pROC: an open-source package for R and S+ to analyze and compare ROC curves

Transcriptomic analysis and validation study of key genes and the HIF‑1α/HO‑1 pathway associated with ferroptosis in neutrophilic asthma

Ferroptosis, as a unique form of cell death caused by iron overload and lipid peroxidation, is involved in the pathogenesis of various inflammatory diseases of the airways. Inhibition of ferroptosis has become a novel strategy for reducing airway epithelial cell death and improving airway inflammation. The aim of the present study was to analyze and validate the key genes and signaling pathways associated with ferroptosis by bioinformatic methods combined with experimental analyzes in vitro and in vivo to aid the diagnosis and treatment of neutrophilic asthma. A total of 1,639 differentially expressed genes (DEGs) were identified in the transcriptome dataset. After overlapping with ferroptosis-related genes, 11 differentially expressed ferroptosis-related genes (DE-FRGs) were obtained. A new diagnostic model was constructed by these DE-FRGs from the transcriptome dataset with those from the GSE108417 dataset. The receiver operating characteristic curve analysis indicated that the area under the curve had good diagnostic performance (>0.8). As a result, four key DE-FRGs (CXCL2, HMOX1, IL-6 and SLC7A5) and biological pathway [hypoxia-inducible factor 1 (HIF-1) signaling pathway] associated with ferroptosis in neutrophilic asthma were identified by the bioinformatics analysis combined with experimental validation. The upstream regulatory network of key DE-FRGs and target drugs were predicted and the molecular docking results from screened 37 potential therapeutic drugs revealed that the 13 small-molecule drugs exhibited a higher stable binding to the primary proteins of key DE-FRGs. The results suggested that four key DE-FRGs and the HIF-1α/heme oxygenase 1 pathway associated with ferroptosis have potential as novel markers or targets for the diagnosis or treatment of neutrophilic asthma.

Integrated bioinformatics analysis identifies autophagy-associated genes as candidate biomarkers and reveals the immune infiltration landscape in psoriasis

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the "limma" package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. CytoHubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. Subsequently, a series of bioinformatics analyses were employed, including protein-protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. Immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease.

Comprehensive analysis of abnormal methylation modification differential expression mRNAs between low-grade and high-grade intervertebral disc degeneration and its correlation with immune cells

BACKGROUND

Intervertebral disc degeneration (IDD) is an important cause of low back pain. The aim of this study is to identify the potential molecular mechanism of abnormal methylation-modified DNA in the progression of IDD, hoping to contribute to the diagnosis and management of IDD.

METHODS

Low-grade IDD (grade I-II) and high-grade IDD (grade III-V) data were downloaded from GSE70362 and GSE129789 datasets. The abnormally methylated modified differentially expressed mRNAs (DEmRNAs) were identified by differential expression analysis (screening criteria were p < .05 and |logFC| > 1) and differential methylation analysis (screening criteria were p < .05 and |δβ| > 0.1). The classification models were constructed, and the receiver operating characteristic analysis was also carried out. In addition, functional enrichment analysis and immune correlation analysis were performed and the miRNAs targeted for the abnormally methylated DEmRNAs were predicted. Finally, expression validation was performed using real-time PCR.

RESULTS

Compared with low-grade IDD, seven abnormal methylation-modified DEmRNAs (AOX1, IBSP, QDPR, ABLIM1, CRISPLD2, ACTC1 and EMILIN1) were identified in high-grade IDD, and the classification models of random forests (RF) and support vector machine (SVM) were constructed. Moreover, seven abnormal methylation-modified DEmRNAs and classification models have high diagnostic accuracy (area under the curve [AUC] > 0.8). We also found that AUC values of single abnormal methylation-modified DEmRNA were all lower than those of RF and SVM classification models. Pearson correlation analysis found that macrophages M2 and EMILIN1 had significant negative correlation, while macrophages M2 and IBSP had significant positive correlation. In addition, four targeted relationship pairs (hsa-miR-4728-5p-QDPR, hsa-miR-4533-ABLIM1, hsa-miR-4728-5p-ABLIM1 and hsa-miR-4534-CRISPLD2) and multiple signalling pathways (for example, PI3K-AKT signalling pathway, osteoclast differentiation and calcium signalling pathway) were also identified that may be involved in the progression of IDD.

CONCLUSION

The identification of abnormal methylation-modified DEmRNAs and the construction of classification models in this study were helpful for the diagnosis and management of IDD progression.

Artificial intelligence for human gunshot wound classification

Certain features are helpful in the identification of gunshot entrance and exit wounds, such as the presence of muzzle imprints, peripheral tears, stippling, bone beveling, and wound border irregularity. Some cases are less straightforward and wounds can thus pose challenges to an emergency room doctor or forensic pathologist. In recent years, deep learning has shown promise in various automated medical image classification tasks. This study explores the feasibility of using a deep learning model to classify entry and exit gunshot wounds in digital color images. A collection of 2418 images of entrance and exit gunshot wounds were procured. Of these, 2028 entrance and 1314 exit wounds were cropped, focusing on the area around each gunshot wound. A ConvNext Tiny deep learning model was trained using the Fastai deep learning library, with a train/validation split ratio of 70/30, until a maximum validation accuracy of 92.6% was achieved. An additional 415 entrance and 293 exit wound images were collected for the test (holdout) set. The model achieved an accuracy of 87.99%, precision of 83.99%, recall of 87.71%, and F1-score 85.81% on the holdout set. Correctly classified were 88.19% of entrance wounds and 87.71% of exit wounds. The results are comparable to what a forensic pathologist can achieve without other morphologic cues. This study represents one of the first applications of artificial intelligence to the field of forensic pathology. This work demonstrates that deep learning models can discern entrance and exit gunshot wounds in digital images with high accuracy.

CST2 promotes cell proliferation and regulates cell cycle by activating Wnt-β-catenin signalling pathway in serous ovarian cancer

BACKGROUND

Cystatin SA (CST2) plays multiple roles in different types of malignant tumours; however, its role in serous ovarian cancer (SOC) remains unclear. Therefore, we aimed to investigate the expression levels, survival outcomes, immune cell infiltration, proliferation, cell cycle, and underlying molecular mechanisms associated with the CST2 signature in SOC.

METHODS

The Cancer Genome Atlas database was used to acquire clinical information and CST2 expression profiles from patients with SOC. Wilcoxon rank-sum tests were used to compare CST2 expression levels between SOC and normal ovarian tissues. A prognostic assessment of CST2 was conducted using Cox regression analysis and the Kaplan-Meier method. Differentially expressed genes were identified using functional enrichment analysis. Immune cell infiltration was examined using a single-sample gene set enrichment analysis. Cell cycle characteristics and proliferation were assessed using a colony formation assay, flow cytometry, and a cell counting kit-8 assay. Western blots and quantitative reverse transcription PCR analyses were employed to examine CST2 expressions and related genes involved in the cell cycle and the Wnt-β-catenin signalling pathway.

RESULTS

Our findings revealed significant upregulation of CST2 in SOC, and elevated CST2 expression was correlated with advanced clinicopathological characteristics and unfavourable prognoses. Pathway enrichment analysis highlighted the association between the cell cycle and the Wnt signalling pathway. Moreover, increased CST2 levels were positively correlated with immune cell infiltration. Functionally, CST2 played vital roles in promoting cell proliferation, orchestrating the G1-to-S phase transition, and driving malignant SOC progression through activating the Wnt-β-catenin signalling pathway.

CONCLUSIONS

The elevated expression of CST2 may be related to the occurrence and progression of SOC by activating the Wnt-β-catenin pathway. Additionally, our findings suggest that CST2 is a promising novel biomarker with potential applications in therapeutic, prognostic, and diagnostic strategies for SOC.

AIMER: A SNP-independent software for identifying imprinting-like allelic methylated regions from DNA methylome

Genomic imprinting is essential for mammalian growth and embryogenesis. High-throughput bisulfite sequencing accompanied with parental haplotype-specific information allows analysis of imprinted genes and imprinting control regions (ICRs) on a large scale. Currently, although several allelic methylated regions (AMRs) detection software were developed, methods for detecting imprinted AMRs is still limited. Here, we developed a SNP-independent statistical approach, AIMER, to detect imprinting-like AMRs. By using the mouse frontal cortex methylome as input, we demonstrated that AIMER performs very well in detecting known germline ICRs compared with other methods. Furthermore, we found the putative parental AMRs AIMER detected could be distinguished from sequence-dependent AMRs. Finally, we found a novel germline imprinting-like AMR using WGBS data from 17 distinct mouse tissue samples. The results indicate that AIMER is a good choice for detecting imprinting-like (parent-of-origin-dependent) AMRs. We hope this method will be helpful for future genomic imprinting studies. The Python source code for our project is now publicly available on both GitHub (https://github.com/ZhaoLab-TMU/AIMER) and Gitee (https://gitee.com/zhaolab_tmu/AIMER).

