What is precision medicine?

Clinical subtypes of older adults starting long-term care in Japan and their association with prognoses: a data-driven cluster analysis

We aimed to identify the clinical subtypes in individuals starting long-term care in Japan and examined their association with prognoses. Using linked medical insurance claims data and survey data for care-need certification in a large city, we identified participants who started long-term care. Grouping them based on 22 diseases recorded in the past 6 months using fuzzy c-means clustering, we examined the longitudinal association between clusters and death or care-need level deterioration within 2 years. We analyzed 4,648 participants (median age 83 [interquartile range 78-88] years, female 60.4%) between October 2014 and March 2019 and categorized them into (i) musculoskeletal and sensory, (ii) cardiac, (iii) neurological, (iv) respiratory and cancer, (v) insulin-dependent diabetes, and (vi) unspecified subtypes. The results of clustering were replicated in another city. Compared with the musculoskeletal and sensory subtype, the adjusted hazard ratio (95% confidence interval) for death was 1.22 (1.05-1.42), 1.81 (1.54-2.13), and 1.21 (1.00-1.46) for the cardiac, respiratory and cancer, and insulin-dependent diabetes subtypes, respectively. The care-need levels more likely worsened in the cardiac, respiratory and cancer, and unspecified subtypes than in the musculoskeletal and sensory subtype. In conclusion, distinct clinical subtypes exist among individuals initiating long-term care.

Single-cell and spatially resolved interactomics of tooth-associated keratinocytes in periodontitis

Periodontitis affects billions of people worldwide. To address relationships of periodontal niche cell types and microbes in periodontitis, we generated an integrated single-cell RNA sequencing (scRNAseq) atlas of human periodontium (34-sample, 105918-cell), including sulcular and junctional keratinocytes (SK/JKs). SK/JKs displayed altered differentiation states and were enriched for effector cytokines in periodontitis. Single-cell metagenomics revealed 37 bacterial species with cell-specific tropism. Fluorescence in situ hybridization detected intracellular 16 S and mRNA signals of multiple species and correlated with SK/JK proinflammatory phenotypes in situ. Cell-cell communication analysis predicted keratinocyte-specific innate and adaptive immune interactions. Highly multiplexed immunofluorescence (33-antibody) revealed peri-epithelial immune foci, with innate cells often spatially constrained around JKs. Spatial phenotyping revealed immunosuppressed JK-microniches and SK-localized tertiary lymphoid structures in periodontitis. Here, we demonstrate impacts on and predicted interactomics of SK and JK cells in health and periodontitis, which requires further investigation to support precision periodontal interventions in states of chronic inflammation.

Using machine learning to develop preoperative model for lymph node metastasis in patients with bladder urothelial carcinoma

BACKGROUND

Lymph node metastasis (LNM) is associated with worse prognosis in bladder urothelial carcinoma (BUC) patients. This study aimed to develop and validate machine learning (ML) models to preoperatively predict LNM in BUC patients treated with radical cystectomy (RC).

METHODS

We retrospectively collected demographic, pathological, imaging, and laboratory information of BUC patients who underwent RC and bilateral lymphadenectomy in our institution. Patients were randomly categorized into training set and testing set. Five ML algorithms were utilized to establish prediction models. The performance of each model was assessed by the area under the receiver operating characteristic curve (AUC) and accuracy. Finally, we calculated the corresponding variable coefficients based on the optimal model to reveal the contribution of each variable to LNM.

RESULTS

A total of 524 and 131 BUC patients were finally enrolled into training set and testing set, respectively. We identified that the support vector machine (SVM) model had the best prediction ability with an AUC of 0.934 (95% confidence interval [CI]: 0.903-0.964) and accuracy of 0.916 in the training set, and an AUC of 0.855 (95%CI: 0.777-0.933) and accuracy of 0.809 in the testing set. The SVM model contained 14 predictors, and positive lymph node in imaging contributed the most to the prediction of LNM in BUC patients.

CONCLUSIONS

We developed and validated the ML models to preoperatively predict LNM in BUC patients treated with RC, and identified that the SVM model with 14 variables had the best performance and high levels of clinical applicability.

Association of lipid-lowering drugs with gut microbiota: A Mendelian randomization study

BACKGROUND

The gut microbiota can be influenced by lipid metabolism. We aimed to evaluate the impact of lipid-lowering medications, such as proproteinconvertase subtilisin/kexin type 9 (PCSK9) inhibitors, Niemann-Pick C1-like protein (NPC1L1) inhibitors, and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibitors, on gut microbiota through drug target Mendelian randomization (MR) investigation.

METHODS

We used genetic variants that were associated with low-density lipoprotein cholesterol (LDL-C) in genome-wide association studies and located within or near drug target genes as proxies for lipid-lowering drug exposure. In addition, expression trait loci in drug target genes were used as complementary genetic tools. We used effect estimates calculated using inverse variance weighted MR (IVW-MR) and summary data-based MR (SMR). Multiple sensitivity analyses were performed.

RESULTS

Genetic proxies for lipid-lowering drugs broadly affected the abundance of gut microbiota. High expression of NPC1L1 was significantly associated with an increase in the genus Eggerthella (β = 1.357, SE = 0.337, P = 5.615 × 10-5). An HMGCR-mediated increase in LDL-C was significantly associated with the order Pasteurellales (β = 0.489, SE = 0.123, P = 6.955 × 10-5) and the genus Haemophilus (β = 0.491, SE = 0.125, P = 8.379 × 10-5), whereas a PCSK9-mediated increase in LDL-C was associated with the genus Terrisporobacter (β = 0.666, SE = 0.127, P = 1.649 × 10-5). No pleiotropy was detected.

CONCLUSIONS

This drug target MR highlighted the potential interventional effects of lipid-lowering drugs on the gut microbiota and separately revealed the possible effects of different types of lipid-lowering drugs on specific gut microbiota.

