BACKGROUND:Stress-induced damage to hippocampal neurons may underlie abnormalities in neuronal structure and function,ultimately leading to mood disorders.G protein-coupled receptors in brain tissue play an important role in mood regulation.OBJECTIVE:To analyze the mechanism of depression-like behavior in chronic social defeat stress mice based on high-throughput sequencing and bioinformatics analysis.METHODS:C57BL/6J mice were randomly divided into control group and model group.There was no special treatment in the control group,while a mouse model of chronic social defeat stress was established in the model group.Depression-like behavior was assessed through the sucrose preference test,tail suspension test,and forced swim test.Anxiety behavior was evaluated using the elevated plus-maze,while social behavior was measured through the social interaction test.Cognitive function was assessed with the Y-maze spontaneous alternation test.Immunofluorescence staining was performed to quantify microglia markers in the mouse hippocampus,and Nissl staining was used to examine neuronal damage in mice.High-throughput sequencing was used to identify differentially expressed genes and gene enrichment in the mouse hippocampus,and qPCR was used to measure the expression of G protein-coupled receptors in the mouse hippocampus.RESULTS AND CONCLUSION:(1)Compared with the control group,chronic social defeat stress mice showed significant behavioral impairments,including increased anxiety,depression,and cognitive deficits.(2)Additionally,the Nissl body light density in hippocampal neurons was significantly reduced in chronic social defeat stress mice.(3)Sequencing results revealed synaptic damage in the neurons after chronic social defeat stress.Microglia activation was also markedly increased in the hippocampus of CSDS mice.Furthermore,the expression of G protein-coupled receptors in the hippocampus was significantly higher in chronic social defeat stress mice compared with the control group.These findings suggest that chronic social defeat stress induces anxiety,depression,and cognitive deficits in mice,accompanied by neuropathological changes in the hippocampus,and that altered G protein-coupled receptors expression may play a key role in these behavioral and neuropathological changes.

BACKGROUND:Obesity is not only related to metabolic diseases such as diabetes and cardiovascular disease,but also closely related to the increased risk of cognitive decline,dementia and other neurodegenerative diseases.Studies have found that aerobic exercise and resistance exercise can help improve obesity-related cognitive impairment,but their therapeutic effects and related mechanisms of action are still unclear.OBJECTIVE:To explore the protective effects of aerobic and resistance exercises on the nervous center of obesity-related cognitive impairment mice.METHODS:Forty-eight 8-week-old C57BL/6J wild-type male mice were randomly divided into four groups:a control group was fed normally for 20 weeks;a high fat group was fed with high fat diet(60％fat energy)for 20 weeks;an aerobic exercise group was fed with 12 weeks of high-fat diet followed by 8 weeks of aerobic exercise;and a resistance exercise group was fed with 12 weeks of high-fat diet followed by 8 weeks of resistance exercise.After the exercise intervention,body mass was weighed,insulin tolerance and glucose tolerance were tested to evaluate insulin resistance,and cognitive function of mice in each group was detected by new object recognition experiment and Y-maze experiment.The morphology of hippocampal and cortical tissue cells was observed by hematoxylin-eosin staining.The mRNA relative expression levels of tumor necrosis factor-α and interleukin-6 were detected by real-time fluorescence quantitative PCR,and the protein expressions of Bax,Bcl-2,nuclear factor-κB,Cleaved Caspase-1,Caspase-3,synapsin 1 and brain-derived neurotrophic factor were detected by western blot.RESULTS AND CONCLUSION:(1)Compared with the control group,the body mass of mice increased in the high-fat group(P＜0.05),accompanied by insulin resistance and cognitive dysfunction,the expression levels of nuclear factor-κB,Bax,Caspase-3,Cleaved Caspase-1 in the hippocampus were significantly increased(P＜0.05),the expression levels of brain-derived neurotrophic factor,synapsin 1and Bcl-2 proteins were significantly decreased(P＜0.05),Bcl-2/Bax ratio was significantly decreased(P＜0.05),and the mRNA levels of inflammatory cytokines,tumor necrosis factor-α and interleukin-6,were significantly up-regulated(P＜0.05).(2)Compared with the high-fat group,the above indexes were significantly improved in the aerobic exercise group(P＜0.05),while in the resistance exercise group,the body mass of mice was significantly decreased,the levels of inflammatory cytokines tumor necrosis factor-α and interleukin-6 mRNA were significantly decreased(P＜0.05),the protein expression of Caspase-3 was significantly decreased(P＜0.05),and the protein expression of brain-derived neurotrophic factor was significantly up-regulated(P＜0.05),but no significant changes were observed in the other indexes(P＞0.05).In conclusion,long-term exercise can reduce insulin resistance,down-regulate the expression of nuclear factor-κB pathway,weaken inflammatory response,inhibit neuronal apoptosis and improve synaptic plasticity,resulting in neuroprotective effects,and effectively alleviate obesity-related cognitive dysfunction in obese mice.The therapeutic effect of aerobic exercise is superior to that of resistance exercise.

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.

Objective To explore the risk factors of progression to dementia within 2 years in patients with recent subcortical small infarction (RSSI) complicated with cognitive dysfunction. Methods A total of 340 patients with RSSI complicated with cognitive dysfunction who were treated in the hospital and completed 2-year follow-up were selected from February 2021 to February 2025. According to whether the patients progressed to dementia, they were classified into dementia group (n=105) and non-dementia group (n=235). The clinical data were compared between both groups, and the independent risk factors were screened by Logistic regression analysis. Results Multivariate logistic regression analysis suggested that history of hypertension (OR=1.919), history of diabetes mellitus (OR=1.597), multiple infarctions (OR=1.455), severe white matter lesions (OR=1.595), no cognitive function training (OR=1.923), increased infarct size (OR=1.069), reduced MMSE score (OR=0.945) and increased levels of NfL (OR=1.049) and IL-6 (OR=1.038) were independent risk factors for the progression to dementia (all P<0.05). Conclusion The progression to dementia in patients with recent subcortical small infarction and cognitive dysfunction is affected by multiple factors. In clinical practice, the integration of vascular risk factors, imaging features, cognitive assessment and serum biomarkers (NfL, IL-6) helps to construct an early risk prediction model and implement targeted interventions for high-risk groups.

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.