Background: Chinese herbal pieces are an essential component of traditional Chinese medicine. Accurate identification and classification of these materials are crucial in clinical practice. Objective: This study aims to enhance the recognition efficiency of Chinese herbal pieces using deep learning technology, while addressing the limitations of traditional manual classification methods in terms of both quality and efficiency. Methods: A comprehensive dataset containing 201 types of Chinese herbal pieces was established. Based on Real-time Detection Transformer (RT-DETR), we designed and integrated a Feature-focused Diffusion Network (FDN), resulting in an improved model termed RT-DETR-FDN. The proposed FDN includes a Feature-focus Module and a feature diffusion mechanism, enabling the model to capture more extensive feature information from Chinese herbal pieces and diffuse it across multiple detection scales. Results: Experimental results show that RT-DETR-FDN achieved a precision of 0.925, a recall of 0.943, and an mAP50-95 of 0.851. In addition, the model was compared with representative You Only Look Once series models commonly used in object detection. Compared with these models, RT-DETR-FDN achieved higher recognition accuracy while maintaining a lightweight architecture. Conclusion: This study integrates deep learning with traditional Chinese medicine, providing a more effective solution for the recognition of Chinese herbal pieces.

Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

ObjectiveTo investigate the efficacy and safety of simplified diagnosis and treatment strategies and regimens in the treatment of patients with human immunodeficiency virus （HIV）/hepatitis C virus （HCV） co-infection. MethodsThis multicenter prospective real-world study was conducted among 198 patients with HIV/HCV co-infection who received hepatitis C treatment in 10 designated primary hospitals for hepatitis C in Yuxi， China from July 2023 to June 2024， and according to whether genotype detection was performed， they were divided into genotype detection group with 122 patients and non-genotype detection group with 76 patients. The patients were observed in terms of sustained virologic response at 12 weeks （SVR12）， safety， and liver biochemical parameters. Propensity score matching was used to match the two cohorts， using caliper matching （caliper value = 0.02） at a ratio of 1∶1. The independent-samples t test or the Wilcoxon signed-rank test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between groups. ResultsAmong the 122 patients in the genotype detection group， 63.11% （77/122） had genotype 3， while the non-genotype detection group had 76 patients； both groups achieved an SVR12 rate of 100%. From baseline to 12 weeks after treatment， both groups had a significant increase in the serum level of albumin， significant reductions in the serum levels of total bilirubin， alanine aminotransferase， aspartate aminotransferase， and alpha-fetoprotein， and a significant reduction in FibroScan value （all P<0.05）. A total of 54 patients （27.27%） experienced at least one adverse reaction， and anemia was the most common adverse reaction （38 patients， 19.19%）， with an incidence rate of 50.00% （33/66） in patients with liver cirrhosis. HIV viral load remained <50 IU/mL before and after treatment， and there was no significant change in CD4⁺ T lymphocyte count after treatment （Z=-0.969， P=0.202）. Compared with the genotype detection group， the non-genotype detection group had a significantly shorter time from positive HCV RNA testing to treatment initiation （4±2 days vs 19±4 days， t=5.321， P<0.05） and significantly lower testing costs （618±97 yuan vs 789±129 yuan， t=3.661， P<0.05）. ConclusionFor patients with HIV/HCV co-infection， the simplified diagnosis and treatment strategies can achieve a relatively high SVR12 rate， improve biochemical indicators， and effectively reduce time cost and economic burden， with a favorable safety profile.

BACKGROUND: Biomarkers-based prediction of long-term risk of acute coronary syndrome (ACS) is scarce. We aim to develop a risk score integrating clinical routine information (C) and plasma biomarkers (B) for predicting long-term risk of ACS patients. METHODS: We included 2729 ACS patients from the OCEA (Observation of cardiovascular events in ACS patients). The earlier admitted 1910 patients were enrolled as development cohort; and the subsequently admitted 819 subjects were treated as validation cohort. We investigated 10-year risk of cardiovascular (CV) death, myocardial infarction (MI) and all cause death in these patients. Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was derived using main part of these variables. RESULTS: During 16,110 person-years of follow-up, there were 238 CV death/MI in the development cohort. The 7 most important predictors including in the final model were NT-proBNP, D-dimer, GDF-15, peripheral artery disease (PAD), Fibrinogen, ST-segment elevated MI (STEMI), left ventricular ejection fraction (LVEF), termed as CB-ACS score. C-index of the score for predication of cardiovascular events was 0.79 (95% CI: 0.76-0.82) in development cohort and 0.77 (95% CI: 0.76-0.78) in the validation cohort (5832 person-years of follow-up), which outperformed GRACE 2.0 and ABC-ACS risk score. The CB-ACS score was also well calibrated in development and validation cohort (Greenwood-Nam-D'Agostino: P = 0.70 and P = 0.07, respectively). CONCLUSIONS CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS. This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.