Precision nanomedicine to treat non-small cell lung cancer

Lung cancer is a major cause of death worldwide, being often detected at a later stage due to the non-appearance of early symptoms. Therefore, specificity of the treatment is of utmost importance for its effective treatment. Precision medicine is a personalized therapy based on the genomics of the patient to design a suitable drug approach. Genetic mutations render the tumor resistant to specific mutations and the therapy is in vain even though correct medications are prescribed. Therefore, Precision medicine needs to be explored for the treatment of Non-small cell lung cancer (NSCLC). Nanoparticles are widely explored to give personalized interventions to treat lung cancer due to their various advantages like the ability to reach cancer cells, enhanced permeation through tissues, specificity, increased bioavailability, etc. Various nanoparticles (NPs) including gold nanoparticles, carbon nanotubes, aptamer-based NPs etc. were conjugated with biomarkers/diagnostic agents specific to cancer type and were delivered. Various biomarker genes have been identified through precision techniques for the diagnosis and treatment of NSCLC like EGFR, RET, KRAS, ALK, ROS-1, NTRK-1, etc. By incorporating of drug with the nanoparticle through bioconjugation, the specificity of the treatment can be enhanced with this revolutionary treatment. Additionally, integration of theranostic cargos in the nanoparticle would allow diagnosis as well as treatment by targeting the site of disease progression. Therefore, to target NSCLC effectively precision nanomedicine has been adopted in recent times. Here, we present different nanoparticles that are used as precision nanomedicine and their effectiveness against NSCLC disease.

Individualized small bowel preparation for computed tomography enterography: A prospective randomized controlled trial

BACKGROUND AND AIM

The study aims to evaluate the feasibility of body mass index (BMI)-based individualized small bowel preparation for computed tomography enterography (CTE).

METHODS

In this prospective randomized controlled study, patients undergoing CTE were randomly assigned to the individualized group or standardized group. Those in individualized group were given different volumes of mannitol solution based on BMI (1000 mL for patients with BMI < 18.5 kg/m2, 1500 mL for patients with 18.5 kg/m2 ≤ BMI < 25 kg/m2 and 2000 mL for patients with BMI ≥ 25 kg/m2) while patients in the standardized group were all asked to consume 1500-mL mannitol solution. CTE images were reviewed by two experienced radiologists blindly. Each segment of the small bowel was assessed for small bowel image quality and disease detection rates. Patients were invited to record a diary regarding adverse events and acceptance.

RESULTS

A total of 203 patients were enrolled and randomly divided into two groups. For patients with BMI < 18.5 kg/m2, 1000-mL mannitol solution permitted a significantly lower rate of flatulence (P = 0.045) and defecating frequency (P = 0.011) as well as higher acceptance score (P = 0.015), but did not affect bowel image quality and diseases detection compared with conventional dosage. For patients with BMI ≥ 25 kg/m2, 2000-mL mannitol solution provided better overall image quality (P = 0.033) but comparable rates of adverse events and patients' acceptance compared with conventional dosage.

CONCLUSIONS

Individualized bowel preparation could achieve both satisfactory image quality and patients' acceptance thus might be an acceptable alternative in CTE.

Anoikis and SPP1 in idiopathic pulmonary fibrosis: integrating bioinformatics, cell, and animal studies to explore prognostic biomarkers and PI3K/AKT signaling regulation

OBJECTIVE

This study aims to explore the relevance of anoikis in idiopathic pulmonary fibrosis (IPF) and identify associated biomarkers and signaling pathways.

METHOD

Unsupervised consensus cluster analysis was employed to categorize IPF patients into subtypes. We utilized Weighted Gene Co-Expression Network Analysis (WGCNA) and Protein-Protein Interaction network construction to identify anoikis-related modules and key genes. A prognostic signature was developed using Lasso and multivariate Cox regression analysis. Single-cell sequencing assessed hub gene expression in various cell types, and both cell and animal experiments confirmed IPF-related pathways.

RESULTS

We identified two distinct anoikis-associated subtypes with differing prognoses. WGCNA revealed essential hub genes, with SPP1 being prominent in the anoikis-related signature. The anoikis-related signature is effective in determining the prognosis of patients with IPF. Single-cell sequencing highlighted significant differences in SPP1 expression, notably elevated in fibroblasts derived from IPF patients. In vivo and in vitro experiments demonstrated that SPP1 enhances fibrosis in mouse lung fibroblasts by regulating p27 through the PI3K/Akt pathway.

CONCLUSION

Our research demonstrates a robust prognostic signature associated with anoikis and highlights SPP1 as a pivotal regulator of the PI3K/AKT signaling pathway in pulmonary fibrosis.

Beyond memory impairment: the complex phenotypic landscape of Alzheimer's disease

Neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD) constitute multifaceted behavioral manifestations that reflect processes of emotional regulation, thinking, and social behavior. They are as prevalent in AD as cognitive impairment and develop independently during the progression of neurodegeneration. As studying NPSs in AD is clinically challenging, most AD research to date has focused on cognitive decline. In this opinion article we summarize emerging literature on the prevalence, time course, and the underlying genetic, molecular, and pathological mechanisms related to NPSs in AD. Overall, we propose that NPSs constitute a cluster of core symptoms in AD, and understanding their neurobiology can lead to a more holistic approach to AD research, paving the way for more accurate diagnostic tests and personalized treatments embracing the goals of precision medicine.

Phenotyping of chronic pain in breast cancer survivors: an original study using the cancer pain phenotyping (CANPPHE) Network multidisciplinary international guidelines

PURPOSE

The primary aim of this cross-sectional study is to examine the prevalence of pain phenotypes in breast cancer survivors (BCS). A secondary aim entails examining whether health related quality of life differs between the main pain phenotypes in BCS.

METHODS

BCS who experienced chronic pain were asked to complete the numeric pain rating scale for pain, Margolis pain diagram, and short form 36 (SF-36). Following administration of questionnaires and quantitative sensory examinations were applied. To determine the prevalence of the predominant type of pain, a recently proposed classification system by the Cancer Pain Phenotyping (CANPPHE) Network was used.

RESULTS

Of the 86 female participants, 19 (22.09%) had dominant neuropathic pain, 18 (20.93%) had dominant nociceptive pain and 14 (16.28%) had dominant nociplastic pain. 35 participants (40.70%) were classified as having mixed pain. One-way ANOVA revealed a significant difference between the four pain groups for the SF-36 general health (F = 3.205, p = 0.027), social functioning (F = 4.093, p = 0.009), and pain (F = 3.603, p = 0.017) subscale scores.

