Abnormal synaptic plasticity is an early pathological feature of Alzheimer disease (AD). Synaptic damage and dysfunction initiate neuronal degeneration and death, ultimately leading to cognitive impairment. Traditional Chinese medicine (TCM) can effectively ameliorate cognitive dysfunction through multitarget regulation of synaptic plasticity. This review summarizes the mechanisms by which TCM, including active components, single herbs, and classical formulas, modulates synaptic plasticity, offering new insights for future research and clinical applications. Relevant experimental studies published between 2020 and 2024 were retrieved from major databases, including China National Knowledge Infrastructure, the National Science and Technology Library, Wanfang Data, Elsevier, ScienceDirect, PubMed, SpringerLink, and Web of Science. Network pharmacology and bioinformatics approaches were used to predict the therapeutic effects and mechanisms of TCM on AD-related synaptic plasticity. In total, 15 TCM single herbs and 11 TCM formulas were identified as enhancing AD-related synaptic plasticity. Additionally, 15 active ingredients targeting synaptic plasticity in AD were retrieved from TCM databases over the past decade. This review provides novel perspectives and strategic directions for future AD research and therapeutic development.

BACKGROUND: Sepsis-associated acute respiratory distress syndrome (SA-ARDS) is frequently accompanied by dysregulated glucose metabolism. The stress-hyperglycemia ratio (SHR) and glycemic variability (GV) have emerged as valuable tools for assessing acute dysglycemia. However, their ability to predict disease severity and mortality in patients with SA-ARDS remains unclear. METHODS: This retrospective study included 3,243 SA-ARDS patients from the MIMIC-IV database. Patients were stratified into four phenotypes based on the cohort median SHR (1.09) and GV (58.42%). Ordinal logistic regression was used to evaluate ARDS severity, while binary logistic and Cox proportional hazards models were used to assess mortality. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC). In addition, five machine learning models with SHapley Additive exPlanations (SHAP) analysis were used to identify clinically relevant risk thresholds. External validation using the eICU database was limited to GV-related findings because of the unavailability of hemoglobin A1c (HbA1c) data. RESULTS: Compared with the low-GV/low-SHR phenotype, the high-GV/high-SHR phenotype had the strongest association with increased ARDS severity (common odds ratio [cOR] 4.37, 95% confidence intervals [95% CI]: 3.60-5.30, P<0.001) and markedly elevated 28-day mortality (hazard ratio [HR] 12.4, 95% CI: 7.95-19.30, P<0.001). However, compared with conventional clinical scores, the combined assessment of the SHR and GV had superior performance in mortality prediction (all AUC=0.748). Furthermore, machine learning and SHAP analyses revealed a non-linear increase in mortality risk when GV exceeded approximately 39.1%. CONCLUSION: Combined assessment of the SHR and GV may provide prognostic information for patients with SA-ARDS. A high-GV/high-SHR phenotype may indicate a high risk of poor prognosis.

Objective To study the photodynamic performance and the killing effect of photodynamic therapy on lung cancer of novel chlorin compounds 2-（4-（5,15,20-triphenyl-7H,8H-porphyrin-10-yl） phenoxy） acetic acid（D1）and 4-（4-（5,15,20-triphenyl-7H,8H-porphyrin-10-yl） phenoxy） butanoic acid （D2）. Methods The ultraviolet visible absorption spectrum and fluorescence spectrum of D1 and D2 were determined. The singlet oxygen generation capacity of D1 and D2 was measured by using DPBF as singlet oxygen capture agent. Fluorescence assay was used to detect the cellular phagocytosis rate of the compounds in A549 cells, and MTT assay was used to detect their dark toxicity and phototoxicity. A nude mouse model of lung cancer was established to investigate the antitumor activity of the compounds mediated photodynamic action in vivo, and the blood concentration of D2 in nude mice, its distribution in tumor tissue and skin tissue were further detected. Results D1 and D2 had strong absorption at 652 nm with the best excitation wavelength at 429 nm and 427 nm, and the optimal emission wavelength was at about 659 nm. They also had a higher singlet oxygen generation rate than the control drug m-THPC. D1 and D2 had no dark toxicity at concentrations below 10 μmol/L, and could be ingested by A549 cells, basically reaching saturation in 18~24 hours. After laser irradiation at 650 nm wavelength, D1 and D2 showed significant antitumor activity in vivo and in vitro （P<0.01）. However, D2 could selectively accumulate in tumor tissues after administration, and the optimal treatment time was less than 30 min after administration. Conclusion D2 had excellent photodynamic antitumor activity and could selectively aggregate in tumor tissues, which had the potential to be a candidate drug for photosensitizer and treatment of lung cancer with independent intellectual property rights, and was worth further research.

