Accurate prediction of myopic progression and high myopia by machine learning.

Background: Myopia is a leading cause of visual impairment in Asia and worldwide. However, accurately predicting the progression of myopia and the high risk of myopia remains a challenge. This study aims to develop a predictive model for the development of myopia. Methods: We first retrospectively gathered 612 530 medical records from five independent cohorts, encompassing 227 543 patients ranging from infants to young adults. Subsequently, we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia. Result: The model to predict the progression of myopia achieved an R2 value of 0.964 vs a mean absolute error (MAE) of 0.119D [95% confidence interval (CI): 0.119, 1.146] in the internal validation set. It demonstrated strong generalizability, maintaining consistent performance across external validation sets: R2 = 0.950 vs MAE = 0.119D (95% CI: 0.119, 1.136) in validation study 1, R2 = 0.950 vs MAE = 0.121D (95% CI: 0.121, 1.144) in validation study 2, and R2 = 0.806 vs MAE = -0.066D (95% CI: -0.066, 0.569) in the Shanghai Children Myopia Study. In the Beijing Children Eye Study, the model achieved an R2 of 0.749 vs a MAE of 0.178D (95% CI: 0.178, 1.557). The model to predict the risk of high myopia achieved an area under the curve (AUC) of 0.99 in the internal validation set and consistently high area under the curve values of 0.99, 0.99, 0.96 and 0.99 in the respective external validation sets. Conclusion: Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.

Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients.

Background: Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC). Methods: We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using consensus clustering to identify subgroups related to ARLncRNAs. Results: The screening process identified 27 ARLncRNAs, with 13 being associated with HCC prognosis. Consequently, a set of signatures comprising 8 ARLncRNAs was successfully constructed as independent prognostic factors for HCC. Patients in the high-risk group showed very poor prognoses in most clinical categories. The Riskscore was closely related to immune cell scores, such as macrophages, and the DEGs between different groups were implicated in metabolism, cell cycle, and mitotic processes. Notably, high-risk group patients demonstrated a significantly lower IC50 for Paclitaxel, suggesting that Paclitaxel could be an ideal treatment for those at elevated risk for HCC. We further identified C2 as the Paclitaxel subtype, where patients exhibited higher Riskscores, reduced survival rates, and more severe clinical progression. Conclusion: The 8 signatures based on ARLncRNAs present novel targets for prognostic prediction in HCC. The drug candidate Paclitaxel may effectively treat HCC by impacting ARLncRNAs expression. With the identification of ARLncRNAs-related isoforms, these results provide valuable insights for clinical exploration of autophagy mechanisms in HCC pathogenesis and offer potential avenues for precision medicine.

Employee perception of precision technology use at the dairy farm.

The adoption of precision technologies on dairy farms has increased significantly in recent decades, leading to the challenge of providing employees with resources to maximize the efficient use of these tools. The objective of this study was to explore how dairy farm employees perceive the available precision technologies and to identify possible challenges they face when adapting to their use at the farm. An online survey consisting of four sections (employee demographics, precision technologies in use, perception of these technologies, and opportunities for adapting to technology use) was completed from September to December 2022 by 266 farm employees from three dairies operated under similar management. Most of the respondents were identified as male (72.2%), Hispanic or Latino (92.5%), aged between 21 and 30 (39.1%) or 31 and 40 yr (36.8%), with a bachelor's degree (34.6%) or completion of middle school (29.3%) and having basic or no English proficiency (74%). Overall, the respondents indicated being comfortable (95.6%) with and understanding (91.8%) the technology they use. Employees recognized precision technology as a tool that helps them to be more efficient (93.7%), identifying the technologies' benefits (92.1%). However, challenges for adapting to these technologies included personal limitations, such as not knowing the language of the technology (31%), visual impairments (24%), light sensitivity (14%), and not being able to read (7%). Environmental limitations were also recognized and included cold weather (64.3%), wind (46%), and surroundings that were too dark (31%) or too bright (21%). Significant associations between perception of the technology and age, level of education, and English proficiency were identified. Respondents indicated their desire to learn more about precision technologies implemented at work, which could eventually lead to improved efficiency at the dairy operation through innovations in the way users interact with these technologies, increasing employees' motivation. This study provides insights that could assist the dairy industry in addressing challenges and enhancing opportunities for a more efficient use of precision technologies at dairy farms.

