From hype to reality: data science enabling personalized medicine

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Objective

Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements.

Methods

Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets.

Results

We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Patient-derived induced pluripotent stem cells: Tools to advance the understanding and drug discovery in Major Depressive Disorder

Major Depressive Disorder (MDD) is a pleomorphic disease with substantial patterns of symptoms and severity with mensurable deficits in several associated domains. The broad spectrum of phenotypes observed in patients diagnosed with depressive disorders is the reflection of a very complex disease where clusters of biological and external factors (e.g., response/processing of life events, intrapsychic factors) converge and mediate pathogenesis, clinical presentation/phenotypes and trajectory. Patient-derived induced pluripotent stem cells (iPSCs) enable their differentiation into specialised cell types in the central nervous system to explore the pathophysiological substrates of MDD. These models may complement animal models to advance drug discovery and identify therapeutic approaches, such as cell therapy, drug repurposing, and elucidation of drug metabolism, toxicity, and mechanisms of action at the molecular/cellular level, to pave the way for precision psychiatry. Despite the remarkable scientific and clinical progress made over the last few decades, the disease is still poorly understood, the incidence and prevalence continue to increase, and more research is needed to meet clinical demands. This review aims to summarise and provide a critical overview of the research conducted thus far using patient-derived iPSCs for the modelling of psychiatric disorders, with a particular emphasis on MDD.

Pan-cancer single-cell landscape of drug-metabolizing enzyme genes

OBJECTIVE

Varied expression of drug-metabolizing enzymes (DME) genes dictates the intensity and duration of drug response in cancer treatment. This study aimed to investigate the transcriptional profile of DMEs in tumor microenvironment (TME) at single-cell level and their impact on individual responses to anticancer therapy.

METHODS

Over 1.3 million cells from 481 normal/tumor samples across 9 solid cancer types were integrated to profile changes in the expression of DME genes. A ridge regression model based on the PRISM database was constructed to predict the influence of DME gene expression on drug sensitivity.

RESULTS

Distinct expression patterns of DME genes were revealed at single-cell resolution across different cancer types. Several DME genes were highly enriched in epithelial cells (e.g. GPX2, TST and CYP3A5 ) or different TME components (e.g. CYP4F3 in monocytes). Particularly, GPX2 and TST were differentially expressed in epithelial cells from tumor samples compared to those from normal samples. Utilizing the PRISM database, we found that elevated expression of GPX2, CYP3A5 and reduced expression of TST was linked to enhanced sensitivity of particular chemo-drugs (e.g. gemcitabine, daunorubicin, dasatinib, vincristine, paclitaxel and oxaliplatin).

CONCLUSION

Our findings underscore the varied expression pattern of DME genes in cancer cells and TME components, highlighting their potential as biomarkers for selecting appropriate chemotherapy agents.

Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure

Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.

Predicting risk of endometrial failure: a biomarker signature that identifies a novel disruption independent of endometrial timing in patients undergoing hormonal replacement cycles

OBJECTIVE

To propose a new gene expression signature that identifies endometrial disruptions independent of endometrial luteal phase timing and predicts if patients are at risk of endometrial failure.

DESIGN

Multicentric, prospective study.

SETTING

Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory.

PATIENTS

Caucasian women (n = 281; 39.4 ± 4.8 years old with a body mass index of 22.9 ± 3.5 kg/m2) undergoing hormone replacement therapy between July 2018 and July 2021. Endometrial samples from 217 patients met RNA quality criteria for signature discovery and analysis.

INTERVENTION(S)

Endometrial biopsies collected in the mid-secretory phase.

MAIN OUTCOME MEASURE(S)

Endometrial luteal phase timing-corrected expression of 404 genes and reproductive outcomes of the first single embryo transfer (SET) after biopsy collection to identify prognostic biomarkers of endometrial failure.

RESULTS

Removal of endometrial timing variation from gene expression data allowed patients to be stratified into poor (n = 137) or good (n = 49) endometrial prognosis groups on the basis of their clinical and transcriptomic profiles. Significant differences were found between endometrial prognosis groups in terms of reproductive rates: pregnancy (44.6% vs. 79.6%), live birth (25.6% vs. 77.6%), clinical miscarriage (22.2% vs. 2.6%), and biochemical miscarriage (20.4% vs. 0%). The relative risk of endometrial failure for patients predicted as a poor endometrial prognosis was 3.3 times higher than those with a good prognosis. The differences in gene expression between both profiles were proposed as a biomarker, coined the endometrial failure risk (EFR) signature. Poor prognosis profiles were characterized by 59 upregulated and 63 downregulated genes mainly involved in regulation (17.0%), metabolism (8.4%), immune response, and inflammation (7.8%). This EFR signature had a median accuracy of 0.92 (min = 0.88, max = 0.94), median sensitivity of 0.96 (min = 0.91, max = 0.98), and median specificity of 0.84 (min = 0.77, max = 0.88), positioning itself as a promising biomarker for endometrial evaluation.

CONCLUSION(S)

The EFR signature revealed a novel endometrial disruption, independent of endometrial luteal phase timing, present in 73.7% of patients. This EFR signature stratified patients into 2 significantly distinct and clinically relevant prognosis profiles providing opportunities for personalized therapy. Nevertheless, further validations are needed before implementing this gene signature as an artificial intelligence (AI)-based tool to reduce the risk of patients experiencing endometrial failure.

