Neurological disorders such as post-stroke hemiplegia, spinal cord injury, and Parkinson disease represent a major global health burden. Brain-computer interface (BCI), which creates direct communication pathways between the nervous system and external devices, offers a promising strategy for functional restoration. The long-term efficacy of such BCI fundamentally depends on the performance of biomaterials at the neural interface. Ideal materials must concurrently satisfy biocompatibility, electrical conductivity, enduring chemical stability, and mechanical compatibility with brain tissue. This review systematically outlines the application of conductive polymers, inorganic nanomaterials, natural biomaterials, and composites in BCI, with a focus on how advanced designs, such as bionic and encapsulated electrodes, improve signal fidelity and surgical feasibility through structural innovation. It further summarizes key material-modification techniques and analyzes the complex foreign-body response orchestrated by microglia, astrocytes, and peripheral immune cells. Finally, it provides insights into future research directions and clinical translation of BCI-based neurorehabilitation, while highlighting critical challenges including long-term biosafety and the establishment of standardized evaluation frameworks, aiming to bridge the gap between laboratory innovation and effective clinical deployment.

Curzerene, a sesquiterpene compound isolated from Curcuma Radix, exhibits various therapeutic effects, such as anti-tumor and anti-hyperlipidemic properties. However, its neuroprotective effects have not yet been reported. This study focused on exploring the neuroprotective effect of curzerene and elucidating its potential mechanism by combining molecular biotechnology with multi-omics approaches. Curzerene was orally administered to LPS-induced depressive-like behaviors and cognitive impairment in mice for 14 days, and the related biochemical parameters were evaluated. The possible mechanisms were elucidated using qRT-PCR, Western Blot, immunofluorescence, untargeted metabolomics, GC-MS and 16S rDNA comprehensively. Curzerene ameliorated depression symptoms and cognitive impairment by increasing the preference for sucrose in SPT and the central area and total distance traveled in OFT, reducing the immobility time in TST and FST, as well as rising the spontaneous alternation ratio in Y maze. Multiple molecular biology techniques analyses indicated the ameliorative effect of curzerene via HMGB1/RAGE/ TLR4 pathway. Moreover, curzerene primarily regulates purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, pyrimidine metabolism, etc. Furthermore, intervention increased the relative abundance of Parabacteroides, Clostridia_UCG-014_unclassified, and Rhodospirillales_unclassified, and enhanced the production of SCFAs. This work demonstrated that curzerene effectively protects against LPS-induced neurological damage, potentially by inhibiting the HMGB1/ RAGE/TLR4 pathway through the restoration of gut microbiota homeostasis, modulation of metabolites, and enhancement of SCFAs. In conclusion, this study offers new perspectives on the therapeutic possibilities of curzerene in mitigating depressive-like behaviors and cognitive impairment.

Lung adenocarcinoma (LUAD) is a global malignancy with significant chemoresistance impacting patient prognosis. The pro-tumorigenic role of hyaluronan-mediated motility receptor (HMMR) in LUAD is recognized. This study was designed to investigate the underlying mechanisms by which HMMR affects chemoresistance in LUAD. Bioinformatics presented the expression patterns of HMMR in LUAD patients and the association between HMMR levels and patient survival, followed by qRT-PCR to verify HMMR expression in LUAD tissues and cells. Further, bioinformatics was leveraged to identify the signaling pathways enriched by HMMR and its relevance to glycolytic genes, we also analyzed changes in the glycolytic activity of LUAD cells by manipulating HMMR expression. Stemness was evaluated through cell aggregation assays and Western blot, and drug responsiveness was gauged using CCK-8 assays, alongside flow cytometry for apoptosis analysis. HMMR was highly expressed in LUAD tissues and cells, and this overexpression correlated with poorer prognoses in patients. GSEA showed that HMMR was notably enriched in the glycolysis and gluconeogenesis pathways, correlating positively with the expression of key glycolytic genes. Cellular experiments confirmed that HMMR knockdown notably suppressed aerobic glycolysis in LUAD cells. Moreover, overexpression of HMMR could further enhance the stemness and cisplatin resistance of LUAD cells by stimulating glycolysis. In brief, this study has validated that high levels of HMMR in LUAD are predictive of poor patient prognosis, and that overexpression of HMMR can catalyze aerobic glycolysis, thus promoting stemness and chemoresistance in LUAD cells. Thus, HMMR could be a target for improving chemosensitivity in LUAD.

