Biology Methods and Protocols

Early identification of patients with advanced chronic conditions (MACA) remains a critical challenge in clinical practice, often relying on retrospective criteria or clinical judgment, which may delay timely and personalized intervention. The increasing availability of electronic health records (EHR) enables the application of Machine Learning (ML) techniques to support more proactive detection. This study aimed to develop and internally validate a ML-based approach for the identification of MACA patients using collected data from Hospital Universitario Parc Tauli (Sabadell, Spain). A retrospective observational study was conducted using a sample of 163 patients. A total of 80 candidate variables were extracted, including clinical, functional, and healthcare utilization indicators. Feature selection methods were applied, reducing the dataset to ten key predictors. Fourteen supervised classification algorithms were evaluated, including linear, probabilistic, and ensemble methods. Model performance was evaluated using various metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). The final cohorts include 80 MACA patients and 83 controls. The bagging classifier achieved the most consistent performance with a sensitivity of 0.91 and an AUC of 0.90. Key predictors include absolute dependency, advanced frailty, functional decline, and healthcare utilization indicators. Cross-validation (CV) confirmed the stability of model performance, with mean AUC values exceeding 0.95. These findings highlight the potential of ML-based tools for early detection and the high discriminative capacity for identifying MACA patients.

Dementia is a progressive neurological condition characterized by cognitive decline and structural brain changes that evolve. Longitudinal modeling of these changes is important for improving disease monitoring, identifying progression patterns, and supporting early risk stratification. This study developed an explainable longitudinal machine-learning framework for dementia progression, using cognitive and Magnetic Resonance Imaging (MRI)-derived biomarkers from the Open Access Series of Imaging Studies (OASIS-2) longitudinal dataset. The dataset included 150 subjects and 373 repeated observations classified as Non-demented, Demented, or Converted. Current-visit features, previous-visit features, and slope-based temporal features were constructed from Mini-Mental State Examination, Clinical Dementia Rating, normalized whole-brain volume, estimated total intracranial volume, atlas scaling factor, Age, and MRI delay. Baseline models were compared with a longitudinal gradient-boosted model, using patient-level splitting to reduce data leakage across repeated visits. The proposed longiGradient Gradient boosting model achieved the best held-out test performance, with an accuracy of 88.16%, a macro F1-score of 0.776, and a weighted F1-score of 0.860. The model showed strong classification performance for Demented and Non-demented individuals, while converted cases remained more difficult to identify. A regularized gradient boosting model was also evaluated as an overfitting sensitivity analysis; although it reduced the perfect training fit, it did not improve held-out test performance. Feature importance, permutation importance, and SHapley Additive exPlanations identified Clinical Dementia Rating as the dominant predictor, with slope-based Clinical Dementia Rating providing additional longitudinal information. These findings suggest that combining cognitive measures, MRI-derived biomarkers, and temporal feature engineering can improve dementia progression modeling, although external validation in larger longitudinal cohorts is needed.

Objective: To develop and validate a supervised text-embedded transformer matching model to identify fall injuries in Medicare data, and evaluate the model's performance -- alongside a validated rule-based algorithm-- against "ground truth" from an external reference standard (self-reported fall injuries leading to medical attention). Materials and Methods: Text embeddings of ICD-10-CM and CPT codes in Medicare claims/encounters from participants in the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial served as model inputs. Trained on annotated claims/encounters occurring within +/- one month of self-reported fall injuries leading to medical attention, the transformer model generated a continuous 0-1 probability that each claim/encounter was for a fall injury. The model was then applied to all claims/encounters in STRIDE and compared alongside the rule-based algorithm to the external reference standard. Results: The model achieved an area under the curve (AUC) of > 0.96 against annotated claims/encounters in 9 out of 10 holdout folds and 0.85 in the remaining fold. In the full STRIDE dataset, the model achieved a peak AUC of 0.86 (95% CI, 0.84-0.87) against the external reference standard, with results comparable to the rule-based algorithm. Discussion: Relative to rule-based approaches, which typically generate binary outcomes, the continuous event probability generated by the transformer model could support clinical endpoint adjudication, with high-probability predictions treated as events, moderate-probability predictions being adjudicated, and low-probability predictions treated as non-events. Conclusion: A text-embedded transformer model identified fall injuries with comparable accuracy to a rule-based algorithm, demonstrating "proof of concept" for use in endpoint adjudication.