External Validation of a Model for Persistent Perfusion Deficit in Patients With Incomplete Reperfusion After Thrombectomy: EXTEND-PROCEED

BACKGROUND AND OBJECTIVES

We recently developed a model (PROCEED) that predicts the occurrence of persistent perfusion deficit (PPD) at 24 hours in patients with incomplete angiographic reperfusion after thrombectomy. This study aims to externally validate the PROCEED model using prospectively acquired multicenter data.

METHODS

Individual patient data for external validation were obtained from the Endovascular Therapy for Ischemic Stroke with Perfusion-Imaging Selection, Tenecteplase versus Alteplase Before Endovascular Therapy for Ischemic Stroke part 1 and 2 trials, and a prospective cohort of the Medical University of Graz. The model's primary outcome was the occurrence of PPD, defined as a focal, wedge-shaped perfusion delay on 24-hour follow-up perfusion imaging that corresponds to the capillary phase deficit on last angiographic series in patients with

RESULTS

We included 371 patients (38% with PPD). The externally validated model had good discrimination (C-statistic 0.81, 95% CI 0.77-0.86) and adequate calibration (intercept 0.25, 95% CI 0.21-0.29 and slope 0.98, 95% CI 0.90-1.12). Across a wide range of probability thresholds (i.e., depending on the physicians' preferences on how the model should be used), the model shows net benefit on clinical decision curves, informing physicians on the likelihood of PPD. If a physician's attitude toward false-positive and false-negative ratings is equal, the model would reduce 13 in 100 unnecessary interventions by correctly predicting complete delayed reperfusion, without missing a patient with PPD.

DISCUSSION

The externally validated model had adequate predictive accuracy and discrimination. Depending on the acceptable threshold probability, the model accurately predicts persistent incomplete reperfusion and may advise physicians whether additional reperfusion attempts should be performed.

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MESH Headings

• Humans
• Thrombectomy/methods
• Male
• Female
• Aged
• Middle Aged
• Reperfusion/methods
• Ischemic Stroke/surgery
• Ischemic Stroke/diagnostic imaging
• Ischemic Stroke/therapy
• Perfusion Imaging
• Prospective Studies
• Cerebrovascular Circulation/physiology
• Aged, 80 and over

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Affiliation(s)

Adnan Mujanovic From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Felix C Ng From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Mattia Branca From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Hannes A Deutschmann From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Thomas R Meinel From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Leonid Churilov From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Oliver Nistl From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Peter J Mitchell From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Nawaf Yassi From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Mark W Parsons From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Gagan J Sharma From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Thomas Gattringer From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Marcel Arnold From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Fabiano Cavalcante From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Eike I Piechowiak From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Timothy J Kleinig From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
David J Seiffge From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Tomas Dobrocky From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Jan Gralla From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Urs Fischer From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Markus Kneihsl From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Bruce C V Campbell From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland
Johannes Kaesmacher From the Departments of Diagnostic and Interventional Neuroradiology (A.M., E.I.P., T.D., J.G., J.K.), Neurology (T.R.M., M.A., D.J.S., U.F.), University Hospital Bern, Inselspital, Graduate School for Health Sciences (A.M.), and CTU Bern (M.B.), University of Bern, Switzerland; Department of Medicine and Neurology, Melbourne Brain Centre (F.C.N., N.Y., G.J.S., B.C.V.C.), Melbourne Medical School (L.C.), and Department of Radiology (P.J.M.), Royal Melbourne Hospital, University of Melbourne, Parkville; Department of Neurology (F.C.N.), Austin Health, Heidelberg, Australia; Division of Neuroradiology, Vascular and Interventional Radiology Department of Radiology (H.A.D., O.N., T.G., M.K.), and Department of Neurology (T.G., M.K.), Medical University of Graz, Austria; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; Department of Neurology (M.W.P.), Liverpool Hospital, University of New South Wales, Sydney, Australia; Department of Radiology and Nuclear Medicine (F.C.), Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Neuroscience, the Netherlands; Department of Neurology (T.J.K.), Royal Adelaide Hospital, Australia; and Department of Neurology (U.F.), University Hospital Basel, University of Basel, Switzerland

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Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana

Genome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs. Here we propose novel GI models to address this issue, exploring the use of two types of transfer learning approaches and proposing a novel Biologically Meaningful Sparse NN layer specifically designed for end-to-end GI. Our models predict the leaf and seed ionome in A.thaliana, obtaining comparable results to our previous over-parameterized model while reducing the number of parameters by 8.8 folds. We also investigate how the effect of population stratification influences the evaluation of the performances, highlighting how it leads to (1) an instance of the Simpson's Paradox, and (2) model generalization limitations.

Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position

Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.

