Hepatic fibrosis is a reversible pathological process in various chronic liver diseases and is closely associated with the development and progression of severe liver diseases such as liver cirrhosis and hepatocellular carcinoma， and it has emerged as a significant global health challenge. In recent years， studies have shown that histone lactylation， a newly discovered epigenetic modification， actively participates in regulating the progression of hepatic fibrosis. This article systematically reviews the core regulatory effect of histone lactylation modification in the interaction between inflammatory microenvironment and hepatic fibrosis， in order to clarify the cascade regulatory mechanism of “inflammation-hepatic fibrosis” and provide new insights for early diagnosis， targeted intervention， and prevention of malignant transformation in hepatic fibrosis.

Liver fibrosis is a critical pathological process in the progression of chronic liver diseases and is mainly driven by the activation of hepatic stellate cells， and it is characterized by excessive deposition of extracellular matrix. Programmed cell death is widely observed in liver fibrosis and plays diverse roles in different types of cells. It is not only a mechanism for maintaining cellular homeostasis through metabolism， but it can also regulate the development and progression of diseases. Ferroptosis has recently emerged as a research focus due to its close association with lipid metabolism. This article systematically reviews the related concepts of lipid metabolism， ferroptosis， and liver fibrosis， analyzes the bridging role and dual regulatory function of ferroptosis in lipid metabolism and liver fibrosis， and further highlights the clinical application value of targeting ferroptosis in hepatic stellate cells for the treatment of liver fibrosis， in order to provide new perspectives for the mechanistic studies on liver fibrosis and the optimization of related clinical intervention strategies.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

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.

Tuberous sclerosis complex（TSC）is an autosomal dominant genetic disorder characterized by benign tumors in multiple systems，with prenatal involvement primarily affecting the heart and nervous system. Its incidence is approximately 1/6 000 - 1/10 000. The pregnant woman was 36-year-old with 26-week pregnancy and no history of hereditary diseases，underwent fetal echocardiography at Shenzhen Luohu People's Hospital. At 21 weeks' gestation，ultrasound showed fetal left atrial mass，suggesting cardiac myxoma. At 23 weeks' gestation，ultrasound showed multiple hyperechoic nodules in the ventricles，suggesting Tuberous sclerosis.Amniocentesis was subsequently performed. At 26 weeks' gestation，multiple intracranial nodules appeared，suggestive of Tuberous sclerosis complex. Finally，genetic testing of amniocentesis revealed heterozygous mutation of c.5228 G>A in the TSC2 gene of the tested individual，which was not carried by parents. The woman ultimately terminate the pregnancy. The author reviews this case and in conjunction with the literature，analyzes the prenatal ultrasonographic features of this disease，in order to remind sonographers to effectively improve the diagnosis of TSC，thereby reducing the rate of missed and misdiagnoses.

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.

