Find the papers that actually matter

Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce useful patterns for survival predictions. This study deals with small sample survival analysis by leveraging transfer learning, a useful machine learning technique that can enhance the target analysis with related knowledge pre-learned from other data. We propose and develop various transfer learning methods designed for common survival models. For parametric models such as DeepSurv, Cox-CC (Cox-based neural networks), and DeepHit (end-to-end deep learning model), we apply standard transfer learning techniques like pretraining and fine-tuning. For non-parametric models such as Random Survival Forest, we propose a new transfer survival forest (TSF) model that transfers tree structures from source tasks and fine-tunes them with target data. We evaluated the transfer learning methods on colorectal cancer (CRC) prognosis. The source data are 27,379 SEER CRC stage I patients, and the target data are 728 CRC stage I patients from the West China Hospital. When enhanced by transfer learning, Cox-CC's Ctd value was boosted from 0.7868 to 0.8111, DeepHit's from 0.8085 to 0.8135, DeepSurv's from 0.7722 to 0.8043, and RSF's from 0.7940 to 0.8297 (the highest performance). All models trained with data as small as 50 demonstrated even more significant improvement. Conclusions: Therefore, the current survival models used for cancer prognosis can be enhanced and improved by properly designed transfer learning techniques. The source code used in this study is available at https://github.com/YonghaoZhao722/TSF.

Effective communication in health care is critical for treatment outcomes and adherence. With patient-provider exchanges shifting to secure messaging, analyzing electronic patient-communication (EPPC) data is both essential and challenging. We introduce EPPCMinerBen, a benchmark for evaluating LLMs in detecting communication patterns and extracting insights from electronic patient-provider messages. EPPCMinerBen includes three sub-tasks: Code Classification, Subcode Classification, and Evidence Extraction. Using 1933 expert-annotated sentences from 752 secure messages of the patient portal at Yale New Haven Hospital, it evaluates LLMs on identifying communicative intent and supporting text. Benchmarks span various LLMs under zero-shot and few-shot settings, with data to be released via the NCI Cancer Data Service. Model performance varied across tasks and settings. Llama-3.1-70B led in evidence extraction (F1: 82.84%) and performed well in classification. Llama-3.3-70b-Instruct outperformed all models in code classification (F1: 67.03%). DeepSeek-R1-Distill-Qwen-32B excelled in subcode classification (F1: 48.25%), while sdoh-llama-3-70B showed consistent performance. Smaller models underperformed, especially in subcode classification (>30% F1). Few-shot prompting improved most tasks. Our results indicate that large, instruction-tuned models tend to achieve higher performance in EPPCMinerBen tasks, particularly evidence extraction while smaller models struggle with fine-grained reasoning. EPPCMinerBen provides a benchmark for discourse-level understanding of patient-provider communication, supporting future work on structured communication analysis and model evaluation.

Rearranged during transfection (RET) kinase mutations are frequently observed in the context of human thyroid and lung cancer treatment. Moreover, a considerable amount of effort has been dedicated by the scientific community to the identification of highly potent and selective RET inhibitors. In this study, we identified a series of pyrazolo[1,5-a]pyridine derivatives, and compound 9 as a candidate drug that targets both wild-type (wt) RET and RETV804M by structure-activity relationship (SAR) study. In addition, 9 exhibited remarkable antitumor activity at a dose of 10 mg/kg/day, indicating that it completely hindered the growth of tumors induced by BAF3-KIF3B-RET-WT xenografts. In summary, 9 can be demonstrated to act as a potential RET inhibitor, as well as a treatment for RET-related cancers.

Neurotrophic tyrosine receptor kinase 2 (NTRK2/TRKB) demonstrates oncogenic roles across cancers, with notable significance in gliomas where its overexpression is linked to aggressive clinical phenotypes. Lucitanib (AL3810), a multi-target tyrosine kinase inhibitor, shows unexplored potential for treating NTRK2-driven gliomas. This study employed an integrated approach combining pan-cancer analysis, computational drug screening, and experimental validation to systematically evaluate the oncogenic function of NTRK2 and the therapeutic efficacy of Lucitanib. Multi-omics analysis across 32 cancer types using TCGA/GTEx/CPTAC datasets revealed NTRK2 overexpression in gliomas (GBMLGG/LGG), correlating with poor prognosis (p < 0.01) and implicating AKT signaling and immune microenvironment modulation. Structure-based virtual screening of 26,996 compounds against NTRK2 identified Lucitanib as a high-affinity binder (ΔG < -8 kcal/mol), forming stable interactions with key residues (MET-636, PHE-633), further validated by 500 ns molecular dynamics simulations. In vitro experiments using U251MG glioblastoma cells and primary astrocytes demonstrated that Lucitanib significantly inhibited proliferation (p < 0.001), suppressed invasion and migration via MMP9 downregulation (p < 0.001), and induced apoptosis through Bcl-2/Bax modulation (p < 0.001). Its efficacy was intermediate between methotrexate and the selective NTRK2 inhibitor ana-12. Mechanistically, Lucitanib targeted the NTRK2-AKT-MMP9 axis while preserving immune effector functions. These findings establish NTRK2 as a viable therapeutic target in gliomas and highlight Lucitanib as a novel multi-mechanistic inhibitor with balanced efficacy and favorable pharmacokinetic properties, supporting its further development for clinical translation in NTRK2-overexpressing gliomas.

