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HypothesisThe main hypothesis is that a digital, biodata-driven, and personalized program would exhibit high user retention and engagement, followed by more effective management of their depressive and anxiety symptoms.ObjectiveThis pilot study explores the feasibility, acceptability, engagement, and potential impact on depressive and anxiety and quality of life outcomes of the 16-week Feel Program. Additionally, it examines potential correlations between engagement and impact on mental health outcomes.MethodsThis single-arm study included 48 adult participants with mild or moderate depressive or anxiety symptoms who joined the 16-week Feel Program, a remote biodata-driven mental health support program created by Feel Therapeutics. The program uses a combination of evidence-based approaches and psychophysiological data. Candidates completed an online demographics and eligibility survey before enrolment. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire and Generalized Anxiety Disorder Scale, respectively. The Satisfaction with Life Scale and the Life Satisfaction Questionnaire were used to assess quality of life. User feedback surveys were employed to evaluate user experience and acceptability.ResultsIn total, 31 participants completed the program with an overall retention rate of 65%. Completed participants spent 60 min in the app, completed 13 Mental Health Actions, including 5 Mental Health Exercises and 4.9 emotion logs on a weekly basis. On average, 96% of the completed participants were active and 76.8% of them were engaged with the sensor during the week. Sixty five percent of participants reported very or extremely high satisfaction, while 4 out of 5 were very likely to recommend the program to someone. Additionally, 93.5% of participants presented a decrease in at least one of the depressive or anxiety symptoms, with 51.6 and 45% of participants showing clinically significant improvement, respectively. Finally, our findings suggest increased symptom improvement for participants with higher engagement throughout the program.ConclusionsThe findings suggest that the Feel Program may be feasible, acceptable, and valuable for adults with mild or moderate depressive and/or anxiety symptoms. However, controlled trials with bigger sample size, inclusion of a control group, and more diverse participant profiles are required in order to provide further evidence of clinical efficacy.

Aims/hypothesisData-driven diabetes subgroups have shown distinct clinical characteristics and disease progression, although there is a lack of evidence that this information can guide clinical decisions. We aimed to investigate whether diabetes subgroups, identified by data-driven clustering or supervised machine learning methods, respond differently to canagliflozin.MethodsWe pooled data from five randomised, double-blinded clinical trials of canagliflozin at an individual level. We applied the coordinates from the All New Diabetics in Scania (ANDIS) study to form four subgroups: mild age-related diabetes (MARD); severe insulin-deficient diabetes (SIDD); mild obesity-related diabetes (MOD) and severe insulin-resistant diabetes (SIRD). Machine learning models for HbA1c lowering (ML-A1C) and albuminuria progression (ML-ACR) were developed. The primary efficacy endpoint was reduction in HbA1c at 52 weeks. Concordance of a model was defined as the difference between predicted HbA1c and actual HbA1c decline less than 3.28 mmol/mol (0.3%).ResultsThe decline in HbA1c resulting from treatment was different among the four diabetes clusters (pinteraction=0.004). In MOD, canagliflozin showed a robust glucose-lowering effect at week 52, compared with other drugs, with least-squares mean of HbA1c decline [95% CI] being 6.6 mmol/mol (4.1, 9.2) (0.61% [0.38, 0.84]) for sitagliptin, 7.1 mmol/mol (4.7, 9.5) (0.65% [0.43, 0.87]) for glimepiride, and 9.8 mmol/mol (9.0, 10.5) (0.90% [0.83, 0.96]) for canagliflozin. This superiority persisted until 104 weeks. The proportion of individuals who achieved HbA1c <53 mmol/mol (<7.0%) was highest in sitagliptin-treated individuals with MARD but was similar among drugs in individuals with MOD. The ML-A1C model and the cluster algorithm showed a similar concordance rate in predicting HbA1c lowering (31.5% vs 31.4%, p=0.996). Individuals were divided into high-risk and low-risk groups using ML-ACR model according to their predicted progression risk for albuminuria. The effect of canagliflozin vs placebo on albuminuria progression differed significantly between the high-risk (HR 0.67 [95% CI 0.57, 0.80]) and low-risk groups (HR 0.91 [0.75, 1.11]) (pinteraction=0.016).Conclusions/interpretationData-driven clusters of individuals with diabetes showed different responses to canagliflozin in glucose lowering but not renal outcome prevention. Canagliflozin reduced the risk of albumin progression in high-risk individuals identified by supervised machine learning. Further studies with larger sample sizes for external replication and subtype-specific clinical trials are necessary to determine the clinical utility of these stratification strategies in sodium–glucose cotransporter 2 inhibitor treatment.Data availabilityThe application for the clinical trial data source is available on the YODA website (http://yoda.yale.edu/).