ZSCAN25 methylation predicts seizures and severe alcohol withdrawal syndrome

Currently, clinicians use their judgement and indices such as the Prediction of Alcohol Withdrawal Syndrome Scale (PAWSS) to determine whether patients are admitted to hospitals for consideration of withdrawal syndrome (AWS). However, only a fraction of those admitted will experience severe AWS. Previously, we and others have shown that epigenetic indices, such as the Alcohol T-Score (ATS), can quantify recent alcohol consumption. However, whether these or other alcohol biomarkers, such as carbohydrate deficient transferrin (CDT), could identify those at risk for severe AWS is unknown. To determine this, we first conducted genome-wide DNA methylation analyses of subjects entering and exiting alcohol treatment to identify loci whose methylation quickly reverted as a function of abstinence. We then tested whether methylation at a rapidly reverting locus, cg07375256, or other existing metrics including PAWSS scores, CDT levels, or ATS, could predict outcome in 125 subjects admitted for consideration of AWS. We found that PAWSS did not significantly predict severe AWS nor seizures. However, methylation at cg07375256 (ZSCAN25) and CDT strongly predicted severe AWS with ATS (p < 0.007) and cg07375256 (p < 6 × 10-5) methylation also predicting AWS associated seizures. We conclude that epigenetic methods can predict those likely to experience severe AWS and that the use of these or similar Precision Epigenetic approaches could better guide AWS management.

Antibody glycan quality predicted from CHO cell culture media markers and machine learning

N-glycosylation can have a profound effect on the quality of mAb therapeutics. In biomanufacturing, one of the ways to influence N-glycosylation patterns is by altering the media used to grow mAb cell expression systems. Here, we explore the potential of machine learning (ML) to forecast the abundances of N-glycan types based on variables related to the growth media. The ML models exploit a dataset consisting of detailed glycomic characterisation of Anti-HER fed-batch bioreactor cell cultures measured daily under 12 different culture conditions, such as changes in levels of dissolved oxygen, pH, temperature, and the use of two different commercially available media. By performing spent media quantitation and subsequent calculation of pseudo cell consumption rates (termed media markers) as inputs to the ML model, we were able to demonstrate a small subset of media markers (18 selected out of 167 mass spectrometry peaks) in a Chinese Hamster Ovary (CHO) cell cultures are important to model N-glycan relative abundances (Regression - correlations between 0.80-0.92; Classification - AUC between 75.0-97.2). The performances suggest the ML models can infer N-glycan critical quality attributes from extracellular media as a proxy. Given its accuracy, we envisage its potential applications in biomaufactucuring, especially in areas of process development, downstream and upstream bioprocessing.

Validation and extension of the quick dementia rating system (QDRS)

Informant report dementia severity staging measures, such as the Quick Dementia Rating System (QDRS) offer clinicians useful diagnostic and staging information. These measures also potentially avoid many of the pitfalls inherent in mental status examinations (e.g., cultural bias, educational bias, floor and ceiling effects). We derive cut points for the QDRS and comprehensively examine their classification accuracy in a large, diagnostically heterogeneous, rural, memory disorder clinic sample. Our findings suggest the QDRS may be helpful when used in the context of a comprehensive diagnostic and staging evaluation. When used in isolation, the QDRS is insufficiently accurate for diagnosis and staging of dementia.

Improving the diagnostic value of lineup rejections

Erroneous eyewitness identification evidence is likely the leading cause of wrongful convictions. To minimize this error, scientists recommend collecting confidence. Research shows that eyewitness confidence and accuracy are strongly related when an eyewitness identifies someone from an initial and properly administered lineup. However, confidence is far less informative of accuracy when an eyewitness identifies no one and rejects the lineup instead. In this study, I aimed to improve the confidence-accuracy relationship for lineup rejections in two ways. First, I aimed to find the lineup that yields the strongest confidence-accuracy relationship for lineup rejections by comparing the standard, simultaneous procedure used by police worldwide to the novel "reveal" procedure designed by scientists to boost accuracy. Second, I aimed to find the best method for collecting confidence. To achieve this secondary aim, I made use of machine-learning techniques to compare confidence expressed in words to numeric confidence ratings. First, I find a significantly stronger confidence-accuracy relationship for lineup rejections in the reveal than in the standard procedure regardless of the method used to collect confidence. Second, I find that confidence expressed in words captures unique diagnostic information about the likely accuracy of a lineup rejection separate from the diagnostic information captured by numeric confidence ratings. These results inform models of recognition memory and may improve the criminal-legal system by increasing the diagnostic value of a lineup rejection.

White blood cell counts, ratios, and C-reactive protein among individuals with schizophrenia spectrum disorder and associations with long-term outcomes: a population-based study

BACKGROUND

Immune mechanisms are associated with adverse outcomes in schizophrenia; however, the predictive value of various peripheral immune biomarkers has not been collectively investigated in a large cohort before.

OBJECTIVE

To investigate how white blood cell (WBC) counts, ratios, and C-Reactive Protein (CRP) levels influence the long-term outcomes of individuals with schizophrenia spectrum disorder (SSD).

METHODS

We identified all adults in the Central Denmark Region during 1994-2013 with a measurement of WBC counts and/or CRP at first diagnosis of SSD. WBC ratios were calculated, and both WBC counts and ratios were quartile-categorized (Q4 upper quartile). We followed these individuals from first diagnosis until outcome of interest (death, treatment resistance and psychiatric readmissions), emigration or December 31, 2016, using Cox regression analysis to estimate adjusted hazard ratios (aHRs).

RESULTS

Among 6,845 participants, 375 (5.5 %) died, 477 (6.9 %) exhibited treatment resistance, and 1470 (21.5 %) were readmitted during follow-up. Elevated baseline levels of leukocytes, neutrophils, monocytes, LLR, NLR, MLR, and CRP increased the risk of death, whereas higher levels of lymphocytes, platelets, and PLR were associated with lower risk. ROC analysis identified CRP as the strongest predictor for mortality (AUC=0.84). Moreover, elevated levels of leukocytes, neutrophils, monocytes, LLR, NLR and MLR were associated with treatment resistance. Lastly, higher platelet counts decreased the risk of psychiatric readmissions, while elevated LLR increased this risk.

CONCLUSIONS

Elevated levels of WBC counts, ratios, and CRP at the initial diagnosis of SSD are associated with mortality, with CRP demonstrating the highest predictive value. Additionally, certain WBC counts and ratios are associated with treatment resistance and psychiatric readmissions.

Discriminative validity and interpretability of the mood and feelings questionnaire

BACKGROUND

Using the Mood and Feelings Questionnaire (MFQ) to differentiate between depression severity levels remains unexplored. We explored the discriminative validity of the MFQ to identify an optimal cut-off MFQ score to distinguish between subthreshold-to-mild and moderate-to-severe depression severity levels.

METHODS

An observational cross-sectional design was used in a sample (N = 67) of help-seeking youth (ages 13 to 18, inclusive) experiencing depressive symptoms. The MFQ was administered verbatim to youth by a research analyst over the phone. Youth were then grouped into subthreshold-to-mild or moderate-to-severe depression severity categories based on scores received on the Kiddie Schedule for Affective Disorders and Schizophrenia-Depression Rating Scale. Receiver Operating Characteristic curve analyses were conducted, with area under the curve (AUC) and Youden Index (J) as primary indices. We hypothesized that the lower limit of the 95 % confidence interval for the area under the curve would be ≥0.70.

RESULTS

The primary analysis yielded an AUC of 0.85 (95 % CI: 0.763-0.947) and an optimal cut-off of ≥43 (J = 0.60, positive predictive value = 91.4 %, negative predictive value = 62.5 %, sensitivity = 72.7 %, specificity = 87.0 %).

LIMITATIONS

Our study collected a small sample, and as such cannot identify how subgroup classification (e.g., based on race or gender) may moderate outcomes. Further, unknown measurement error of the predictor and reference variable measures can bias the estimates.

CONCLUSIONS

Our preliminary findings highlight the potential for the MFQ to support clinical decision-making relevant to adolescents experiencing varying severities of depressive symptoms in secondary care settings.

Polyhexamethylene guanidine aerosol causes irreversible changes in blood proteins that associated with the severity of lung injury

Polyhexamethylene guanidine (PHMG) is a positively charged polymer used as a disinfectant that kills microbes but can cause pulmonary fibrosis if inhaled. After the long-term risks were confirmed in South Korea, it became crucial to measure toxicity through diverse surrogate biomarkers, not only proteins, especially after these hazardous chemicals had cleared from the body. These biomarkers, identified by their biological functions rather than simple numerical calculations, effectively explained the imbalance of pulmonary surfactant caused by fibrosis from PHMG exposure. These long-term studies on children exposed to PHMG has shown that blood protein indicators, primarily related to apolipoproteins and extracellular matrix, can distinguish the degree of exposure to humidifier disinfectants (HDs). We defined the extreme gradient boosting models and computed reflection scores based on just ten selected proteins, which were also verified in adult women exposed to HD. The reflection scores successfully discriminated between the HD-exposed and unexposed groups in both children and adult females (AUROC: 0.957 and 0.974, respectively) and had a strong negative correlation with lung function indicators. Even after an average of more than 10 years, blood is still considered a meaningful specimen for assessing the impact of environmental exposure to toxic substances, with proteins providing in identifying the pathological severity of such conditions.