CONCLUSION

This study found that pain in BCS was mostly of mixed phenotype, followed by predominantly neuropathic and nociplastic pain. Furthermore, it was found that, compared to BCS with predominant neuropathic and nociceptive pain, BCS with predominant nociplastic pain have lower health related quality of life in the areas of bodily pain and social functioning.

Bio-Nano Toolbox for Precision Alzheimer's Disease Gene Therapy

Alzheimer's disease (AD) is the most burdensome aging-associated neurodegenerative disorder, and its treatment encounters numerous failures during drug development. Although there are newly approved in-market β-amyloid targeting antibody solutions, pathological heterogeneity among patient populations still challenges the treatment outcome. Emerging advances in gene therapies offer opportunities for more precise personalized medicine; while, major obstacles including the pathological heterogeneity among patient populations, the puzzled mechanism for druggable target development, and the precision delivery of functional therapeutic elements across the blood-brain barrier remain and limit the use of gene therapy for central neuronal diseases. Aiming for "precision delivery" challenges, nanomedicine provides versatile platforms that may overcome the targeted delivery challenges for AD gene therapy. In this perspective, to picture a toolbox for AD gene therapy strategy development, the most recent advances from benchtop to clinics are highlighted, possibly available gene therapy targets, tools, and delivery platforms are outlined, their challenges as well as rational design elements are addressed, and perspectives in this promising research field are discussed.

Personalized medicine in old age psychiatry and Alzheimer's disease

Elderly patients show us unfolded lives with unique individual characteristics. An increasing life span is associated with increasing physical and mental disease burden. Alzheimer's disease (AD) is an increasing challenge in old age. AD cannot be cured but it can be treated. The complexity of old age and AD offer targets for personalized medicine (PM). Targets for stratification of patients, detection of patients at risk for AD or for future targeted therapy are plentiful and can be found in several omic-levels.

Research Hotspots and Trends in the Diagnosis of Chorioamnionitis: Based on Bibliometric Analysis from 2010 to 2022

OBJECTIVE

 This study aimed to conduct a bibliometric analysis of literature related to the diagnosis of chorioamnionitis (CAM) and to point out the current research progress, hotspots, and development trends of CAM research.

STUDY DESIGN

 Literature on the diagnosis of CAM from the Web of Science Core Collection (WoSCC) between 2010 and 2022 was retrieved. CiteSpace, VOSviewer, and Online Analysis Platform (OALM) were used to draw maps of authors, articles, journals, institutions, countries/regions, and keywords.

RESULTS

 A total of 312 articles were included, and the number of articles gradually increased over the study period. The author with the largest number of articles was Roberto Romero. The institution with the largest number of articles was Wayne State University School of Medicine, and the United States was the country that produced the largest number of articles. Analysis of keywords and outbreak words suggested that future research hotspots and trends may focus on early treatment of CAM and more precise, noninvasive, and more sensitive diagnoses.

CONCLUSION

 In this study, visualization software and data information mining were innovatively used to conduct a bibliometric analysis of articles in the field of CAM diagnosis, and the current status, hotspots, and development of this field were obtained. Future research hotspots may be the precision diagnosis and treatment of CAM.

KEY POINTS

· There is no bibliometric study on CAM diagnosis in the existing literature.. · The prediction of CAM diagnosis is an important topic to improve maternal and infant prognosis.. · Bibliometrics can effectively guide the direction of future research..

Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project

Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians.

Towards voltammetric point of care detection of leucovorin

Current healthcare trends have seen an increased emphasis on the move towards personalised precision medicine to tailor treatments to the individual and their response to diseases and disease therapies. This highlighting a transition from traditional "one size fits all" to a more nuanced approach. Despite advancements in fundamental knowledge to facilitate personalised prevision approaches, lack of resources to implement such plans remains one of the largest hurdles to overcome. Monitoring of drug therapies is one key aspect that could aid in the evolution of precision medicine alongside the development of drugs and targeted treatment systems. This contribution illustrates the potential of square wave voltammetry (SWV) as a proof-of-concept for monitoring of circulating blood concentrations of treatment therapies within artificial urine, using leucovorin calcium (LV) as a model cancer therapy drug. A low cost, easy-to-use and portable sensor has been developed and successfully employed for the detection of LV over the linear range 0.5-30 μM which represents the therapeutically relevant concentrations for LV within artificial urine without any prior sample preparation required with a limit of detection of 2.63 μM and initial investigations into saliva and serum as biological matrices. The developed sensor describe herein exhibits a proof-of-concept for the engagement of such electrochemical sensors as point-of-care devices, where the sensors ease of use and removal of time-consuming and complex sample preparation methods will ultimately increase its usability by physicians, widening the avenues where electrochemical sensors could be employed.

A latent class analysis of family resilience and its relationship with fear of recurrence in lung cancer patients: a cross-sectional study

PURPOSE

Family resilience helps cancer-affected families overcome challenges and may influence an individual's fear of cancer recurrence (FCR). Identifying distinct classes of family resilience among lung cancer patients is crucial for tailored interventions. This study aimed to identify latent classes of family resilience in lung cancer patients and explore their relationships with FCR.

METHODS

Three hundred ten lung cancer patients from three hospitals in Fujian were recruited from June to September 2021. Clinical data were extracted from medical records, while sociodemographic details, family resilience, and FCR were self-reported. A latent class analysis was performed to identify family resilience classes.

RESULTS

A 4-class solution showed the best fit. Compared to Class 1, the patients who had no comorbidities (ORs = 3.480-16.005) had an increased likelihood of belonging to Class 2 and 3, while those who were not family breadwinners (ORs = 0.118-0.176) had a decreased likelihood. Further, the patients who (1) did not lack interest/pleasure in doing things during the past 2-week period (OR = 7.057), (2) were never smokers (OR = 6.230), and (3) were urban residents (OR = 8.985) had an increased likelihood of belonging to Class 4, while those who were (1) male (OR = 0.167), (2) not the family breadwinner (OR = 0.152), and (3) had none or only one child (OR = 0.203) had a decreased likelihood of belonging to Class 4. The FCR level differed significantly among these four classes.

CONCLUSION

Our study identified four distinct classes of family resilience among Chinese lung cancer patients. FCR severity decreased with increasing levels of family resilience.

