Faculty Bibliography

BACKGROUND & AIM/OBJECTIVE:Accurate extraction of clinical and methodological features from published literature is essential for supporting evidence-based cardiovascular rehabilitation. With the rapid expansion of research outputs, manual data extraction is time-consuming and prone to errors. Large language models (LLMs) may help automate evidence synthesis tasks, but their performance in document extraction for cardiovascular rehabilitation nursing research remains unclear. We aimed to evaluate the ability of LLMs to extract key literature features in the field of cardiovascular exercise rehabilitation nursing. METHOD/METHODS:A diagnostic accuracy study was conducted using a reference standard established by two independent experts in cardiovascular rehabilitation. A structured extraction framework covering study characteristics, participant profiles, intervention components, and outcome domains was applied. LLM outputs were compared with the reference standard, and sensitivity, specificity, and area under the curve (AUC) were calculated. RESULTS:LLMs demonstrated high sensitivity (0.96-0.98) in identifying essential study features, indicating a low risk of missed information. Specificity ranged from moderate to high (0.70-0.77) and improved notably after incorporating nursing-specific chain-of-thought prompting. Overall diagnostic performance was excellent, with a post-hoc analysis AUC reaching 0.98. Error patterns were primarily related to ambiguous intervention descriptions and multi-component rehabilitation programs. CONCLUSIONS:LLMs show strong potential as an efficient and accurate tools for automating literature feature extraction in cardiovascular exercise-based rehabilitation nursing. Their use may enhance the speed and reliability of evidence synthesis, and support clinical decision-making. Further optimisation of prompting strategies and domain-specific tuning is warranted before widespread clinical adoption.

Non-homologous end-joining (NHEJ) is a major double-stranded DNA (dsDNA) break repair pathway essential for V(D)J recombination during lymphocyte development. The Ku70/Ku80 heterodimer (Ku) initiates NHEJ by encircling dsDNA ends and recruiting DNA-PKcs. Ku70 in plants and mammals acquired a C-terminal SAP domain implicated in nucleic acid binding. Here, we show that in murine models, the SAP domain is dispensable for Ku stability and recruitment to DNA breaks. Unlike Ku70-/- mice, Ku70ΔSAP/ΔSAP mice exhibit normal lymphocyte development despite mild radiation sensitivity. Structural modeling places the SAP domain in adjacent DNA grooves, where it can restrict Ku's lateral movement along dsDNA. Correspondingly, in mice lacking DNA-PKcs that caps the ends, Ku70ΔSAP reduces T cell counts and deletion sizes, consistent with Ku translocating off DNA. Moreover, SAP deletion reduced DNA-end affinity, increased dissociation, and exchange of purified Ku at low concentrations, and increased multiple-loading at high concentrations, consistent with increased lateral movement. In DNA-PKcs-/- murine fibroblasts, deletion or lysine mutation (K593/4A, corresponding to K595/6A in human Ku70) in the SAP domain decreased the relative intensity of laser-induced Ku spots, revealing a role of the SAP domain in constraining Ku lateral movement on dsDNA in the absence of DNA-PKcs (or in the short-range complex).

Polar surfaces, known for their unique electronic and chemical properties, play vital roles in catalysis and electronics, yet their atomic structures remain difficult to resolve due to intrinsic instability often mitigated by surface reconstruction. However, the role of entropy, particularly vibrational entropy, in stabilizing such reconstructions is not well understood. Herein, we combine in situ atomic-resolution spherical aberration-corrected scanning transmission electron microscopy, density-functional theory calculations, and a differential evolution algorithm for global structural search to resolve the (1×1) reconstructed structure of the classical polar rutile TiO_{2}(111) surface. Crucially, we demonstrate that this reconstruction is predominantly stabilized by vibrational entropy at high temperatures. These findings not only highlight the importance of vibrational entropy in surface reconstructions but also advance our understanding of compensation mechanisms on polar surfaces by highlighting entropy as a critical stabilizing factor.

BACKGROUND:Effective screening and cohort enrichment remain major challenges in clinical trials for Alzheimer's disease (AD), where traditional diagnostic pathways rely on costly, invasive, and time-consuming procedures. Speech and language analysis has emerged as a scalable, low-burden approach for detecting subtle cognitive-linguistic and motor-speech changes that may appear early in the disease course. SUMMARY/CONCLUSIONS:This review synthesizes current evidence on acoustic, prosodic, lexical, semantic, and syntactic speech features associated with AD and mild cognitive impairment (MCI) and evaluates their reported utility across a range of elicitation tasks including picture description, verbal fluency, narrative recall, spontaneous speech, and reading. Across studies, machine-learning models trained on speech and language features have reported consistent performance, although results vary substantially depending on task design, feature sets, and cohort characteristics. Task-dependent variability is evident, with picture description and verbal fluency tasks capturing lexical-semantic and timing markers, while narrative and spontaneous speech tasks capture impairments in coherence, information content, and prosody. Hybrid approaches integrating hand-crafted and machine-extracted features have also been explored to improve interpretability and model performance. Speech and language analytics may support digital prescreening, cohort enrichment, and quality-assurance monitoring within clinical trials; however, their application depends on methodological considerations and validation across diverse settings. KEY MESSAGES/CONCLUSIONS:Despite encouraging findings, several methodological challenges persist, including interindividual variability, limited dataset sizes, differences in recording conditions, and limitations in automatic speech recognition performance in cognitively impaired populations. Continued development of standardized protocols, disorder-adapted speech models, and multimodal analytic pipelines is needed to support clinical translation. Collectively, current evidence suggests that speech and language features represent candidate digital markers that may improve screening efficiency and support clinical trial enrichment in AD, although further validation is required to establish their reliability and generalizability.