Transcriptome combined with single cell to explore hypoxia-related biomarkers in osteoarthritis

Osteoarthritis (OA) is a prevalent degenerative condition among the elderly on a global scale. Research has demonstrated that hypoxia can promote chondrocyte apoptosis and autophagy leading to OA. Hence, it was vital to screen the hypoxia related biomarkers in OA. We introduced transcriptome data to screen out differentially expressed genes (DEGs) in GSE114007 and GSE57218 (OA samples vs control samples). We performed differential expression analysis in key annotated cell to obtain differentially expressed marker genes at the single-cell level (GSE169454). Venn diagram was executed to identify hypoxia related differentially expressed genes (HR-DEGs) associated with OA. Further, feature genes were obtained through the application of least absolute shrinkage and selection operator (LASSO) regression and the Random Forest (RF) algorithm. Receiver operating characteristic (ROC) and expression level analysis were used to identify hypoxia related biomarkers in OA. We further performed immune infiltration and gene set enrichment analysis (GSEA) based on hypoxia related biomarkers. Finally, we analyzed the expression of biomarkers in single-cell level. We identified 2351 DEGs associated with OA. At the single-cell level, 242 differentially expressed marker genes were obtained. 12 HR-DEGs were retained venn diagram. Subsequently, three hypoxia related biomarkers (ADM, DDIT3 and MAFF) were identified. Moreover, we got 15 significantly different immune cells. Finally, we found a lower expression of ADM, DDIT3 and MAFF in OA group compared to the control group in ECs. Overall, we obtained three hypoxia related biomarkers (ADM, DDIT3 and MAFF) associated with OA, which established a theoretical basis for addressing OA.

m6A/m1A/m5C-Associated Methylation Alterations and Immune Profile in MDD

Major depressive disorder (MDD) is a prevalent psychiatric condition often accompanied by severe impairments in cognitive and functional capacities. This research was conducted to identify RNA modification-related gene signatures and associated functional pathways in MDD. Differentially expressed RNA modification-related genes in MDD were first identified. And a random forest model was developed and distinct RNA modification patterns were discerned based on signature genes. Then, comprehensive analyses of RNA modification-associated genes in MDD were performed, including functional analyses and immune cell infiltration. The study identified 29 differentially expressed RNA modification-related genes in MDD and two distinct RNA modification patterns. TRMT112, MBD3, NUDT21, and IGF2BP1 of the risk signature were detected. Functional analyses confirmed the involvement of RNA modification in pathways like phosphatidylinositol 3-kinase signaling and nucleotide oligomerization domain (NOD)-like receptor signaling in MDD. NUDT21 displayed a strong positive correlation with type 2 T helper cells, while IGF2BP1 negatively correlated with activated CD8 T cells, central memory CD4 T cells, and natural killer T cells. In summary, further research into the roles of NUDT21 and IGF2BP1 would be valuable for understanding MDD prognosis. The identified RNA modification-related gene signatures and pathways provide insights into MDD molecular etiology and potential diagnostic biomarkers.

Revealing Academic Evolution and Frontier Pattern in the Field of Uveitis Using Bibliometric Analysis, Natural Language Processing, and Machine Learning

PURPOSE

Numerous uveitis articles were published in this century, underneath which hides valuable intelligence. We aimed to characterize the evolution and patterns in this field.

METHODS

We divided the 15,994 uveitis papers into four consecutive time periods for bibliometric analysis, and applied latent Dirichlet allocation topic modeling and machine learning techniques to the latest period. .

RESULTS

The yearly publication pattern fitted the curve: 1.21335x2 - 4,848.95282x + 4,844,935.58876 (R2 = 0.98311). The USA, the most productive country/region, focused on topics like ankylosing spondylitis and biologic therapy, whereas China (mainland) focused on topics like OCT and Behcet disease. The logistic regression showed the highest accuracy (71.6%) in the test set.

CONCLUSION

In this century, a growing number of countries/regions/authors/journals are involved in the uveitis study, promoting the scientific output and thematic evolution. Our pioneering study uncovers the evolving academic trends and frontier patterns in this field using bibliometric analysis and AI algorithms.

Bioinformatics Analysis and Identification of Ferroptosis-Related Hub Genes in Intervertebral Disc Degeneration

Approximately 80% of individuals encounter lower back pain (LBP), a prevalent clinical issue largely attributed to intervertebral disc degeneration (IDD). Ferroptosis is an iron-dependent lipid peroxidation-driven cell death, and there is growing evidence that ferroptosis plays an important role in various human diseases. However, the underlying mechanism of ferroptosis in IDD remains unclear. This study aims to reveal the potential hub genes and related pathways of ferroptosis in the pathogenesis and progression of IDD. In this study, we analyzed three microarray datasets from the GEO database. Additionally, we downloaded ferroptosis-related genes from FerrDb-V2 and extracted apoptosis-related genes from UniProt as a control to show the specificity of ferroptosis. Weighted gene co-expression network analysis (WGCNA) was performed to identify the IDD-related module genes. Then, ferroptosis-related genes and apoptosis-related genes were separately overlapped with the IDD-related module genes, resulting in the identification of 35 ferroptosis-related module genes (FRMG) and 142 apoptosis-related module genes (ARMG). Furthermore, we performed functional enrichment analysis and protein-protein interaction network, and Cytoscape along with CytoHubba was used to identify the hub genes. Finally, logistic regression models were constructed and identified two hub FRMGs (PTEN and EGFR) and one hub ARMG (CTNNB1), which could distinguish IDD patients from controls (P < 0.05). The areas under the ROC curves were 0.792 and 0.730, respectively, suggesting that ferroptosis is more specific than apoptosis in IDD. In conclusion, this study provided fresh perspectives on ferroptosis in the pathogenesis and progression of IDD that can be used to evaluate potential biomarker genes and therapeutic targets.

The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone

Antenatal hydronephrosis (HN) impacts up to 5% of pregnancies and requires close, frequent follow-up monitoring to determine who may benefit from surgical intervention. To create an automated HN Severity Index (HSI) that helps guide clinical decision-making directly from renal ultrasound images. We applied a deep learning model to paediatric renal ultrasound images to predict the need for surgical intervention based on the HSI. The model was developed and studied at four large quaternary free-standing paediatric hospitals in North America. We evaluated the degree to which HSI corresponded with surgical intervention at each hospital using area under the receiver-operator curve, area under the precision-recall curve, sensitivity, and specificity. HSI predicted subsequent surgical intervention with > 90% AUROC, > 90% sensitivity, and > 70% specificity in a test set of 202 patients from the same institution. At three external institutions, HSI corresponded with AUROCs ≥ 90%, sensitivities ≥ 80%, and specificities > 50%. It is possible to automatically and reliably assess HN severity directly from a single ultrasound. The HSI stratifies low- and high-risk HN patients thus helping to triage low-risk patients while maintaining very high sensitivity to surgical cases. HN severity can be predicted from a single patient ultrasound using a novel image-based artificial intelligence system.

Risk factors for long COVID syndrome in postmenopausal women with previously reported diagnosis of COVID-19

PURPOSE

Long COVID-19 syndrome occurs in 10-20 % of people after a confirmed/probable SARS-COV-2 infection; new symptoms begin within three months of COVID-19 diagnosis and last > 8 weeks. Little is known about risk factors for long COVID, particularly in older people who are at greater risk of COVID complications.

METHODS

Data are from Women's Health Initiative (WHI) postmenopausal women who completed COVID surveys that included questions on whether they had ever been diagnosed with COVID and length and nature of symptoms. Long COVID was classified using standard consensus criteria. Using WHI demographic and health data collected at study enrollment (1993-98) through the present day, machine learning identified the top 20 risk factors for long COVID. These variables were tested in logistic regression models.

RESULTS

Of n = 37,280 survey respondents, 1237 (mean age = 83 years) reported a positive COVID-19 test and 425 (30 %) reported long COVID. Symptoms included an array of neurological, cardio-pulmonary, musculoskeletal, and general fatigue, and malaise symptoms. Long COVID risk factors included weight loss, physical and mobility limitations, and specific heath conditions (e.g., history of heart valve procedure, rheumatoid arthritis).

CONCLUSIONS

Knowledge of risk factors for long COVID may be the first step in understanding the etiology of this complex disease.

Internet-Delivered Psychological Pain Management: A Prospective Cohort Study Examining Routine Care Delivery by a Specialist Regional Multidisciplinary Pain Service

Several clinical trials have demonstrated the effectiveness of internet-delivered psychological-based pain management programs (PMPs). However, to date, no large studies have reported the outcomes of PMPs when delivered by specialist multidisciplinary pain services in routine care. The present study reports (n = 653) the outcomes of an internet-delivered PMP provided as routine care by a specialist Australian regional pain service over a 6-year period. High levels of treatment commencement (85%) and completion (72%) were observed, with more than 80% of patients reporting they were satisfied with the intervention. Clinical improvements were observed from pretreatment to post-treatment (% change, 95% confidence intervals (CI)) in pain-related disability (8.8%; 4.5, 12.8), depression (28.4%; 23.0, 33.4), anxiety (21.9%; 14.6, 28.5), and pain intensity (7%; 3.5, 10.5), which were maintained to 3-month follow-up. At 3-month follow-up, 27% (23, 31), 46% (41, 51), 44% (39, 49), and 22% (19, 26) reported clinically meaningful (defined as ≥ 30%) improvements in pain-related disability, depression, anxiety, and pain intensity, respectively. These results were obtained with relatively little therapist time per patient (M = 30.0, (standard deviation) SD = 18.8) to deliver the intervention. The current findings highlight the potential of internet-delivered PMPs as part of the services provided by specialist pain services, particularly those servicing large geographical regions and for patients unable to travel to clinics for face-to-face care. PERSPECTIVE: This study reports the outcomes of the routine delivery of an internet-delivered psychological PMP by a specialist pain service. The findings highlight the potential of this model of care when provided by specialist pain services, particularly for patients not unable to attend and not requiring intensive face-to-face care.