Background The European Association for the Study of the Liver (ESAL) - Steatotic Liver Disease (SLD) screening algorithm involves two steps; initial screening with FIB-4 followed by referral for vibration-controlled transient elastography (VCTE) in patients likely to have significant fibrosis (SF). However, VCTE is not widely available in resource-limited settings. Aim To optimise the EASL SLD screening algorithm for resource-poor settings using machine learning (ML). Methods We analysed data from 964 adults aged [≥]35 years who underwent VCTE at a tertiary referral centre in Sri Lanka between November 2024 and 2025. Multiple ML models using different methods and variable combinations were trained on 80% of the dataset and tested on the remaining 20%. Best models were selected based on performance and externally validated using data from 430 patients who underwent VCTE before November 2024. Model performance was compared with the FIB-4 using confusion matrices. Results A Random Forest model incorporating age, AST, ALT, and platelet count separately, rather than using FIB-4, outperformed. The all-variable ML model showed the best predictive performance for SF, with accuracy of 77.2%, recall of 0.762, precision of 0.778, and AUC-ROC of 0.818. The variables used in the model, in descending order of feature importance, were AST, platelet count, BMI, ALT, age, diabetes mellitus, hypertension, dyslipidaemia, sex, family history, hypothyroidism, diabetes complication and smoking. External validation demonstrated 75.1% accuracy and an AUC of 0.779. When used as the first step of the SLD screening algorithm, the all-variable ML model identified 37 (17.1%) additional true positives and reduced false-negative diagnoses by 50% compared with FIB-4. Conclusions ML-based models were more effective than the FIB-4 score as the first-line screening tool for VCTE referral, substantially improving the identification of patients with significant fibrosis in this South Asian cohort.

Background: Postoperative delirium (POD) is a complication associated with most types of surgery, and is associated with a number of detrimental effects. Therefore, it is of interest to determine which patients may be at higher risk of POD so that mitigating steps may be taken. We sought to determine whether POD can be accurately predicted with common machine learning (ML) models. Methods: Using the Medical Information Mart for Intensive Care (MIMIC)-IV database, we identified 8026 abdominal surgery procedures across 7215 adult patients. Using demographic information, such as age, type of surgery, sex; as well as commonly measured laboratory values (such as electrolytes and blood counts) and comorbidity indices, we determined to what extend common ML models, such as random forests, support vector machines, extreme gradient boosted machines, and neural networks, could predict POD. Results: Random forests outperformed logistic regression, support vector machines, extreme gradient boosted machines, and neural networks, with respect to individual t-tests. The random forest model had a sensitivity of 73.11, a specificity of 71.14, and an area under the receiver operator characteristic curve of 0.800. Age, comorbidity indices, gender, and alcohol use carried significant predictive weight in this cohort. Conclusions: Machine learning models are effective predictors of postoperative delirium, although further work is required to increase clinical utility of such tools. Markers of inflammation, comorbidity indices, and alcohol use are important predictive features alongside better-known features such as age.

Objective: To evaluate the case-level performance of deep-learning segmentation models for detecting extrahepatic bile duct stones on representative intraoperative cholangiography images (IOC) and to characterize the completeness of individual-stone localization. Background: Retained bile duct stones can cause biliary obstruction, cholangitis, and pancreatitis. However false-positive interpretation of filling defects may prompt additional downstream procedures. Computer vision has been applied to biliary anatomy recognition and IOC adequacy assessment, but patient-level stone detection and individual-stone localization remain insufficiently studyed. Methods: Representative IOC images were annotated for extrahepatic biliary anatomy and stones, with case-level stone status established using a composite clinical reference standard. Two deep-learning models were developed to delineate the common bile duct and common hepatic duct and to detect and localize stones. Case-level diagnostic performance was evaluated against the composite clinical reference standard, and individual-stone localization was evaluated against expert-reviewed annotations. Results: On the held-out 125 patients test set, MiT-B2-UNet identified 23 of 25 stone-positive cases and 95 of 100 stone-negative cases, corresponding to a sensitivity of 0.920, specificity of 0.950, and AUC of 0.986. nnU-Net identified 19 of 25 stone-positive cases and 98 of 100 stone-negative cases, corresponding to a sensitivity of 0.760, specificity of 0.980, and AUC of 0.959. At the individual-stone level, MiT-B2-UNet and nnU-Net localized 31 of 59 and 25 of 59 annotated stones, respectively; all annotated stones were localized in 13 of 25 and 12 of 25 stone-positive cases. Conclusions: Deep-learning models can identify stone-positive IOC cases and localize individual stones. This technology may help inte