New clinical trial design in precision medicine: discovery, development and direction

In the era of precision medicine, it has been increasingly recognized that individuals with a certain disease are complex and different from each other. Due to the underestimation of the significant heterogeneity across participants in traditional "one-size-fits-all" trials, patient-centered trials that could provide optimal therapy customization to individuals with specific biomarkers were developed including the basket, umbrella, and platform trial designs under the master protocol framework. In recent years, the successive FDA approval of indications based on biomarker-guided master protocol designs has demonstrated that these new clinical trials are ushering in tremendous opportunities. Despite the rapid increase in the number of basket, umbrella, and platform trials, the current clinical and research understanding of these new trial designs, as compared with traditional trial designs, remains limited. The majority of the research focuses on methodologies, and there is a lack of in-depth insight concerning the underlying biological logic of these new clinical trial designs. Therefore, we provide this comprehensive review of the discovery and development of basket, umbrella, and platform trials and their underlying logic from the perspective of precision medicine. Meanwhile, we discuss future directions on the potential development of these new clinical design in view of the "Precision Pro", "Dynamic Precision", and "Intelligent Precision". This review would assist trial-related researchers to enhance the innovation and feasibility of clinical trial designs by expounding the underlying logic, which be essential to accelerate the progression of precision medicine.

Artificial intelligence applications in prostate cancer

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.

Endophytic fungi as a potential source of anti-cancer drug

Endophytes are considered one of the major sources of bioactive compounds used in different aspects of health care including cancer treatment. When colonized, they either synthesize these bioactive compounds as a part of their secondary metabolite production or augment the host plant machinery in synthesising such bioactive compounds. Hence, the study of endophytes has drawn the attention of the scientific community in the last few decades. Among the endophytes, endophytic fungi constitute a major portion of endophytic microbiota. This review deals with a plethora of anti-cancer compounds derived from endophytic fungi, highlighting alkaloids, lignans, terpenes, polyketides, polyphenols, quinones, xanthenes, tetralones, peptides, and spirobisnaphthalenes. Further, this review emphasizes modern methodologies, particularly omics-based techniques, asymmetric dihydroxylation, and biotic elicitors, showcasing the dynamic and evolving landscape of research in this field and describing the potential of endophytic fungi as a source of anticancer drugs in the future.

On minimising tumoural growth under treatment resistance

Drug resistance is a major challenge for curative cancer treatment, representing the main reason of death in patients. Evolutionary biology suggests pauses between treatment rounds as a way to delay or even avoid resistance emergence. Indeed, this approach has already shown promising preclinical and early clinical results, and stimulated the development of mathematical models for finding optimal treatment protocols. Due to their complexity, however, these models do not lend themself to a rigorous mathematical analysis, hence so far clinical recommendations generally relied on numerical simulations and ad-hoc heuristics. Here, we derive two mathematical models describing tumour growth under genetic and epigenetic treatment resistance, respectively, which are simple enough for a complete analytical investigation. First, we find key differences in response to treatment protocols between the two modes of resistance. Second, we identify the optimal treatment protocol which leads to the largest possible tumour shrinkage rate. Third, we fit the "epigenetic model" to previously published xenograft experiment data, finding excellent agreement, underscoring the biological validity of our approach. Finally, we use the fitted model to calculate the optimal treatment protocol for this specific experiment, which we demonstrate to cause curative treatment, making it superior to previous approaches which generally aimed at stabilising tumour burden. Overall, our approach underscores the usefulness of simple mathematical models and their analytical examination, and we anticipate our findings to guide future preclinical and, ultimately, clinical research in optimising treatment regimes.

From genome to clinic: The power of translational bioinformatics in improving human health

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

Bone Organoids: Recent Advances and Future Challenges

Bone defects stemming from tumorous growths, traumatic events, and diverse conditions present a profound conundrum in clinical practice and research. While bone has the inherent ability to regenerate, substantial bone anomalies require bone regeneration techniques. Bone organoids represent a new concept in this field, involving the 3D self-assembly of bone-associated stem cells guided in vitro with or without extracellular matrix material, resulting in a tissue that mimics the structural, functional, and genetic properties of native bone tissue. Within the scientific panorama, bone organoids ascend to an esteemed status, securing significant experimental endorsement. Through a synthesis of current literature and pioneering studies, this review offers a comprehensive survey of the bone organoid paradigm, delves into the quintessential architecture and ontogeny of bone, and highlights the latest progress in bone organoid fabrication. Further, existing challenges and prospective directions for future research are identified, advocating for interdisciplinary collaboration to fully harness the potential of this burgeoning domain. Conclusively, as bone organoid technology continues to mature, its implications for both clinical and research landscapes are poised to be profound.

tRNA-derived fragments: mechanism of gene regulation and clinical application in lung cancer

Lung cancer, being the most widespread and lethal form of cancer globally, has a high incidence and mortality rate primarily attributed to challenges associated with early detection, extensive metastasis, and frequent recurrence. In the context of lung cancer development, noncoding RNA molecules have a crucial role in governing gene expression and protein synthesis. Specifically, tRNA-derived fragments (tRFs), a subset of noncoding RNAs, exert significant biological influences on cancer progression, encompassing transcription and translation processes as well as epigenetic regulation. This article primarily examines the mechanisms by which tRFs modulate gene expression and contribute to tumorigenesis in lung cancer. Furthermore, we provide a comprehensive overview of the current bioinformatics analysis of tRFs in lung cancer, with the objective of offering a systematic and efficient approach for studying the expression profiling, functional enrichment, and molecular mechanisms of tRFs in this disease. Finally, we discuss the clinical significance and potential avenues for future research on tRFs in lung cancer. This paper presents a comprehensive systematic review of the existing research findings on tRFs in lung cancer, aiming to offer improved biomarkers and drug targets for clinical management of lung cancer.

Investigation of community pharmacists' knowledge and attitudes of pharmacogenomics testing: implication for improved pharmacogenomic testing practice

BACKGROUND

Community pharmacists must be well-equipped to advance pharmacogenomics services. Nevertheless, limited data is available regarding pharmacists' knowledge and attitudes toward pharmacogenomics testing. The present study aimed to evaluate community pharmacists' knowledge and attitudes toward pharmacogenomics testing in the UAE.