The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI's ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.

Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. Results We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone ,  ACT and L R W 5 are the top 3 best performers on all five datasets. Conclusions This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.

The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.

The integration of high-throughput experimentation and machine learning is transforming data-driven antibody engineering, revolutionizing the discovery and optimization of antibody therapeutics. These approaches employ extensive datasets comprising antibody sequences, structures, and functional properties to train predictive models that enable rational design. This review highlights the significant advancements in data acquisition and feature extraction, emphasizing the necessity of capturing both sequence and structural information. We illustrate how machine learning models, including protein language models, are used not only to enhance affinity but also to optimize other crucial therapeutic properties, such as specificity, stability, viscosity, and manufacturability. Furthermore, we provide practical examples and case studies to demonstrate how the synergy between experimental and computational approaches accelerates antibody engineering. Finally, this review discusses the remaining challenges in fully realizing the potential of artificial intelligence (AI)-powered antibody discovery pipelines to expedite therapeutic development.

Multiple sclerosis (MS) is an incurable autoimmune disease marked by heterogeneous progression and a lack of reliable biomarkers, complicating prognosis and individualized care. This study introduces a novel trajectory-based statistical approach designed to identify patterns in patient histories within MS populations. Using longitudinal clinical data from a real-world cohort of 1,025 MS patients (median follow-up: 6.75 years), two complementary analyses were conducted based on patient trajectory analysis. In the first analysis, the technique is applied to the complete dataset after removal of missing values (n = 985; 11,048 events) to uncover latent progressive trajectories. The second analysis evaluated the techniques' performance on a smaller, limited-sample cohort (n = 83; 282 events). Across both analyses, the approach revealed previously unrecognized progression patterns, giving rise to new hypotheses, including an effect of Alemtuzumab on the bowel/bladder function (p 0.01, RR = 2.83) and glatiramer acetate on the occurrence of relapses (p 0.01, RR = 1.49). Known associations were also confirmed, such as the relationship between relapse activity and brain lesions (p 0.01, RR = 1.20). The results demonstrate the method's robustness across varying dataset sizes, highlight its methodological limitations, and show its potential to uncover previously unseen relationships among MS-specific diagnostic events. These findings provide a foundation for generating novel hypotheses relevant to biomarker discovery and therapeutic optimization.

Objective Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer treatment for patients who are not candidates for surgery/radiotherapy. However, a major challenge for MWA is its relatively high tumor recurrence rates, due to incomplete treatment as a result of inaccurate planning. We introduce a patient-specific, deep-learning model to accurately predict post-treatment ablation zones to aid planning and enable effective treatments. Materials and methods Our IRB-approved retrospective study consisted of ablations with a single applicator/burn/vendor between 01/2015 and 01/2019. The input data included pre-procedure computerized tomography (CT), ablation power/time, and applicator position. The ground truth ablation zone was segmented from follow-up CT post-treatment. Novel deformable image registration optimized for ablation scans and an applicator-centric co-ordinate system for data analysis were applied. Our prediction model was based on the U-net architecture. The registrations were evaluated using target registration error (TRE) and predictions using Bland-Altman plots, Dice co-efficient, precision, and recall, compared against the applicator vendor’s estimates. Results The data included 113 unique ablations from 72 patients (median age 57, interquartile range (IQR) (49–67); 41 women). We obtained a TRE ≤ 2 mm on 52 ablations. Our prediction had no bias from ground truth ablation volumes ( p  = 0.169) unlike the vendor’s estimate ( p  < 0.001) and had smaller limits of agreement ( p  < 0.001). An 11% improvement was achieved in the Dice score. The ability to account for patient-specific in-vivo anatomical effects due to vessels, chest wall, heart, lung boundaries, and fissures was shown. Conclusions We demonstrated a patient-specific deep-learning model to predict the ablation treatment effect prior to the procedure, with the potential for improved planning, achieving complete treatments, and reduce tumor recurrence. Clinical relevance statement Our method addresses the current lack of reliable tools to estimate ablation extents, required for ensuring successful ablation treatments. The potential clinical implications include improved treatment planning, ensuring complete treatments, and reducing tumor recurrence. Key Points Reliable tools to predict the extent of ablation treatments are currently lacking. Our novel patient-specific deep-learning algorithm was shown to predict ablation zones with higher accuracy and less bias compared to the currently used estimates provided by applicator vendor. Our method for ablation prediction allows for real-time clinical deployment, with potential for improved treatment planning and reduced tumor recurrence.