Background. Definitive diagnosis of Hirschsprung's disease (HD) requires pathological identification of enteric ganglion cells. This process is time-consuming and subject to inter-observer variability. Artificial intelligence (AI) tools have the potential to standardize and accelerate this workflow, but no study has determined which AI approach best serves intraoperative HD pathology diagnostics. Method. This study compared the U-Net and You Only Look Once version 26 (YOLO26) frameworks for ganglion cell detection using a single-centre retrospective dataset of 54 whole-slide images (WSIs) from rectal biopsies. WSIs were tiled into 397,731 image patches (128x128 pixels), further partitioned into training (70%), validation (15%), and testing (15%) sets. Models were evaluated on tile- and patient-level diagnostic metrics and processing latency. Results. The U-Net achieved a tile-level sensitivity of 82.9%, showing no statistically significant difference compared to YOLO26 (79.1%; p = 0.097). However, YOLO26 demonstrated a statistically significant advantage in tile-level specificity (96.1% vs. 93.9%; p < 0.001) and reduced mean inference latency (7.64 ms vs. 11.57 ms/tile). At the patient level, both models achieved 100% diagnostic sensitivity. Despite low patient-level specificity (0.0% U-Net; 11.8% YOLO26), the tissue-level diagnostic burden of false positives was 6.00% for U-Net and 3.50% for YOLO26. Conclusion. The U-Net is preferred when nominal gains in sensitivity are prioritized, while the YOLO26 is an alternative that optimizes efficiency and false positive suppression. Both models serve as robust screening filters to augment the pathologist's workflow and should be selected based on workflow requirements. Prospective validation on larger, multi-centre datasets is required before clinical implementation.

Laser Interstitial Thermal Therapy (LITT) is a minimally invasive neurosurgical technique in which a stereotactically-implanted fiber delivers thermal energy to ablate intracranial lesions. Existing computer-assisted planning systems optimize trajectories against a one-dimensional line abstraction, then approximate the ablation zone as a fixed-radius cylinder post-hoc to estimate coverage. Trajectories selected as optimal under this model are not guaranteed to remain optimal once the cylindrical extent is applied, which introduces a mismatch between predicted and true ablation coverage. This may also underestimate spillover into surrounding healthy tissue. We present OptiLITT, a treatment planning system that represents the laser probe as a cylindrical ablation volume from the onset of optimization, jointly solving dual-fiber placement, lesion coverage, and healthy-tissue spillover as a single coupled problem. All planning parameters are exposed through a user-configurable graphical user interface supporting intraoperative refinement between planning stages.

Background Existing insulin resistance (IR) indices are predominantly developed in diabetic cohorts, limiting their generalizability. We developed a novel deep neural network-derived IR index (DNN-IR) using a Mixture-of-Experts (MoE) framework and evaluated its predictive performance for incident cardiovascular disease (CVD) and mortality in general populations. Methods We utilized data from three cohorts: the cross-sectional REACTION study (Fujian subcohort, 2011-2012) for DNN-IR derivation and internal validation; and two prospective cohorts, NHANES (1999-2018, linked to the National Death Index) and CHARLS (2011-2018), for external validation. The DNN-IR was developed using a deep learning model based on a Mixture-of-Experts (MoE) architecture, trained on the REACTION dataset. We evaluated the DNN-IR's utility in predicting incident CVD, cardiovascular mortality, and non-cardiovascular mortality among 13,889 NHANES and 7,047 CHARLS participants. Predictive performance was assessed via the area under the receiver operating characteristic curve (AUC). Multivariable logistic regression, restricted cubic splines, and Kaplan-Meier analyses characterized the associations between DNN-IR and clinical outcomes. Results In the REACTION cohort, DNN-IR demonstrated superior predictive performance for atherosclerotic outcomes, achieving AUROCs of 0.89 (training) and 0.84 (internal validation). In the external CHARLS cohort (median follow-up: 7 years; 1,135 incident CVD cases [16.1%]), DNN-IR yielded AUROCs of 0.72 for incident CVD and 0.77 for all-cause mortality. Fully adjusted models showed that each 1-SD increment in DNN-IR was associated with a 23% higher CVD risk (OR=1.23, 95% CI: 1.14-1.32), exhibiting a predominantly linear dose-response relationship (P-nonlinearity=0.453). In NHANES, DNN-IR robustly predicted cardiovascular (AUROC=0.77) and all-cause mortality (AUROC=0.72), alongside specific mortalities like diabetes (0.91), Alzheimer's disease (0.88), and kidney disease (0.96). Higher DNN-IR levels correlated with stepwise increases in cumulative mortality (log-rank P<0.001). Conclusions The MoE-derived DNN-IR index demonstrated robust and stable performance in predicting atherosclerosis, incident CVD, cardiovascular mortality, and all-cause mortality in the general population. Further validation in larger, more diverse cohorts is warranted to support its broad clinical applicability.

