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PubMed2026

The potential of clustering methods for pre-test triage in sleep medicine: A systematic review.

Sleep disorders exhibit substantial heterogeneity, and traditional classifications may not fully capture clinically relevant subtypes. Clustering techniques can identify patient subgroups that improve phenotypic characterization and may support personalized management. This systematic review evaluated the application of clustering in sleep medicine, with particular focus on its potential use as a pre-test triage tool prior to formal sleep testing. PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched to February 2025. Eligible studies applied clustering to classify sleep disorders in adults. Two reviewers independently conducted screening, data extraction, and risk-of-bias assessment using QUADAS-2. The protocol was registered on PROSPERO. Fifty-one studies (1983-2025) were included, predominantly focused on obstructive sleep apnea (OSA) (n = 38, 74%). Hierarchical clustering (n = 20) and K-means clustering (n = 14) were the most frequently used techniques. Internal validation was reported in only 18% of studies, and external validation was reported in only 1 study. Seven studies relied exclusively on baseline clinical, demographic, or questionnaire data, representing pre-test scenarios, whereas most incorporated polysomnography-derived variables, limiting their applicability to early clinical stratification. Hierarchical clustering was the most commonly applied method; however, the overall lack of validation limits confidence in the robustness and clinical applicability of identified phenotypes. The potential role of clustering as a pre-test triage strategy remains largely unexplored, as most studies focused on post-diagnostic phenotyping and were affected by incorporation bias. Future research should prioritize pre-test clinical variables, rigorously validate internally and externally, and adopt standardized methodological and reporting practices to facilitate clinical translation.

Humans↗

PubMed2026

Unravelling bioanalytical innovations, degradation processes, and impurity landscapes of VEGFR inhibitors.

From pre-formulation studies to clinical trials, VEGFR-targeted small-molecule tyrosine kinase inhibitors (TKIs) require rigorous analytical standards. Bioanalysis, stability-indicating studies, and impurity profiling are used to examine chromatographic advances for VEGFR-targeted TKIs like sunitinib, pazopanib, axitinib, sorafenib, cabozantinib, vandetanib, apatinib, lenvatinib, nintedanib, and regorafenib. An LC-MS/MS and UPLC-MS/MS routinely show sub ng/mL performance, as shown by LLOQs (0.2 ng/mL) for sunitinib and axitinib, 1 ng/mL for pazopanib, 5-7 ng/mL for sorafenib, 0.5-1.5 ng/mL for regorafenib metabolic products, and 0.1-0.5 ng/mL for lenvatinib. These approaches are used for pharmacokinetics and therapeutic drug monitoring due to their good correlation coefficient of 0.1-10,000 ng/mL, accuracy of 95%-108%, and precision of 15% RSD. UPLC-QTOF-MS/MS distinguishes degradants and metabolites during forced degradation studies, enabling structural elucidation following ICH M7 risk evaluation protocol. HPTLC/MLC offers fast, sensitive screenings, while RP-HPLC/DAD or HPLC-UV offer reliable, cost-effective routine quality-control solutions with LOD/LOQ in the μg/mL range and linearity of 10-240 μg/mL. This review lists the structures and CAS numbers of ten VEGFR-2 TKI degradants and metabolites, as well as pharmacopeial impurities in SMILES forms. It will be useful for future method development and regulatory applications. To ensure VEGFR-targeted TKI quality, safety, and therapeutic efficacy, LC-MS/MS for trace quantification and HRMS for structure elucidation provide a robust, future-oriented framework. To improve VEGFR-targeted TKI quality, safety, and regulatory compliance, analytical development should focus on HRMS-based impurity characterization, AI-assisted degradation prediction, green chromatography, and harmonized bioanalytical validation.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Vortex-assisted liquid-liquid microextraction based on natural deep eutectic solvents for the determination of pyrethroid pesticides in urine.