Lung adenocarcinoma (LUAD) is a global malignancy with significant chemoresistance impacting patient prognosis. The pro-tumorigenic role of hyaluronan-mediated motility receptor (HMMR) in LUAD is recognized. This study was designed to investigate the underlying mechanisms by which HMMR affects chemoresistance in LUAD. Bioinformatics presented the expression patterns of HMMR in LUAD patients and the association between HMMR levels and patient survival, followed by qRT-PCR to verify HMMR expression in LUAD tissues and cells. Further, bioinformatics was leveraged to identify the signaling pathways enriched by HMMR and its relevance to glycolytic genes, we also analyzed changes in the glycolytic activity of LUAD cells by manipulating HMMR expression. Stemness was evaluated through cell aggregation assays and Western blot, and drug responsiveness was gauged using CCK-8 assays, alongside flow cytometry for apoptosis analysis. HMMR was highly expressed in LUAD tissues and cells, and this overexpression correlated with poorer prognoses in patients. GSEA showed that HMMR was notably enriched in the glycolysis and gluconeogenesis pathways, correlating positively with the expression of key glycolytic genes. Cellular experiments confirmed that HMMR knockdown notably suppressed aerobic glycolysis in LUAD cells. Moreover, overexpression of HMMR could further enhance the stemness and cisplatin resistance of LUAD cells by stimulating glycolysis. In brief, this study has validated that high levels of HMMR in LUAD are predictive of poor patient prognosis, and that overexpression of HMMR can catalyze aerobic glycolysis, thus promoting stemness and chemoresistance in LUAD cells. Thus, HMMR could be a target for improving chemosensitivity in LUAD.

Lung adenocarcinoma (LUAD) is a global malignancy with significant chemoresistance impacting patient prognosis. The pro-tumorigenic role of hyaluronan-mediated motility receptor (HMMR) in LUAD is recognized. This study was designed to investigate the underlying mechanisms by which HMMR affects chemoresistance in LUAD. Bioinformatics presented the expression patterns of HMMR in LUAD patients and the association between HMMR levels and patient survival, followed by qRT-PCR to verify HMMR expression in LUAD tissues and cells. Further, bioinformatics was leveraged to identify the signaling pathways enriched by HMMR and its relevance to glycolytic genes, we also analyzed changes in the glycolytic activity of LUAD cells by manipulating HMMR expression. Stemness was evaluated through cell aggregation assays and Western blot, and drug responsiveness was gauged using CCK-8 assays, alongside flow cytometry for apoptosis analysis. HMMR was highly expressed in LUAD tissues and cells, and this overexpression correlated with poorer prognoses in patients. GSEA showed that HMMR was notably enriched in the glycolysis and gluconeogenesis pathways, correlating positively with the expression of key glycolytic genes. Cellular experiments confirmed that HMMR knockdown notably suppressed aerobic glycolysis in LUAD cells. Moreover, overexpression of HMMR could further enhance the stemness and cisplatin resistance of LUAD cells by stimulating glycolysis. In brief, this study has validated that high levels of HMMR in LUAD are predictive of poor patient prognosis, and that overexpression of HMMR can catalyze aerobic glycolysis, thus promoting stemness and chemoresistance in LUAD cells. Thus, HMMR could be a target for improving chemosensitivity in LUAD.

Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model's applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.