Introduction: Precise anatomical navigation is fundamental to safe endoscopic pituitary surgery, a high-stakes procedure characterised by a challenging learning curve. While traditional navigation systems often rely on workflow-disrupting probes or static preoperative imaging, advancements in computer vision AI (CVAI) now enable dynamic, real-time anatomical segmentation directly from live surgical video1-3. Our group has previously conducted a series of preclinical human-computer interaction studies to refine the system's design, alongside digital and high-fidelity physical simulations demonstrating the benefit of AI assistance in improving overall performance, training, and safety4-8. Building on this foundation, the current study represents a first-in-human application of real-time CVAI assistance in the neurosurgical operating room, serving to assess feasibility and safety, and to iteratively improve the system. Method: Guided by DECIDE-AI and IDEAL frameworks, this single-centre evaluation comprises an initial proof-of-concept phase (n=6) for endoscopic transsphenoidal pituitary surgeries. The AI model utilised a DINOv3-derived vision transformer architecture, deployed via a high-performance edge computing unit to achieve low-latency, real-time inference without reliance on cloud infrastructure2. Given the high-risk nature of the procedure and the early stage of clinical AI integration, the system was initially deployed as an educational adjunct on a secondary monitor, ensuring the primary surgical feed remains uncompromised. Functionality and safety were assessed via structured questionnaire, prospective observation, and blinded retrospective review of the recordings of the endoscopic surgical video feed and wider operating room environment. Continuous multi-stakeholder feedback through validated human factors surveys drove iterative technical refinements between cases. Results: Six patients with pituitary adenomas were enrolled. The CVAI system was successfully deployed in four cases, demonstrating acceptable real-time sella segmentation accuracy. Deployment failed pre-operatively in two cases owing to a single recurring system reboot bug. Iterative refinement between cases were driven by our experience and surgical team feedback. This resulted in the integration of additional anatomical structure segmentations (e.g., carotid arteries), enhanced model accuracy via training dataset expansion, and hardware firmware upgrades. Multi-stakeholder surveys demonstrated satisfactory system feasibility, usability, and acceptability among the surgical team. Both prospective observation and retrospective video review confirmed the absence of adverse events, including no significant distraction to the primary surgeon, and there were no AI-related clinical complications. Conclusion: This first-in-human early clinical evaluation demonstrates the feasibility, safety and iterative development of real-time, CVAI-based anatomical navigation during high-stakes neurosurgery. Future work will include a larger single-centre case series (IDEAL Stage 2a) with more surgical teams to further iterate the system and explore its impact on training and workflow. As the underpinning technology improves, deployment will transition to direct intra-operative decision support and integration with other intra-operative navigational technologies.

To explore adapting qPCR systems for end-point nucleic acid quantification using dyes such as SYTO-9, we quantified serial dilutions of DNA and RNA standards in the range of 0.75 - 200 ng/{micro}l on 384-well qPCR devices. SYTO-9 fluorescence was successfully measured using standard SYBR Green settings. Blank-subtracted relative SYTO-9 signal showed a logarithmic dependence on DNA/RNA concentration (R2 > 0.95). Measurements were highly stable with different incubation times, temperatures of up to 95{degrees}C, and photobleaching. The described approach is a valuable QC option for high-throughput DNA/RNA isolations and could be adapted to additional fluorometric assays beyond nucleic acids.

