This article systematically reviews the current research status as well as diagnosis and treatment strategies of traditional Chinese medicine （TCM） for gastroesophageal reflux disease （GERD）. Studies demonstrate that TCM， based on the "disease-syndrome combination" approach， exhibits multi-target advantages in alleviating symptoms of various GERD subtypes， promoting mucosal repair， regulating emotions， and facilitating the reduction of western medication. To address clinical challenges such as symptom overlap and limited therapeutic efficacy， strategies have been proposed including "treating different diseases with the same method" and integrated regulation based on viscera correlation. Future efforts should focus on elucidating the mechanisms of compound prescriptions， promoting TCM drug development under the "three-combination" evaluation framework that integrates TCM theory， human experience and clinical trial evidence， and optimizing integrated traditional and western medicine models to enhance GERD management.

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.

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.

Background and aims:Primary sclerosing cholangitis(PSC)is an autoimmune liver disease characterized by complex pathogenesis and limited available therapeutic options.The mechanisms underlying the development and progression of PSCs remain unclear.Liver X receptor beta(LXR-β)is recognized to modulate lipid metabolism and immune response,but its specific involvement in the PSC has not been elucidated.Here,we explored the role and mechanism of LXR-β in PSC induced by 3,5-diethoxycarbonyl-1,4-dihydro-2,4,6-collidine(DDC).Methods:CRISPR-Cas9 technology was applied to generate Abcb4(coding MDR2,next named as Mdr2),Nr1h2(coding LXR-β,next named as Lxrβ),and Rag2(coding RAG2)knockout mice.DDC was used to induce PSC.Hematoxylin and eosin and Sirius red staining were used to assess the extent of hepatic injury and fibrosis.Flow cytometry was used to observe immune cell subsets.Results:We observed a declining trend in hepatic Lxrβ in the PSC model.Unexpectedly,Lxrβ knockout failed to modulate DDC-induced PSC pathogenesis.Concomitantly,assessment of the influence of Rag2 deficiency on PSC progression revealed the absence of aggravated or alleviated hepatic injury or fibrosis in the Rag2-/-DDC mice.However,Lxrβ depletion intensified DDC-induced PSC in the Rag2-/-mice,with more abundant infiltrative inflammatory cells and more severe liver fibrosis.Compared with Rag2-/-DDC mice,Lxrβ-/-Rag2-/-DDC mice had higher serum ALT and AST levels and mRNA expression of proinflammatory and profibrotic genes.Flow cytometry showed that LXR-β deficiency resulted in a diminished population of hepatic innate immune cells.Conclusion:This study indicated innate immune cell LXR-β deficiency can exacerbate hepatic injury and fibrosis in murine models of PSC suggesting that LXR-β may regulate the function of innate immunity in the fibrotic advancement of PSC.

ObjectiveTo assess the efficiency of a Sophora flavescens Ait (S. flavescens, Ku Shen)-soluble microneedle (SFA-MN) for improving skin lesion symptoms in mice with psoriasis.MethodsSFA-MNs were prepared using a two-mold molding process with 20% w/v polyvinylpyrrolidone and 15% w/v polyvinyl alcohol. The SFA-MNs were assessed for morphology, mechanical properties, in vitro dissolution, identification of components, and skin lesion improvement in imiquimod-induced psoriasis mice.ResultsThe SFA-MNs demonstrated good mechanical properties for efficiently penetrating the dermis, facilitating efficient drug delivery. Furthermore, they effectively inhibited mast cell levels in the dorsal lesion area of psoriasis mice and reduced the expression of the T-lymphocyte factor cluster of differentiation 3 and tumor necrosis factor-α. In addition, this system alleviated skin inflammation, splenic swelling, and thymic atrophy in the psoriasis-like mouse model. Seven major components were detected from SFA-MNs by comparison of the mass-to-nucleus ratios (m/z) of the secondary fragments N-methylcytisine, 5α, 9α-dihydroxymatrine, sophoramine, matrine, oxysophocarpine, oxymatrine, and kushenol O.ConclusionThe drug delivery strategy combining traditional herbal S. flavescens with soluble microneedle technology provides more targeted and effective immune regulation for treating psoriasis-like mice models, enabling enhanced therapeutic effects compared with the control group.

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 se-rotonin 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.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

Histotripsy is a non-invasive ultrasound tissue fractionation technique that utilizes focused ultrasound waves to induce cavitation within tissues, thus generating microbubbles. Subsequently, these microbubbles rapidly expand and collapse, generating mechanical forces that disrupt targeted tissues without the use of heat, with the characteristics of non-thermal and non-ionizing modality. Preclinical studies in animal models have demonstrated the potential of histotripsy in treating both benign and malignant tumors, cardiovascular lesions, thrombi, and hematomas. Preliminary evaluations from randomized controlled clinical trials have also been conducted for liver tumors and benign prostatic hyperplasia. This review summarizes the principles and development of histotripsy, and its clinical applications, particularly in the treatment of liver cancer, and compares them with current clinical ablation technologies.