A novel, facile, and environmentally friendly analytical method was developed based on vortex-assisted liquid-liquid microextraction and high-performance liquid chromatography with diode-array detection for detecting pyrethroid pesticides (PPs) in urine. Natural deep eutectic solvents (NADESs) were prepared using plant essential oil-derived monoterpenoids (thymol, carvacrol, and menthol) combined with aromatic primary alcohols (benzyl alcohol, phenethyl alcohol, and phenylpropyl alcohol) as hydrogen bond donors and acceptors. These solvents served as environmentally benign extraction media, thereby avoiding the use of conventional volatile, toxic organic solvents. NADESs are naturally derived, easy to prepare, biodegradable, and environmentally friendly solvents. Hydrophobic and π-π interactions between the NADESs and PPs may contribute to enhancing the affinity of PPs toward the NADESs phase. Vortex technology, accelerating mass transfer between the sample and extractant phases, enables fast extraction of PPs. Under optimized conditions, the method achieved a low detection limit (0.002 mg L-1), satisfactory precision with relative standard deviations (0.3%-2.4%), and acceptable recovery (80.7%-86.2%). The method demonstrated excellent performance in urine analysis and was feasible as a facile and green strategy for monitoring the content of PPs in biological matrices and assessing exposure risk.

Liquid Phase Microextraction↗

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

Methods for defining equity-stratifying variables: a systematic review of validation studies.

BACKGROUND AND OBJECTIVE: Disease burden is often disproportionally higher among those who are socially disadvantaged by factors defined in the PROGRESS-Plus framework (ie, Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital, with "Plus" covering features like age and disability). The accuracy and applicability of case definitions to identify these variables from administrative and clinical health data are unknown. We conducted a systematic review to explore how equity-stratifying variables, as categorized by the PROGRESS-Plus framework, have been defined and validated in epidemiologic studies using administrative health, population-level, or electronic health record (EHR) data. METHODS: Medline, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from the inception of the databases to 2024 for validation studies of equity-stratifying variables in adults using administrative health datasets, health registries, or EHR data. Titles and abstracts, followed by relevant full-text articles, were screened in duplicate by two reviewers for eligibility. The data sources utilized, algorithms employed, and their associated performance measures were extracted and synthesized from included studies. Given substantial heterogeneity in study design, equity-stratifying variable definition, and performance metrics, meta-analysis was not possible. RESULTS: Of the 9099 unique citations screened, 188 full texts were reviewed and 116 were included in this review. Most studies were published between 2019 and 2024 (n = 64, 55%) and were validation studies of race/ethnicity definitions that used race/ethnicity codes or surname list algorithms (n = 66, 57%). No studies examined religion. Regarding the reported performance measure estimates, the race/ethnicity/culture/language equity-stratifying variables category had the largest variability across sensitivity, positive predictive value (PPV), and Cohen's Kappa. Occupation validation studies had the lowest variation in sensitivity and PPV. CONCLUSION: Despite an increasing number of publications reporting on the validation of equity-stratifying variables relevant to the PROGRESS-Plus framework, performance measures varied widely across studies. The significant heterogeneity in equity-stratifying variable definitions and methods used to validate them support the need for further rigorous validation of equity-stratifying variables in administrative and clinical health data. PLAIN LANGUAGE SUMMARY: Disease burden is often higher in people who experience financial hardships, lower level of education, discrimination due to race/ethnicity, and unstable housing. These social factors can be considered health equity factors and are important for understanding health inequalities. Health researchers often use large datasets, such as hospital or electronic health records (EHRs), to study these health equity factors. However, it is not clear how accurately these data sources capture information about people's social circumstances and how these factors are defined. In this study, we reviewed existing research to understand how health equity factors have been defined across health data sources and how accurate they are at measuring aspects of health equity and social disadvantage. Of the more than 9000 studies we identified, we included 116 that met our criteria for this systematic review. Most included studies focused on identifying race and ethnicity, often using codes or surname-based methods. We found that the accuracy of these methods varied widely across studies, meaning results may not always be reliable or comparable. Overall, our findings show that there are inconsistencies in how social factors are defined and measured in health data. This makes it difficult to fully understand and address health inequalities using routinely collected health data. More work is needed to develop and validate better quality and more consistent methods for capturing these important social factors.

Humans↗

PubMed2026

Pictographs: feasibility and acceptability of a novel method of newborn identification to reduce wrong-patient errors in the NICU.

