Druggable proteins are proteins that can be specifically bound and modulated by drug molecules, with such modulation expected to produce therapeutic effects. The identification and validation of druggable proteins are central steps in modern drug discovery. With the rapid advancements in biological big data and artificial intelligence, computational methods based on bioinformatics have become essential for large-scale screening of druggable proteins. This review systematically summarizes the latest research progress in this field, providing a comprehensive analysis across three dimensions: data resources, feature engineering, and predictive models. The main contributions are 4-fold: First, we systematically describe three major types of databases: pharmacological annotation databases, quantitative affinity databases, and multi-source integrated databases, and dataset construction strategies as well as strategies for handling imbalanced samples are explored. Second, multi-level feature representations across four dimensions are categorized into sequencederived features, physicochemical properties, evolutionary information, and structural information, and the corresponding methods and tools are summarized. Third, we provide a detailed review of the evolution and application paradigms of predictive models, from classical machine learning and ensemble learning to deep learning architectures and protein large language models. Fourth, based on a systematic evaluation of representative studies, current limitations in areas such as few-shot learning and model interpretability are highlighted, and offer a forward-looking perspective on future research directions. This review aims to serve as a comprehensive reference for druggable protein prediction algorithms and to provide a solid theoretical and technical roadmap for computationally driven discovery of novel drug targets.

Pediatric voice disorders are common conditions that may significantly affect communication, psychosocial development, and quality of life in children. Despite their clinical importance, considerable variation exists in diagnostic approaches and treatment strategies, partly due to difficulties in performing invasive examinations and limited high-quality evidence. To address these challenges, the Korean Society of Laryngology, Phoniatrics and Logopedics developed evidence-based clinical practice guidelines for the diagnosis and management of pediatric voice disorders. These guidelines were developed de novo through a systematic literature review addressing predefined key clinical questions related to pediatric vocal fold mucosal lesions and vocal fold paralysis. Searches were conducted in Medline, Embase, the Cochrane Library, and KoreaMed for studies published up to May 31, 2025. Evidence was critically appraised using standardized tools according to study design, and the certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Recommendations were formulated by considering the balance of benefits and harms, quality of evidence, patient values and preferences, clinical feasibility, and resource implications. A total of nine key clinical questions were addressed, covering diagnostic strategies, voice assessment, noninvasive imaging, voice therapy, surgical intervention, and postoperative management in children with voice disorders. Most recommendations were conditional due to low or very low certainty of evidence, reflecting current limitations in pediatric voice research. These guidelines aim to support consistent, patient-centered clinical decision-making, reduce unwarranted practice variation, and promote optimal outcomes in the care of children with voice disorders.

Melanoma remains a highly lethal malignancy, often associated with limited therapeutic efficacy and substantial systemic toxicity caused by conventional chemotherapy. Triptolide (TP) exhibits potent antitumor activity, but its clinical application is limited by poor aqueous solubility and a narrow therapeutic window. In this study, folate-decorated Pluronic nanomicelles (TP-FPNPs) were developed to enhance TP delivery. The resulting spherical micelles, with an average diameter of 152 nm, exhibited an encapsulation efficiency of 79% and sustained drug release for up to 48 h. By targeting folate receptors (FRs), TP-FPNPs increased cellular uptake in B16F10 and reduced the IC50 to 18.94 nm (2.08-fold decrease). Conversely, the IC50 in HUVECs increased from 19.51 to 37.89 nm (1.94-fold), indicating reduced toxicity toward normal endothelial cells. Mechanistically, TP-FPNPs downregulated glutathione peroxidases (GPx), increased intracellular Reactive Oxygen Species (ROS) levels, and promoted apoptosis. In vivo, TP-FPNPs achieved a tumor inhibition rate of 89.6% with negligible toxicity, significantly outperforming free TP. These results suggest that TP-FPNPs are a promising targeted delivery system that improves the therapeutic index of TP for melanoma therapy.

Chiral halide perovskites are promising materials for circularly polarized light (CPL) optoelectronics owing to their strong light absorption, long carrier diffusion lengths, and intrinsic chiroptical activity. However, most reported CPL perovskite devices still suffer from limited chiroptical selectivity and poor operational stability, hindering practical applications. Here, we report a facile interfacial passivation strategy using an atomic layer deposition (ALD)-grown AlF3 film for high-performance and stable CPL photodetectors (PDs). The dense AlF3 underlayer modifies the local interfacial environment of the perovskite, promotes grain growth, and reduces defect sites in the chiral perovskite films. Consequently, the AlF3-based films exhibit enhanced apparent chiroptical properties, with circular dichroism (CD) and circularly polarized photoluminescence (CPL-PL) g-factors increasing by approximately 3.8-fold and 2-fold, respectively, compared with pristine films. CPL PDs fabricated on the AlF3 underlayer achieve a photoresponsivity of 54 mA W-1, a specific detectivity of 1.68 × 1011 Jones, and a photocurrent g-factor of 0.48, representing an 84% enhancement over pristine devices. Interfacial AlF3 passivation also significantly improves device durability, providing a T70 operational lifetime more than 9.3 times longer under ambient conditions. This work demonstrates an effective interface-engineering strategy for simultaneously enhancing chiroptical selectivity, device performance, and operational stability in CPL perovskite optoelectronics.