Sex differences in sleep, circadian rhythms, and metabolism: Implications for precision medicine.

The number of individuals experiencing sleep loss has exponentially risen over the past decades. Extrapolation of laboratory findings to the real world suggests that females are more affected by extended wakefulness and circadian misalignment than males are. Therefore, long-term effects such as sleep and metabolic disorders are likely to be more prevalent in females than in males. Despite emerging evidence for sex differences in key aspects of sleep-wake and circadian regulation, much remains unknown, as females are often underrepresented in sleep and circadian research. This narrative review aims at highlighting 1) how sex differences systematically impinge on the sleep-wake and circadian regulation in humans, 2) how sex differences in sleep and circadian factors modulate metabolic control, and 3) the relevance of these differences for precision medicine. Ultimately, the findings justify factoring in sex differences when optimizing individually targeted sleep and circadian interventions in humans.

Skin Microbial Composition and Genetic Mutation Analysis in Precision Medicine for Epidermolysis Bullosa.

Epidermolysis bullosa (EB) is an inherited skin disease representing a spectrum of rare genetic disorders. These conditions share the common trait that causes fragile skin, resulting in the development of blisters and erosions. The inheritance follows an autosomal pattern, and the array of clinical presentations leads to significant physical suffering, considerable morbidity, and mortality. Despite EB having no cure, effectively managing EB remains an exceptional challenge due to its rarity and complexity, occasionally casting a profound impact on the lives of affected individuals. Considering that EB management requires a multidisciplinary approach, this sometimes worsens the condition of patients with EB due to inappropriate handling. Thus, more appropriate and precise treatment management of EB is essentially needed. Advanced technology in medicine and health comes into the bioinformatics era. Including treatment for skin diseases, omics-based approaches aim to evaluate and handle better disease management and treatment. In this work, we review several approaches regarding the implementation of omics-based technology, including genetics, pathogenic mutation, skin microbiomics, and metagenomics analysis for EB. In addition, we highlight recent updates on the potential of metagenomics analysis in precision medicine for EB.

Recent Advances in the Treatment and Management of Alzheimer's Disease: A Precision Medicine Perspective.

About 60% to 70% of people with dementia have Alzheimer's Disease (AD), a neuro-degenerative illness. One reason for this disorder is the misfolding of naturally occurring proteins in the human brain, specifically ß-amyloid (Aß) and tau. Certain diagnostic imaging techniques, such as amyloid PET imaging, tau PET imaging, Magnetic Resonance Imaging (MRI), Comput-erized Tomography (CT), and others, can detect biomarkers in blood, plasma, and cerebral spinal fluids, like an increased level of ß-amyloid, plaques, and tangles. In order to create new pharma-cotherapeutics for Alzheimer's disease, researchers must have a thorough and detailed knowledge of amyloid beta misfolding and other related aspects. Dolopezil, rivastigmine, galantamine, and other acetylcholinesterase inhibitors are among the medications now used to treat Alzheimer's disease. Another medication that can temporarily alleviate dementia symptoms is memantine, which blocks the N-methyl-D-aspartate (NMDA) receptor. However, it is not able to halt or re-verse the progression of the disease. Medication now on the market can only halt its advance-ment, not reverse it. Interventions to alleviate behavioral and psychological symptoms, exhibit an-ti-neuroinflammation and anti-tau effects, induce neurotransmitter alteration and cognitive en-hancement, and provide other targets have recently been developed. For some Alzheimer's pa-tients, the FDA-approved monoclonal antibody, aducanumab, is an option; for others, phase 3 clinical studies are underway for drugs, like lecanemab and donanemab, which have demonstrat-ed potential in eliminating amyloid protein. However, additional study is required to identify and address these limitations in order to reduce the likelihood of side effects and maximize the thera-peutic efficacy.