METHODS

In this cross-sectional study, a validated, online, self-administered survey, was randomly distributed to community pharmacists across the United Arab Emirates (UAE).

RESULTS

The participants demonstrated poor knowledge about pharmacogenomic testing (median score < 8). Having 10-29 (Adjusted odds ration [AOR]: 0.038; 95% CI: 0.01-0.146, p = 0.001) and 30-49 (AOR: 0.097; 95% CI: 0.04-0.237, p = 0.001) patients per day was associated with poorer knowledge. Also, receiving 10-29 (AOR: 0.046; 95% CI: 0.005-0.401, p = 0.005), 30-49 (AOR: 0.025; 95% CI: 0.003-0.211, p = 0.001), and > 50 (AOR: 0.049; 95% CI: 0.005-0.458, p = 0.008) prescriptions decreased the odds of having good knowledge. Around half (43.9%) of the participants did not show a positive attitude toward pharmacogenomic testing (median score < 11). Having 30-49 patients per day (AOR: 5.351; 95% CI: 2.414-11.860, p = 0.001) increased the odds of good knowledge while receiving 10-29 (AOR: 0.133; 95% CI: 0.056-0.315, p = 0.001) and 30-49 (AOR: 0.111; 95% CI: 0.049-0.252, p = 0.001) prescriptions a day were associated with decreased odds of positive attitude toward the pharmacogenomics testing.

CONCLUSIONS

The findings indicate a lack of knowledge and less-than-ideal attitudes among community pharmacists regarding pharmacogenomics testing. Enhanced efforts focused on educational initiatives and training activities related to pharmacogenomics testing is needed. Additionally, reducing workload can facilitate better knowledge acquisition and help mitigate unfavorable attitudes.

Information Transmission in G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are the largest class of receptors in the human genome and constitute about 30% of all drug targets. In this article, intended for a non-mathematical audience, both experimental observations and new theoretical results are compared in the context of information transmission across the cell membrane. The amount of information actually currently used or projected to be used in clinical settings is a small fraction of the information transmission capacity of the GPCR. This indicates that the number of yet undiscovered drug targets within GPCRs is much larger than what is currently known. Theoretical studies with some experimental validation indicate that localized heat deposition and dissipation are key to the identification of sites and mechanisms for drug action.

Emerging applications of single-cell profiling in precision medicine of atherosclerosis

Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.

Cancer epigenetics: from laboratory studies and clinical trials to precision medicine

Epigenetic dysregulation is a common feature of a myriad of human diseases, particularly cancer. Defining the epigenetic defects associated with malignant tumors has become a focus of cancer research resulting in the gradual elucidation of cancer cell epigenetic regulation. In fact, most stages of tumor progression, including tumorigenesis, promotion, progression, and recurrence are accompanied by epigenetic alterations, some of which can be reversed by epigenetic drugs. The main objective of epigenetic therapy in the era of personalized precision medicine is to detect cancer biomarkers to improve risk assessment, diagnosis, and targeted treatment interventions. Rapid technological advancements streamlining the characterization of molecular epigenetic changes associated with cancers have propelled epigenetic drug research and development. This review summarizes the main mechanisms of epigenetic dysregulation and discusses past and present examples of epigenetic inhibitors in cancer diagnosis and treatment, with an emphasis on the development of epigenetic enzyme inhibitors or drugs. In the final part, the prospect of precise diagnosis and treatment is considered based on a better understanding of epigenetic abnormalities in cancer.

Biomarkers of response to treatment in acromegaly

INTRODUCTION

Medical treatment of acromegaly is based in a `trial and error` approach. First-generation somatostatin receptor ligands (fg-SRL) are prescribed as first-line medical therapy to the vast majority of patients, despite lack of disease control in approximately 60% of patients. However, other drugs used in acromegaly treatment are available (cabergoline, pasireotide and pegvisomant).

AREAS COVERED

In this article, we review and discuss the biomarkers of response to medical treatment in acromegaly.

EXPERT OPINION

Biomarkers for fg-SRL that can already be applied in clinical practice are: gender, age, pretreatment GH and IGF-I levels, cytokeratin granulation pattern, and the expression of somatostatin receptor type 2. Using biomarkers of response could guide treatment towards precision medicine with greater efficacy and lower costs.

Including supramaximal verification reduced uncertainty in VO2peak response rate

Many reports describe using a supramaximal verification phase-exercising at a power output higher than the highest power output recorded during an incremental cardiopulmonary test-to validate VO2max. The impact of verification phases on estimating the proportion of individuals who increased VO2peak in response to high-intensity interval training (HIIT) remains an underexplored area in the individual response literature. This analysis investigated the influence of same-day and separate-day verification phases during repeated measurements (incremental tests-INCR1 and INCR2; incremental tests + supramaximal verification phases-INCR1+ and INCR2+) of VO2peak on typical error (TE) and the proportion of individuals classified as responders (i.e., the response rate) following 4 weeks of HIIT (n = 25) or a no-exercise control period (n = 9). Incorporation of supramaximal verification consistently reduced the standard deviation of individual response, TE, and confidence interval (CI) widths. However, variances were statistically similar across all groups (p > 0.05). Response rates increased when incorporating either one (INCR1 to INCR1+; 24%-48%, p = 0.07) or two (INCR2 to INCR2+; 28%-48%, p = 0.063) supramaximal verification phases. However, response rates remained unchanged when either zero-based thresholds or smallest worthwhile difference response thresholds were used (50% and 90% CIs, all p > 0.05). Supramaximal verification phases reduced random variability in VO2peak response to HIIT. Compared with separate-day testing (INCR2 and INCR2+), the incorporation of a same-day verification (INCR1+) reduced CI widths the most. Researchers should consider using a same-day verification phase to reduce uncertainty and better estimate VO2peak response rate to HIIT.

Empowering Healthcare through Precision Medicine: Unveiling the Nexus of Social Factors and Trust

This study investigated the impact of social factors on the acceptance of precision medicine (PM) using a quantitative survey grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The findings revealed that social influence has a significantly positive effect on PM acceptance, while the influence of social media is found to be insignificant. Performance expectancy emerged as the most influential factor, demonstrating a significant relationship with PM acceptance. Trust plays a crucial moderating role, mitigating the impact of social factors on PM acceptance. While exploring the mediating effects of trust, we identified a significant mediation effect for social influence and performance expectancy on PM acceptance. However, the mediation effect of social media influence is insignificant. These findings highlight the importance of trust in shaping decisions regarding PM acceptance. These findings have significant implications for healthcare practitioners and policymakers aiming to promote the adoption of precision medicine in clinical practice.