Diabetic chronic wounds are difficult to heal because of persistent infection, oxidative stress, inflammation, and hyperglycemia. Herein, a bilayer multifunctional dressing (GCP/GAEu@H) was developed for wound monitoring and diabetic wound repair. The lower layer comprised a glucose- and pH-responsive self-healing hydrogel formed from phenylboronic acid-modified chitosan (CS-PBA) and oxidized hyaluronic acid (OHA) through dynamic boronate ester and Schiff base linkages. The upper layer was a glutaraldehyde-crosslinked chitosan/poly(vinyl alcohol) (CS/PVA) electrospun nanofibrous membrane. This bilayer configuration increased the tensile strength to 278.94 kPa, provided strong resistance to compressive fatigue, and preserved structural integrity over 50 compression cycles at 60% strain. The incorporated GA-loaded Eu-MOF (GAEu) clusters supplied pH-sensitive fluorescence for real-time assessment of wound status and enabled acid-responsive release of active species. Antibacterial efficiencies against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) exceeded 99%, and 70% of 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals were scavenged within 30 min. In addition, it displayed good hemocompatibility and cytocompatibility. Animal experiments revealed enhanced collagen deposition and angiogenesis, together with 98.8% wound closure by day 12. These findings offer an alternative route for designing intelligent dressings for diabetic wounds.

BACKGROUND:Anticancer treatments are associated with cutaneous adverse events (cAEs) that can severely impact patients' quality of life (QoL) and interfere with treatment outcomes. LACOM aims to support clinicians in preventing and managing cAEs to optimize patient outcomes. METHODS:A panel of dermatologists, clinical oncologists, and radiation oncologists developed an evidence-based algorithm for the prevention and treatment of cancer treatment-related cAEs using a skincare regimen that includes hygiene, moisturization, sun protection, and camouflage products. RESULTS:The LACOM II algorithm discusses patient education before cancer treatment, appropriate skincare, triage, and the importance of treating emerging cAEs with a multidisciplinary team. CONCLUSIONS:Integrating proactive education, safe and effective skincare, triage, and reaction-specific management of cAEs is essential to optimize the care of patients living with cancer. The LACOM II algorithm provides evidence- and opinion-based best-practice recommendations to support clinicians working with oncology patients throughout the continuum of care, achieving optimal outcomes and improving patients' QoL. &nbsp.

Automatic segmentation of medical ultrasound images is critical for computer-aided diagnosis (CAD). However, most existing methods primarily exploit within-image semantics while neglecting cross-image semantic modeling. By leveraging cohort-level shape and echotexture priors, cross-image semantic modeling improves robustness against inter-patient and inter-scanner variations. Nevertheless, positional correlations in cross-image scenarios may incorrectly map foreground features from previous images onto background regions of the current one, causing semantic conflicts that degrade segmentation reliability. Moreover, many methods emphasize global context while overlooking fine-grained lesion information. To address these limitations, we propose Multi-granularity Cross-image Semantic Modeling (MCSM), where multi-granularity denotes the joint modeling of global (whole-image) and local (lesion-centric local patch) representations. Specifically, we first design a Multi-Granularity Patch Extraction Process (MGPEP) that extracts lesion patches and fuses them with whole-image features to strengthen cross-image semantics. We then propose a Frequency-domain Feature Dependency Module (FFDM) that performs multi-granularity spectral filtering to capture cross-image dependencies. During training, features from preceding batches (i.e., previous images) are mapped to the frequency domain to extract latent semantic correlations and are integrated into current batches (i.e., current image) representation, thereby enhancing foreground-background discrimination. In addition, we employ dual-mean filtering to suppress high-frequency noise in the global spectrum. Extensive experiments demonstrate the effectiveness and robustness of MCSM over state-of-the-art methods, highlighting its potential for clinical application. Code and models are available at https://github.com/XyL215/MCSM.

Boiogito (BOT), a traditional Kampo herbal medicine, has been reported to exhibit anti-inflammatory and anti-obesity properties. However, its potential role in protecting adipocyte function under inflammatory conditions at different stages of adipocyte development remains unclear. This study investigated the effects of BOT on adipogenesis and tumor necrosis factor-α (TNF-α)-induced inflammatory responses in differentiating and mature 3T3-L1 adipocytes. In this study, 3T3-L1 preadipocytes were induced to differentiate and exposed to TNF-α in the presence or absence of BOT during differentiation or after full adipocyte maturation. Lipid accumulation was assessed by Oil Red O staining, while adipokine secretion and inflammatory cytokine production were evaluated by ELISA. The expression of adipogenic markers and inflammatory signaling molecules was analyzed using quantitative PCR and Western blotting. TNF-α significantly inhibited the expression of adipogenesis-related factors at the transcriptional level in adipocytes, reduced lipid accumulation and adiponectin expression, and enhanced inflammatory cytokine production. BOT treatment dose-dependently attenuated these effects, restoring adipogenic capacity and suppressing inflammatory responses in both differentiating and mature adipocytes. Mechanistically, BOT reduced TNF-α-induced activation of the NF-κB pathway, as evidenced by decreased phosphorylation of NF-κB p65 and IκB. These findings demonstrate that BOT preserves adipocyte function and mitigates inflammation-associated adipocyte dysfunction throughout adipocyte development. The protective effects of BOT may contribute to the regulation of obesity-associated metabolic inflammation, partly through modulation of NF-κB signaling.