Ethical and Equitable Digital Health Research: Ensuring Self-Determination in Data Governance for Racialized Communities.

Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance approach honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management.

Cerebrospinal Fluid Testing in Leptomeningeal Progression of HER2-Negative Breast Cancer Reveals HER2 Positivity, Leading to HER2-Targeted Therapy: A Case Report.

The treatment of breast cancer is largely determined by protein expression assays of estrogen receptor, progesterone receptor, and Her2/neu (HER2) status. These prognostic markers may vary due to tumor heterogeneityor the evolution of prognostic markers throughout the course of treatment. This report presents a case of a patient who initially presented with HER2-negative breast cancer and had rapidly progressed on numerous lines of treatment. An analysis of cerebrospinal fluid via next-generation sequencing and biopsy of metastasis to the liver identified HER2-positive cancer, which allowed for the use of trastuzumab deruxtecan, a HER2-targeted therapy. This led to an excellent clinical response with improvement in performance status and quality of life. This case report demonstrates the importance of continuing to follow a patient's cancer pathology to open the doors for other opportunities for treatment. Cancer has the potential to evolve and there is a benefit of obtaining rebiopsies to ensure the correct targeted therapies are provided to the patient.

Safely balancing a double-edged blade: identifying and mitigating emerging biosecurity risks in precision medicine.

Tools and methods of precision medicine are developing rapidly, through both iterative discoveries enabled by innovations in biomedical research (e.g., genome editing, synthetic biology, bioengineered devices). These are strengthened by advancements in information technology and the increasing body of data-as assimilated, analyzed, and made accessible-and affectable-through current and emerging cyber-and systems- technologies. Taken together, these approaches afford ever greater volume and availability of individual and collective human data. Machine learning and/or artificial intelligence approaches are broadening this dual use risk; and in the aftermath of COVID-19, there is growing incentive and impetus to gather more biological data from individuals and their environments on a routine basis. By engaging these data-and the interventions that are based upon them, precision medicine offer promise of highly individualized treatments for disease and injury, optimization of structure and function, and concomitantly, the potential for (mis) using data to incur harm. This double-edged blade of benefit and risk obligates the need to safeguard human data from purloinment, through systems, guidelines and policies of a novel discipline, cyberbiosecurity, which, as coupled to ethical precepts, aims to protect human privacy, agency, and safety in ways that remain apace with scientific and technological advances in biomedicine. Herein, current capabilities and trajectories precision medicine are described as relevant to their dual use potential, and approaches to biodata security (viz.- cyberbiosecurity) are proposed and discussed.

Clinical efficacy of tumor organoid-guided cancer therapy for locally advanced unresectable or metastatic breast cancer.

Patient-derived organoids (PDOs) may facilitate treatment selection. This retrospective cohort study evaluated the feasibility and clinical benefit of using PDOs to guide personalized treatment in metastatic breast cancer (MBC). Patients diagnosed with MBC were recruited between January 2019 and August 2022. PDOs were established and the efficacy of customized drug panels was determined by measuring cell mortality after drug exposure. Patients receiving organoid-guided treatment (OGT) were matched 1:2 by nearest neighbor propensity scores with patients receiving treatment of physician's choice (TPC). The primary outcome was progression-free survival. Secondary outcomes included objective response rate and disease control rate. Targeted gene sequencing and pathway enrichment analysis were performed. Forty-six PDOs (46 of 51, 90.2%) were generated from 45 MBC patients. PDO drug screening showed an accuracy of 78.4% (95% CI 64.9%-91.9%) in predicting clinical responses. Thirty-six OGT patients were matched to 69 TPC patients. OGT was associated with prolonged median progression-free survival (11.0 months vs. 5.0 months; hazard ratio 0.53 [95% CI 0.33-0.85]; p = .01) and improved disease control (88.9% vs. 63.8%; odd ratio 4.26 [1.44-18.62]) compared with TPC. The objective response rate of both groups was similar. Pathway enrichment analysis in hormone receptor-positive, human epidermal growth factor receptor 2-negative patients demonstrated differentially modulated pathways implicated in DNA repair and transcriptional regulation in those with reduced response to capecitabine/gemcitabine, and pathways associated with cell cycle regulation in those with reduced response to palbociclib. Our study shows that PDO-based functional precision medicine is a feasible and effective strategy for MBC treatment optimization and customization.