Global and Gender Equity in Oligodendroglioma Research: A Comprehensive Bibliometric Analysis Following the COVID-19 Pandemic

Oligodendrogliomas are rare brain tumors arising from oligodendrocytes; there is a limited understanding of their pathogenesis, which leads to challenges in diagnosis, prognosis, and treatment. This study aimed to conduct a comprehensive bibliometric analysis of the oligodendroglioma literature to assess the current state of research, identify research trends, and elucidate implications for future research. The Lens® database was used to retrieve journal articles related to "oligodendroglioma" without geographic or temporal restrictions. Year-on-year trends in publication and funding were analyzed. Global and gender equity were assessed using the Namsor® Application programming interface. Collaboration patterns were explored using network visualizations. Keyword analysis revealed the most prominent themes in oligodendroglioma research. Out of 9701 articles initially retrieved, 8381 scholarly journal articles were included in the final analysis. Publication trends showed a consistent increase until 2020, followed by a sharp decline likely due to the COVID-19 pandemic. Global representation revealed researchers from 86 countries, with limited participation from low and middle-income countries (LMICs). Gender inequity was evident, with 78.7% of researchers being male. Collaboration analysis revealed a highly interconnected research community. Prognosis, genetic aberrations (particularly "IDH" mutations), and therapeutic options (including chemotherapy and radiotherapy) emerged as dominant research themes. The COVID-19 pandemic impacted oligodendroglioma research funding and publication trends, highlighting the importance of robust funding mechanisms. Global and gender inequities in research participation underscore the need for fostering inclusive collaboration, especially in LMICs. The interconnected research community presents opportunities for knowledge exchange and innovation. Keyword analysis highlights current research trends and a shift to genetic and molecular understanding.

Multipotent Mesenchymal Cells Homing and Differentiation on Poly(ε-caprolactone) Blended with 20% Tricalcium Phosphate and Polylactic Acid Incorporating 10% Hydroxyapatite 3D-Printed Scaffolds via a Commercial Fused Deposition Modeling 3D Device

As highlighted by the 'Global Burden of Disease Study 2019' conducted by the World Health Organization, ensuring fair access to medical care through affordable and targeted treatments remains crucial for an ethical global healthcare system. Given the escalating demand for advanced and urgently needed solutions in regenerative bone procedures, the critical role of biopolymers emerges as a paramount necessity, offering a groundbreaking avenue to address pressing medical needs and revolutionize the landscape of bone regeneration therapies. Polymers emerge as excellent solutions due to their versatility, making them reliable materials for 3D printing. The development and widespread adoption of this technology would impact production costs and enhance access to related healthcare services. For instance, in dentistry, the use of commercial polymers blended with β-tricalcium phosphate (TCP) is driven by the need to print a standardized product with osteoconductive features. However, modernization is required to bridge the gap between biomaterial innovation and the ability to print them through commercial printing devices. Here we showed, for the first time, the metabolic behavior and the lineage commitment of bone marrow-derived multipotent mesenchymal cells (MSCs) on the 3D-printed substrates poly(e-caprolactone) combined with 20% tricalcium phosphate (PCL + 20% β-TCP) and L-polylactic acid (PLLA) combined with 10% hydroxyapatite (PLLA + 10% HA). Although there are limitations in printing additive-enriched polymers with a predictable and short half-life, the tested 3D-printed biomaterials were highly efficient in supporting osteoinductivity. Indeed, considering different temporal sequences, both 3D-printed biomaterials resulted as optimal scaffolds for MSCs' commitment toward mature bone cells. Of interest, PLLA + 10% HA substrates hold the confirmation as the finest material for osteoinduction of MSCs.

Therapeutic Phage Monitoring: A Review

With the global rise in antimicrobial resistance, there has been a renewed interest in the application of therapeutic phages to treat bacterial infections. Therapeutic phage monitoring (TPM) is proposed as an essential element of phage therapy (PT) protocols to generate data and fill knowledge gaps regarding the in vivo efficacy of therapeutic phages, patients' immune responses to PT, and the wider ecological effects of PT. By monitoring phage concentrations in blood and tissues, together with immune responses and possible ecological changes during PT, TPM may enable the optimization of dosing and the implementation of precision medicine approaches. Furthermore, TPM can validate diagnostic surrogates of efficacy, direct research efforts, and establish quality assurance indicators for therapeutic phage products. Thus, TPM holds great potential for enhancing our understanding of the multidirectional phage-bacteria-host interactions and advancing "best practice" PT, ultimately improving patient care.

New management strategies for primary headache disorders: Insights from P4 medicine

Primary headache disorder is the main cause of headache attacks, leading to significant disability and impaired quality of life. This disorder is increasingly recognized as a heterogeneous condition with a complex network of genetic, environmental, and lifestyle factors. However, the timely diagnosis and effective treatment of these headaches remain challenging. Precision medicine is a potential strategy based on P4 (predictive, preventive, personalized, and participatory) medicine that may bring new insights for headache care. Recent machine learning advances and widely available molecular biology and imaging data have increased the usefulness of this medical strategy. Precision medicine emphasizes classifying headaches according to their risk factors, clinical presentation, and therapy responsiveness to provide individualized headache management. Furthermore, early preventive strategies, mainly utilizing predictive tools, are critical in reducing headache attacks and improving the quality of life of individuals with headaches. The current review comprehensively discusses the potential application value of P4 medicine in headache management.

Candidate composite biomarker to inform drug treatments for diabetic kidney disease

Introduction

Current guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy.

Results

Using data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment.

Discussion

Higher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment.

Progress in overactive bladder: novel avenues from psychology to clinical opinions

Rationale

Overactive bladder (OAB) is a common, distressing condition that worsens with age and impacts quality of life significantly. As a results of its clinical symptoms, patients suffer from serious physical and mental health issues, have a poor quality of life, and participate in a serious economic burden. The key social-psychological factors include living habits, eating habits, and personality characteristics on this disease, even though the pathogenesis of OAB is complex. However, there is few cognitions and research on OAB in the field of psychology.