Liver cancer, particularly hepatocellular carcinoma (HCC), is a significant global health concern due to its asymptomatic early stages, biological diversity, and frequent late diagnoses that hinder effective treatment and survival rates. Traditional diagnostic methods, such as serum biomarker assays and imaging techniques, often lack the necessary sensitivity and specificity and highlight the urgent need for innovative, non-invasive diagnostic alternatives. This review emphasizes the potential of combining nanobiosensor technologies with physics-informed machine learning (PIML) to address these diagnostic challenges. Nanobiosensors utilize advanced materials like gold nanoparticles and graphene to achieve highly sensitive, real-time detection of HCC biomarkers, including alpha-fetoprotein (AFP) and non-coding RNAs, with detection limits reaching sub-nanomolar to femtomolar levels through various mechanisms. However, the clinical application of nanobiosensors is hindered by issues such as signal instability and environmental interference. PIML offers a solution by incorporating fundamental physical principles into machine learning models which is enhancing their predictive accuracy and robustness against data noise. This hybrid approach facilitates effective signal denoising, adaptive calibration, and the integration of multimodal data, thereby improving the overall diagnostic process. Main findings indicate that PIML-enhanced nanobiosensing systems significantly outperform traditional AI models in biomedical applications, demonstrating superior generalization and biologically relevant outputs even in the presence of limited data. The integration of these technologies creates a promising framework for advanced liver cancer diagnostics, enabling precise, non-invasive detection and personalized clinical decision-making. In conclusion, the convergence of nanobiosensors and PIML holds the potential to revolutionize liver cancer diagnostics, offering improved early detection and dynamic monitoring. However, to realize this potential, ongoing challenges related to computational scalability, sensor reproducibility, and regulatory validation must be systematically addressed through collaborative interdisciplinary efforts.

Tumor-infiltrating lymphocytes (TILs) are an important indicator of immune activity in breast cancer, yet scoring them consistently on H&E slides remains challenging in routine pathology. This work presents a modular deep learning pipeline that delivers fully automated and continuous stromal TILs (sTILs) scores in line with the International Immuno-Oncology Biomarker Working Group (IIOBWG) guidelines. The pipeline combines three components: a TIL segmentation model refined through pathologist-guided active learning, a robust stroma segmentation network based on an enhanced DeepLabV3+, and a lightweight regression module that learns how TILs distribute within stromal regions. A new adaptive aggregation strategy integrates patch-level predictions into a single, clinically meaningful score while accounting for heterogeneous infiltration. The system was evaluated on two independent datasets (60 and 112 WSIs) with expert-annotated ROIs, achieving strong agreement with pathologists (Pearson of 0.814; ICC of 0.808). Importantly, the pipeline is interpretable: each stage produces human-readable outputs (stroma masks, TIL-in-stroma maps), and SegGradCAM visualizations confirm that predictions rely on biologically relevant tissue regions. These findings demonstrate the pipeline's potential as a reliable and clinically adaptable tool for standardized, fully automated TILs quantification in breast cancer pathology. The source code and pretrained models are publicly available at https://github.com/Shrief-Abdelazeez/TILs-Scoring.

Androgen deprivation therapy has a negative effect on bone mineral density and trabecular bone score in prostate cancer patients. The addition of androgen receptor pathway inhibitors can result in worsened skeletal fragility. BonEnza is a prospective phase II trial in which metastatic hormone sensitive prostate cancer patients were randomized to receive androgen deprivation therapy plus enzalutamide with (EZ arm) or without (E arm) the addition of zoledronic acid. Bone quantity and quality parameters were evaluated by dual-energy x-ray absorptiometry (DXA) scan at baseline and after 18 months of therapy. Alkaline phosphatase (ALP) and C-terminal telopeptide of type I collagen (CTX) were assessed at baseline and after 18 months of treatment. Eighty-nine patients had paired DXA evaluation at both timepoints. After 18 months of treatment femoral neck and lumbar spine bone mineral density significantly decreased in E arm (-8.6% and - 9.26% respectively; p < 0.001), while improved in EZ arm (+1.83%, p 0.019; and + 5.47%, p < 0.001). Trabecular bone score significantly worsened in E arm (-3.35%, p < 0.001) and improved in EZ arm (+3.01%, p 0.004). Both ALP and CTX showed marked reduction overtime among patients receiving zoledronic acid (-35.6%, p < 0.0001, and - 58.9%, p < 0.0001, respectively), while remaining stable (-0.6%, p 0.934) or significantly increasing (39.5%, p 0.011) respectively among patients from E arm. The addition of zoledronic acid to enzalutamide and androgen deprivation improved bone mineral density, trabecular bone score, and reduced bone turnover markers. Future studies in mHSPC should consider the use of lower doses of bone protecting agents and regard the reduction in morphometric fractures by DXA as a primary endpoint.