Comparing human evaluations of eyewitness statements to a machine learning classifier under pristine and suboptimal lineup administration procedures

Recent work highlights the ability of verbal machine learning classifiers to distinguish between accurate and inaccurate recognition memory decisions (Dobbins, 2022; Dobbins & Kantner, 2019; Seale-Carlisle, Grabman, & Dodson, 2022). Given the surge of interest in these modeling techniques, there is an urgent need to investigate verbal classifiers' limitations - particularly in applied contexts such as when police collect eyewitness's confidence statements. We find that confirmatory feedback (e.g., "This study now has a total of 87 participants, 84 of them made the same decision as you!") weakens the relationship between identification accuracy and verbal classifier scores to a similar degree as mock witnesses' numeric confidence judgments (Experiment 1). Crucially, for the first time, we compare the discriminative value of verbal classifier scores to the ratings of human evaluators who assessed the identical verbal confidence statements (Experiment 2). Our results suggest that human evaluators outperform the classifier when mock witnesses received no feedback; however, the classifier matches (or exceeds) the performance of human evaluators when mock witnesses received confirmatory feedback. Providing lineup information to human evaluators resulted in a worse ability to distinguish between correct and filler identifications, suggesting that this particular information may encourage the use of inappropriate heuristics when rendering accuracy judgments. Overall, these results suggest that the utility of verbal classifiers may be enhanced when contextual effects (e.g., lineup presence) impair human estimates of others' performance, but that translating witnesses' statements into classifier scores will not fix the problems of an improperly conducted lineup procedure.

Voxel-based tumor dose correlates to complete pathologic necrosis after transarterial radioembolization for hepatocellular carcinoma

PURPOSE

The transarterial radioembolization (TARE) dose is traditionally calculated using the single-compartment Medical Internal Radiation Dose (MIRD) formula. This study utilized voxel-based dosimetry to correlate tumor dose with explant pathology in order to identify dose thresholds that predicted response.

METHODS

All patients with HCC treated with TARE using yttrium-90 [90Y] glass microspheres at a single institution between January 2015 - June 2023 who underwent liver transplantation were eligible. The [90Y] distribution and dose-volume histograms were determined using Simplicity90 (Mirada Medical, Oxford UK) with a Bremsstrahlung SPECT/CT. A complete response was assigned if explant pathology showed complete necrosis and the patient had not undergone additional treatments to the same tumor after TARE. Logistic regression and receiver operator characteristic (ROC) curves were constructed to evaluate dose thresholds correlated with response.

RESULTS

Forty-one patients were included. Twenty-six (63%) met criteria for complete response. Dose to 95% (D95), 70% (D70), and 50% (D50) of the tumor volume were associated with likelihood of complete response by logistic regression (all p < 0.05). For lesions with complete response versus without, the median D95 was 813 versus 232 Gy, D70 was 1052 versus 315 Gy, and D50 was 1181 versus 369 Gy (all p < 0.01). A D95 > 719 Gy had the highest accuracy at 68% (58% sensitivity, 87% specificity) for predicting complete response. Median percent of tumor volume receiving at least 100 Gy (V100), 200 Gy (V200), 300 Gy (V300), and 400 Gy (V400) also differed by pathologic response: the median V100, V200, V300, and V400 was 100% versus 99%, 100% versus 97%, 100% versus 74%, and 100% versus 43% in the complete response versus non-complete response groups, respectively (all p < 0.05).

CONCLUSION

Voxel-based dosimetry was well-correlated with explant pathology. The D95 threshold had the highest accuracy, suggesting the D95 may be a relevant target for multi-compartment dosimetry.

Deep Learning Virtual Contrast-Enhanced T1 Mapping for Contrast-Free Myocardial Extracellular Volume Assessment

BACKGROUND

The acquisition of contrast-enhanced T1 maps to calculate extracellular volume (ECV) requires contrast agent administration and is time consuming. This study investigates generative adversarial networks for contrast-free, virtual extracellular volume (vECV) by generating virtual contrast-enhanced T1 maps.

METHODS AND RESULTS

This retrospective study includes 2518 registered native and contrast-enhanced T1 maps from 1000 patients who underwent cardiovascular magnetic resonance at 1.5 Tesla. Recent hematocrit values of 123 patients (hold-out test) and 96 patients from a different institution (external evaluation) allowed for calculation of conventional ECV. A generative adversarial network was trained to generate virtual contrast-enhanced T1 maps from native T1 maps for vECV creation. Mean and SD of the difference per patient (ΔECV) were calculated and compared by permutation of the 2-sided t test with 10 000 resamples. For ECV and vECV, differences in area under the receiver operating characteristic curve (AUC) for discriminating hold-out test patients with normal cardiovascular magnetic resonance versus myocarditis or amyloidosis were tested with Delong's test. ECV and vECV showed a high agreement in patients with myocarditis (ΔECV: hold-out test, 2.0%±1.5%; external evaluation, 1.9%±1.7%) and normal cardiovascular magnetic resonance (ΔECV: hold-out test, 1.9%±1.4%; external evaluation, 1.5%±1.2%), but variations in amyloidosis were higher (ΔECV: hold-out test, 6.2%±6.0%; external evaluation, 15.5%±6.4%). In the hold-out test, ECV and vECV had a comparable AUC for the diagnosis of myocarditis (ECV AUC, 0.77 versus vECV AUC, 0.76; P=0.76) and amyloidosis (ECV AUC, 0.99 versus vECV AUC, 0.96; P=0.52).

CONCLUSIONS

Generation of vECV on the basis of native T1 maps is feasible. Multicenter training data are required to further enhance generalizability of vECV in amyloidosis.

Identification and experimental validation of immune-related gene PPARG is involved in ulcerative colitis

BACKGROUND

The pathophysiology of ulcerative colitis (UC) is believed to be heavily influenced by immunology, which presents challenges for both diagnosis and treatment. The main aims of this study are to deepen our understanding of the immunological characteristics associated with the disease and to identify valuable biomarkers for diagnosis and treatment.

METHODS

The UC datasets were sourced from the GEO database and were analyzed using unsupervised clustering to identify different subtypes of UC. Twelve machine learning algorithms and Deep learning model DNN were developed to identify potential UC biomarkers, with the LIME and SHAP methods used to explain the models' findings. PPI network is used to verify the identified key biomarkers, and then a network connecting super enhancers, transcription factors and genes is constructed. Single-cell sequencing technology was utilized to investigate the role of Peroxisome Proliferator Activated Receptor Gamma (PPARG) in UC and its correlation with macrophage infiltration. Furthermore, alterations in PPARG expression were validated through Western blot (WB) and immunohistochemistry (IHC) in both in vitro and in vivo experiments.

RESULT

By utilizing bioinformatics techniques, we were able to pinpoint PPARG as a key biomarker for UC. The expression of PPARG was significantly reduced in cell models, UC animal models, and colitis models induced by dextran sodium sulfate (DSS). Interestingly, overexpression of PPARG was able to restore intestinal barrier function in H2O2-induced IEC-6 cells. Additionally, immune-related differentially expressed genes (DEGs) allowed for efficient classification of UC samples into neutrophil and mitochondrial metabolic subtypes. A diagnostic model incorporating the three disease-specific genes PPARG, PLA2G2A, and IDO1 demonstrated high accuracy in distinguishing between the UC group and the control group. Furthermore, single-cell analysis revealed that decreased PPARG expression in colon tissue may contribute to the polarization of M1 macrophages through activation of inflammatory pathways.

CONCLUSION

In conclusion, PPARG, a gene related to immunity, has been established as a reliable potential biomarker for the diagnosis and treatment of UC. The immune response it controls plays a key role in the progression and development of UC by enabling interaction between characteristic biomarkers and immune infiltrating cells.

LncRNA XIST/miR-381-3P/STAT1 axis as a potential biomarker for lupus nephritis

OBJECTIVE

We aim to investigate the potential roles of key genes in the development of lupus nephritis (LN), screen key biomarkers, and construct the lncRNA XIST/miR-381-3P/STAT1 axis by using bioinformatic prediction combined with clinical validation, thereby providing new targets and insights for clinical research.