Purpose: To evaluate whether fluorescence lifetime imaging ophthalmoscopy (FLIO) combined with deep learning can detect metabolic signatures for classification of type 2 diabetes mellitus (T2DM). Design: Cross-sectional analysis of participants included AI-READI dataset (version 3) with FLIO imaging and and hemoglobin A1c (HbA1c) measurement. Subjects: 1,783 participants from the AI-READI dataset (version 3) with HbA1c measurements and FLIO imaging scans (6,912 total): 671 normoglycemic, 726 prediabetic, and 386 diabetic. Methods: Mean fluorescence lifetime maps were generated using a center-of-mass approach and used as inputs to AI models. We trained convolutional neural networks (CNNs), ResNet-18, and XGBoost under three-class (normal, prediabetic, diabetic) and two binary (normal vs. impaired; normal vs. diabetic) classification schemes, using nested 5-fold cross-validation with participant-level grouping. Main Outcome Measures: Macro-averaged accuracy, F1 score, area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value (PPV). Results: Group-averaged lifetime maps demonstrated consistent spatial differences across glycemic groups, with progressively longer lifetimes from normal to diabetic participants. The CNN achieved the best overall performance in the 3-class classification (accuracy 0.41 +/- 0.03, F1 score 0.39 +/- 0.02, AUROC 0.58 +/- 0.02), compared to the random classifier for 3-class classification (AUROC = 0.50; accuracy = F1 = 0.33). ResNet-18 and XGBoost showed similar performance (AUROC 0.53-0.58). Confusion matrices revealed substantial overlap between classes, with frequent misclassification toward the prediabetes group. Binary reformulation (normal vs. diabetic) improved performance substantially, with the CNN resulting in AUROC 0.63 +/- 0.02 and XGBoost 0.67 +/- 0.07. Conclusions: FLIO-derived lifetime maps capture metabolic signals associated with glycemic status but yield modest classification performance with current AI models. These findings highlight both the potential and the challenges of using FLIO for early metabolic screening and monitoring, informing future development of clinically applicable imaging biomarkers.

Machine learning workflows frequently incorporate data preprocessing to enhance predictive performance. However, the need for Super Learner ensembles made up only of preprocessing-invariant tree-based algorithms remains unexplored. Using three benchmark clinical classification datasets, this study examined how preprocessing affected the Super Learner's prediction performance, learner weight distribution, and oracle behavior. The Heart Disease (207 observations), Indian Liver Patient Dataset (583 observations), and Pima Indians Diabetes (768 observations) datasets were used to create a Super Learner ensemble model that included Classification and Regression Trees (CART), Random Forest, Ranger, and Extreme Gradient Boosting (XGBoost). Models were evaluated under raw and preprocessed data conditions using repeated cross-validation. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), and Brier score. Learner weight allocation and Oracle Gap were compared using paired Wilcoxon signed-rank tests with Benjamini-Hochberg adjustment. Preprocessing produced negligible changes in predictive performance for the Heart Disease and Pima datasets. For the ILPD dataset, preprocessing significantly improved AUC (0.746 to 0.752; adjusted p = 0.0017) and reduced the Brier score (0.177 to 0.175; adjusted p < 0.001). Learner weights remained largely stable, although Random Forest replaced Ranger as the dominant learner for the Heart Disease dataset. Oracle Gaps remained extremely small (<0.002) across all datasets and did not differ significantly between preprocessing conditions. Preprocessing provides limited benefit for Super Learner ensembles composed of preprocessing-invariant learners and does not materially alter their oracle behavior. Preprocessing decisions should therefore be guided by dataset characteristics rather than adopted as a universal modelling practice.

Background: Rapid detection of viral pathogens can be challenging, especially when routine PCR fails. Conventional assays typically detect known viruses which are specifically targeted by the assay, which may result in the failure to identify novel or non-targeted viruses. Broad-range hybrid-capture sequencing enables unbiased detection of viruses, including those that are uncommon or divergent. Methods: We combined the TWIST Comprehensive Viral Research Panel (>3,000 virus species) with Oxford Nanopore Flongle sequencing for easy and quick viral genome detection. The workflow includes random-primed cDNA synthesis, dsDNA conversion, TWIST probe enrichment, and Nanopore sequencing. Performance was evaluated using the QCMD 2024 Viral Metagenomics EQA panel and one clinical sample. Results: All expected targets of the QCMD 2024 Viral Metagenomics EQA panel were detected; eight of thirteen viruses achieved [&ge;]90% genome coverage. The negative control showed no targeted viral reads. Mixed infections of DNA and RNA viruses were resolved accurately. The workflow from nucleic acid extraction to obtaining sequence data was completed within 3 days.