Wrong-patient errors cause serious harm in newborns. These errors involve ordering and administering tests, procedures, medications, and breast milk to an unintended patient. Newborns receiving care in neonatal intensive care units (NICUs) are at particularly high risk. Although more distinct newborn naming conventions as recommended by the Joint Commission significantly reduce wrong-patient orders, name similarities among multiple-birth infants and truncation of differentiating information in some electronic health record (EHR) systems contribute to this persistent increased risk. Accordingly, novel newborn identifiers are urgently needed. We propose Pictographs - images that are appealing, recognizable, and appropriate - to serve as visual identifiers for newborns in NICUs. Pictographs are selected by caregivers, uploaded into the EHR, and displayed at bedside. As part of a multicenter randomized controlled trial assessing effectiveness of Pictographs to prevent wrong-patient order errors, we initially evaluated feasibility and acceptability of Pictographs at two study sites. Pictographs as novel visual identifiers for newborns in the NICU were generally well received by caregivers and clinicians, and the vast majority of caregivers selected a Pictograph for their infant(s), which was posted at the bedside and uploaded into the EHR. Ordering clinicians - the primary target of the intervention to prevent wrong-patient errors - recognized the potential for Pictographs to provide a visual cue when placing orders, particularly for multiple-birth infants. Here, we describe the rationale, implementation, framework, feasibility, usefulness, and acceptability of Pictographs among key stakeholders. If found effective for preventing wrong-patient errors, Pictographs could be adopted as a patient safety solution in hospitals worldwide.

Female↗

PubMed2026

Measuring Coping Strategies in Daily Life: A Systematic Review of Experience Sampling Methodology and Daily Diary Studies.

Advances in daily diary methods and experience sampling method (ESM) have improved the study of coping strategies in daily life and their role in shaping health and well-being. In this review, we examine study designs, measurement approaches, and analytical practices used to investigate coping in natural contexts. We performed a systematic review of studies published before 5 December 2025 that used daily diary or ESM to measure coping strategies over multiple days or moments. Studies were examined with regard to sampling schemes, assessment frequency and duration, measurement of coping strategies, incorporation of stressor appraisals, and analytic techniques used to model coping processes. Fifty-five studies met the inclusion criteria. Results indicated that 80% employed end-of-day diary designs, generally lasting 1-3 weeks, whereas higher-frequency ESM protocols were less common and ranged 2-14 days. Coping strategies were often assessed using abbreviated or single-item measures, frequently adapted from established questionnaires. Many studies incorporated appraisals such as perceived stressor intensity or controllability, enabling tests of coping flexibility. Multilevel modelling was the dominant analytic approach, allowing researchers to distinguish within-person dynamics from between-person differences. However, analyses were predominantly concurrent, and temporally ordered models remained comparatively rare. Overall, the literature demonstrates substantial progress in capturing coping in everyday contexts, yet heterogeneity in measurement and limited use of temporal modelling constrain cumulative knowledge about the temporal links between coping and psychological and physiological health outcomes. Future research would benefit from greater alignment between theoretical assumptions, assessment strategies, and analytic methods.

Humans↗

PubMed2026

Memantine Augmentation for Obsessive-Compulsive Symptoms in Bipolar Disorder: A Randomized, Double-Blind, Placebo-Controlled Trial.

BACKGROUND: Obsessive-compulsive symptoms are frequently observed in patients with bipolar disorder and present a significant therapeutic challenge. This study evaluated the efficacy and safety of memantine as an adjunctive therapy for obsessive-compulsive disorder in patients with bipolar disorder. METHODS: In this randomized, double-blind, placebo-controlled trial, 46 patients with bipolar disorder and obsessive-compulsive disorder, stabilized on quetiapine and lithium, were randomly assigned to receive either memantine (n = 23) or placebo (n = 23) for 6 weeks. RESULTS: The memantine group showed a significant reduction in Yale-Brown Obsessive-Compulsive Scale scores compared with the placebo group (Cohen d = 1.57 vs 0.42, P < 0.001). Nausea was the most common side effect, but overall adverse effects were minimal. CONCLUSION: Memantine appears to be a safe and effective adjunctive treatment for obsessive-compulsive disorder in patients with bipolar disorder, warranting further investigation.