METHODS

Gene expression microarrays GSE157293 and GSE112943 were downloaded from the GEO database to obtain differentially expressed genes (DEGs), followed by enrichment analyses on these DEGs, which were enriched and analyzed to construct a protein-protein interaction (PPI) network to screen core genes. The lncRNA-miRNA-mRNA regulatory network was predicted and constructed based on the miRNA database. 37 female patients with systemic lupus erythematosus (SLE) were recruited to validate the bioinformatics results by exploring the diagnostic value of the target ceRNA axis in LN by dual luciferase and real-time fluorescence quantitative PCR (RT-qPCR) and receiver operating characteristic (ROC).

RESULTS

The data represented that a total of 133 differential genes were screened in the GSE157293 dataset and 2869 differential genes in the GSE112943 dataset, yielding a total of 26 differentially co-expressed genes. Six core genes (STAT1, OAS2, OAS3, IFI44, DDX60, and IFI44L) were screened. Biological functional analysis identified key relevant pathways in LN. ROC curve analysis suggested that lncRNA XIST, miR-381-3P, and STAT1 could be used as potential molecular markers to assist in the diagnosis of LN.

CONCLUSION

STAT1 is a key gene in the development of LN. In conclusion, lncRNA XIST, miR-381-3P, and STAT1 can be used as new molecular markers to assist in the diagnosis of LN, and the lncRNA XIST/miR-381-3P/STAT1 axis may be a potential therapeutic target for LN.

Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020

OBJECTIVES

To improve firearm injury encounter classification (new vs follow-up) using machine learning (ML) and compare our ML model to other common approaches.

MATERIALS AND METHODS

This retrospective study used data from the St Louis region-wide hospital-based violence intervention program data repository (2010-2020). We randomly selected 500 patients with a firearm injury diagnosis for inclusion, with 808 total firearm injury encounters split (70/30) for training and testing. We trained a least absolute shrinkage and selection operator (LASSO) regression model with the following predictors: admission type, time between firearm injury visits, number of prior firearm injury emergency department (ED) visits, encounter type (ED or other), and diagnostic codes. Our gold standard for new firearm injury encounter classification was manual chart review. We then used our test data to compare the performance of our ML model to other commonly used approaches (proxy measures of ED visits and time between firearm injury encounters, and diagnostic code encounter type designation [initial vs subsequent or sequela]). Performance metrics included area under the curve (AUC), sensitivity, and specificity with 95% confidence intervals (CIs).

RESULTS

The ML model had excellent discrimination (0.92, 0.88-0.96) with high sensitivity (0.95, 0.90-0.98) and specificity (0.89, 0.81-0.95). AUC was significantly higher than time-based outcomes, sensitivity was slightly (but not significantly) lower than other approaches, and specificity was higher than all other methods.

DISCUSSION

ML successfully delineated new firearm injury encounters, outperforming other approaches in ruling out encounters for follow-up.

CONCLUSION

ML can be used to identify new firearm injury encounters and may be particularly useful in studies assessing re-injuries.

The Impact of Body Mass Index on Adverse Outcomes Associated with Panniculectomy: A Multimodal Analysis

BACKGROUND

Overhanging pannus may be detrimental to ambulation, urination, sexual function, and social well-being. Massive weight loss patients often have high residual body mass index (BMI) and comorbidities presenting a unique challenge in panniculectomy patient selection. This study aims to better characterize the role of BMI in postoperative complications following panniculectomy.

METHODS

A meta-analysis attempted to assess the impact of BMI on complications following panniculectomy. Cochrane Q and I2 test statistics measured study heterogeneity, with subsequent random effects meta-regression investigating these results. After this, all panniculectomy patients in the National Surgical Quality Improvement Program database in the years 2007 to 2019 were analyzed. Univariate and multivariable tests assessed the relative role of BMI on 30-day postoperative complications.

RESULTS

Thirty-four studies satisfied inclusion criteria, revealing very high heterogeneity (Cochrane Q = 2453.3; I2 = 99.1%), precluding further meta-analysis results. Receiver operating characteristic curves demonstrated BMI was a significant predictor of both all causes (area under the curve, 0.64; 95% CI, 0.62 to 0.66) and wound complications (area under the curve, 0.66; 95% CI, 0.63 to 0.69). BMI remained significant following multivariable regression analyses. Restricted cubic spines demonstrated marginal increases in complication incidence above 33.2 and 35 kg/m 2 for all-cause and wound complications, respectively.

CONCLUSIONS

Reported literature regarding postoperative complications in panniculectomy patients is highly heterogeneous and may limit evidence-based care. Complication incidence positively correlated with BMI, although the receiver operating characteristic curve demonstrated its limitations as the sole predictive variable. Furthermore, restricted cubic splines demonstrated diminishing marginal predictive capacity of BMI for incremental increases in BMIs above 33.2 to 35 kg/m 2 . These findings support a reevaluation of the role of BMI cutoffs in panniculectomy patient selection.

Developing diagnostic biomarkers for Alzheimer's disease based on histone lactylation-related gene

Background

Research underscores the significant influence of histone lactylation pathways in the progression of Alzheimer's disease (AD), though the molecular mechanisms associated with histone lactylation-related genes (HLRGs) in AD are still insufficiently investigated.

Methods

This study employed datasets GSE85426 and GSE97760 to identify candidate genes by intersecting weighted gene co-expression network analysis (WGCNA) module genes with AD-control differentially expressed genes (DEGs). Subsequently, machine learning refined key genes, validated by receiver operating characteristic (ROC) curve performance. Gene-set enrichment analysis (GSEA) explored the molecular mechanisms of these diagnostic markers. Concurrently, the association between the diagnostic genes and both differential immune cells and immune responses was examined. Furthermore, a ceRNA and gene-drug network was developed. Finally, the expression of the selected genes was validated using brain tissues from AD model mice.

Results

This study identified five genes (ARID5B, NSMCE4A, SESN1, THADA, and XPA) with significant diagnostic utility, primarily enriched in olfactory transduction and N-glycan biosynthesis pathways. Correlation analysis demonstrated a strong positive association between all diagnostic genes and naive B cells. The ceRNA regulatory network comprised 7 miRNAs, 2 mRNAs, and 25 lncRNAs. Additionally, 33 drugs targeting the diagnostic genes were predicted. Following expression validation through training and validation sets, three genes (ARID5B, SESN1, XPA) were ultimately confirmed as biomarkers for this study. RT-qPCR and Western blot analyses revealed upregulated expression of ARID5B, SESN1, and XPA in the cerebral tissue of AD model mice.

Conclusion

Three histone lactylation-linked genes (ARID5B, SESN1, XPA) were identified as potential AD biomarkers, indicating a strong association with disease progression.

Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank

AIMS

Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank.

METHODS AND RESULTS

A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1 mm isotropic resolution from CMR short- and long-axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate, and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P-values), particularly for sex, age, and body mass index. AUCs for all logistic regressions were higher for deep learning volumes than standard volumes (P < 0.001 for all four chambers at ED and ES).

CONCLUSION

Neural network reconstructions of whole heart volumes had significantly stronger associations with CVD and risk factors than standard volume estimation methods in an automatic processing pipeline.

Progression from cardiomyopathy to heart failure with reduced ejection fraction: A CORIN deficient course

Cardiomyopathies, encompassing hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), constitute a diverse spectrum of heart muscle diseases that often culminating in heart failure (HF). The inherent molecular heterogeneity of these conditions has implications for prognosis and therapeutic strategies. Publicly available microarray and RNA sequencing (RNA-seq) data sets of HCM (n = 106 from GSE36961) and DCM (n = 18 from GSE135055 and 166 from GSE141910) patients were employed for our analysis. The Non-negative Matrix Factorization (NMF) algorithm was applied to explore the molecular stratification within HCM and DCM, and enrichment analysis was performed to delineate their biological characteristics. By integrating bulk and single-nucleus RNA-seq (snRNA-seq) data, we identified a potential biomarker for HCM progression and cardiac fibrosis, which was subsequently validated using mendelian randomization and in vitro. Our application of NMF identified two distinct molecular clusters. Particularly, a profibrotic, heart failure with reduced ejection fraction (HFrEF)-resembling Cluster 1 emerged, characterized by diminished expression of CORIN and a high degree of fibroblast activation. This cluster also exhibited lower left ventricular ejection fraction (LVEF) and worse prognostic outcomes, establishing the significance of this molecular subclassification. We further found that overexpression of CORIN could mitigate TGFβ1-induced expression of col1a1 and α-SMA in neonatal rat cardiac fibroblasts. Our results indicated the heterogeneity of HCM population, and further evidenced the participation of corin in the progression of HCM, DCM and HFrEF. Nevertheless, our study is constrained by the lack of corresponding clinical data and experimental validation of the identified subtypes. Therefore, further studies are warranted to elucidate the downstream pathways of corin and to validate these findings in independent patient cohorts.

AGBL2 promotes renal cell carcinoma cells proliferation and migration via α-tubulin detyrosination

Background

AGBL2's role in tumorigenesis and cancer progression has been reported in several cancer studies, and it is closely associated with α-tubulin detyrosination. The roles of AGBL2 and α-tubulin detyrosination in renal cell carcinoma (RCC) pathogenesis remain unclear and require further investigation.

