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PubMed2026

Navigating Social Media: Balancing Connectivity With Media Literacy to Combat Misinformation and Protect Mental Well-Being.

BACKGROUND: The pervasive use of social media has created a complex digital ecosystem where high connectivity coexists with significant challenges, including the rapid spread of misinformation, particularly regarding mental health, and documented negative impacts on psychological well-being. Platform architectures designed for engagement maximization have been identified as central factors in both issues. OBJECTIVE: This paper critically analyzes the interconnected relationships between social media use, misinformation dissemination, and mental health impacts, with particular attention to psychiatric misinformation across diagnostic categories (e.g., depression, anxiety, ADHD). A primary objective is to evaluate the potential of advanced critical digital literacy frameworks to serve as protective mechanisms against these dual threats. METHODS: A systematic search was conducted following PRISMA 2020 guidelines across APA PsycInfo, PubMed, JSTOR, and Google Scholar for literature published between January 2018 and March 2026 (updated from the original 2023 search). The search yielded 2672 records. After removing 624 duplicates, 2048 records underwent title and abstract screening, with 1802 excluded. The remaining 246 full-text articles were assessed for eligibility, resulting in 86 studies included in the final qualitative synthesis. Inter-rater reliability was established (Cohen's κ = 0.82). Quality assessment was conducted using the Joanna Briggs Institute Checklist, AXIS, and CASP tools, with findings weighted by methodological quality. A thematic analysis was undertaken to synthesize findings. RESULTS: The analysis reveals that core architectural features of social media platforms, algorithmic curation and engagement-based metrics, simultaneously foster environments ripe for misinformation spread and contribute to psychological distress, including anxiety, depression, and harmful social comparison. Psychiatric misinformation specifically (e.g., inaccurate claims about treatment effectiveness, diagnostic criteria, and medication side effects) represents a growing concern, particularly on image- and video-based platforms. The findings indicate that conventional media literacy approaches focused solely on fact-checking are insufficient. Instead, a critical digital literacy framework encompassing algorithmic awareness, data literacy, and emotional awareness is essential for building user resilience, with evidence from high-quality systematic reviews supporting this approach. CONCLUSIONS: Navigating the complexities of modern social media requires an integrated approach combining "pedagogies of play" for experiential skill development with advocacy for structural change (e.g., algorithmic transparency, well being by design principles). This dual strategy empowers individual users to critically engage with digital content while advocating for ethical platform design, thereby safeguarding both mental well-being and democratic discourse. Implications for educators, mental health professionals (including competencies for addressing patient encounters with psychiatric misinformation), policymakers, and platform designers are discussed.

Humans↗

PubMed2026

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans↗

PubMed2026

Psychological consequences of AI-assisted training and the buffering role of mindfulness.

The integration of artificial intelligence (AI) into athletic training is accelerating, yet its psychological implications for athletes remain insufficiently understood. Drawing on the transactional model of stress and the stress-buffering framework of mindfulness, this study examined whether mindfulness training can mitigate adverse psychological responses associated with AI-assisted training. Using a randomized controlled factorial design, 160 collegiate athletes were assigned to AI-assisted training or standard training, with or without concurrent mindfulness intervention, and assessed at baseline, week 4, and week 8. Athletes exposed to AI-assisted training without psychological support exhibited increases in perceived stress and AI dependence over time. In contrast, these stress increases were substantially attenuated when mindfulness training was implemented alongside AI-assisted training. A significant AI × Mindfulness × Time interaction emerged for perceived stress at post-intervention, and difference-in-differences analyses corroborated a robust buffering effect. Mediation analyses further indicated that mindfulness training reduced stress partially through enhancing mindful awareness; a three-wave cross-lagged analysis showed that mindful awareness and stress were reciprocally related over time, with the hypothesized awareness-to-stress pathway remaining robust. Together, these findings suggest that AI-assisted training introduces a distinct form of evaluative pressure, and that mindfulness training may serve as an effective psychological buffer during the adoption of continuous algorithmic performance evaluation systems.

Humans↗

PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2026

Antibiotic Self-Medication and Public Awareness During the 2023 Gaza War: A Cross-Sectional Investigation.