Antimicrobial resistance (AMR) poses a critical global health threat. This review systematically examines AMR surveillance technology advances, from conventional culture methods to modern molecular diagnostics (e.g., whole-genome sequencing) and artificial intelligence-assisted approaches. It focuses on the current application of mass spectrometry, machine learning predictive models, and real-time surveillance networks. To address challenges including inadequate technical standardization, clinical translation barriers, and data-sharing limitations, we propose integrated ^"genotype-phenotype^" strategies and global standardization framework, while exploring future applications of CRISPR-based portable detection, single-cell sequencing, and blockchain technologies.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

OBJECTIVE To establish a method for simultaneous determination of venetoclax， busulfan and voriconazole in plasma of patients with acute myeloid leukemia （AML）， and apply it clinically. METHODS Plasma samples were subjected to protein precipitation using acetonitrile and subsequently analyzed by liquid chromatography-tandem mass spectrometry （LC-MS/MS） using venetoclax-D 8 ， busulfan-D 8 and posaconazole as internal standards. The separation was performed on a Phenomenex Kinetex ® C 18 column with a mobile phase composed of 0.1% formic acid solution （2 mmol/L ammonium acetate）-0.1% formic acid in acetonitrile （gradient elution） at a flow rate of 0.8 mL/min. The column temperature was set at 40 ℃， the sample size was 5 μL， and the total run time was 3.10 min. An electrospray ionization source was employed， and positive ion scanning was conducted using multiple reaction monitoring mode. The ion pairs used for quantitative analysis included m/z 868.4→636.3 （venetoclax）， m/z 264.1→151.1 （busulfan）， and m/z 350.1→224.0 （voriconazole）. The above LC-MS/MS method was adopted to determine plasma concentrations of venetoclax and voriconazole in 10 AML patients， as well as plasma concentration of busulfan in 5 patients undergoing conditioning treatment for allogeneic hematopoietic stem cell transplantation. RESULTS The linear ranges of venetoclax， busulfan and voriconazole were 50-10 000， 15-3 000 and 50-10 000 ng/mL， respectively （ R 2 ≥0.999 0）， with lower limits of quantification of 50， 15 and 50 ng/mL， respectively. The RSDs of intra-day and inter-day precision tests for all three analytes were all less than 10%， with accuracy （relative errors） ranging from －10.00% to 12.96%. The average extraction recovery ranged from 92.54% to 100.95%， and the average matrix effect was 89.98%-101.49%. Dilution reliability covered all dilution factors used in the test samples， and the absolute values of relative errors in stability tests were all≤16.25%. The plasma concentrations of venetoclax， busulfan and voriconazole in enrolled patients were 496.20-4 250.45， 233.48-2 002.28 and 475.51-5 710.18 ng/mL， respectively. CONCLUSIONS The LC-MS/MS method established in this study is rapid， sensitive and easy to operate， and can be used for the therapeutic drug monitoring of venetoclax， busulfan and voriconazole.