Objective:To analyze the effect of joint management of type 2 diabetes mellitus (T2DM) between specialty and community under the model of National Diabetes Prevention and Control Center (DPCC).Methods:A total of 2 527 T2DM patients managed by DPCC Pingshan Center of Shenzhen from January 1, 2022 to December 31, 2024 were retrospectively included. After management, the rate of downturn, reexamination rate, blood pressure compliance rate, metabolic indicators (waist circumference, body mass index, fasting blood glucose, glycosylated hemoglobin, blood lipids) and screening rate of chronic complications of diabetes (atherosclerotic cardiovascular disease, microvascular disease, diabetic peripheral neuropathy) were analyzed. Those included 2022 ( n=564), 2023 ( n=1 477), and 2024 ( n=2 527). Results:The downturn rate in 2022, 2023 and 2024 increased year by year (22.8% vs 67.2% vs 89.9%, P<0.01), and the review rate (41.1% vs 62.2% vs 52.7%, P<0.01), complication screening rate (51.6% vs 85.3% vs 62.2%, P<0.01), blood pressure compliance rate (53.1% vs 78.0% vs 67.2%, P<0.01), body mass index compliance rate (13.2% vs 17.3% vs 28.6%, P<0.01), fasting blood glucose meeting rate (46.4% vs 60.2% vs 68.5%, P<0.01), glycated hemoglobin meeting rate (58.4% vs 63.2% vs 45.6%, P<0.01) were relatively improved. Waist circumference compliance rate (30.6% vs 27.7% vs 21.6%) and blood lipid compliance rate (33.6% vs 35.5% vs 31.9%) were not significantly improved, and the review rate, blood pressure compliance rate and complication screening rate in 2024 were lower than those in 2023 and higher than those in 2022. Conclusions:The combined management of type 2 diabetes under the DPCC model has significant effects on improving the down-conversion rate, rediagnosis rate, blood pressure compliance rate, metabolic index compliance rate and the screening rate of diabetes-related chronic complications in patients with diabetes. At the same time, it was also found that with the progress of hierarchical diagnosis and treatment, the review rate, complication screening rate, blood pressure, waist circumference, blood lipid and glycosylated hemoglobin reached the standard of patients decreased compared with the previous situation, which needs to be further analyzed and improved.

Objective:To analyze the effect of joint management of type 2 diabetes mellitus (T2DM) between specialty and community under the model of National Diabetes Prevention and Control Center (DPCC).Methods:A total of 2 527 T2DM patients managed by DPCC Pingshan Center of Shenzhen from January 1, 2022 to December 31, 2024 were retrospectively included. After management, the rate of downturn, reexamination rate, blood pressure compliance rate, metabolic indicators (waist circumference, body mass index, fasting blood glucose, glycosylated hemoglobin, blood lipids) and screening rate of chronic complications of diabetes (atherosclerotic cardiovascular disease, microvascular disease, diabetic peripheral neuropathy) were analyzed. Those included 2022 ( n=564), 2023 ( n=1 477), and 2024 ( n=2 527). Results:The downturn rate in 2022, 2023 and 2024 increased year by year (22.8% vs 67.2% vs 89.9%, P<0.01), and the review rate (41.1% vs 62.2% vs 52.7%, P<0.01), complication screening rate (51.6% vs 85.3% vs 62.2%, P<0.01), blood pressure compliance rate (53.1% vs 78.0% vs 67.2%, P<0.01), body mass index compliance rate (13.2% vs 17.3% vs 28.6%, P<0.01), fasting blood glucose meeting rate (46.4% vs 60.2% vs 68.5%, P<0.01), glycated hemoglobin meeting rate (58.4% vs 63.2% vs 45.6%, P<0.01) were relatively improved. Waist circumference compliance rate (30.6% vs 27.7% vs 21.6%) and blood lipid compliance rate (33.6% vs 35.5% vs 31.9%) were not significantly improved, and the review rate, blood pressure compliance rate and complication screening rate in 2024 were lower than those in 2023 and higher than those in 2022. Conclusions:The combined management of type 2 diabetes under the DPCC model has significant effects on improving the down-conversion rate, rediagnosis rate, blood pressure compliance rate, metabolic index compliance rate and the screening rate of diabetes-related chronic complications in patients with diabetes. At the same time, it was also found that with the progress of hierarchical diagnosis and treatment, the review rate, complication screening rate, blood pressure, waist circumference, blood lipid and glycosylated hemoglobin reached the standard of patients decreased compared with the previous situation, which needs to be further analyzed and improved.