Contextualising flexible nonlinear periodization as a person-adaptive behavioral model for exercise maintenance

There is a growing focus on developing person-adaptive strategies to support sustained exercise behaviour, necessitating conceptual models to guide future research and applications. This paper introduces Flexible nonlinear periodisation (FNLP) - a proposed, but underdeveloped person-adaptive model originating in sport-specific conditioning - that, pending empirical refinement and evaluation, may be applied in health promotion and disease prevention settings. To initiate such efforts, the procedures of FNLP (i.e., acutely and dynamically matching exercise demand to individual assessments of mental and physical readiness) are integrated with contemporary health behaviour evidence and theory to propose a modified FNLP model and to show hypothesised pathways by which FNLP may support exercise adherence (e.g., flexible goal setting, management of affective responses, and provision of autonomy/variety-support). Considerations for future research are also provided to guide iterative, evidence-based efforts for further development, acceptability, implementation, and evaluation.

Targeting PI3K/AKT/mTOR signaling to overcome drug resistance in cancer

This review comprehensively explores the challenge of drug resistance in cancer by focusing on the pivotal PI3K/AKT/mTOR pathway, elucidating its role in oncogenesis and resistance mechanisms across various cancer types. It meticulously examines the diverse mechanisms underlying resistance, including genetic mutations, feedback loops, and microenvironmental factors, while also discussing the associated resistance patterns. Evaluating current therapeutic strategies targeting this pathway, the article highlights the hurdles encountered in drug development and clinical trials. Innovative approaches to overcome resistance, such as combination therapies and precision medicine, are critically analyzed, alongside discussions on emerging therapies like immunotherapy and molecularly targeted agents. Overall, this comprehensive review not only sheds light on the complexities of resistance in cancer but also provides a roadmap for advancing cancer treatment.

Development and internal validation of machine learning models for personalized survival predictions in spinal cord glioma patients

BACKGROUND CONTEXT

Numerous factors have been associated with the survival outcomes in patients with spinal cord gliomas (SCG). Recognizing these specific determinants is crucial, yet it is also vital to establish a reliable and precise prognostic model for estimating individual survival outcomes.

OBJECTIVE

The objectives of this study are twofold: first, to create an array of interpretable machine learning (ML) models developed for predicting survival outcomes among SCG patients; and second, to integrate these models into an easily navigable online calculator to showcase their prospective clinical applicability.

STUDY DESIGN

This was a retrospective, population-based cohort study aiming to predict the outcomes of interest, which were binary categorical variables, in SCG patients with ML models.

PATIENT SAMPLE

The National Cancer Database (NCDB) was utilized to identify adults aged 18 years or older who were diagnosed with histologically confirmed SCGs between 2010 and 2019.

OUTCOME MEASURES

The outcomes of interest were survival outcomes at three specific time points postdiagnosis: 1, 3, and 5 years. These outcomes were formed by combining the "Vital Status" and "Last Contact or Death (Months from Diagnosis)" variables. Model performance was evaluated visually and numerically. The visual evaluation utilized receiver operating characteristic (ROC) curves, precision-recall curves (PRCs), and calibration curves. The numerical evaluation involved metrics such as sensitivity, specificity, accuracy, area under the PRC (AUPRC), area under the ROC curve (AUROC), and Brier Score.

METHODS

We employed five ML algorithms-TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest-along with the Optuna library for hyperparameter optimization. The models that yielded the highest AUROC values were chosen for integration into the online calculator. To enhance the explicability of our models, we utilized SHapley Additive exPlanations (SHAP) for assessing the relative significance of predictor variables and incorporated partial dependence plots (PDPs) to delineate the influence of singular variables on the predictions made by the top performing models.

RESULTS

For the 1-year survival analysis, 4,913 patients [5.6% with 1-year mortality]; for the 3-year survival analysis, 4,027 patients (11.5% with 3-year mortality]; and for the 5-year survival analysis, 2,854 patients (20.4% with 5-year mortality) were included. The top models achieved AUROCs of 0.938 for 1-year mortality (TabPFN), 0.907 for 3-year mortality (LightGBM), and 0.902 for 5-year mortality (Random Forest). Global SHAP analyses across survival outcomes at different time points identified histology, tumor grade, age, surgery, radiotherapy, and tumor size as the most significant predictor variables for the top-performing models.

CONCLUSIONS

This study demonstrates ML techniques can develop highly accurate prognostic models for SCG patients with excellent discriminatory ability. The interactive online calculator provides a tool for assessment by physicians (https://huggingface.co/spaces/MSHS-Neurosurgery-Research/NCDB-SCG). Local interpretability informs prediction influences for a given individual. External validation across diverse datasets could further substantiate potential utility and generalizability. This robust, interpretable methodology aligns with the goals of precision medicine, establishing a foundation for continued research leveraging ML's predictive power to enhance patient counseling.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.

Grilling the data: application of specification curve analysis to red meat and all-cause mortality

OBJECTIVES

To present an application of specification curve analysis-a novel analytic method that involves defining and implementing all plausible and valid analytic approaches for addressing a research question-to nutritional epidemiology.

STUDY DESIGN AND SETTING

We reviewed all observational studies addressing the effect of red meat on all-cause mortality, sourced from a published systematic review, and documented variations in analytic methods (eg, choice of model, covariates, etc.). We enumerated all defensible combinations of analytic choices to produce a comprehensive list of all the ways in which the data may reasonably be analyzed. We applied specification curve analysis to data from National Health and Nutrition Examination Survey 2007 to 2014 to investigate the effect of unprocessed red meat on all-cause mortality. The specification curve analysis used a random sample of all reasonable analytic specifications we sourced from primary studies.