Humans↗

PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗

PubMed2026

Non-parametric differential methylation analysis characterizes histotype-specific promoter regions in epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is a heterogenous disease with frequent late-stage diagnosis and high mortality rates, for which no reliable screening tests exist. In recent years, epigenetic biomarkers in the form of DNA methylation in CpG-rich regions have gained increased attention in the scientific community due to their robust nature and accessibility, allowing for diagnosis without the need for invasive surgery. In this study, we investigated the aberrant methylation of promoter regions in early stage EOC through non-parametric methods, with the purpose of characterizing candidate epigenetic biomarkers. The approach was used on a cohort of early stage EOC samples, and results were compared to existing programs for differential methylation. Significant regions were then used to construct a CpG panel for stratifying EOC histotypes through predictive classification in external data. Identified promoter regions were highly reproducible across cohorts, and the constructed CpG model stratified histotypes in external cohorts through predictive classification. Comparisons against other DMP and DMR callers showed a degree of homogeneity between results but also revealed promoter regions that were overlooked despite clear signs of aberrant methylation. Finally, EOC histotypes were found to differ in their methylation distribution types, and results indicate that methods sensitive to non-normally distributed data may be poorly suited to compare groups with different distribution types. The non-parametric approach identified aberrantly methylated promoter regions that were highly reproducible across cohorts. Results from predictive classification indicate that these regions may be useful for the purpose of EOC histotype stratification.

Humans↗

PubMed2026

Socket motility assessment of anophthalmic sockets: a systematic review.

PURPOSE: Systematically review and categorize the methods used to assess socket and prosthetic motility in anophthalmic patients following enucleation or evisceration. METHODS: A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Embase, Web of Science, and Scopus were searched from inception through September 2024. Studies reporting qualitative or quantitative assessments of motility in anophthalmic sockets or ocular prostheses were included. Motility assessment methods were categorized as qualitative (descriptive or graded clinical evaluation) or quantitative (numerical measurements in millimeters, degrees, or objective tracking systems). RESULTS: Thirty-five studies encompassing 1,819 patients met inclusion criteria. Nineteen studies used qualitative assessment methods, including subjective observation, graded scales based on cardinal gaze positions, or comparison with the contralateral eye. Sixteen studies employed quantitative techniques, such as the Kestenbaum limbus test, Lister perimeter measurements, conjunctival or limbal markings, photographic image analysis, infrared eye-tracking systems, and magnetic search-coil technology. Considerable heterogeneity was observed in measurement techniques, reporting standards, timing of assessment, and distinction between socket and prosthetic motility. CONCLUSIONS: Substantial variability exists in the methods used to assess motility in anophthalmic sockets, limiting comparability across studies. Establishing standardized, feasible, and reproducible assessment approaches may improve outcome reporting and facilitate meaningful comparisons in future oculoplastic research.

Humans↗

PubMed2026

K-wire versus screw fixation in Scarf-Akin osteotomy for hallux valgus: A retrospective cohort study.

BACKGROUND: Retention of metal implants after Scarf-Akin osteotomy (SAO) may cause irritation and psychological discomfort, often necessitating a hardware removal procedure. This study aimed to introduce K-wire fixation, allowing for outpatient removal, and to compare it with screw fixation. METHODS: This retrospective study included 64 patients with hallux valgus, comprising 32 in the K-wire fixation group and 32 in the screw fixation group. Clinical outcomes were assessed using the American Orthopaedic Foot and Ankle Society (AOFAS) score, visual analogue scale (VAS), and patient satisfaction. Radiographic parameters included hallux valgus angle(HVA), intermetatarsal angle(IMA), and distal metatarsal articular angle(DMAA). RESULTS: Both groups showed significant clinical and radiographic improvement (P < 0.01). No significant between-group differences were observed in the other clinical or radiographic outcomes (P > 0.05). Treatment costs were significantly lower in the K-wire group (P < 0.001). CONCLUSIONS: K-wire fixation provides clinical and radiographic outcomes comparable to screw fixation, while avoiding the need for an additional procedure to remove the implant. LEVEL OF EVIDENCE: Level III.

Humans↗

PubMed2026

A phase I clinical study of the safety, tolerability, pharmacokinetics and pharmacodynamics of SHR-2106, an anti-CD40 antibody, following single intravenous or subcutaneous administration in healthy participants.