ObjectiveTo investigate the optimal termination time for acupuncture in treating patients with acute peripheral facial paralysis and its cost-effectiveness. MethodsA total of 120 eligible patients with acute-stage peri-pheral facial paralysis were randomly assigned to either the mild dysfunction termination group and the complete recovery termination group， with 60 patients in each group. Both groups received the standard acupuncture treatment protocol. Treatment in the mild dysfunction termination group was terminated when the Sunnybrook facial grade scale （SFGS） score first reached or exceeded 83 points， while that in the complete recovery termination group was terminated when the SFGS score first reached or exceeded 95 points. Assessments were conducted before treatment， 6 and 12 months after onset. SFGS， facial disability index （FDI） including physical function （FDIp） and social function （FDIs）， self-rating anxiety scale （SAS）， and self-rating depression scale （SDS） scores were assessed before treatment， and 6 and 12 months after onset. Any acupuncture-related adverse events during treatment were recorded for safety evaluation. Treatment sessions and medical costs including direct costs， indirect costs， insurance coverage， total societal costs， and patient out-of-pocket expenses were also recorded， and an economic evaluation was conducted including cost-effectiveness ratio （CER） and incremental cost-effectiveness ratio （ICER）. ResultsUltimately， 56 patients in the mild dysfunction termination group and 55 in the complete recovery termination group completed the follow-up. At 6 and 12 months after onset， SFGS and FDIp scores in both groups improved significantly while FDIs， SAS and SDS scores decreased （P＜0.05）. Comparison of scores between groups 6 months and 12 months after onset showed no statistically significant differences （P＞0.05）. During the trial， the incidence of adverse events was 13.3% （8/60） in the mild dysfunction termination group and 18.3% （11/60） in the complete recovery termination group， with no statistically significant difference （P＞0.05）. The number of treatment sessions， total social costs， and out-of-pocket expenses in the mild dysfunction termination group were significantly lower than those in the complete recovery termination group （P＜0.05）. The CER of the mild dysfunction termination group in SFGS， FDIp， FDIs， SAS， and SDS scores was lower than that of the complete recovery termination group. The ICER analysis showed that continuing treatment until full recovery incurred an additional cost of 573.30 CNY/point in SFGS improvement， whereas 1-point improvement in FDIp， FDIs， SAS， and SDS required 21,355.25 CNY， 1779.60 CNY， 3713.96 CNY， and 2755.52 CNY， respectively. ConclusionFor acupuncture in treating acute peripheral facial palsy， terminating treatment when mild dysfunction is achieved yields long-term efficacy comparable to that of continuing treatment until complete recovery， while significantly reducing medical costs and socioeconomic burden.

ObjectiveTo investigate the role of runt-related transcription factor 3 （RUNX3） in transforming growth factor-β₁ （TGF-β₁）-induced activation of mouse primary pulmonary fibroblasts （PFs）， and its effects on fibroblast activation protein （FAP） expression， cell proliferation， and collagen synthesis. MethodsPFs were isolated from C57BL/6 mice and cultured. A RUNX3 knockdown model was established using small interfering RNA （siRNA）. Cells were assigned to the control group （Control）， TGF-β₁-treated group （TGF-β₁）， negative control group （TGF-β₁+siRNA-NC）， and RUNX3-silenced group （TGF-β₁+si-RUNX3）. In addition， a RUNX3 overexpression rescue experiment was performed based on TGF-β₁ stimulation. Protein and mRNA levels of RUNX3， FAP， and typeⅠcollagen （COL1A1） were measured by Western blot and reverse transcription quantitative real-time PCR （RT-qPCR）. Cell proliferation was assessed using CCK-8 and EdU assays. Co-expression of COL1A1 and FAP was examined by double immunofluorescence staining. ResultsCompared with the Control group， RUNX3， FAP， and COL1A1 expression levels were upregulated in PFs in the TGF-β₁ group （P<0.01）. The CCK-8 assay showed that the absorbance value was reduced in the RUNX3 knockdown group compared with the negative control group （P<0.01）. Consistently， the EdU assay demonstrated a lower proportion of EdU-positive cells in the RUNX3 knockdown group than in the negative control group （P<0.01）. Immunofluorescence double staining revealed decreased fluorescence intensities of COL1A1 and FAP in the RUNX3 knockdown group relative to the negative control. Under RUNX3 overexpression conditions， these fluorescence signals exhibited a partial rebound （P<0.01）. ConclusionRUNX3 in TGF-β1-induced PFs may promote cell proliferation and collagen synthesis by positively regulating FAP expression. Targeting the RUNX3/FAP axis may represent a potential therapeutic strategy for pulmonary fibrosis.

Health economics evidence plays an important role in linking clinical value evidence with health resource allocation decisions in the development of clinical practice guidelines. It can not only effectively balance clinical effectiveness and economic feasibility but also avoid forming "idealized" recommendations that are detached from the affordability of the healthcare system or the burden-bearing capacity of patients. To promote guideline developers to use health economics evidence more standardizedly and fully, this paper conducts an in-depth analysis of the current application status, existing challenges, access channels, and application processes of health economics evidence in current guidelines, and on this basis, puts forward considerations and suggestions for strengthening and standardizing the application of health economics evidence in China's clinical practice guidelines.