RESULTS

Among 15 publications reporting on 24 cohorts included in the systematic review on red meat and all-cause mortality, we identified 70 unique analytic methods, each including different analytic models, covariates, and operationalizations of red meat (eg, continuous vs quantiles). We applied specification curve analysis to National Health and Nutrition Examination Survey, including 10,661 participants. Our specification curve analysis included 1208 unique analytic specifications, of which 435 (36.0%) yielded a hazard ratio equal to or more than 1 for the effect of red meat on all-cause mortality and 773 (64.0%) less than 1. The specification curve analysis yielded a median hazard ratio of 0.94 (interquartile range: 0.83-1.05). Forty-eight specifications (3.97%) were statistically significant, 40 of which indicated unprocessed red meat to reduce all-cause mortality and eight of which indicated red meat to increase mortality.

CONCLUSION

We show that the application of specification curve analysis to nutritional epidemiology is feasible and presents an innovative solution to analytic flexibility.

A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals

The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient's condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.

AI in medical diagnosis: AI prediction & human judgment

AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, 'explainability', and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.

The paradigm change from reactive medical services to 3PM in ischemic stroke: a holistic approach utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation

Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.

Finding biomarkers of experience in animals

At a time when there is a growing public interest in animal welfare, it is critical to have objective means to assess the way that an animal experiences a situation. Objectivity is critical to ensure appropriate animal welfare outcomes. Existing behavioural, physiological, and neurobiological indicators that are used to assess animal welfare can verify the absence of extremely negative outcomes. But welfare is more than an absence of negative outcomes and an appropriate indicator should reflect the full spectrum of experience of an animal, from negative to positive. In this review, we draw from the knowledge of human biomedical science to propose a list of candidate biological markers (biomarkers) that should reflect the experiential state of non-human animals. The proposed biomarkers can be classified on their main function as endocrine, oxidative stress, non-coding molecular, and thermobiological markers. We also discuss practical challenges that must be addressed before any of these biomarkers can become useful to assess the experience of an animal in real-life.

Microbiome Dynamics: A Paradigm Shift in Combatting Infectious Diseases

Infectious diseases have long posed a significant threat to global health and require constant innovation in treatment approaches. However, recent groundbreaking research has shed light on a previously overlooked player in the pathogenesis of disease-the human microbiome. This review article addresses the intricate relationship between the microbiome and infectious diseases and unravels its role as a crucial mediator of host-pathogen interactions. We explore the remarkable potential of harnessing this dynamic ecosystem to develop innovative treatment strategies that could revolutionize the management of infectious diseases. By exploring the latest advances and emerging trends, this review aims to provide a new perspective on combating infectious diseases by targeting the microbiome.

Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs

The current management of patients with multimorbidity is suboptimal, with either a single-disease approach to care or treatment guideline adaptations that result in poor adherence due to their complexity. Although this has resulted in calls for more holistic and personalized approaches to prescribing, progress toward these goals has remained slow. With the rapid advancement of machine learning (ML) methods, promising approaches now also exist to accelerate the advance of precision medicine in multimorbidity. These include analyzing disease comorbidity networks, using knowledge graphs that integrate knowledge from different medical domains, and applying network analysis and graph ML. Multimorbidity disease networks have been used to improve disease diagnosis, treatment recommendations, and patient prognosis. Knowledge graphs that combine different medical entities connected by multiple relationship types integrate data from different sources, allowing for complex interactions and creating a continuous flow of information. Network analysis and graph ML can then extract the topology and structure of networks and reveal hidden properties, including disease phenotypes, network hubs, and pathways; predict drugs for repurposing; and determine safe and more holistic treatments. In this article, we describe the basic concepts of creating bipartite and unipartite disease and patient networks and review the use of knowledge graphs, graph algorithms, graph embedding methods, and graph ML within the context of multimorbidity. Specifically, we provide an overview of the application of graph theory for studying multimorbidity, the methods employed to extract knowledge from graphs, and examples of the application of disease networks for determining the structure and pathways of multimorbidity, identifying disease phenotypes, predicting health outcomes, and selecting safe and effective treatments. In today's modern data-hungry, ML-focused world, such network-based techniques are likely to be at the forefront of developing robust clinical decision support tools for safer and more holistic approaches to treating older patients with multimorbidity.

High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Introduction: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed. Methods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models. Results and Discussion: For the CRC GEO dataset samples from patients who received either FOLFOX or FOLFIRI, validation and test sets were >90% correctly classified (accuracy), with specificity and sensitivity ranging between 85%-95%. In the datasets used from the GEO database, 28.6% of patients who failed the treatment therapy they received are predicted to benefit from the alternative treatment. Analysis of the gene signature suggests the mechanistic difference between colorectal cancers that respond and those that do not respond to FOLFOX and FOLFIRI. Application of this machine learning approach could lead to improvements in treatment outcomes for patients with CRC and other cancers after additional appropriate clinical validation.