Reliable DNA isolation is a critical prerequisite for PCR-based food authentication, particularly for meat products where complex matrices may compromise DNA quality and amplification efficiency. This study aimed to analytically validate an automated DNA extraction method from meat matrices using Qiagen QIAcube Connect in combination with the DNeasy(R) Mericon Food Kit. Validation parameters included DNA concentration, total yield, purity, integrity, and assessment of PCR inhibitors using real-time PCR targeting the porcine cytochrome b gene. The method produced a mean DNA concentration of 219.5 ng/{micro}L with an average yield of 21,519.7 ng, exceeding predefined acceptance criteria. Agarose gel electrophoresis confirmed DNA fragment sizes larger than the target amplicon, indicating suitability for PCR analysis. Real-time PCR evaluation demonstrated excellent linearity (R2 = 0.99-1.00), amplification efficiencies between 90.34% and 99.84%, and mean {Delta}Ct values of 0.10, confirming the absence of PCR inhibition. These results indicate that the validated automated method is robust, reproducible, and suitable for routine PCR-based meat species authentication in food control laboratories.

Background: For patients with metastatic gastrointestinal cancers, chemotherapy resistance is a common phenomenon that, if known in advance, would allow for individualized treatment decisions. This study aimed to test the feasibility of developing a deep learning computer vision system that uses laparoscopy images depicting peritoneal surface metastases (i.e., capturing the in-vivo optical appearance of metastases as a summary of their molecular makeup) to predict whether a patient is resistant to standard chemotherapy. Methods: The retrospective observational feasibility study included 35 adult patients who underwent staging laparoscopy for non-colon gastrointestinal adenocarcinoma with biopsy-confirmed peritoneal surface metastases and who underwent chemotherapy as their only treatment modality. Chemotherapy resistance was determined based on each patient's observed cancer-specific survival after controlling for confounders. Results: Of 35 patients, 17 were assigned to the chemotherapy sensitive group and 18 to the chemotherapy resistant group. The study cohort provided 1010 laparoscopy image patches of 101 biopsy-confirmed metastases. A densely connected convolutional neural network with cross-validation provided the best results for correctly predicting chemotherapy resistance at the patient level (accuracy 0.80 (95%CI 0.63-0.92), sensitivity 0.72, specificity 0.88, AUC-ROC 0.78). Saliency maps demonstrated the system's trustworthiness. Conclusion: In this study, a prototype surgical computer vision system designed to determine chemotherapy resistance from operative images of peritoneal surface metastases demonstrated its technical feasibility. Further development and validation in a multi-institutional clinical study are pending.

Background Hepatocellular carcinoma (HCC) treatment selection demands nuanced integration of heterogeneous patient data, yet prevailing predictive models rely on restricted data modalities and oversimplified therapeutic frameworks, compromising clinical translation. Objective We developed and validated a multimodal artificial intelligence framework to guide optimal treatment strategy selection across the full spectrum of HCC interventions. Methods This retrospective study comprised 1,043 HCC patients (development cohort, January 2017-December 2023) and 55 external validation patients (2023) from Wuxi Peoples Hospital. We engineered Embedding-Augmented Extra Trees (ET-Emb), a novel model fusing structured clinical variables with contextual text embeddings derived from medical histories and radiology reports. ET-Emb quantifies probabilities for five primary treatments: open/laparoscopic resection, transarterial chemoembolization, radiofrequency ablation (RFA), and chemotherapy. Model performance was rigorously assessed via 10-fold cross-validation and external validation using ROC-AUC and PR-AUC metrics. Results ET-Emb demonstrated robust performance in the development cohort (ROC-AUC: 0.84 {+/-} 0.04; PR-AUC: 0.55 {+/-} 0.06), significantly outperforming established benchmarks. This generalizability was preserved in external validation (ROC-AUC: 0.77 {+/-} 0.02; PR-AUC: 0.47 {+/-} 0.03). SHAP analysis identified textual clinical narratives and socioeconomic determinants as critical predictive drivers. Conclusions By unifying structured and unstructured data modalities, ET-Emb delivers accurate, multi-treatment strategy prediction for HCC. Its clinical validity and the demonstrated significance of textual features establish multimodal AI as an essential paradigm for simulating complex oncological decision-making, positioning ET-Emb as a transformative tool for precision HCC management.