Type 2 diabetes (T2D) places substantial physiological and psychological demands on patients and is independently linked to an elevated risk of depression. Intellectual developmental disability (IDD) is likewise associated with metabolic disorders and a high prevalence of mood disturbances, yet communication barriers often delay diagnosis. Whether coexistence of IDD further amplifies the likelihood of new-onset depression in people with T2D remains unclear. We aimed to investigate the association between IDD and incident depression among Korean adults with T2D. We analyzed 1,819,869 adults (≥ 20 years) with T2D who underwent the 2015-2016 Korea National Health Screening Program. Participants were classified as either IDD (n = 3665) or non-IDD groups. The primary outcome was new-onset depression identified up to 31 December 2022 following the health-screening date. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident depression. Participants with IDD were younger (mean age: 49.2 vs. 58.0 years) and had a lower proportion of men (55.9% vs. 61.8%) than those without IDD. Over a median follow-up of 5.8 years, 14.8% developed depression, with an elevated risk in the IDD group (HR 1.65, 95% CI: 1.53-1.77). This association was consistent across IDD severity and was especially marked in individuals under 65 and with T2D duration under five years. Coexisting IDD and T2D are linked to higher depression risk. Our finding underscores the need for tailored interventions, improved caregiver awareness, and enhanced screening to address mental health disparities.

The development of targeted, multifunctional protein hydrogel systems offers a safe and robust platform for advancing precision cancer theranostics. Here, we construct an anti-HER2 mediated injectable hydrogel by self-assembling bovine serum albumin within a tetraaza-macrocycle scaffold, integrating crosslinking and antibody bioconjugation strategies. Hydrogel structural regulation through gadolinium (Gd3+)-tetraaza coordination and cystamine linkages yields a tunable framework for drug loading. With a gelation temperature near 37 °C, the hydrogel enables minimally invasive in situ tumor injection and simultaneous delivery of imaging probes and therapeutic agents. Stimulus-responsive drug release was realized by the tumor-associated reductive microenvironment and acidic pH, which trigger disulfide bond cleavage and destabilize Gd-chelate interactions. The hydrogel loaded with imaging probes and contrast agents enables multimodal tumor diagnosis, integrating fluorescence, photoacoustic, and magnetic resonance (MR) modalities to support real-time monitoring of intratumoral distribution and redox-responsive behavior. The hydrogel system provides the synergistic combination therapy that integrates anti-HER2 mediated immunotherapy, monomethyl auristatin E-based chemotherapy, and IR780-induced photothermal and photodynamic treatments, collectively enhancing cytotoxic efficacy through targeted cellular uptake and prolonged tumoral retention. The anti-HER2 guided hydrogel serves as an injectable, versatile vehicle for tumor theranostics, enabling effective tumor imaging and imaging-guided combination therapy for biomedical applications.

Histone deacetylase 2 (HDAC2) plays a critical role in the pathogenesis of diffuse large B-cell lymphoma (DLBCL), positioning it as an attractive therapeutic target. In this study, we applied an integrated computational strategy that combined machine learning-based quantitative structure-activity relationship (QSAR) modelling, molecular docking, ADMET filtering, and molecular dynamics (MD) simulations to identify and optimize potential HDAC2 inhibitors. A dataset of 1995 HDAC2-active molecules was assembled and reduced to 25 key molecular descriptors, enabling the development of a robust Random Forest QSAR model (R2 = 0.926, CCC = 0.956). Top-predicted compounds underwent docking and pharmacokinetic evaluation, highlighting compound 10 as a promising lead (binding energy ≈ -10.2 kcal·mol-1). Guided optimization produced analogue 10 g, which displayed enhanced affinity (-10.9 kcal·mol-1), stable protein-ligand interactions in MD simulations, and favourable MM-GBSA binding free energy (ΔG_bind ≈ -40.3 kcal·mol-1). In silico pharmacokinetic analysis further suggested acceptable safety and drug-likeness. This multi-stage pipeline prioritizes 10 g as a strong candidate for experimental validation and demonstrates the value of integrating predictive modelling with structure-based refinement in anti-lymphoma drug discovery.