A Comprehensive Review of Genetic Variations in Collagen-Encoding Genes and Their Implications in Intervertebral Disc Degeneration

This comprehensive review examines the intricate relationship between genetic variations in collagen-encoding genes and their implications in intervertebral disc degeneration (IVDD). Intervertebral disc degeneration is a prevalent spinal condition characterized by structural and functional changes in intervertebral discs (IVDs), and understanding its genetic underpinnings is crucial for advancing diagnostic and therapeutic strategies. The review begins by exploring the background and importance of collagen in IVDs, emphasizing its role in providing structural integrity. It then delves into the significance of genetic variations within collagen-encoding genes, categorizing and discussing their potential impact on disc health. The methods employed in studying these variations, such as genome-wide association studies (GWASs) and next-generation sequencing (NGS), are also reviewed. The subsequent sections analyze existing literature to establish associations between genetic variations and IVDD, unraveling molecular mechanisms linking genetic factors to disc degeneration. The review concludes with a summary of key findings, implications for future research and clinical practice, and a reflection on the importance of understanding genetic variations in collagen-encoding genes to diagnose and treat IVDD. The insights gleaned from this review contribute to our understanding of IVDD and hold promise for the development of personalized interventions based on individual genetic profiles.

Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper

Background

Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed.

Objective

As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool.

Methods

We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model.

Results

Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables.

Conclusion

AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.

Artificial intelligence in rheumatoid arthritis: potential applications and future implications

The widespread adoption of digital health records, coupled with the rise of advanced diagnostic testing, has resulted in an explosion of patient data, comparable in scope to genomic datasets. This vast information repository offers significant potential for improving patient outcomes and decision-making, provided one can extract meaningful insights from it. This is where artificial intelligence (AI) tools like machine learning (ML) and deep learning come into play, helping us leverage these enormous datasets to predict outcomes and make informed decisions. AI models can be trained to analyze and interpret patient data, including physician notes, laboratory testing, and imaging, to aid in the management of patients with rheumatic diseases. As one of the most common autoimmune diseases, rheumatoid arthritis (RA) has attracted considerable attention, particularly concerning the evolution of diagnostic techniques and therapeutic interventions. Our aim is to underscore those areas where AI, according to recent research, demonstrates promising potential to enhance the management of patients with RA.

Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients

High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn's Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.

Personalized antiseizure medication therapy in critically ill adult patients

Precision medicine has the potential to have a significant impact on both drug development and patient care. It is crucial to not only provide prompt effective antiseizure treatment for critically ill patients after seizures start but also have a proactive mindset and concentrate on epileptogenesis and the underlying cause of the seizures or seizure disorders. Critical illness presents different treatment issues compared with the ambulatory population, which makes it challenging to choose the best antiseizure medications and to administer them at the right time and at the right dose. Since there is a paucity of information available on antiseizure medication dosing in critically ill patients, therapeutic drug monitoring is a useful tool for defining each patient's personal therapeutic range and assisting clinicians in decision-making. Use of pharmacogenomic information relating to pharmacokinetics, hepatic metabolism, and seizure etiology may improve safety and efficacy by individualizing therapy. Studies evaluating the clinical implementation of pharmacogenomic information at the point-of-care and identification of biomarkers are also needed. These studies may make it possible to avoid adverse drug reactions, maximize drug efficacy, reduce drug-drug interactions, and optimize medications for each individual patient. This review will discuss the available literature and provide future insights on precision medicine use with antiseizure therapy in critically ill adult patients.

Identifying cancer driver genes in individual tumours

Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.

Machine Learning Applied to Cholesterol-Lowering Pharmacotherapy: Proof-of-Concept in High-Risk Patients Treated in Primary Care

Objectives: Machine learning has potential to improve the management of lipid disorders. We explored the utility of machine learning in high-risk patients in primary care receiving cholesterol-lowering medications. Methods: Machine learning algorithms were created based on lipid management guidelines for England [National Institute for Health and Care Excellence (NICE) CG181] to reproduce the guidance with >95% accuracy. Natural language processing and therapy identification algorithms were applied to anonymized electronic records from six South London primary care general practices to extract medication information from free text fields. Results: Among a total of 48,226 adult patients, a subset of 5630 (mean ± standard deviation, age = 67 ± 13 years; male:female = 55:45) with a history of lipid-lowering therapy were identified. Additional major cardiometabolic comorbidities included type 2 diabetes in 13% (n = 724) and hypertension in 32% (n = 1791); all three risk factors were present in a further 28% (n = 1552). Of the 5630 patients, 4290 (76%) and 1349 (24%) were in primary and secondary cardiovascular disease prevention cohorts, respectively. Statin monotherapy was the most common current medication (82%, n = 4632). For patients receiving statin monotherapy, 71% (n = 3269) were on high-intensity therapy aligned with NICE guidance with rates being similar for the primary and secondary prevention cohorts. In the combined cohort, only 46% of patients who had been prescribed lipid-lowering therapy in the previous 12 months achieved the NICE treatment goal of >40% reduction in non-high-density lipoprotein cholesterol from baseline pretreatment levels. Based on the most recent data entry for patients not at goal the neural network recommended either increasing the dose of statin, adding complementary cholesterol-lowering medication, or obtaining an expert lipid opinion. Conclusions: Machine learning can be of value in (a) quantifying suboptimal lipid-lowering prescribing patterns, (b) identifying high-risk patients who could benefit from more intensive therapy, and (c) suggesting evidence-based therapeutic options.