Prospective validation of a model-informed precision dosing tool for vancomycin treatment in neonates.

We recruited 48 neonates (50 vancomycin treatment episodes) in a prospective study to validate a model-informed precision dosing (MIPD) software. The initial vancomycin dose was based on a population pharmacokinetic model and adjusted every 36-48 h. Compared with a historical control group of 53 neonates (65 episodes), the achievement of a target trough concentration of 10-15 mg/L improved from 37% in the study to 62% in the MIPD group (P = 0.01), with no difference in side effects.

An enriched approach to combining high-dimensional genomic and low-dimensional phenotypic data.

We describe an approach for combining and analyzing high-dimensional genomic and low-dimensional phenotypic data. The approach leverages a scheme of weights applied to the variables instead of observations and, hence, permits incorporation of the information provided by the low dimensional data source. It can also be incorporated into commonly used downstream techniques, such as random forest or penalized regression. Finally, the simulated lupus studies involving genetic and clinical data are used to illustrate the overall idea and show that the proposed enriched penalized method can select significant genetic variables while keeping several important clinical variables in the final model.

The molecular subtypes of autoimmune diseases.

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.

Objectives: We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. Materials and Methods: The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification. Results: By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention. Discussion: We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection. Conclusion: The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology.

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

Redefining Rare Disease Care in the Digital Age: Insights and Key Takeaways from a Digital Health Symposium Focused on Empowering Rare Disease Communities.

At the Stanford-UCB Rare Disease Digital Health Symposium held in Stanford, California, on September 8, 2023, researchers, clinicians, payers, thought leaders, and rare disease caregivers and advocates discussed the current state of care delivery and future perspectives of digitally-enabled care for rare disease patient populations. Digital health aims to improve healthcare delivery through novel ways of providing access to more precise diagnosis, monitoring of disease progression, treatment, prognosis, and care management for rare disease patients. The meeting focused on highlighting challenges and unmet needs, data infrastructure and analytics, the need for targeted and effective personalized therapies, and the importance of digital care transformation. The meeting also covered the social and ethical impact of access to digitally delivered, patient-centered care, as well as views on implementation and patient autonomy and empowerment.

Trends and distribution of external radiation therapy facilities in Japan based on Survey of Medical Institutions from the Ministry of Health, Labour and Welfare.

This study aimed to explore the distribution of external radiation therapy (RT) facilities, the status of related device installations and the adoption of high-precision RT using Survey of Medical Institutions from the Ministry of Health, Labour and Welfare in Japan. Analysis, categorized by the hospital size and prefecture, provides specific insights into the trends in treatment facility healthcare capabilities. Data on the number of RT facilities, high-precision RT facilities, RT devices and treatment planning systems (TPS) categorized by the number of beds and prefecture from 1996 to 2020 were analyzed. In addition, the study examined the correlation between the high-precision implementation rate and the number of TPSs or radiation oncologists and other medical staff. High-precision RT exceeded 95% in large facilities (800+ beds) but remained <50% in medium-sized facilities (300-499 beds). In a prefecture-by-prefecture analysis, calculation of the maximum-minimum ratio of RT facilities per million population and per 30 km2 revealed a disparity of 3.7 and 73.1 times in the population ratio and the density ratio, respectively. Although a correlation was found between the number of TPSs per RT device or the number of medical physicists per million population and the rate of high-precision RT implementation, no correlation was found among other professions. Detailed analysis based on the hospital size and prefecture provided more specific information on the medical functions of RT facilities in Japan. These findings can potentially contribute to the future development of RT, including the standardization of treatment techniques and optimal resource allocation.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.