Methods

In this study, we conducted an analysis of AGBL2 expression differences between renal clear cell carcinoma tissues and normal tissues using data from The Cancer Genome Atlas (TCGA). We performed a comprehensive prognostic analysis of AGBL2 in Kidney Renal Clear Cell Carcinoma (KIRC) using univariate and multivariate Cox regression. Based on the results of the Cox analysis, we constructed a prognostic model to assess its predictive capabilities. Receiver Operating Characteristic (ROC) analysis confirmed the diagnostic value of AGBL2 in renal cancer. We conducted further validation by analyzing cancer tissue samples and renal cancer cell lines, which confirmed the role of AGBL2 in promoting RCC cell proliferation and migration through in vitro experiments. Additionally, we verified the impact of AGBL2's detyrosination on α-tubulin using the tubulin carboxypeptidase (TCP) inhibitor parthenolide. Finally, we performed sequencing analysis on AGBL2 knockdown 786-O cells to investigate the correlation between AGBL2, immune infiltration, and AKT phosphorylation. Moreover, we experimentally demonstrated the enhancing effect of AGBL2 on AKT phosphorylation.

Results

TCGA analysis revealed a significant increase in AGBL2 expression in RCC patients, which was correlated with poorer overall survival (OS), disease-specific survival (DSS), and progression-free intervals (PFI). According to the analysis results, we constructed column-line plots to predict the 1-, 3-, and 5-year survival outcomes in RCC patients. Additionally, the calibration plots assessing the model's performance exhibited favorable agreement with the predicted outcomes. And the ROC curves showed that AGBL2 showed good diagnostic performance in KIRC (AUC = 0.836)). Cell phenotyping assays revealed that AGBL2 knockdown in RCC cells significantly inhibited cell proliferation and migration. Conversely, overexpression of AGBL2 resulted in increased cell proliferation and migration in RCC cells. We observed that AGBL2 is predominantly located in the nucleus and can elevate the detyrosination level of α-tubulin in RCC cells. Moreover, the enhancement of RCC cell proliferation and migration by AGBL2 was partially inhibited after treatment with the TCP inhibitor parthenolide. Analysis of the sequencing data revealed that AGBL2 is associated with a diverse array of biological processes, encompassing signal transduction and immune infiltration. Interestingly, AGBL2 expression exhibited a negative correlation with the majority of immune cell infiltrations. Additionally, AGBL2 was found to enhance the phosphorylation of AKT in RCC cells.

Conclusion

Our study suggests that AGBL2 fosters RCC cell proliferation and migration by enhancing α-tubulin detyrosination. Moreover, elevated AGBL2 expression increases phosphorylation of AKT in RCC cells.

Identification of diagnostic markers and molecular clusters of cuproptosis-related genes in alcohol-related liver disease based on machine learning and experimental validation

Background and aims

Alcohol-related liver disease (ALD) is a worldwide burden. Cuproptosis has been shown to play a key role in the development of several diseases. However, the role and mechanisms of cuproptosis in ALD remain unclear.

Methods

The RNA-sequencing data of ALD liver samples were downloaded from the Gene Expression Omnibus (GEO) database. Bioinformatical analyses were performed using the R data package. We then identified key genes through multiple machine learning methods. Immunoinfiltration analyses were used to identify different immune cells in ALD patients and controls. The expression levels of key genes were further verified.

Results

We identified three key cuproptosis-related genes (CRGs) (DPYD, SLC31A1, and DBT) through an in-depth analysis of two GEO datasets, including 28 ALD samples and eight control samples. The area under the curve (AUC) value of these three genes combined in determining ALD was 1.0. In the external datasets, the three key genes had AUC values as high as 1.0 and 0.917, respectively. Nomogram, decision curve, and calibration curve analyses also confirmed these genes' ability to predict the diagnosis. These three key genes were found to be involved in multiple pathways associated with ALD progression. We confirmed the mRNA expression of these three key genes in mouse ALD liver samples. Regarding immune cell infiltration, the numbers of B cells, CD8 (+) T cells, NK cells, T-helper cells, and Th1 cells were significantly lower in ALD patient samples than in control liver samples. Single sample gene set enrichment analysis (ssGSEA) was then used to estimate the immune microenvironment of different CRG clusters and CRG-related gene clusters. In addition, we calculated CRG scores through principal component analysis (PCA) and selected Sankey plots to represent the correlation between CRG clusters, gene clusters, and CRG scores. Finally, the three key genes were confirmed in mouse ALD liver samples and liver cells treated with ethanol.

Conclusions

We first established a prognostic model for ALD based on 3 CRGs and robust prediction efficacy was confirmed. Our investigation contributes to a comprehensive understanding of the role of cuproptosis in ALD, presenting promising avenues for the exploration of therapeutic strategies.

Pre-challenge gut microbial signature predicts RhCMV/SIV vaccine efficacy in rhesus macaques

Rhesus cytomegalovirus expressing simian immunodeficiency virus (RhCMV/SIV) vaccines protect ~59% of vaccinated rhesus macaques against repeated limiting-dose intra-rectal exposure with highly pathogenic SIVmac239M, but the exact mechanism responsible for the vaccine efficacy is unknown. It is becoming evident that complex interactions exist between gut microbiota and the host immune system. Here, we aimed to investigate if the rhesus gut microbiome impacts RhCMV/SIV vaccine-induced protection. Three groups of 15 rhesus macaques naturally pre-exposed to RhCMV were vaccinated with RhCMV/SIV vaccines. Rectal swabs were collected longitudinally both before SIV challenge (after vaccination) and post-challenge and were profiled using 16S rRNA based microbiome analysis. We identified ~2,400 16S rRNA amplicon sequence variants (ASVs), representing potential bacterial species/strains. Global gut microbial profiles were strongly associated with each of the three vaccination groups, and all animals tended to maintain consistent profiles throughout the pre-challenge phase. Despite vaccination group differences, by using newly developed compositional data analysis techniques, we identified a common gut microbial signature predictive of vaccine protection outcome across the three vaccination groups. Part of this microbial signature persisted even after SIV challenge. We also observed a strong correlation between this microbial signature and an early signature derived from whole blood transcriptomes in the same animals. Our findings indicate that changes in gut microbiomes are associated with RhCMV/SIV vaccine-induced protection and early host response to vaccination in rhesus macaques.IMPORTANCEThe human immunodeficiency virus (HIV) has infected millions of people worldwide. Unfortunately, still there is no vaccine that can prevent or treat HIV infection. A promising pre-clinical HIV vaccine based on rhesus cytomegalovirus (RhCMV) expressing simian immunodeficiency virus (SIV) antigens (RhCMV/SIV) provides sustained, durable protection against SIV challenge in ~59% of vaccinated rhesus macaques. There is an urgent need to understand the cause of this protection vs non-protection outcome. In this study, we profiled the gut microbiomes of 45 RhCMV/SIV vaccinated rhesus macaques and identified gut microbial signatures that were predictive of RhCMV/SIV vaccination groups and vaccine protection outcomes. These vaccine protection-associated microbial features were significantly correlated with early vaccine-induced host immune signatures in whole blood from the same animals. These findings show that the gut microbiome may be involved in RhCMV/SIV vaccine-induced protection, warranting further research into the impact of the gut microbiome in human vaccine trials.

Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers

The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensitivity or specificity. We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver cancer mutations. The algorithm was trained with cancer gene-specific mutation datasets of cancer patients to identify cancer drivers. DriverDetect performed better than the individual tools or their combinations in the validation test. It has the potential to incorporate future novel prediction algorithms and can be retrained with new datasets, offering an expanded application to pan-cancer analysis for cross-cancer study. (115 words).

Exploring a new signature for lung adenocarcinoma: analyzing cuproptosis-related genes through Integrated single-cell and bulk RNA sequencing

OBJECTIVES

Lung adenocarcinoma (LUAD) continues to pose a significant global health challenge. This research investigates cuproptosis and its association with LUAD progression. Employing various bioinformatics techniques, the study explores the heterogeneity of LUAD cells, identifies prognostic cuproptosis-related genes (CRGs), examines cell-to-cell communication networks, and assesses their functional roles.

METHODS

We downloaded single-cell RNA sequencing data from TISCH2 and bulk RNA sequencing data from TCGA for exploring LUAD cell heterogeneity. Subsequently, "CellChat" package was employed for intercellular communication network analysis, while weighted correlation network analysis was applied for identification of hub CRGs. Further, A cuproptosis related prognostic signature was constructed via LASSO regression, validated through survival analysis, nomogram development, and ROC curves. We assessed immune infiltration, gene mutations, and GSEA of prognostic CRGs. Finally, in vitro experiments were applied to validate CDC25C's role in LUAD.

RESULTS

We identified 15 clusters and nine cell type in LUAD. Malignant cells showed active communication and pathway enrichment in "oxidative phosphorylation" and "glycolysis". Meanwhile, prognostic hub CRGs including PFKP, CDC25C, F12, SIGLEC6, and NLRP7 were identified, with a robust prognostic signature. Immune infiltration, gene mutations, and functional enrichment correlated with prognostic CRGs. In vitro cell experiments have shown that CDC25C-deficient LUAD cell lines exhibited reduced activity.

