Medicine & Health

This case report describes a 23-year-old man who presented with a 3×4 cm cystic left-sided neck swelling that moved with deglutition but not clearly with tongue protrusion; ultrasound and contrast-enhanced neck CT showed a cystic lesion below the hyoid and above the thyroid cartilage extending laterally to the left, FNAC suggested a benign cystic lesion possibly a thyroglossal duct cyst, and the patient underwent a Sistrunk procedure removing the cyst, tract and body of the hyoid, with an uneventful postoperative course and histopathology confirming a left thyroglossal duct cyst.

The work presents a weakly-supervised, detection-free multiple instance learning framework that uses dual-branch gated attention pooling to make slide-level predictions on whole-slide cervical cytology images, treating each slide as a bag of local instance patches so that single-cell bounding boxes or pixel-level annotations are not required; evaluated on internal multi-center cohorts (SIPaKMeD, Herlev, and CRIC) and on the unannotated, out-of-distribution Mendeley LBC validation cohort processed via an unsupervised marker-controlled watershed pipeline, it reports that the lightweight MobileNetV2 backbone optimizes in-distribution multi-center accuracy (90.96% accuracy, 0.9800 ROC-AUC) while the higher-capacity Xception provides better out-of-distribution robustness under domain shift (80.

The work introduces an interpretable Fuzzy Rank-H2O AutoML framework in which a Fuzzy Rank Feature Selection (FRFS) algorithm picks predictors by combining statistical significance, information gain, clinical importance, and uncertainty, H2O AutoML then automatically builds ensemble models, and SHAP and LIME supply global and patient-level explanations; evaluated on two public cervical cancer datasets with stratified five-fold cross-validation where SMOTE is applied only to training folds to avoid information leakage, it reports a highest accuracy of 95.1% and AUC of 98.1%, outperforming traditional machine learning models and the baseline H2O AutoML framework.

This survey reviews AI, machine learning, deep learning, and generative adversarial network (GAN) approaches to autism spectrum disorder (ASD) screening, focusing on multimodal learning across behavioral, genetic, environmental, neuroimaging, physiological, and clinical data and on the role of GANs in synthetic-data generation and augmentation, concluding that multimodal AI may represent ASD-related characteristics more comprehensively than single-modality approaches while facing challenges of limited and heterogeneous datasets, class imbalance, multimodal integration, GAN training instability, synthetic-data quality, privacy and security, interpretability, generalizability, and limited external clinical validation.

Ibáñez and colleagues report in Science Advances a 'speech clock': they recorded 2,928 Spanish speakers from Argentina, Chile, Colombia, Mexico and Peru across various speech tasks, used machine learning to extract more than 700 speech features that change with ageing and dementia (such as pitch and vocabulary range), trained a model to predict age and compute a 'speech age gap', and found the clock could distinguish healthy individuals from those with some form of cognitive impairment, rating the speech of people with cognitive issues as older than expected for their chronological age while healthy people's speech generally matched their age.

Microsoft Research introduces Quine, a research system combining a world model of biology trained jointly across modalities including sequence, structure, function, cellular state, and imaging with a harness connecting scientific tools, literature, the wet lab, and researchers; with the Broad Institute it used the system in pancreatic ductal adenocarcinoma (PDAC) to predict and prioritize thousands of compounds by their potential to shift tumor cells between therapeutically relevant states, wet-lab assays showed Quine's highest-ranked compounds produced the largest intended shifts, the whole process from narrowing the search space to prioritizing a handful of candidates took just one weekend, and the experiments also bore out the model's prediction of a distinct third phenotype.