BACKGROUND: Antimicrobial resistance is driven by inappropriate antibiotic use, particularly self-medication. The 2023 Gaza War disrupted healthcare services, increasing resistant infections and reliance on self-treatment practices. OBJECTIVES: To determine the prevalence of antibiotic self-medication and assess public awareness during the 2023 Gaza War. METHODS: A cross-sectional survey was conducted between May and October 2025 among adults residing in the Gaza Strip during the 2023 Gaza War. A multistage non-probability recruitment strategy, organized across predefined governorate and residential-setting strata, was used to recruit 422 participants from primary healthcare centers, community pharmacies, and displacement shelters. The questionnaire demonstrated good internal consistency (Cronbach's α = 0.857). Ethical approval was obtained from the relevant institutional review board, and informed consent was secured from all participants. RESULTS: The overall KAP score was moderate (67.46%), with attitudes scoring highest (74.62%), followed by knowledge (66.23%) and practices (61.52%). Reported antibiotic self-medication increased from 60.4% before the war to 80.6% during the war (p < 0.001), with significant changes in reasons for self-medication and antibiotic procurement sources. The mean antimicrobial-resistance perception score was 82.19 ± 11.10%, and knowledge was strongly correlated with the total KAP score (r = 0.836, p < 0.001). CONCLUSIONS: Reported antibiotic self-medication was markedly higher during the 2023 Gaza War despite moderate public awareness, highlighting the urgent need for education, antibiotic stewardship, and improved healthcare accessibility.

Gaza War 2023↗

PubMed2026

Primary ACL Repair and Reconstruction in Isolated ACL Ruptures: Forgotten Joint Scores and the Association Between Residual Laxity and Joint Awareness.

BACKGROUND: Primary anterior cruciate ligament (ACL) repair has recently reemerged as a treatment option for carefully selected proximal ACL tears. However, evidence regarding patient-reported joint awareness and the relationship between postoperative laxity and joint awareness remains limited. PURPOSE: To compare joint awareness, clinical outcomes, and postoperative laxity between primary ACL repair and hamstring tendon autograft reconstruction in carefully selected patients with isolated ACL rupture, and to explore the association between residual laxity and postoperative joint awareness. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: This retrospective cohort study included 85 patients with isolated ACL rupture treated with either primary ACL repair (n = 26) or hamstring autograft reconstruction (n = 59), with a minimum follow-up of 24 months. Clinical outcomes, including visual analog scale score, Lysholm score, International Knee Documentation Committee (IKDC) score, Tegner activity scale score, postoperative knee laxity, return to sport, and rerupture rates, were evaluated. The authors also used the Forgotten Joint Score-12 (FJS-12), a measure of joint awareness, to evaluate patients. A higher score reflects lower joint awareness, suggesting function more similar to a native knee. Multivariable regression analyses were performed to evaluate variables associated with postoperative FJS-12 values. RESULTS: No differences were observed between groups in postoperative Lysholm, IKDC, and Tegner activity scale scores; Lachman- and pivot-shift-assessed postoperative laxity; and return-to-sport rates. However, postoperative FJS-12 values were significantly higher in the repair group compared with the reconstruction group (87.0 ± 15.0 vs 77.5 ± 14.2; mean difference, 9.5 points [95% CI, 2.8-16.3]; P = .006), corresponding to a moderate effect size (Cohen d = 0.66). Secondary exploratory regression analyses demonstrated that residual postoperative laxity was associated with lower postoperative FJS-12 values in both Lachman- and pivot-shift-based models (R2 = 0.649 and 0.680, respectively; P < .001 for both). CONCLUSION: In carefully selected patients with proximal ACL tears and adequate tissue quality, no significant between-group differences were detected in conventional clinical outcomes, postoperative laxity, return-to-sport rates, or rerupture rates between primary ACL repair and reconstruction. Primary ACL repair was associated with higher postoperative FJS-12 values at short- to midterm follow-up, suggesting function more similar to that of a native knee. Residual postoperative laxity was associated with lower FJS-12 values in secondary exploratory analyses.

Humans↗

PubMed2026

Glycemic and safety outcomes of the insulin-only bionic pancreas in older adults and individuals with impaired awareness of Hypoglycemia: a post hoc analysis of a randomized pivotal trial.