Immunotherapy has emerged as a revolutionary approach to treat immune-related diseases. Dendritic cells (DCs) play a pivotal role in orchestrating immune responses, making them an attractive target for immunotherapeutic interventions. Modulation of gene expression in DCs using genome editing techniques, such as the CRISPR-Cas system, is important for regulating DC functions. However, the precise delivery of CRISPR-based therapies to DCs has posed a significant challenge. While lipid nanoparticles (LNPs) have been extensively studied for gene editing in tumor cells, their potential application in DCs has remained relatively unexplored. This study investigates the important role of cholesterol in regulating the efficiency of BAMEA-O16B lipid-assisted nanoparticles (BLANs) as carriers of CRISPR/Cas9 for gene editing in DCs. Remarkably, BLANs with low cholesterol density exhibit exceptional mRNA uptake, improved endosomal escape, and efficient single-guide RNA release capabilities. Administration of BLAN_mCas9/gPD-L1 results in substantial PD-L1 gene knockout in conventional dendritic cells (cDCs), accompanied by heightened cDC1 activation, T cell stimulation, and significant suppression of tumor growth. The study underscores the pivotal role of cholesterol density within LNPs, revealing potent influence on gene editing efficacy within DCs. This strategy holds immense promise for the field of cancer immunotherapy, offering a novel avenue for treating immune-related diseases.

Coronavirus-related diseases pose a significant challenge to the global health system. Given the diversity of coronaviruses and the unpredictable nature of disease outbreaks, the traditional "one bug, one drug" paradigm struggles to address the growing number of emerging crises. Therefore, there is an urgent need for therapeutic agents with broad-spectrum anti-coronavirus activity. Here, we provide evidence that ATV006, an anti-SARS-CoV-2 nucleoside analog targeting RNA-dependent RNA polymerase (RdRp), has broad antiviral activity against human and animal coronaviruses. Using mouse hepatitis virus (MHV) and human coronavirus NL63 (HCoV-NL63) as a model, we show that ATV006 has potent prophylactic and therapeutic activity against murine coronavirus infection in vivo. Remarkably, ATV006 successfully inhibits viral replication in mice even when administered 96 h after infection. Due to its oral bioavailability and potency against multiple coronaviruses, ATV006 has the potential to become a useful antiviral agent against SARS-CoV-2 and other circulating and emerging coronaviruses in humans and animals.

Lacking therapeutic targets highlights the crucial roles of chemotherapy and radiotherapy in the clinical management of triple-negative breast cancer (TNBC). To relieve the side effects of the chemoradiotherapy combination regimen, we design and develop a self-assembled micelle nanosystem consisting of perfluorocarbon chain-modified cisplatin prodrug. By incorporating perfluorodecalin, this nanosystem can effectively carry ozone and promote irradiation-derived reactive oxygen species (ROS) production. By leveraging the perfluorocarbon sidechain, the nanosystem exhibits efficient internalization by TNBC cells and effectively escapes from lysosomal entrapment. Under X-ray irradiation, ozone-generated ROS disrupts the intracellular redox balance, thereby facilitating the release of cisplatin in a reduction-responsive manner mediated by reduced glutathione. Moreover, oxygen derived from ozone decomposition enhances the efficacy of radiotherapy by alleviating tumor hypoxia. Notably, the combination of irradiation with ozone-loaded cisplatin prodrug nano system synergistically prompts antitumor efficacy and reduces cellular/systemic toxicity in vitro and in vivo. Furthermore, the combo regimen remodels the tumor microenvironment into an immune-favored state by triggering immunogenic cell death and relieving hypoxia, which provides a promising foundation for a combination regimen of immunotherapy. In conclusion, our nanosystem presents a novel strategy for integrating chemotherapy and radiotherapy to optimize the efficacy and safety of TNBC clinical treatment.