Multimodal diagnostic strategies and risk stratification are essential for improving early lung cancer detection, clinical outcomes, and personalized interventions. To develop a predictive model for lung cancer incidence by integrating plasma proteomics, genetic factors, and demographic data. We analyzed data from the UK Biobank, which included 47,655 adults without a history of lung cancer at baseline, with 641 incident cases and a median follow-up duration of 13.87 years. COX proportional hazards modeling with SHapley Additive exPlanations (SHAP) sequential feature selection was used to identify significant plasma proteins associated with lung cancer. An XGBoost-based survival model was constructed and evaluated to assess its predictive utility. Proteomic risk score (ProRS), demographic, and Combined risk scores were developed to evaluate their predictive capabilities across genetic risk strata. Among 2911 plasma proteins, we identified 15 significant plasma proteins associated with the development of lung cancer. The XGBoost survival model demonstrated promising predictive performance, achieving a C-statistics of 0.803. Risk stratification revealed that high-risk groups had up to a 101-fold increased risk of lung cancer compared to low-risk groups. We analyzed expression patterns of 15 carcinogenesis-associated proteins during the 15-year period preceding diagnosis. TNR and CLEC3B were identified as the earliest dysregulated proteins, with expression levels consistently downregulated beginning as early as 14 years before diagnosis. GDF15 exhibited sustained and pronounced upregulation throughout the entire 14-year observation window. Integrating plasma proteomics, polygenic risk scores, and demographic factors improves personalized lung cancer risk stratification, offering potential for refined screening strategies.

Microfluidic paper-based analytical devices (µPADs) have gained attention for clinical diagnostics due to low cost, simplicity, and suitability for point-of-care testing. However, high reagent and sample consumption, limited analytical efficiency, and non-optimized designs still restrict broader use. To overcome these limitations, we propose a miniaturized and re-optimized µPAD design for colorimetric dopamine that reduces the required assay time and minimizes reagent and sample consumption while maintaining reproducible analytical performance in biological samples. To achieve this goal, six distinct µPAD geometries were designed and evaluated to examine the effect of device architecture on colorimetric signal development, followed by optimization of the selected geometry and reaction conditions. Based on its temporal color development profile and signal intensity, the most suitable configuration was selected for further optimization This optimized platform was further evaluated or reagent consumption, response time, reproducibility, and analytical performance in biological samples. The optimized miniaturized µPAD design achieved more than a six-fold reduction in reagent usage and a two-minute decrease in drying time. The optimized configuration showed an LOQ of 0.0517 µmol/L in buffer, while the selectivity experiments showed signal deviations within ± 3% for the tested interferents. Furthermore, validation in spiked human serum and plasma samples yielded recoveries of 101-102.5%, supporting the feasibility of the proposed µPAD for dopamine analysis in complex biological matrices. Overall, these findings demonstrate the potential of the proposed design as a low-volume and reagent-efficient platform for point-of-care colorimetric sensing and provide a practical basis for further evaluation toward preliminary clinical screening applications.

Sarcopenia poses a significant health burden in aging populations, particularly among patients with rheumatoid arthritis (RA), but current diagnostic approaches face limitations in early detection. This study aims to identify plasma-derived exosomal miRNA features associated with low muscle mass in Tibetan patients with RA. High-throughput sequencing identified 101 candidate dysregulated miRNAs in Tibetan patients with RA and low muscle mass, including 61 upregulated and 40 downregulated miRNAs, using exploratory thresholds of |log2 fold change| ≥ 0.58 and nominal P < 0.05. Most candidates did not remain significant after Benjamini-Hochberg false discovery rate correction; therefore, the broader candidate set was considered exploratory. Machine learning algorithms consistently identified hsa-miR-1294 and hsa-let-7c-5p as candidate miRNA features. Integrated miRNA-mRNA regulatory network analysis retained 50 candidate target genes for the construction of two direction-specific putative interaction networks involving 43 miRNAs. Functional enrichment analysis identified nominally enriched metabolic pathways, including sphingolipid metabolism and oxidative phosphorylation, while immune microenvironment analysis revealed differences in estimated muscle satellite-cell and mast-cell enrichment scores. Internally evaluated miRNA-based and target gene-based nomogram models both showed preliminary apparent discrimination, with an area under the receiver operating characteristic curve of 0.905 in their respective development datasets. Exploratory computational drug screening identified several candidate compounds for future investigation, including deferasirox, acarbose, taxifolin, pterostilbene, and ercalcitriol. This study prioritized hsa-miR-1294 and hsa-let-7c-5p as candidate exosomal miRNA features associated with low muscle mass in Tibetan patients with RA. These findings provide hypothesis-generating evidence for the further evaluation of minimally invasive miRNA features associated with low muscle mass.

This review examines the transport, fate, modelling, and mitigation of Microplastics (MPs) in urban stormwater infrastructure, with emphasis on pavements, runoff pathways, micro-drainage, and macro-drainage systems. Following a systematic review approach, more than 1000 records were screened and approximately 50 core studies were retained when they addressed urban stormwater or drainage-related MP transport with adequate methodological reporting; marine-only studies and biological-effect studies without direct relevance to transport processes were excluded. The evidence shows that stormwater systems function not merely as passive conduits but as dynamic reactive transport systems with temporary storage, where particle mobilisation, sedimentation, resuspension, and temporary retention regulate MP export. Road surfaces, especially high-traffic areas, are major reservoirs of tyre wear, road-marking, atmospheric, and litter-derived particles that are rapidly mobilised during rainfall. Conventional grab sampling may underestimate MP loads, which in some cases exceed treated wastewater effluent loads by up to six-fold. Drainage structures such as manholes can immobilise up to 17.3% of near-neutrally buoyant particles, while biofouling and aggregation may shift buoyant polymers from wash-load to bedload. Mitigation systems, including permeable pavements, bioretention, wetlands, and technical inserts, can achieve high removal of coarse MPs, but performance declines for fine particles below 100 µm. The review highlights the need for standardised flow-proportional sampling, physically informed modelling, and treatment-train strategies targeting both surface sources and in-network storage.