AIMS: Evaluate the efficacy and safety of iLet Bionic Pancreas (BP) in older adults and individuals with impaired awareness of hypoglycemia (IAH). METHODS: This post hoc analysis used individual participant-level data from the Insulin-Only Bionic Pancreas Pivotal Trial (n = 440; NCT04200313). Eligible participants (n = 96) with type 1 diabetes, aged ≥ 60 years and/or had IAH (Clarke score ≥ 4), were randomized to BP with aspart/lispro (BP-Asp/Lis; n = 45), BP with fast-acting aspart configuration (BP-Fiasp; n = 31), or standard care (SC; n = 20) for 13 weeks. RESULTS: Compared with SC, time-in-range (70-180 mg/dL) significantly increased by 7.49 % (95 % CI: 2.61 to 12.38; ∼1.8 h/day) with BP-Asp/Lis and by 8.28 % (95 % CI: 3.15 to 13.41; ∼2.0 h/day) with BP-Fiasp, driven by reduced hyperglycemia. No significant differences were observed in hypoglycemia exposure. Severe hypoglycemia occurred in four participants (four events) on BP-Asp/Lis and one participant (two events) on SC. One diabetic ketoacidosis event occurred on BP-Fiasp due to an infusion set failure. CONCLUSIONS: In high-risk, clinically vulnerable populations, the BP system significantly improved glycemic control while maintaining safety parity with respect to hypoglycemia risk, providing a resilient therapeutic alternative for vulnerable cohorts.

Humans↗

PubMed2026

Parents' Awareness of Their Adolescents' Sexual Behaviors and Experiences: A Concordance Analysis.

PURPOSE: Adolescence is a developmental period during which sexual exploration is normative. Parental awareness of adolescents' sexual behaviors can be protective during this stage; however, research suggests both parents and adolescents tend to avoid open conversations about these sensitive issues. This study aimed to examine concordance between parent proxy-reports and adolescent self-reports across three domains of adolescents' sexual behaviors and experiences. METHODS: Data were collected from a national sample of 522 parent-adolescent dyads (aged 15-17 years) from the AmeriSpeak panel (data collected May-September 2022). Prevalence-adjusted bias-adjusted kappa (PABAK) statistics were calculated to evaluate agreement. RESULTS: Findings revealed weak-to-moderate concordance regarding adolescents sending and receiving sexual photos (PABAKs = 0.54-0.79), whereas stronger concordance was observed for experiences related to nonconsensual sharing of sexual photos (PABAKs = 0.92-0.94), with high agreement largely driven by low reported prevalence of these experiences. Strong concordance regarding whether adolescents had ever had sex (PABAK = 0.80) was largely driven by both parents and adolescents reporting the adolescent had not had sex. Concordance regarding sexually transmitted infection and pregnancy prevention methods varied, ranging from weak to strong agreement across specific methods (or lack thereof; PABAKs = -0.07-0.79). DISCUSSION: Results highlight significant discrepancies in parents' knowledge about their adolescents' sexual behaviors and experiences. Interventions aimed at enhancing trust and open dialogue between parents and adolescents about sexuality-related topics, as well as ensuring adolescents have a variety of trusted sources to consult for sexual information, can help adolescents make informed sexual health decisions.

Humans↗

PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans↗

PubMed2026

Alternative genetic codes in bacteria and archaea identified with a fast k-mer-based algorithm.

The genetic code is conserved across all domains of life and is often described as universal. Nevertheless, many exceptions to the "universal" code have now been documented, most of these through manual or semiautomated inspection of highly conserved genes. Modern bioinformatics tools improved our ability to find alternative genetic codes but remain computationally expensive, preventing widespread use on thousands of new species identified by sequencing environmental samples. Here, I report a >100-fold accelerated method for inferring the genetic code directly from assembled genomes and apply it to thousands of previously uncharacterized assemblies from archaea and bacteria. I describe three candidate genetic code variations, one of which, an alternative genetic code used by a family of Asgard archaea, is a unique example of sense codon reassignments for this domain. Identifying genetic code variations is important for understanding evolution of the standard code and improving accuracy of protein databases and open reading frame identification.

Genetic Code↗

PubMed2026

Meniscal preservation in the age of biologics: toward a quantitative decision algorithm for personalized repair.

BACKGROUND: Despite advances in arthroscopic repair and biologic augmentation, surgical indication for meniscal tears remains heterogeneous. No standardized framework currently integrates biomechanical, clinical, and biological determinants to guide repair versus resection. PURPOSE: To develop a quantitative decision model-the Meniscal Preservation Score (MPS)-that unifies biomechanical and biological evidence to stratify reparability potential and standardize treatment selection in meniscal surgery. METHODS: A systematic evidence synthesis conducted in accordance with PRISMA 2020 reporting standards of studies published from 2000 to 2025 in PubMed, Embase, and Scopus identified key determinants of meniscal healing. Five consistent predictors-patient age, vascularity, tear morphology, associated pathology, and activity profile-were weighted through a two-round modified Delphi consensus among ten experienced knee surgeons. The resulting 0-9-point MPS was incorporated into a stepwise decision tree linking lesion morphology, biological context, and surgical strategy. Conceptual validation used 50 simulated cases and a retrospective cohort of 45 patients to test agreement between algorithm recommendations and expert surgical decisions. RESULTS: The MPS achieved 86% concordance with expert judgment in simulation and 84% agreement in clinical validation. In this retrospective exploratory cohort, cases in which surgical management was concordant with MPS recommendations demonstrated higher mean IKDC scores at 24 months and lower observed reoperation rates. These findings should be interpreted as associative rather than causal, as treatment allocation was not controlled and discordant cases may have represented inherently more complex pathology. CONCLUSION: The MPS represents an evidence-informed decision-support framework designed to systematize reparability assessment. While exploratory analyses suggest structural coherence with expert reasoning, prospective implementation and external validation are required before clinical adoption as a predictive tool. LEVEL OF EVIDENCE: conceptual model with exploratory validation.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Fundamentals of pacemakers ECG interpretation - part 2.