Objective To develop and validate an early venous thromboembolism(VTE)risk assessment scale for emergency observation patients.Methods ① Based on the characteristics of emergency observation patients,the Delphi expert consultation method and literature review were used to determine scale items and construct a scoring system.②The newly developed VTE scale and its scoring system were analyzed for reliability and validity.③Primary application:collect non acute traumatic observation and rescue patients admitted to Zhejiang Hospital from June 2022 to June 2023 as the research subjects.Patients were divided into survival and non-survival groups based on 28-day outcomes.Differences in VTE scores between the two groups using the new scale,Caprini,and Padua models were compared.The optimal cut-off point was determined using the receiver operator characteristic curve(ROC curve),according to the optimal cut-off value of the new scale score,patients were divided into two groups,and Kaplan-Meier survival curves were used to analyze the 28-day cumulative survival of the two groups of patients.Results ①The preliminary version of the early VTE risk assessment scale for emergency observation patients was developed,comprising 8 items:age,pre examination triage level,underlying diseases,D-dimer levels,activities of daily living(ADL)assessment,coagulation-related indicators,anticoagulants and(or)antiplatelet drugs use,and unhealthy habits.② A total of 121 emergency observation patients were included in the analysis.The test-retest reliability correlation coefficient(R)of the new scale was 0.945(>0.850),split-half reliability was 0.741(>0.700),and Cronbach'sαcoefficient was>0.700.KMO value was 0.715(>0.700),and Bartlett's sphericity test yieldedχ2=167.079,P<0.001,confirming the suitability of the scale for factor analysis.Three factors were identified:basic information,initial assessment,and blood test indicators.Pearson correlation analysis showed the correlation coefficients between the new scale and the Caprini and Padua scores were 0.842 and 0.307,respectively,both P<0.01.③Area under the curve(AUC)of the new scale was 0.566,95%confidence interval(95%CI)was 0.444-0.688,with an optimal diagnostic cut-off value of 13.5 points based on the maximum Youden index.The results of the Kaplan-Meier regression indicated that survival analysis using the 13.5-point cut-off revealed that patients with scores≥13.5 had significantly lower 28-day survival rates than those with scores<13.5(Log-Rank test:χ2=5.609,P=0.018).④The survival group had significantly lower scores than the non-survival group across all scales(new score:10.06±2.84 vs.12.69±3.06,Caprini model:7.22±2.48 vs.9.41±2.64,Padua model:2.91±1.97 vs.4.59±1.07,all P<0.05).Conclusion The early VTE risk assessment scale for emergency observation patients was successfully developed,demonstrating good reliability and validity through statistical analysis.The new scale effectively predicts disease severity and prognosis in emergency observation patients.

Objective:To evaluate the accuracy of the oral healthcare information provided by different large language models (LLM) to explore their feasibility and limitations in the application of oral auxiliary, treatment and health consultation.Methods:This study designed eight items comprising 47 questions in total related to the diagnosis and treatment of oral diseases [to assess the performance of LLM as an artificial intelligence (AI) medical assistant], and five items comprising 35 questions in total about oral health consultations (to assess the performance of LLM as a simulated doctor). These questions were answered individually by the five LLM models (Erine Bot, HuatuoGPT, Tongyi Qianwen, iFlytek Spark, ChatGPT). Two attending physicians with more than 5 years of experience independently rated the responses using the 3C criteria (correct, clear, concise), and the consistency between the raters was assessed using the Spearman rank correlation coefficient, and the Kruskal-Wallis test and Dunn post hoc test were used to assess the statistical differences between the models. Additionally, this study used 600 questions from the 2023 dental licensing examination to evaluate the time taken to answer, scores, and accuracy of each model.Results:As an AI medical assistant, LLM can assist doctors in diagnosis and treatment decision-making, with an inter-evaluator Spearman coefficient of 0.505 ( P<0.01). As a simulated doctor, LLM can carry out patient popularization, with an inter-evaluator Spearman coefficient of 0.533 ( P<0.01). The 3C scores of each model as an AI medical assistant and a simulated doctor were respectively: 2.00 (1.00, 3.00) and 2.00 (2.00, 3.00) points of Erine Bot, 1.00 (1.00, 2.00) and 2.00 (1.00, 2.00) points of HuatuoGPT, 2.00 (1.00, 2.00) and 2.00 (1.00, 3.00) points of Tongyi Qianwen, 2.00 (1.00, 2.00) and 2.00 (1.75, 2.25) points of iFlytek Spark, 3.00 (2.00, 3.00) and 3.00 (2.00, 3.00) points of ChatGPT (full score of 4 points). The Kruskal-Wallis test results showed that, as an AI medical assistant or a simulated doctor, there were statistically differences in the 3C scores among the five large language models (all P<0.001). The average score of the 5 LLMs on the dental licensing examination was 370.2, with an accuracy rate of 61.7% (370.2/600) and a time consumption of 94.6 min. Specifically, Erine Bot took 115 min, scored 363 points with an accuracy rate of 60.5% (363/600), HuatuoGPT took 224 min and scored 305 points with an accuracy rate of 50.8% (305/600), Tongyi Qianwen took 43 min, scored 438 points with an accuracy rate of 73.0% (438/600), iFlytek Spark took 32 min, scored 364 points with an accuracy rate of 60.7% (364/600), and ChatGPT took 59 min, scored 381 points with an accuracy rate of 63.5% (381/600). Conclusions:Based on the evaluation of LLM′s dual roles as an AI medical assistant and a simulated doctor, ChatGPT performes the best, with basically correct, clear and concise answers, followed by Erine Bot, Tongyi Qianwen and iFlytek Spark, with HuatuoGPT lagging behind significantly. In the dental licensing examination, all the 4 LLM, except for HuatuoGPT, reach the passing level, and the time consumpution for answering is significantly reduced compared to the 8 h required for the exam regulations in all of the five models. LLM has the feasibility of application in oral auxiliary, treatment and health consultation, and it can help both doctors and patients obtain medical information quickly. Howere, their outputs carry a risk of errors (since the 3C scoring results do not reach the full marks), so prudent judgment should be exercised when using them.