Genome-scale CRISPR-Cas9 screening stratifies pancreatic cancer with distinct outcomes and immunotherapeutic efficacy

Pancreatic cancer (PC) was featured by dramatic heterogeneity and dismal outcomes. An ideal classification strategy capable of achieving risk stratification and individualized treatment is urgently needed to significantly improve prognosis. In this study, using the 105 prognostic cancer essential genes identified by genome-scale CRISPR-Cas9 screening and univariate Cox analysis, we established and verified three heterogeneous subtypes via non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithms in the TCGA-PAAD cohort (176 samples) and four multi-center cohorts (233 samples), respectively. Among them, C1 with the worst prognosis was enriched in immune-related pathways, possessed superior infiltration abundance of immune cells and immune checkpoint molecules expression, and might be most sensitive to immunotherapy. C3, owing a moderate prognosis, might be featured by proliferative biological function, and despite its highest immunogenicity, the defects in antigen processing and presentation ability coupled with barren immune environment render it ineffective for immunotherapy, while it had potential sensitivity to paclitaxel and methotrexate. Besides, C2 harbored the best prognosis and was characterized by metabolism-related functions. These results could deepen our understanding of PC molecular heterogeneity and provide a trustworthy reference for prognostic stratification management and precision medicine in clinical practice.

PTOLEMI: Personalized Cancer Treatment through Machine Learning-Enabled Image Analysis of Microfluidic Assays

Contemporary personalized cancer diagnostic approaches encounter multiple challenges. The presence of cellular and molecular heterogeneity in patient samples introduces complexities to analysis protocols. Conventional analyses are manual, reliant on expert personnel, time-intensive, and financially burdensome. The copious data amassed for subsequent analysis strains the system, obstructing real-time diagnostics at the "point of care" and impeding prompt intervention. This study introduces PTOLEMI: Python-based Tensor Oncological Locator Examining Microfluidic Instruments. PTOLEMI stands out as a specialized system designed for high-throughput image analysis, particularly in the realm of microfluidic assays. Utilizing a blend of machine learning algorithms, PTOLEMI can process large datasets rapidly and with high accuracy, making it feasible for point-of-care diagnostics. Furthermore, its advanced analytics capabilities facilitate a more granular understanding of cellular dynamics, thereby allowing for more targeted and effective treatment options. Leveraging cutting-edge AI algorithms, PTOLEMI rapidly and accurately discriminates between cell viability and distinct cell types within biopsy samples. The diagnostic process becomes automated, swift, precise, and resource-efficient, rendering it well-suited for point-of-care requisites. By employing PTOLEMI alongside a microfluidic cell culture chip, physicians can attain personalized diagnostic and therapeutic insights. This paper elucidates the evolution of PTOLEMI and showcases its prowess in analyzing cancer patient samples within a microfluidic apparatus. While the integration of machine learning tools into biomedical domains is undoubtedly in progress, this study's innovation lies in the fusion of PTOLEMI with a microfluidic platform-an integrated, rapid, and independent framework for personalized drug screening-based clinical decision-making.

Exploring the potential impact of multi-factor precision interventions in Alzheimer's disease with system dynamics

Numerous clinical trials based on a single-cause paradigm have not resulted in efficacious treatments for Alzheimer's disease (AD). Recently, prevention trials that simultaneously intervened on multiple risk factors have shown mixed results, suggesting that careful design is necessary. Moreover, intensive pilot precision medicine (PM) trial results have been promising but may not generalize to a broader population. These observations suggest that a model-based approach to multi-factor precision medicine (PM) is warranted. We systematically developed a system dynamics model (SDM) of AD for PM using data from two longitudinal studies (N=3660). This method involved a model selection procedure in identifying interaction terms between the SDM components and estimating individualized parameters. We used the SDM to explore simulated single- and double-factor interventions on 14 modifiable risk factors. We quantified the potential impact of double-factor interventions over single-factor interventions as 1.5 [95% CI: 1.5-2.6] and of SDM-based PM over a one-size-fits-all approach as 3.5 [3.1, 3.8] ADAS-cog-13 points in 12 years. Although the model remains to be validated, we tentatively conclude that multi-factor PM could come to play an important role in AD prevention.

Temporal uncertainty in disease diagnosis

There is a profound paradox in modern medical knowledge production: The more we know, the more we know that we (still) do not know. Nowhere is this more visible than in diagnostics and early detection of disease. As we identify ever more markers, predictors, precursors, and risk factors of disease ever earlier, we realize that we need knowledge about whether they develop into something experienced by the person and threatening to the person's health. This study investigates how advancements in science and technology alter one type of uncertainty, i.e., temporal uncertainty of disease diagnosis. As diagnosis is related to anamnesis and prognosis it identifies how uncertainties in all these fields are interconnected. In particular, the study finds that uncertainty in disease diagnosis has become more subject to prognostic uncertainty because diagnosis is more connected to technologically detected indicators and less closely connected to manifest and experienced disease. These temporal uncertainties pose basic epistemological and ethical challenges as they can result in overdiagnosis, overtreatment, unnecessary anxiety and fear, useless and even harmful diagnostic odysseys, as well as vast opportunity costs. The point is not to stop our quest for knowledge about disease but to encourage real diagnostic improvements that help more people in ever better manner as early as possible. To do so, we need to pay careful attention to specific types of temporal uncertainty in modern diagnostics.

Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach

BACKGROUND

Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach.

METHODS

The current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguously clustered pairs (PAC) analysis. We calculated the standardized mean difference (SMD) of each variable and used the cutoff of ± 0.3 to identify each cluster's key features. We examined the relationship between the phenotypes and several clinical endpoints utilizing logistic regression (LR) analysis.

RESULTS

The consensus cluster analysis identified two clusters (Cluster 1: n = 9,848; Cluster 2: n = 12,077). The key features of patients in Cluster 1, compared with Cluster 2, included: lower blood pressure, lower eGFR (estimated glomerular filtration rate), higher BUN (blood urea nitrogen), higher creatinine, lower albumin, higher potassium, lower bicarbonate, lower red blood cell (RBC), higher red blood cell distribution width (RDW), higher SOFA score, higher APS III score, and higher APACHE IV score on admission. The results of LR analysis showed that the Cluster 2 was associated with lower in-hospital mortality (odds ratio [OR]: 0.374; 95% confidence interval [CI]: 0.347-0.402; P < 0.001), ICU mortality (OR: 0.349; 95% CI: 0.318-0.382; P < 0.001), and the incidence of acute kidney injury (AKI) after admission (OR: 0.478; 95% CI: 0.452-0.505; P < 0.001).

CONCLUSIONS

ML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes.

Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective

This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.

The Future of Precision Oncology

Our understanding of the molecular mechanisms underlying cancer development and evolution have evolved rapidly over recent years, and the variation from one patient to another is now widely recognized. Consequently, one-size-fits-all approaches to the treatment of cancer have been superseded by precision medicines that target specific disease characteristics, promising maximum clinical efficacy, minimal safety concerns, and reduced economic burden. While precision oncology has been very successful in the treatment of some tumors with specific characteristics, a large number of patients do not yet have access to precision medicines for their disease. The success of next-generation precision oncology depends on the discovery of new actionable disease characteristics, rapid, accurate, and comprehensive diagnosis of complex phenotypes within each patient, novel clinical trial designs with improved response rates, and worldwide access to novel targeted anticancer therapies for all patients. This review outlines some of the current technological trends, and highlights some of the complex multidisciplinary efforts that are underway to ensure that many more patients with cancer will be able to benefit from precision oncology in the near future.

Wearables for personalized monitoring of masticatory muscle activity - opportunities, challenges, and the future

Wearable devices are worn on or remain in close proximity of the human body. The use of wearable devices specific to the orofacial region is steadily increasing. Orofacial applications of wearable devices include supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and understanding oral parafunctional behaviours. In this short communication, the role of wearable devices in advancing personalized dental medicine are highlighted with a specific focus on masticatory muscle activity monitoring in naturalistic settings. Additionally, challenges, opportunities, as well as future research areas for successful use of wearable devices for precise, personalized care of muscle disorders are discussed.

Unraveling the epigenetic landscape of pulmonary arterial hypertension: implications for personalized medicine development

Pulmonary arterial hypertension (PAH) is a multifactorial disease associated with the remodeling of pulmonary blood vessels. If left unaddressed, PAH can lead to right heart failure and even death. Multiple biological processes, such as smooth muscle proliferation, endothelial dysfunction, inflammation, and resistance to apoptosis, are associated with PAH. Increasing evidence suggests that epigenetic factors play an important role in PAH by regulating the chromatin structure and altering the expression of critical genes. For example, aberrant DNA methylation and histone modifications such as histone acetylation and methylation have been observed in patients with PAH and are linked to vascular remodeling and pulmonary vascular dysfunction. In this review article, we provide a comprehensive overview of the role of key epigenetic targets in PAH pathogenesis, including DNA methyltransferase (DNMT), ten-eleven translocation enzymes (TET), switch-independent 3A (SIN3A), enhancer of zeste homolog 2 (EZH2), histone deacetylase (HDAC), and bromodomain-containing protein 4 (BRD4). Finally, we discuss the potential of multi-omics integration to better understand the molecular signature and profile of PAH patients and how this approach can help identify personalized treatment approaches.

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues

BACKGROUND

P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data.

RESULTS

We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors).