BACKGROUND: SHR-2106 is a humanized IgG1 monoclonal antibody that blocks CD40-CD40L interactions and has demonstrated immunosuppressive activity and graft-prolonging effects in preclinical studies. This first-in-human Phase I study evaluated the safety, pharmacokinetics, pharmacodynamics, and immunogenicity of single intravenous or subcutaneous doses of SHR-2106 in healthy adults. METHODS: This randomized, double-blind, placebo-controlled Phase I study enrolled healthy participants. Fifty-one participants were enrolled in seven cohorts and received five intravenous doses (50-1200 mg) or two subcutaneous doses (300 and 600 mg). Safety, serum pharmacokinetics, CD40 occupancy on B cells, and anti-drug antibodies were assessed using standard clinical and bioanalytical methods. RESULTS: SHR-2106 demonstrated a favorable safety and tolerability profile, and most treatment-emergent adverse events were mild to moderate laboratory abnormalities with incidence rates comparable to placebo. SHR-2106 exhibited nonlinear pharmacokinetics consistent with target-mediated drug disposition, with a dose-dependent increase in geometric mean terminal half-life following intravenous administration (1.83-10.7 days). Absolute bioavailability after subcutaneous administration was approximately 60%. CD40 occupancy exceeded 80% within 24 h at all doses, with saturation duration increasing from 7 to 70 days across the intravenous dose range and remaining comparable between routes at matched doses. Anti-drug antibody incidence decreased with increasing intravenous dose and did not significantly affect pharmacokinetics or pharmacodynamics. CONCLUSION: SHR-2106 was well tolerated and achieved rapid and sustained CD40 engagement, supporting dose and route selection for Phase II studies.

Humans↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Improving Patient Comfort of Vibratory Anesthetic Devices With a Dampener: A Pilot Study.

BACKGROUND: Vibratory anesthetic devices (VADs) reduce dermatologic injection pain, but their vibration can feel harsh at sensitive anatomical sites. Simple modifications improving patient comfort may enhance VAD adoption. OBJECTIVE: The authors evaluated whether dampening VAD vibration with a cotton buffer improves patient comfort and characterized tactile features influencing preferences. MATERIALS AND METHODS: In a single-site, participant-blinded pilot study (N = 53), adults received a dampened VAD (D-VAD) and standard VAD (S-VAD) at 5 sites-lateral nasal wall, submalar cheek, ear helix, lateral neck, and dorsal forearm-in randomized, contralateral application. Site-specific preference was analyzed with binomial and Cochran Q tests; demographic associations with univariate analyses. Word2vec and hierarchical clustering analyzed qualitative reasons behind patient preference. RESULTS: D-VAD was preferred at all sites across demographics-lateral nasal wall (88.7%), submalar cheek (84.9%), ear helix (88.7%), lateral neck (77.4%), and dorsal forearm (75.5%) (all p < .001), with strongest preference at face and head/neck (p = .018). Computational semantics analysis of qualitative responses identified 6 themes driving preference: Smoothness, Gentleness, Controlled, Low Frequency, Low Intensity, and Less Bothersome. CONCLUSION: Dampening VAD vibration with a cotton buffer enhances comfort across sensitive sites, with reduced harshness and smoother sensation underlying preference. This simple modification may improve patient experience, encouraging broader VAD adoption.

Humans↗

PubMed2026

Physical reconfiguration of limb electrodes for Precordial Bipolar Lead acquisition: Morphological validation against digital subtraction.

BACKGROUND: The V2 - V1 Precordial Bipolar Lead (PBL) selectively evaluates the right-to-left retrosternal axis and has shown diagnostic value beyond the standard 12‑lead electrocardiogram. However, its use has been limited by the need for raw electrocardiographic data and post-processing software. This study evaluated whether a simple physical reconfiguration of limb electrodes could reproduce the digitally derived V2 - V1 morphology with sufficient accuracy for clinical application. METHODS: Thirty-seven subjects underwent two sequential 10-s 12‑lead recordings using a Cardiovit FT-1 electrocardiograph sampled at 1000 Hz. In the standard recording, the digital PBL was calculated as V2 - V1. In the second recording, the right-arm and left-arm electrodes were repositioned to the V1 and V2 sites so that Lead I directly recorded the retrosternal dipole. Signals were filtered, synchronized, and analyzed using median beats. Morphological agreement was assessed with Pearson correlation on Z-normalized signals, while absolute agreement was evaluated using Lin's concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC (Lewis, 1931; Nehb, 1938 [1,2])), root mean square error (RMSE), and Bland-Altman analysis. RESULTS: Mean Pearson correlation between digital and physical PBL was 0.955 (SD 0.043), with segment-specific correlations of 0.953 (SD 0.054) for QRS and 0.967 (SD 0.052) for ST-T. Lin's CCC and ICC(2,1) were both 0.871 (SD 0.110), and RMSE was 0.091 (SD 0.049) mV. Bland-Altman analysis showed minimal bias (-0.008 mV). CONCLUSIONS: Physical acquisition of the V2 - V1 PBL achieved high agreement with the digitally derived signal, supporting a simplified analog method for broader clinical implementation.

Humans↗