Methods/Search Strategy

Between 2000 and 2022, two electronic databases were systematically searched in accordance with Cochrane library guidelines (PubMed/Medline, Web of Science). An analysis of the remaining articles with relevant information was conducted using a data extraction sheet. An itemized flow diagram was adopted and used to report systematic reviews and meta-analysis. A systematic review of studies published from 2000 to 2022 in English language were conducted and included in the review.

The intended audience

Urological surgeon and psychologists majoring in urinary diseases.

Implication

As a result of this information, we are able to develop a better understanding of the role of psychological factors in the development of OAB and suggest potential therapeutic directions for OAB patients. This may benefit the recovery of OAB patients.

Transform diabetes care with precision medicine

Background and Aims

Diabetes is a global concern. This article took a closer look at diabetes and precision medicine.

Methods

A literature search of studies related to the use of precision medicine in diabetes care was conducted in various databases (PubMed, Google Scholar, and Scopus).

Results

Precision medicine encompasses the integration of a wide array of personal data, including clinical, lifestyle, genetic, and various biomarker information. Its goal is to facilitate tailored treatment approaches using contemporary diagnostic and therapeutic techniques that specifically target patients based on their genetic makeup, molecular markers, phenotypic traits, or psychosocial characteristics. This article not only highlights significant advancements but also addresses key challenges, particularly focusing on the technologies that contribute to the realization of personalized and precise diabetes care.

Conclusion

For the successful implementation of precision diabetes medicine, collaboration and coordination among multiple stakeholders are crucial.

Network medicine for patients' stratification: From single-layer to multi-omics

Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings. This article is categorized under: Cancer > Biomedical Engineering Cancer > Computational Models Cancer > Genetics/Genomics/Epigenetics.

Drug-microbiota interactions: an emerging priority for precision medicine

Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.

Senescence gene expression in clear cell renal cell carcinoma: Role of tumor immune microenvironment and senescence-associated survival prediction

Clear cell renal cell carcinoma (ccRCC) constitutes the most prevalent histopathologic subtype of renal cell carcinoma. The interplay between aging and cancer is complicated, and we provide a relatively new set of senescence genes that has not yet been used in the study of clear cell renal cell carcinoma. Our objective is to investigate the involvement of senescence in the development and diagnosis of ccRCC. RNA-seq and clinical data for ccRCC was obtained from the cancer genome atlas and gene expression omnibus databases. Consensus clustering analysis was performed to identify novel molecular subgroups. Tumor immune status was assessed using estimating stromal and immune cells in malignancy using expression data, microenvironment cell populations, and single-sample gene set enrichment analysis analyses. Functional analysis, including gene ontology, gene set variation analysis, and gene set enrichment analysis, was conducted to explore potential mechanisms. A prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Decision trees and nomograms were developed for survival prediction. SenMayo classified ccRCC patients into 2 molecular subtypes with significantly different survival rates, and significant differences in their immune status, characterized by poor prognosis with relatively high immune status. Besides, the differentially expressed genes between the 2 subgroups were mainly enriched in immune-related pathways. The burden of aging tissues and cells may lead to immune dysregulation and drug resistance, which could contribute to poor prognosis in ccRCC patients. Risk models, decision trees, and nomogram for ccRCC survival prediction have great potential applications. In conclusion, our study establishes a clear association between aging in ccRCC and the immune microenvironment, demonstrating the predictive potential of senescence genes for ccRCC prognosis.

Gene variation profile and it's potential correlation with clinical characteristics in HBV-associated HCC patients of Sichuan Han nationality in China

OBJECTIVE

To explore the correlation between hepatocellular carcinoma (HCC) gene variation profile and clinical characteristics in Han nationality with HBV infection in Sichuan province.

METHODS

The clinical data and HCC tissues were obtained from the enrolled patients. Whole exome sequencing and bioinformatics analysis were performed on formalin-fixed and paraffin-embedded samples from HCC. Tumor mutational burden (TMB) was measured by an algorithm developed in-house.

RESULTS

Sixteen high-frequency mutated genes with differential expressions were identified by WES. SMG1 gene variation could be positively correlated with satellite lesions. AMY2B and RGPD4 gene mutation seemed to have a greater chance of vascular invasion. The patients with TATDN1 variation have bigger diameters and greater chances of vascular and microvascular invasion (all P < 0.05). Univariate analysis indicated patients with gene TATDN1 variation had worse prognoses both in disease free survival (DFS) and overall survival (OS). In addition, the enrichment analysis showed many pathways, including the cell cycle pathway, viral oncogene pathway, MAPK pathway, PI3K-AKT pathway, etc., may be associated with HCC.

CONCLUSION

This study explores the gene variation profile of HCC patients with HBV infection in Han nationality of Sichuan Province for the first time, which confirmed the existence of some high-frequency mutated genes and the possibility that the gene variations are involved in the tumorigenesis of HCC through multiple signal pathways. Also, patients with TATDN1 wild type showed a trend of better prognosis both in DFS and OS.

Individualisation, personalisation and person-centredness in mental healthcare: a scoping review of concepts and linguistic network visualisation

BACKGROUND

Targeted mental health interventions are increasingly described as individualised, personalised or person-centred approaches. However, the definitions for these terms vary significantly. Their interchangeable use prevents operationalisations and measures.

OBJECTIVE

This scoping review provides a synthesis of key concepts, definitions and the language used in the context of these terms in an effort to delineate their use for future research.

STUDY SELECTION AND ANALYSIS

Our search on PubMed, EBSCO and Cochrane provided 2835 relevant titles. A total of 176 titles were found eligible for extracting data. A thematic analysis was conducted to synthesise the underlying aspects of individualisation, personalisation and person-centredness. Network visualisations of co-occurring words in 2625 abstracts were performed using VOSViewer.

