Medicine & Health

This study developed and deployed a transformer-based natural language processing pipeline to identify incidental thyroid findings in radiology reports from 115,683 adults without prior thyroid disease across Mayo Clinic sites from July 1, 2017, to September 30, 2023, finding that 7.8% had such findings (92.9% nodular) and that these findings were associated with higher odds of downstream thyroid nodule diagnosis, biopsy, thyroidectomy, and thyroid cancer diagnosis, with most cancers being papillary.

This review synthesizes the 15 MAPK homologues across pathogenic Leishmania species, linking individual kinases to parasite differentiation, intracellular survival, stress tolerance, motility, virulence and drug response, distinguishing experimentally validated mechanistic targets from computationally proposed candidates, and evaluating small-molecule and natural-product inhibitors, drug repurposing, structure-guided approaches, and emerging contributions from molecular dynamics, AlphaFold-based modelling, artificial intelligence and nanotechnology, while noting evidence for MAPK10 as a vaccine-associated antigen.

This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.

This study retrospectively analyzed 615 ECGs from 270 emergency medical service patient encounters with concern for acute myocardial infarction, using cardiology activation of the STEMI pathway as the reference standard, and compared three multimodal large language models with an ECG machine algorithm, finding that Gemini had the highest sensitivity (95.3%) but extremely poor specificity (9.4%), ChatGPT and Claude showed moderate sensitivity (68.1% and 67.2%) with limited specificity (42.3% and 46.5%), while the ECG machine algorithm was more balanced with sensitivity of 67.7% and specificity of 64.2%, suggesting that general-purpose large language models are not appropriate for ECG-based catheterization laboratory activation decisions in time-sensitive emergency workflows.

In the context of family caregivers of people living with Alzheimer's Disease and Related Dementias (ADRD), this work compares caregiver support exchanges from online communities with peer-like responses prompted from three LLMs (LLaMA, GPT-4o-mini, and MedGemma), using psycholinguistic and qualitative analysis to show that peer responses used significantly more first-person and past-focused language than peer-like AI responses, identifies seven types of personal narratives in human peer support, and finds that AI often captures their emotional work while potentially fabricating experiential grounding, thereby naming a narrative authenticity gap and a synthetic lived experience paradox.

Using AlphaGenome and AlphaMissense to quantify the disruption imposed by somatic mutations across 8,800 patients and 33 cancer types from The Cancer Genome Atlas, this study found that recurrent hotspot mutations showed substantially larger predicted protein-level effects while non-hotspot mutations exhibited larger regulatory effects across most cancer types; aggregating variant-level predictions into patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing yielded gene- and modality-specific profiles that reflected tissue of origin, cancer type, and microsatellite-instability status while retaining information beyond tumor mutational burden; among patients lacking recurrent hotspot mutations, higher predicte

This study implemented AP-FLART, which integrates dosimetric score-based beam angle selection, multi-modality-guided dose prediction, and function-guided dose mimicking, within RayStation and evaluated it on a test dataset of 33 lung cancer patients who underwent SPECT ventilation or perfusion imaging and lung radiotherapy, finding that automatic FLART plans significantly reduced high-function lung mean dose by 15.1% versus manual conventional radiotherapy plans, lowered the probability of grade >=2 radiation pneumonitis by 6.25 percentage points (27%) among FLART-benefiting patients, achieved clinical benefits similar to manual FLART plans, were clinically acceptable without modification in 87.9% of cases, and cut planning time from 2-3 hours to approximately 8 minutes.