BACKGROUND: Modern pacemakers incorporate arrhythmia-response algorithms, ventricular pacing minimization protocols, and safety mechanisms that generate ECG patterns indistinguishable from pathological AV block, sensing malfunction, or device-mediated tachycardia. Failure to recognize these algorithm-driven signatures leads to unnecessary interventions, misdiagnosis, and inappropriate device reprogramming. This manuscript is the second in a two-part series on pacemaker ECG interpretation. METHODS: We conducted a narrative review of peer-reviewed literature and device-specific documentation on algorithm-driven ECG behavior, synthesizing evidence across arrhythmia recognition, upper rate physiology, ventricular pacing minimization, mode switching, safety mechanisms, and hysteresis algorithms. RESULTS: Pacemaker-mediated tachycardia produces regular paced wide-complex tachycardia locked at the upper tracking rate, initiated by any event with retrograde VA conduction. Ventricular tachycardia is identified by QRS morphology diverging from the known paced pattern, absent pacing spikes, and AV dissociation. Upper rate Wenckebach behavior mimics Mobitz type I AV block; 2:1 upper rate response mimics second-degree AV block. Ventricular pacing minimization algorithms produce isolated nonconducted P waves and prolonged AV intervals that simulate pathological conduction disease. Mode switching causes abrupt rate drops misidentified as output failure. Ventricular safety pacing generates a conspicuously short, fixed AV interval. Three discrete pacing artifacts reflect AV-sequential cardiac resynchronization therapy (CRT), ventricular safety pacing in CRT, or His-bundle pacing with backup RV output. Rate and AV hysteresis produce pauses and wandering AV intervals mimicking oversensing or Wenckebach periodicity. CONCLUSIONS: Recognizing algorithm-driven ECG patterns requires knowledge of device timing intervals and refractory periods, which lets clinicians distinguish programmed behavior from true malfunction or cardiac arrhythmia.

Humans↗

PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗

PubMed2026

RR-interval-based atrial fibrillation detection and burden estimation: cross-dataset validation and calibration-aware probability analysis.

Objective.Atrial fibrillation (AF) burden has become an increasingly important endpoint in long-duration rhythm monitoring, but reliable burden estimation requires more than accurate AF detection alone. In particular, when burden is derived by aggregating predicted AF probabilities over time, probability calibration may directly affect burden validity under external dataset shift.Approach.This study developed an interpretable-interval feature model for AF detection and evaluated it using record-wise cross-validation on a development cohort and independent cross-dataset external validation on public Holter electrocardiographic databases. Window-level performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve (PR-AUC), Brier score, expected calibration error (ECE), and calibration intercept and calibration slope. Recording-level AF burden was estimated using both probability-based and hard-label aggregation and evaluated using mean absolute error (MAE) and agreement analyses.Main results.The model showed high discrimination in both development and external evaluation, with external ROC-AUC ofand PR-AUC of. However, external calibration deteriorated despite preserved ranking performance, with Brier score of, ECE(15) of, calibration intercept of, and calibration slope of. In the external cohort, probability-based burden estimation preserved strong association with reference burden but showed weaker raw agreement than hard-label aggregation, with MAE ofversus, consistent with systematic probability underprediction. Repeated external recalibration across record-level splits substantially improved probability quality and probability-based burden estimation. Median probability-burden MAE decreased fromwithout recalibration toafter Platt recalibration andafter isotonic recalibration, while median ECE(15) decreased fromtoand, respectively.Significance.These findings indicate that-interval-based AF detection maintained strong ranking performance in the tested external cohort, but probability calibration should be evaluated explicitly when predicted probabilities are aggregated into AF-burden estimates.

Atrial Fibrillation↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