Objective:To explore the safety and efficacy of intraosseous regional administration (IORA) of vancomycin for preventing infection in primary total knee arthroplasty (TKA).Methods:A total of 124 patients with knee osteoarthritis undergoing TKA between February 2024 and May 2024 at nine hospitals were enrolled. Preoperative infection prophylaxis involved either IORA (0.5 g vancomycin administered via intraosseous regional infusion before incision) or intravenous infusion (1 g vancomycin via peripheral vein). The IORA group included 15 males and 47 females with a median age of 66.5 years (range, 60.0-70.0 years), while the intravenous group included 14 males and 48 females with a median age of 66.0 years (range, 61.8-70.3 years) years. Intraoperative samples were collected including fat and synovium tissues after incision, before prosthesis placement, and after tourniquet release; distal femoral cancellous bone during femoral osteotomy; proximal tibial cancellous bone during tibial osteotomy; proximal intercondylar cancellous bone before prosthesis placement; and peripheral blood from non-infused arms at surgery initiation and after tourniquet release. Vancomycin concentrations were measured using liquid chromatography-tandem mass spectrometry. Vital sign changes were recorded from admission to 5~10 minutes post-IORA (IORA group) or post-incision (intravenous group). Follow-ups were conducted on postoperative day 1 and 3, and at 1 and 3 months, to document complications including IORA-related adverse events, periprosthetic joint infections, surgical site infections, red man syndrome, acute kidney injury, deep vein thrombosis and so on.Results:Vancomycin concentrations in bone, fat, and synovial tissue samples were significantly higher in the IORA group than in the intravenous group ( P<0.05), while vancomycin concentrations in blood samples were significantly lower in the IORA group than in the intravenous group ( P<0.05). Only 7.3%(41/558) of tissue samples in the IORA group had vancomycin concentrations below 2.0 μg/g (the minimum inhibitory concentration of vancomycin against coagulase-negative staphylococcus), compared to 59.3%(331/558) in the intravenous group (χ 2=11.285, P<0.001). In the intravenous group, 16.9%(21/124) of blood samples had vancomycin concentrations exceeding 15.0 mg/L (the threshold associated with a significantly increased risk of nephrotoxicity), while all concentrations in the IORA group were below this threshold, the difference was statistically significant (χ 2=22.943, P<0.001). There were no statistically significant difference ( P>0.05) in vital signs changes before and after vancomycin administration between the two groups. Two patients in the intravenous group experienced incision exudate, while no other related complications occurred in either group. Conclusions:Compared to the traditional intravenous infusion of 1 g vancomycin, intraosseous injection of a low dose (0.5 g) of vancomycin achieves higher local tissue concentrations in the knee joint with a lower incidence of adverse reactions and is safe for infection prophylaxis. Despite guidelines not recommending the routine use of vancomycin for preventing infection after primary TKA, intraosseous injection of 0.5 g vancomycin may be considered intraoperatively for primary TKA in the following scenarios: patients in medical institutions with a high prevalence of methicillin-resistant staphylococcus aureus (MRSA) infections, patients with potential preoperative MRSA colonization, or patients with cephalosporin allergy.

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.