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors

ObjectiveTo evaluate the clinical effectiveness and safety of Modified Zhigancao Granules （炙甘草汤加味颗粒） for preventing the early recurrence following radiofrequency ablation in patients with atrial fibrillation （AF） of qi-yin deficiency syndrome. MethodsA multi-center， randomized， double-blind， placebo-controlled trial was designed. A total of 116 patients with atrial fibrillation of qi-yin deficiency syndrome who underwent radiofrequency ablation for the first time were enrolled from 3 centers， and they were randomly divided into a treatment group （59 cases） and a control group （57 cases）. Both groups received basic western medicine treatment after surgery. In addition， the treatment group was given oral Modified Zhigancao Granules， while the control group was given oral placebo granules. The dosage for both groups was 20 g each time， twice a day， with continuous treatment for 12 weeks. The recurrence of atrial fibrillation in both groups was recorded at 24 hours， 4 weeks， 8 weeks， and 12 weeks after surgery. The serum levels of B-type natriuretic peptide （BNP）， interleukin-6 （IL-6）， high-sensitivity C-reactive protein （hs-CRP）， and tumor necrosis factor-α （TNF-α） were detected before treatment and 12 weeks after treatment in both groups. The scores of Atrial Fibrillation Effect on QualiTy-of-life （AFEQT） Questionnaire （including scores of the daily life dimension， symptom dimension， treatment worry dimension， treatment satisfaction dimension， and total score） and traditional Chinese medicine （TCM） syndrome scores were compared before treatment and at 4 weeks， 8 weeks， and 12 weeks between groups. Safety indicators such as blood routine， urine routine， liver function， and renal function were monitored before and after treatment. ResultsNine of the treatment group and seven of the control group dropped out. Finally， 50 patients in each group were included in the statistical analysis. At 24 hours， 4 weeks， 8 weeks， and 12 weeks after surgery， the recurrence rates of AF in the treatment group were 2.0% （1/50）， 2.0% （1/50）， 4.0% （2/50）， and 10.0% （5/50）， respectively； while those in the control group were 2.0% （1/50）， 26.0% （13/50）， 28.0% （14/50）， and 34.0% （17/50）， respectively. Compared with the control group at the same time points， the early recurrence rates of AF in the treatment group were significantly lower at 4 weeks， 8 weeks， and 12 weeks after surgery （P＜0.01）. Compared with the baseline within group， BNP， hs-CRP， IL-6， and TNF-α in the treatment group all decreased after 12 weeks of treatment （P＜0.05）； the difference in hs-CRP levels （before vs. after treatment） in the treatment group was higher than that in the control group （P＜0.01）. Compared with the baseline within group， both groups showed decreases in the total score of AFEQT Questionnaire， scores of the daily life dimension， treatment worry dimension， symptom dimension， and TCM syndrome scores at 4 weeks， 8 weeks， and 12 weeks after treatment. Meanwhile， the score of the treatment satisfaction dimension of AFEQT increased in both groups （P＜0.01）， and the improvements in all the above scores in the treatment group were superior to those in the control group at all time points （P＜0.05 or P＜0.01）. All safety indicators of patients in both groups were within the normal range before treatment and at 12 weeks after treatment， and no adverse reactions or adverse events occurred in either group. ConclusionModified Zhigancao Granules can reduce the early recurrence rate following radiofrequency ablation in AF patients with qi-yin deficiency syndrome， improve clinical symptoms and quality of life， suppress inflammatory response， and show good safety.

The conversion of abundant and low-cost biomass waste into highly efficient two-electron oxygen reduction(ORR)electrocatalyst is an important link in the degradation of pollutants in industrial wastewater through the electro-Fenton process.In this work,porous biocarbon materials doped with manganese and nitrogen(MnNBC)were prepared from corn stalk.The H2O2 selectivity of MnNBC in acidic media was up to 81% @0.6 Vvs RHE,also MnNBC exhibited a long-term stability in a 10-h uninterrupted lifetime test.The ORR activity of MnNBC could be attributed to the synergistic effect of the hierarchical porous structure,improved defect level and heteroatom doping.Moreover,MnNBC as a cathode material for the electro-Fenton system could completely degrade four kinds of common organic dye pollutants,e.g.,Rhodamine B,methyl orange,methylene blue and crystal violet(25 mg/L),respectively,within 40?60 min.The present study provided valuable insights into the transformation of corn stalk waste into efficient cathode materials for the electro-Fenton process.

Objective To explore the value of support vector machine(SVM)model based on gray matter volume(GMV)for identifying amyotrophic lateral sclerosis(ALS),also to analyze the relevant brain regions.Methods MR 3D T1WI data of 60 ALS patients(ALS group)and 60 healthy volunteers(control group)were retrospectively analyzed.Taken GMV of each brain region obtained by voxel-based morphometry as the input features.F-score analysis was used to select feature with the highest classification accuracy to construct SVM model.Receiver operating characteristic curve was drawn to evaluate the efficacy of SVM model for identifying ALS,and top 10％was used as the weight threshold to obtain gray matter brain regions contributed the most to this model.Results SVM model constructed based on the top 40％GMV features had the highest classification accuracy(82.50％),with sensitivity,specificity and area under the curve(AUG)of 85.05％,80.40％and 0.890,respectively.The left precentral gyrus,left anterior cingulate gyrus and paracingulate gyrus,right middle temporal gyrus,opercular part of left inferior frontal gyrus,right dorsolateral superior frontal gyrus,left temporal pole:middle temporal gyrus,right superior occipital gyrus,orbital part of right middle frontal gyrus,right calcarine fissure and surrounding cortex,right fusiform gyrus were the top 1-10 gray matter brain regions contributed to this model.Conclusion ALS had specific GMV change pattern.SVM model based on GMV could be used to effectively identify ALS,while the left precentral gyrus was the most contributive brain region to this model.