FINDINGS

Overall, 106 out of 176 (60.2%) articles provided concepts for individualisation, personalisation and person-centredness. Studies using person-centredness provided a conceptualisation more often than the others. A thematic analysis revealed medical, psychological, sociocultural, biological, behavioural, economic and environmental dimensions of the concepts. Practical frameworks were mostly found related to person-centredness, while theoretical frameworks emerged in studies on personalisation. Word co-occurrences showed common psychiatric words in all three network visualisations, but differences in further contexts.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The use of individualisation, personalisation and person-centredness in mental healthcare is multifaceted. While individualisation was the most generic term, personalisation was often used in biomedical or technological studies. Person-centredness emerged as the most well-defined concept, with many frameworks often related to dementia care. We recommend that the use of these terms follows a clear definition within the context of their respective disorders, treatments or medical settings.

SCOPING REVIEW REGISTRATION

Open Science Framework: osf.io/uatsc.

Probiotics and the Potential of Genetic Modification as a Possible Treatment for Food Allergy

Food allergy is a common condition that affects millions of people worldwide. It is caused by an abnormal immune response to harmless food antigens, which is influenced by genetics and environmental factors. Modulating the gut microbiota and immune system with probiotics or genetically modified probiotics confers health benefits to the host and offers a novel strategy for preventing and treating food allergy. This systematic review aims to summarize the current proof of the role of probiotics in food allergy and propose a promising future research direction of using probiotics as a possible strategy of treatment for food allergy.

Genetic Correlates as a Predictor of Bariatric Surgery Outcomes after 1 Year

This study analyzed genetic risk assessments in patients undergoing bariatric surgery to serve as a predictive factor for weight loss parameters 1 year after the operation. Thirty (30) patients were assessed for Genetic Addiction Risk Severity (GARS), which analyzes neurogenetic polymorphisms involved in addiction and reward deficiency. Genetic and psychosocial data collected before the operation were correlated with weight loss data, including changes in weight, body mass index (BMI), and percent of expected weight loss (%EWL). Results examined correlations between individual gene risk alleles, 1-year body weight data, and psychosocial trait scores. Spearman's correlations revealed that the OPRM1 (rs1799971) gene polymorphism had significant negative correlation with 1-year weight (rs = -0.4477, p < 0.01) and BMI (rs = -0.4477, p < 0.05). In addition, the DRD2 risk allele (rs1800497) was correlated negatively with BMI at 1 year (rs = -0.4927, p < 0.05), indicating that one risk allele copy was associated with lower BMI. However, this allele was positively correlated with both ∆Weight (rs = 0.4077, p < 0.05) and %EWL (rs = 0.5521, p < 0.05) at 1 year post-surgery. Moreover, the overall GARS score was correlated with %EWL (rs = 0.4236, p < 0.05), ∆Weight (rs = 0.3971, p < 0.05) and ∆BMI (rs = 0.3778, p < 0.05). Lastly, Food Cravings Questionnaire (FCQ) scores were negatively correlated with %EWL (rs = -0.4320, p < 0.05) and ∆Weight at 1 year post-surgery (rs = -0.4294, p < 0.05). This suggests that individuals with a higher genetic addiction risk are more responsive to weight loss treatment, especially in the case of the DRD2 polymorphism. These results should translate clinically to improve positivity and attitude related to weight management by those individuals born with the risk alleles (rs1800497; rs1799971).

Porting and Optimizing BWA-MEM2 Using the Fujitsu A64FX Processor

Sequence alignment pipelines for human genomes are an emerging workload that will dominate in the precision medicine field. BWA-MEM2 is a tool widely used in the scientific community to perform read mapping studies. In this paper, we port BWA-MEM2 to the AArch64 architecture using the ARMv8-A specification, and we compare the resulting version against an Intel Skylake system both in performance and in energy-to-solution. The porting effort entails numerous code modifications, since BWA-MEM2 implements certain kernels using x86_64 specific intrinsics, e.g., AVX-512. To adapt this code we use the recently introduced Arm's Scalable Vector Extensions (SVE). More specifically, we use Fujitsu's A64FX processor, the first to implement SVE. The A64FX powers the Fugaku Supercomputer that led the Top500 ranking from June 2020 to November 2021. After porting BWA-MEM2 we define and implement a number of optimizations to improve performance in the A64FX target architecture. We show that while the A64FX performance is lower than that of the Skylake system, A64FX delivers 11.6% better energy-to-solution on average. All the code used for this article is available at https://gitlab.bsc.es/rlangari/bwa-a64fx.

Flourishing tumor organoids: History, emerging technology, and application

Malignant tumors are one of the leading causes of death which impose an increasingly heavy burden on all countries. Therefore, the establishment of research models that closely resemble original tumor characteristics is crucial to further understanding the mechanisms of malignant tumor development, developing safer and more effective drugs, and formulating personalized treatment plans. Recently, organoids have been widely used in tumor research owing to their advantages including preserving the structure, heterogeneity, and cellular functions of the original tumor, together with the ease of manipulation. This review describes the history and characteristics of tumor organoids and the synergistic combination of three-dimensional (3D) culture approaches for tumor organoids with emerging technologies, including tissue-engineered cell scaffolds, microfluidic devices, 3D bioprinting, rotating wall vessels, and clustered regularly interspaced short palindromic repeats-CRISPR-associated protein 9 (CRISPR-Cas9). Additionally, the progress in research and the applications in basic and clinical research of tumor organoid models are summarized. This includes studies of the mechanism of tumor development, drug development and screening, precision medicine, immunotherapy, and simulation of the tumor microenvironment. Finally, the existing shortcomings of tumor organoids and possible future directions are discussed.

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%-50% of DME patients do not respond well to anti-VEGF agents. In addition, there is no evidence-based and universally accepted administration regimen. The identification of DME patients not responding to anti-VEGF agents and the determination of the optimal administration interval are the 2 major challenges of DME, which are difficult to achieve with the coarse granularity of conventional health care modality. Therefore, more and more retina specialists have pointed out the necessity of introducing precision medicine into the management of DME and have conducted related studies in recent years. One of the most frontier methods is the targeted extraction of individualized disease features from optical coherence tomography images based on artificial intelligence technology, which provides precise evaluation and risk classification of DME. This review aims to provide an overview of the progress of artificial intelligence-enabled precision medicine in automated screening, precise evaluation, prognosis prediction, and follow-up monitoring of DME. Further, the challenges ahead of real-world applications and the future development of precision medicine in DME will be discussed.