CONCLUSION

This research reveals the heterogeneity of LUAD cells, identifies key prognostic CRGs, and maps intercellular communication networks, providing insights into LUAD pathogenesis. These findings pave the way for developing targeted therapies and precision medicine approaches.

Comparison of a machine learning model with a conventional rule-based selective dry cow therapy algorithm for detection of intramammary infections

We trained machine learning models to identify intramammary infections (IMI) in late lactation cows at dry-off to guide antibiotic treatment, and compared their performance to a rule-based algorithm that is currently used on dairy farms in the US. We conducted an observational test-characteristics study using a data set of 3,645 cows approaching dry-off from 68 US dairy herds. The outcome variables of interest were cow-level IMI caused by all pathogens, major pathogens, and Streptococcus and Strep-like organisms (SSLO), which were determined using aerobic culture of aseptic quarter-milk samples and identification of isolates using MALDI-TOF. Individual cow records were extracted from the farm software to create 53 feature variables at the cow and 39 at the herd-level which were derived from cow-level descriptive data, records of clinical mastitis events, results from routine testing of milk for volume and concentrations of somatic cell count (SCC), fat, and protein. ML algorithms evaluated were logistic regression, decision tree, random forest, light gradient-boosting machine, naïve bayes, and neural networks. For comparison, cows were also classified according to a conventional rule-based algorithm that considered a cow as high risk for IMI if she had at one or more high SCC (>200,000 cells/ml) tests or ≥2 cases of clinical mastitis during the lactation of enrollment. Area under the curve (AUC) and Youden's index were used to compare models, in addition to binary classification metrics, including sensitivity, specificity, and predictive values. ML models had slightly higher AUC and Youden's index values than the rule-based algorithm for all IMI outcomes of interest. However, these improvements in prediction accuracy were substantially less than what we had considered necessary for the technology to be a worthwhile alternative to the rule-based algorithm. Therefore, evidence is lacking to support the wholesale use of ML-guided selective dry cow therapy at the moment. We recommend that producers wanting to implement algorithm-guided SDCT use a rule-based method.

ParSE-seq: a calibrated multiplexed assay to facilitate the clinical classification of putative splice-altering variants

Interpreting the clinical significance of putative splice-altering variants outside canonical splice sites remains difficult without time-intensive experimental studies. To address this, we introduce Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed assay to quantify variant effects on RNA splicing. We first apply this technique to study hundreds of variants in the arrhythmia-associated gene SCN5A. Variants are studied in 'minigene' plasmids with molecular barcodes to allow pooled variant effect quantification. We perform experiments in two cell types, including disease-relevant induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The assay strongly separates known control variants from ClinVar, enabling quantitative calibration of the ParSE-seq assay. Using these evidence strengths and experimental data, we reclassify 29 of 34 variants with conflicting interpretations and 11 of 42 variants of uncertain significance. In addition to intronic variants, we show that many synonymous and missense variants disrupted RNA splicing. Two splice-altering variants in the assay also disrupt splicing and sodium current when introduced into iPSC-CMs by CRISPR-Cas9 editing. ParSE-seq provides high-throughput experimental data for RNA-splicing to support precision medicine efforts and can be readily adopted to study other loss-of-function genotype-phenotype relationships.

A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes

BACKGROUND

Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.

METHODS

To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model.

RESULTS

We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs.

CONCLUSION

The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Construction and evaluation of two nomograms for screening major depressive disorder and subthreshold depression individuals based on anxiety, depression, and sleep items

BACKGROUND

Current evidence is insufficient to support specific tools for screening Major Depressive Disorder (MDD). Early detection of subthreshold depression (SD) is crucial in preventing its progression to MDD. This study aims to develop nomograms that visualize the weights of predictors to improve the performance of screening tools.

METHODS

Participants were recruited from Peking University Sixth Hospital and Beijing Physical Examination Center between October 2022 and April 2024. The Mini-International Neuropsychiatric Interview (MINI) 5.0.0 was employed as the diagnostic gold standard, and Generalized Anxiety Disorder questionnaire-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Pittsburgh Sleep Quality Index (PSQI) were employed to assess anxiety, depression, and sleep state. The nomograms were constructed by incorporating optimal predictors, selected through the Least Absolute Shrinkage and Selection Operator (LASSO), into a multivariate logistic regression model to estimate the probability of MDD and SD.

RESULTS

After matching age and education, 164 participants were included in each group for analysis. Both nomograms demonstrated superior discrimination, calibration, and clinical applicability compared to PHQ-9. Anxiety emerged as a most significant predictor for SD, while sleep problems exhibited high rankings for both SD and MDD. The two predictors subsequently affect concentration and daytime functioning.

LIMITATIONS

With a lack of external validation data, the performance of nomograms may be overestimated.

CONCLUSIONS

This study is the first attempt to develop a nomogram for predicting SD, while also providing a nomogram for MDD. The crucial predictors offer valuable insights into potential variables for clinical intervention.

Systematic discovery of DNA-binding tandem repeat proteins

Tandem repeat proteins (TRPs) are widely distributed and bind to a wide variety of ligands. DNA-binding TRPs such as zinc finger (ZNF) and transcription activator-like effector (TALE) play important roles in biology and biotechnology. In this study, we first conducted an extensive analysis of TRPs in public databases, and found that the enormous diversity of TRPs is largely unexplored. We then focused our efforts on identifying novel TRPs possessing DNA-binding capabilities. We established a protein language model for DNA-binding protein prediction (PLM-DBPPred), and predicted a large number of DNA-binding TRPs. A subset was then selected for experimental screening, leading to the identification of 11 novel DNA-binding TRPs, with six showing sequence specificity. Notably, members of the STAR (Short TALE-like Repeat proteins) family can be programmed to target specific 9 bp DNA sequences with high affinity. Leveraging this property, we generated artificial transcription factors using reprogrammed STAR proteins and achieved targeted activation of endogenous gene sets. Furthermore, the members of novel families such as MOON (Marine Organism-Originated DNA binding protein) and pTERF (prokaryotic mTERF-like protein) exhibit unique features and distinct DNA-binding characteristics, revealing interesting biological clues. Our study expands the diversity of DNA-binding TRPs, and demonstrates that a systematic approach greatly enhances the discovery of new biological insights and tools.

Targeted proteomics profiling reveals valuable biomarkers in the diagnosis of primary immune thrombocytopaenia

The lack of biomarkers for accurate diagnosis and prognosis is a major clinical challenge of primary immune thrombocytopaenia (ITP). Using an Olink proteomics platform with a 92 immune response-related human protein panel, we analysed plasma samples from ITP patients (ITP, n = 40), patients with thrombocytopaenia secondary to other causes (Non-ITP, n = 19) and healthy controls (NC, n = 18), of a discovery cohort as well as a validation cohort (ITP, n = 36; NC, n = 20). A total of 10 differentially expressed proteins (DEPs) were identified in the ITP group compared with the non-ITP and NC groups of the discovery cohort. These include CXCL11, GZMH, ARG1, TGF-β1, ANGPT1, CXCL12, CD40-L, PDGF subunit B, IL4 and TNFSF14. Furthermore, least absolute shrinkage and selection operator regression analysis showed some of these DEPs, such as CXCL11, TGF-β1, ARG1 and GZMH to be significant in differentiating between patients with ITP and healthy controls (validation area under the curve = 0.87). The analysis demonstrated that the ITP group has a specific proteomic profile relative to non-ITP and NC groups. In summary, we report for the first time that Olink precision proteomics can specifically detect up-regulated inflammatory proteins as potential diagnostic biomarkers for ITP.

Integrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score

Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors. We evaluated the performance of this metaPRS and benchmarked it against existing genome-wide PRS in 620,059 participants and 50,572 T2D cases amongst six diverse genetic ancestries from UK Biobank, INTERVAL, the All of Us Research Program, and the Singapore Multi-Ethnic Cohort. We show that our metaPRS was the most powerful PRS for predicting T2D in European population-based cohorts and had comparable performance to the top ancestry-specific PRS, highlighting its transferability. In UK Biobank, we show the metaPRS had stronger predictive power for 10-year risk than all individual risk factors apart from BMI and biomarkers of dysglycemia. The metaPRS modestly improved T2D risk stratification of QDiabetes risk scores for 10-year risk prediction, particularly when prioritising individuals for blood tests of dysglycemia. Overall, we present a highly predictive and transferrable PRS for T2D and demonstrate that the potential for PRS to incrementally improve T2D risk prediction when incorporated into UK guideline-recommended screening and risk prediction with a clinical risk score.

Identification and validation of diagnostic and prognostic biomarkers in prostate cancer based on WGCNA

BACKGROUND

Prostate cancer (PCa) represents a significant health challenge for men, and the advancement of the disease often results in a grave prognosis for patients. Therefore, the identification of biomarkers associated with the diagnosis and prognosis of PCa holds paramount importance in patient health management.

METHODS

The datasets pertaining to PCa were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to investigate the modules specifically associated with the diagnosis of PCa. The hub genes were identified using the LASSO regression analysis. The expression levels of these hub genes were further validated by qRT-PCR experiments. Receiver operating characteristic (ROC) curves and nomograms were employed as evaluative measures for assessing the diagnostic value.