Developments in Nanopore sequencing have enabled telomere to telomere genomic assembly as a routine technique in genomic research. Nanopore DNA sequencing for genomic assembly is typically performed on native DNA molecules, making it particularly sensitive to the quality of input DNA, with contaminating molecules limiting data yields and reducing read quality. As pangenome analysis gains interest, particularly in non-model plant species which are often rich in inhibitory secondary metabolites, the development of methods which can improve the quality and throughput of nanopore sequencing is essential. Here we describe a method for isolation of total DNA from the leaf tissues of diverse Viridiplantae species. The initial lysis buffer consists of a modified CTAB buffer, incorporating dimethyl sulfoxide for the reduction of viscosity, which can be problematic in many plant DNA preparations. An organic extraction with 2-butoxyethanol is utilized to further extract phenolic compounds which may be sufficiently hydrophilic to evade chloroform extraction, while reducing aqueous phase volume. Further cleanup via cesium chloride (CsCl) ultracentrifugation is performed to minimize the carryover of residual contaminating macromolecules. Samples prepared using this method are of consistent high quality, even when extracted from challenging late season leaf tissue or secondary metabolite rich species. Sequencing results from samples prepared by this method outperform those obtained from typical modified CTAB DNA isolation techniques in both quantity and quality. We tested sequencing performance from Vitis DNA isolated using a modified CTAB method and Vitis DNA isolated using the CsCl ultracentrifugation-based method described here. DNA isolated via the method described here produced 83% more >Q10 sequence data (52.61 Gb vs. 28.8 Gb), resulted in a 60% greater read N50 despite more handling steps (32.78kb vs. 20.45kb), and resulted in a higher modal read quality (Q27 vs. Q24). The consistency of this method across diverse plant taxa suggests its use as a general method for DNA isolation prior to Nanopore sequencing and genomic assembly for diverse plant taxa.

Deep learning reads 12 lead electrocardiograms at close to expert level on public benchmarks, yet most reports give one accuracy figure for the whole test set and stop there. We trained three architectures that are standard in this field, a 1D ResNet, a convolutional network with a bidirectional LSTM, and a convolutional network with a bidirectional LSTM followed by a transformer encoder, on the PTB-XL dataset to classify the five diagnostic superclasses, and then looked at how each one performed across sex and age. On the held out fold all three reached a macro AUC near 0.92, in line with the strongest published results on this benchmark, and the simplest model, the 1D ResNet, was marginally the best at 0.9241. The averages hid a steady pattern. Every model scored lower for female patients than for male patients, and every model scored lowest for patients aged 80 and over, where the 1D ResNet fell to 0.8878 and the transformer to 0.8693. Adding complexity did not close either gap and slightly widened the gap by age. Overall accuracy on PTB-XL is close to solved for these model families, but the benefit is not shared evenly, and a single headline number hides the patients a model serves worst. We release the full stratified evaluation to support fairness aware reporting.

Huntington's disease (HD) presents a heterogeneous neurodegenerative course, with motor, cognitive, and functional symptoms progressing differently across individuals. This atypical progression complicates the definition of discrete disease stages, hindering understanding of disease trajectories, timely pa- tient care, and therapy development. Consequently, current clinical staging systems rely heavily on clinician-defined, domain-specific criteria and fixed clinical measurement boundaries for stage assignment, reducing objectivity and often leading to overlapping clinical measurements across stages. While machine learning methods can help, existing approaches cannot fully capture complex temporal relationships within and across patients. We propose URL- STFN, a dynamic graph-based representation learning model that encodes both inter- and intra-patient temporal patterns from longitudinal clinical measures. We then evaluate disease stages formed through clustering and stability analysis of URL-STFN latent representations, and compare them with representations obtained from conventional embedding approaches. We further benchmark these clustering-based stages against states derived from conventional temporal models, including DHMM. We hypothesize that clustering URL-STFN latent representations enables identification of HD stages with reduced overlap in clinical measurements. The proposed framework is evaluated using 1,477 clinical visits from the Enroll-HD dataset, a large lon- gitudinal cohort with repeated clinical assessments. For staging, we used 44 clinical measurements spanning motor, cognitive, and functional domains. URL-STFN identifies clinically meaningful HD stages consistent with estab- lished disease progression while reducing overlap in clinical feature values compared with DHMM-derived and clinical staging approaches. These find- ings highlight the potential of a dynamic graph-based representation learning and clustering framework to support more objective, data-driven, and precise HD staging.