Tailored lipid nanoparticles (LNPs)-mediated small interfering RNA (siRNA) nanomedicines show promise in treating liver disease, such as acute liver injury (ALI) and non-alcoholic steatohepatitis (NASH). However, constructing LNPs that address biosafety concerns, ensure efficient delivery, and target specific hepatic subcellular fractions has been challenging. To evade above obstacles, we develop three novel self-degradable "gemini-like" ionizable lipids (SS-MA, SS-DC, SS-MH) by incorporating disulfide bonds and modifying the length of ester bond and tertiary amino head. Our findings reveal that the disulfide-bond-bridged LNPs exhibit reduction-responsive drug release, improving both biosafety and siRNA delivery efficiency. Furthermore, the distance of ester bond and tertiary amino head significantly influences the LNPs' pK _a, thereby affecting endosomal escape, hemolytic efficiency, absorption capacity of ApoE, uptake efficiency of hepatocytes and liver accumulation. We also develop the modified-mannose LNPs (M-LNP) to target liver macrophages specifically. The optimized M-MH_LNP@TNFα exhibits potential in preventing ALI by decreasing tumor necrosis factor α (TNFα) levels in the macrophages, while MH_LNP@DGAT2 could treat NASH by selectively degrading diacylglycerol O-acyltransferase 2 (DGAT2) in the hepatocytes. Our findings provide new insights into developing novel highly effective and low-toxic "gemini-like" ionizable lipids for constructing LNPs, potentially achieving more effective treatment for hepatic diseases.

OBJECTIVES: To analyze the molecular mechanism of Xixian Pills for treatment of rheumatoid arthritis (RA). METHODS: Forty-eight rats were randomized into 6 groups (n=8), including a normal control group, a collagen-induced arthritis (CIA) model group, 3 Xixian Pills treatment (200, 400 and 800 mg/kg) groups, and a Tripterygium glycosides tablet (TGT) treatment group. In the latter 4 groups, the rats were treated with daily gavage of Xixian Pills or TGT 2 weeks after CIA modeling for 3 consecutive weeks. The differentially expressed proteins in high-dose Xixian Pills group and the model group compared with the normal control group were screened based on the tandem mass spectrometry tag (TMT) technology, and the core targets and signaling pathways were analyzed. The immune cell infiltration and gene expression data were analyzed using ggplot2 and tidyverse packages, and the correlation coefficients between the core targets and the immune cells were calculated. RESULTS: The CIA rats showed significantly increased serum levels of TNF-α and IL-6 and lowered serum IL-10 level. Treatments with high- and medium-dose Xixian Pills and TGT all significantly reduced serum TNF‑α and IL-6 and increased IL-10 levels in CIA rats. Proteomic analysis identified 160 differential proteins between the model group and high-dose Xixian Pills group, and the core targets included CCL5, STAT1, GZMB and IL7R. The areas under the ROC curve of CCL5 and STAT1 were both greater than 0.9. Immunohistochemical and immunofluorescence staining revealed increased levels of CCL5 and STAT1 in the ankle joints of CIA rats, which were significantly decreased after treatment with Xixian Pills. CONCLUSIONS Treatment with Xixian Pills offers protection of the joints in CIA rats possibly by inhibiting joint inflammation via regulating protein expressions of CCL5 and STAT1.
Animals ; Drugs, Chinese Herbal/pharmacology* ; Rats ; Arthritis, Rheumatoid/metabolism* ; Proteomics ; Tripterygium/chemistry* ; Arthritis, Experimental/metabolism* ; Tumor Necrosis Factor-alpha/blood* ; Interleukin-10/blood* ; Interleukin-6/blood* ; Male ; Rats, Sprague-Dawley ; Signal Transduction

Combined with elastic network model (ENM), the perturbation response scanning (PRS) has emerged as a robust technique for pinpointing allosteric interactions within proteins. Here, we proposed the PRS analysis of drug-target networks (DTNs), which could provide a promising avenue in network medicine. We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework, for drug repurposing of multiple sclerosis (MS). First, the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes. Then, based on topological analysis and functional annotation, the neurotransmission module was identified as the "therapeutic module" of MS. Further, perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis, giving a list of repurposable drugs for MS. Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of serotonin 2B receptor (HTR2B). Finally, we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex. These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS. As a useful systematic method, our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.