CONCLUSIONS

Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

Modular Clinical Decision Support Networks (MoDN)-Updatable, interpretable, and portable predictions for evolving clinical environments

Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study

The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depression in primary care based on Information and Communication Technologies, predictive risk algorithms, decision support systems (DSSs), and personalized prevention plans (PPPs). A multicenter cluster randomized trial with GPs randomly assigned to the e-predictD intervention + care-as-usual (CAU) group or the active-control + CAU group and 1-year follow-up is being conducted. The required sample size is 720 non-depressed patients (aged 18-55 years), with moderate-to-high depression risk, under the care of 72 GPs in six Spanish cities. The GPs assigned to the e-predictD-intervention group receive brief training, and those assigned to the control group do not. Recruited patients of the GPs allocated to the e-predictD group download the e-predictD app, which incorporates validated risk algorithms to predict depression, monitoring systems, and DSSs. Integrating all inputs, the DSS automatically proposes to the patients a PPP for depression based on eight intervention modules: physical exercise, social relationships, improving sleep, problem-solving, communication skills, decision-making, assertiveness, and working with thoughts. This PPP is discussed in a 15-min semi-structured GP-patient interview. Patients then choose one or more of the intervention modules proposed by the DSS to be self-implemented over the next 3 months. This process will be reformulated at 3, 6, and 9 months but without the GP-patient interview. Recruited patients of the GPs allocated to the control-group+CAU download another version of the e-predictD app, but the only intervention that they receive via the app is weekly brief psychoeducational messages (active-control group). The primary outcome is the cumulative incidence of major depression measured by the Composite International Diagnostic Interview at 6 and 12 months. Other outcomes include depressive symptoms (PHQ-9) and anxiety symptoms (GAD-7), depression risk (predictD risk algorithm), mental and physical quality of life (SF-12), and acceptability and satisfaction ('e-Health Impact' questionnaire) with the intervention. Patients are evaluated at baseline and 3, 6, 9, and 12 months. An economic evaluation will also be performed (cost-effectiveness and cost-utility analysis) from two perspectives, societal and health systems.

Trial registration

ClinicalTrials.gov, identifier: NCT03990792.

Joint Modeling of Clinical and Biomarker Data in Acute Kidney Injury Defines Unique Subphenotypes with Differing Outcomes

BACKGROUND

AKI is a heterogeneous syndrome. Current subphenotyping approaches have only used limited laboratory data to understand a much more complex condition.

METHODS

We focused on patients with AKI from the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI (ASSESS-AKI). We used hierarchical clustering with Ward linkage on biomarkers of inflammation, injury, and repair/health. We then evaluated clinical differences between subphenotypes and examined their associations with cardiorenal events and death using Cox proportional hazard models.

RESULTS

We included 748 patients with AKI: 543 (73%) of them had AKI stage 1, 112 (15%) had AKI stage 2, and 93 (12%) had AKI stage 3. The mean age (±SD) was 64 (13) years; 508 (68%) were men; and the median follow-up was 4.7 (Q1: 2.9, Q3: 5.7) years. Patients with AKI subphenotype 1 ( N =181) had the highest kidney injury molecule (KIM-1) and troponin T levels. Subphenotype 2 ( N =250) had the highest levels of uromodulin. AKI subphenotype 3 ( N =159) comprised patients with markedly high pro-brain natriuretic peptide and plasma tumor necrosis factor receptor-1 and -2 and low concentrations of KIM-1 and neutrophil gelatinase-associated lipocalin. Finally, patients with subphenotype 4 ( N =158) predominantly had sepsis-AKI and the highest levels of vascular/kidney inflammation (YKL-40, MCP-1) and injury (neutrophil gelatinase-associated lipocalin, KIM-1). AKI subphenotypes 3 and 4 were independently associated with a higher risk of death compared with subphenotype 2 and had adjusted hazard ratios of 2.9 (95% confidence interval, 1.8 to 4.6) and 1.6 (95% confidence interval, 1.01 to 2.6, P = 0.04), respectively. Subphenotype 3 was also independently associated with a three-fold risk of CKD and cardiovascular events.

CONCLUSIONS

We discovered four AKI subphenotypes with differing clinical features and biomarker profiles that are associated with longitudinal clinical outcomes.

Prenatal androgen influences on the brain: A review, critique, and illustration of research on congenital adrenal hyperplasia

Sex hormones, especially androgens, contribute to sex and gender differences in the brain and behavior. Organizational effects are particularly important because they are thought to be permanent, reflecting hormone exposure during sensitive periods of development. In human beings, they are often studied with natural experiments in which sex hormones are dissociated from other biopsychosocial aspects of development, such as genes and experiences. Indeed, the greatest evidence for organizational effects on sex differences in human behavior comes from studies of females with congenital adrenal hyperplasia (CAH), who have heightened prenatal androgen exposure, female-typical rearing, and masculinized toy play, activity and career interests, spatial skills, and some personal characteristics. Interestingly, however, neuroimaging studies of females with CAH have revealed few neural mechanisms underlying these hormone-behavior links, with the exception of emotion processing; studies have instead shown reduced gray matter volumes and reduced white matter integrity most consistent with other disease-related processes. The goals of this narrative review are to: (a) describe methods for studying prenatal androgen influences, while offering a brief overview of behavioral outcomes; (b) provide a critical methodological review of neuroimaging research on females with CAH; (c) present an illustrative analysis that overcomes methodological limitations of previous work, focusing on person-specific neural reward networks (and their associations with sensation seeking) in women with CAH and their unaffected sisters in order to inform future research questions and approaches that are most likely to reveal organizational hormone effects on brain structure and function.

Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data

IMPORTANCE

Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings.

OBJECTIVE

To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets.

DESIGN, SETTING, AND PARTICIPANTS

This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study.

MAIN OUTCOMES AND MEASURES

The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested.

RESULTS

Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.