Before meaningful progress toward precision psychiatry is possible, objective (unbiased) assessment of patient mental well-being must be validated and adopted broadly. This study aims to compare the fidelity of a precision psychiatry therapy recommendation algorithm when trained with an objective quantification of psychological stress versus subjective ecological momentary assessments (EMAs) of stress and mood. From 2786 unique individuals engaging between March 2015 and December 2022 in English language psychotherapy sessions and providing pre- and postsession self-report and facial biometric data via a mobile health platform (Mobio Interactive Pte Ltd, Singapore), analysis was conducted on 67 \"super users\" that completed a minimum of 28 sessions with all pre- and postsession measures. The platform used has previously demonstrated reduced psychiatric symptom severity and improved overall mental well-being. Psychotherapy recordings (\"sessions\") within the platform, available asynchronously and on demand, span mindfulness, meditation, cognitive behavioral therapy, client-centered therapy, music therapy, and self-hypnosis. The platform also has the unusual ability to rapidly assess mental well-being without bias via an easy-to-use objective measure of psychological stress derived from artificial intelligence-based analysis of facial biomarkers (objective stress level [OSL]). In tandem with the objective measure, EMAs obtain self-reported values of stress (SRS) and mood (SRM). ∆OSL, ∆SRS, and ∆SRM (with delta referring to the presession subtracted from the postsession measurement) were used to independently train a therapy recommendation algorithm designed to predict what future sessions would prove most efficacious for each individual. Algorithm predictions were compared against the efficacy of the individual's self-selected sessions. The objective measure of psychological stress provided a differentiated delta for the measurement of therapeutic efficacy compared to the 2 EMA deltas, as shown by clear divergence in ∆OSL vs ∆SRS or ∆SRM (r<0.03), while the EMA deltas showed significant convergence (r=0.53, P<.01). The recommendation algorithm selected increasingly efficacious therapy sessions as a function of training data when trained with either ∆OSL (F =5.37, P=.03) or ∆SRM data (F =3.69, P<.05). However, the sequential improvement in prediction efficacy only surpassed the efficacy of self-selected therapy when the algorithm was trained using objective data (P<.01). Training the algorithm with EMA data showed potential trends that did not reach significance (∆SRS: P=.09; ∆SRM: P=.12). As a final insight, self-selected therapy sessions were overrepresented among the algorithmically recommended sessions, an effect most pronounced when the algorithm was trained with ∆OSL data (F =30.94, P<.001). These prospective data demonstrate that a rapid, scalable, and objective measure of psychological stress, in combination with a robust recommendation algorithm, can autonomously identify clinically meaningful therapy for individuals. More broadly, this work illustrates the potential for objective data on mental well-being to improve precision psychiatry and the capacity for mental health care professionals to match global demand. ClinicalTrials.gov NCT06265909; https://clinicaltrials.gov/ct2/show/NCT06265909.

The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the potential to create clinical decision support systems (CDSS) that assist clinicians in making prediction-informed treatment decisions. This study aims to develop a novel predictive-prescriptive analytics framework that leverages ML to enhance medication prescriptions for T2D patients. The framework is designed as a data-driven CDSS to determine the best medication strategies based on individual patient profiles, including demographics, comorbidities, and medications. Utilizing a comprehensive dataset of electronic health records from 17,773 patients across various U.S. Veterans Administration Medical Centers collected over 12 years, the study employs the Bayesian Network (BN) as the ML model of choice. The BN’s unique dual capability serves both predictive and prescriptive functions. Several BN learning algorithms are applied to map the relationships among patient features and decision variables for predicting the outcome. The prescriptive stage includes three strategies, i.e., forward, backward, and guideline-based, to identify optimal treatment recommendations. Next, the complex treatment pathways identified through the prescriptive stage were illustrated using rule-based and decision-tree presentations to improve interpretability for actionable insights and clinical usability. Finally, our empirical analysis examines the alignment between recommended treatment strategies and actual physician prescriptions. ML exhibited strong predictive performance with a precision of 0.789, a recall of 0.879, and an F1-score of 0.831. The recommended treatment strategies aligned with physician prescriptions in simpler treatment scenarios. However, the alignment decreased as the complexity of medication prescription increased, highlighting the challenges of achieving physician compliance with optimal strategies in complex scenarios. This underscores the greater need for CDSS, particularly in situations involving complex combination therapy. This study presents a novel ML-based CDSS framework for personalized T2D treatment. Leveraging ML, the framework offers a promising approach to optimizing medication prescriptions and improving patient outcomes.