RESULTS

The blue module identified by WGCNA exhibited a strong association with PCa. Six hub genes (SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3) were identified by LASSO regression analysis. Further verification confirmed that these six genes were significantly downregulated in tumor tissues and cells. The six hub genes and the nomogram demonstrated substantial diagnostic value, with area under the curve (AUC) values ranging from 0.754 to 0.961. Moreover, patients with low expression levels of these six genes exhibited elevated T/N pathological stage and Gleason score, implying a more advanced disease state. Meanwhile, their progression-free survival (PFS) was observed to be potentially poorer. Finally, a significant association could be observed between the expression of these genes and the dysregulation of immune cells, along with drug sensitivity.

CONCLUSIONS

In summary, our study identified six hub genes, namely SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3, which can be utilized to establish a diagnostic model for PCa. The discovery may offer potential molecular targets for clinical diagnosis and treatment of PCa.

Lipid profiling identifies modifiable signatures of cardiometabolic risk in children and adolescents with obesity

Pediatric obesity is a progressive, chronic disease that can lead to serious cardiometabolic complications. Here we investigated the peripheral lipidome in 958 children and adolescents with overweight or obesity and 373 with normal weight, in a cross-sectional study. We also implemented a family-based, personalized program to assess the effects of obesity management on 186 children and adolescents in a clinical setting. Using mass spectrometry-based lipidomics, we report an increase in ceramides, alongside a decrease in lysophospholipids and omega-3 fatty acids with obesity metabolism. Ceramides, phosphatidylethanolamines and phosphatidylinositols were associated with insulin resistance and cardiometabolic risk, whereas sphingomyelins showed inverse associations. Additionally, a panel of three lipids predicted hepatic steatosis as effectively as liver enzymes. Lipids partially mediated the association between obesity and cardiometabolic traits. The nonpharmacological management reduced levels of ceramides, phospholipids and triglycerides, indicating that lowering the degree of obesity could partially restore a healthy lipid profile in children and adolescents.

Identification of prognostic biomarkers in neuroblastoma using WGCNA and multi-omics analysis

BACKGROUND

Neuroblastoma (NB) is one of the most frequent parenchymal tumors among children, with a high degree of heterogeneity and wide variation in clinical presentation. Despite significant therapeutic advances in recent years, long-term survival in high-risk patients remains low, emphasizing the urgent need to find new biomarkers and construct reliable prognostic models.

METHODS

In this study, data from neuroblastoma samples in the ArrayExpress database were utilized to identify key gene modules and pivotal genes associated with NB prognosis by weighted gene co-expression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis was performed using the DAVID database. Based on these hub genes, survival prognosis models were constructed and validated on an independent validation set in the Gene Expression Omnibus (GEO) database. Differences in biological functions and immune microenvironments and the sensitivity to pharmacological and immunotherapeutic treatments of patients in the high- and low-risk groups were examined by gene set enrichment analysis (GSEA) and immune infiltration analysis.

RESULTS

WGCNA identified 14 gene modules and screened the module with the highest relevance to the International Neuroblastoma Staging System (INSS), containing 60 pivotal genes. GO and KEGG analyses demonstrated that these pivotal genes were mainly implicated in biological processes and signaling pathways including DNA replication, cell division, mitotic cell cycle, and cell cycle. Based on Lasso regression and COX regression analysis, a prognostic model containing DHFR, GMPS and E2F3 was constructed, and the RiskScore was significantly correlated with the 1-, 3- and 5-year survival of the patients. GSEA and immune infiltration analyses revealed significant differences in the levels of cell cycle-related pathways and immune cell infiltration between the high and low RiskScore groups. In particular, patients in the high-risk group are less likely to benefit from immunotherapy and may be better suited for treatment with drugs such as Oxaliplatin and Alpelisib.

CONCLUSION

This research systematically identified biomarkers related to NB prognosis and developed a reliable prognostic model applying WGCNA and multiple bioinformatics methods. The model has important application value in predicting patients' prognosis, evaluating drug sensitivity and immunotherapy effect, and provides new ideas and directions for precise treatment of neuroblastoma.

Unveiling differential gene co-expression networks and its effects on levodopa-induced dyskinesia

Levodopa-induced dyskinesia (LID) refers to involuntary motor movements of chronic use of levodopa in Parkinson's disease (PD) that negatively impact the overall well-being of people with this disease. The molecular mechanisms involved in LID were investigated through whole-blood transcriptomic analysis for differential gene expression and identification of new co-expression and differential co-expression networks. We found six differentially expressed genes in patients with LID, and 13 in patients without LID. We also identified 12 co-expressed genes exclusive to LID, and six exclusive hub genes involved in 23 gene-gene interactions in patients with LID. Convergently, we identified novel genes associated with PD and LID that play roles in mitochondrial dysfunction, dysregulation of lipid metabolism, and neuroinflammation. We observed significant changes in disease progression, consistent with previous findings of maladaptive plastic changes in the basal ganglia leading to the development of LID, including a chronic pro-inflammatory state in the brain.

Peptide-Bound Glycative, AGE and Oxidative Modifications as Biomarkers for the Diagnosis of Alzheimer's Disease-A Feasibility Study

Background: The diagnosis of Alzheimer's disease (AD) relies on core cerebrospinal fluid (CSF) biomarkers, amyloid beta (Aβ) and tau. As the brain is then already damaged, researchers still strive to discover earlier biomarkers of disease onset and the progression of AD. Glycation, advanced glycation end products (AGEs) and oxidative modifications on proteins in CSF mirror the underlying biological mechanisms that contribute to early AD pathology. However, analyzing free AGEs in the body fluids of AD patients has led to controversial results. Thus, this pilot study aimed to test the feasibility of detecting, identifying and quantifying differentially glycated, AGE or oxidatively modified peptides in CSF proteins of AD patients (n = 5) compared to a control group (n = 5). Methods: To this end, we utilized a data-dependent (DDA) nano liquid chromatography (LC) linear ion trap-Orbitrap tandem mass spectrometry (MS/MS) ) approach and database search that included over 30 glycative and oxidative modifications in four search nodes to analyze endogenous modifications on individual peptides. Furthermore, we quantified candidate peptide abundance using LC Quan. Results: We identified 299 sites of early and advanced glycation and 53 sites of oxidatively modified tryptophan. From those, we identified 17 promising candidates as putative biomarkers (receiver operating curve-area under the curve (ROC-AUC) > 0.8), albeit without statistical significance. Conclusions: The potential candidates with higher discrimination power showed correlations with established diagnostic markers, thus hinting toward the potential of those peptides as biomarkers.

The ATOM-Seq sequence capture panel can accurately predict microsatellite instability status in formalin-fixed tumour samples, alongside routine gene mutation testing

Microsatellite instability (MSI) occurs across a number of cancers and is associated with different clinical characteristics when compared to microsatellite stable (MSS) cancers. As MSI cancers have different characteristics, routine MSI testing is now recommended for a number of cancer types including colorectal cancer (CRC). Using gene panels for sequencing of known cancer mutations is routinely performed to guide treatment decisions. By adding a number of MSI regions to a small gene panel, the efficacy of simultaneous MSI detection in a series of CRCs was tested. Tumour DNA from formalin-fixed, paraffin-embedded (FFPE) tumours was sequenced using a 23-gene panel kit (ATOM-Seq) provided by GeneFirst. The mismatch repair (MMR) status was obtained for each patient from their routine pathology reports, and compared to MSI predictions from the sequencing data. By testing 29 microsatellite regions in 335 samples the MSI status was correctly classified in 314/319 samples (98.4% concordance), with sixteen failures. By reducing the number of regions in silico, comparable performance could be reached with as few as eight MSI marker positions. This test represents a quick, and accurate means of determining MSI status in FFPE CRC samples, as part of a routine gene mutation assay, and can easily be incorporated into a research or diagnostic setting. This could replace separate mutation and MSI tests with no loss of accuracy, thus improving testing efficiency.

CSF proteomics identifies early changes in autosomal dominant Alzheimer's disease

In this high-throughput proteomic study of autosomal dominant Alzheimer's disease (ADAD), we sought to identify early biomarkers in cerebrospinal fluid (CSF) for disease monitoring and treatment strategies. We examined CSF proteins in 286 mutation carriers (MCs) and 177 non-carriers (NCs). The developed multi-layer regression model distinguished proteins with different pseudo-trajectories between these groups. We validated our findings with independent ADAD as well as sporadic AD datasets and employed machine learning to develop and validate predictive models. Our study identified 137 proteins with distinct trajectories between MCs and NCs, including eight that changed before traditional AD biomarkers. These proteins are grouped into three stages: early stage (stress response, glutamate metabolism, neuron mitochondrial damage), middle stage (neuronal death, apoptosis), and late presymptomatic stage (microglial changes, cell communication). The predictive model revealed a six-protein subset that more effectively differentiated MCs from NCs, compared with conventional biomarkers.

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention

OBJECTIVE

Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU).

METHODS

Analyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI).

RESULTS

A slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment.

CONCLUSIONS

PAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.