Acupotomy therapy demonstrates the definite clinical efficacy on knee osteoarthritis (KOA). After reviewing systematically the mechanism studies on acupotomy for KOA over the past 5 years, It is revealed that acupotomy synergistically intervenes in the pathological progression of KOA through multi-target approaches, such as regulating cartilage homeostasis, restoring skeletal muscle function, alleviating synovial inflammatory responses, remodeling subchondral bone, and neuromodulation. But the current research still limits to single-tissue phenotypic observation, and is insufficiency in the in-depth exploration of multi-tissue synergistic interactions and molecular upstream-downstream regulatory mechanisms. Future studies should focus on the inheritance and innovation of acupotomy theory, and integrating multi-omics analytical technologies, artificial intelligence, and novel biochemical detection methods. The mechanism research targets on the interaction mechanisms among tissues, direct effects of acupotomy, immune-inflammatory regulatory mechanisms, and analgesic mechanisms, so as to comprehensively elucidate the therapeutic mechanism of acupotomy for KOA.
Humans ; Acupuncture Therapy ; Osteoarthritis, Knee/genetics* ; Animals

Objective:To analyze the use of medicare antiviral drugs (ART) and related factors among HIV-infected people in Ningbo City.Methods:The retrospective data was collected related to infection and treatment of HIV-infected people in ART in Ningbo up to February 2023 through the National Infectious Disease Surveillance System. Binary logistic regression was used to analyze the factors related to medicare antiviral drug use in HIV-infected people. R 4.2.2 software was used for statistical analysis.Results:A total of 6 433 HIV-infected people with ART records were collected, among which 5 783 were in ART. The prevalence of medicare drugs use among people in ART was 24.8% (1 435/5 783, 95% CI: 23.7%-25.9%). Beilun District (8.7%, 43/497) and Fenghua District (5.7%, 14/247) had the lowest level in medicare drugs use. Among people in ART using medicare or out‐of‐pocket drugs, the prevalence of those who had at least one viral load test in the last year (84.9%, 1 352/1 593) was significantly lower than that of those using free drugs (91.4%, 3 829/4 190) ( χ2=52.50, P<0.001). The results of multivariate logistic analysis showed that the factors influencing medicare drug use included low educational level (junior high school and below: a OR=0.24, 95% CI:0.17-0.34), farmer or worker (farmer: a OR=0.60, 95% CI: 0.39-0.91; worker: a OR=0.42, 95% CI: 0.27-0.64), low monthly income (<3 000 Yuan: a OR=0.29, 95% CI: 0.18-0.45), the longer interval time between diagnosis and treatment (≥21 days: a OR=0.47, 95% CI: 0.30-0.74). Conclusions:Significant regional differences on the prevalence of medicare antiviral drugs use in HIV-infected people exist in Ningbo City. Follow-up management program of patients should be improved to strengthen patient compliance to mobilize medicare drug promotion. Meanwhile, publicity of medicare drugs should be strengthened for farmers or workers with low education level and patients with delayed treatment.

Objective:To screen the interacting protein of ubiquitin-conjugating enzyme E2S(UBE2S)and construct the hepatocellular carcinoma(HCC)based on UBE2S interacting protein prognosis model(UIPM),and to discuss the value of UIPM in assessing the prognosis of the HCC patients.Methods:Co-immunoprecipitation(Co-IP)was used to screen the protein complexes binding to Flag-UBE2S.After validation by sodium dodecyl sulphate-polyacrylamide gel electrophoresis(SDS-PAGE)and Western blotting methods;liquid chromatography-mass spectrometer(LC-MS)was used to identify the UBE2S interacting proteins;Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis were conducted on these proteins;the prognosis-related proteins from The Cancer Genome Atlas(TCGA)were cross-referenced with UBE2S interacting proteins by survival package of R software;the key proteins were extracted through LASSO regression analysis to build the UIPM;the prognostic model risk scoring formula was established.The HCC patients in TCGA were divided into high risk group and low risk group based on median value of the risk scores.The predictive accuracy of UIPM was evaluated by receiver operating characteristic curve(ROC),and the predictive accuracy was further validated by International Cancer Genome Consortium(ICGC)Database;univariate regression analysis and multivariate Cox regression analysis were used to detect whether the UIPM risk score was an independent prognostic factor for HCC.Furthermore,the nomogram model was built.Results:A total of 97 UBE2S interacting proteins were identified through Co-IP combined with LC-MS analysis.The GO functional enrichment analysis and KEGG signaling pathway enrichment analysis results showed that the interacting proteins were closely associated with cysteine-type endopeptidase activity,oxidative stress,and cell death.The TCGA revealed 5 163 HCC prognosis-related proteins;after intersecting with UBE2S interacting proteins,40 prognosis-related interacting proteins were found.Seven key proteins were determined through LASSO regression analysis,including UBE2S,heat shock protein family A member 8(HSPA8),heterogeneous nuclear ribonucleoprotein H1(HNRNPH1),chaperonin containing TCP1 subunit 3(CCT3),eukaryotic translation initiation factor 2 subunit 1(EIF2S1),receptor for activated C kinase 1(RACK1),and actin related protein 2/3 complex subunit 4(ARPC4),and the UIPM was constructed.There was significant difference in survival rate of the patients between high risk group and low risk group(P<0.05).The ROC curve analysis results showed the area under ROC curve(AUC)values of UIPM for predicting 1-year,2-year,and 3-year survival risk scores of the HCC patients were all greater than 0.7,indicating the model had high predictive accuracy.This was also confirmed by ICGC Database data.The univariate and multivariate Cox regression analysis results showed that the UIPM risk score was an independent prognostic risk factor for the HCC patients(P<0.05).The nomogram results showed good consistency between predicted survival rate and actual survival rate of the patient.Conclusion:A total of 97 interacting proteins that interact with UBE2S may promote the occurence and devolopment of HCC through oxidative stress and dysregulation of ferroptosis pathways.The UIPM risk score is an independent risk factor for the prognosis of HCC and can be used to predict the outcomes of the patients.UBE2S,HSPA8,HNRNPH1,CCT3,EIF2S1,RACK1,and ARPC4 could be regarded as the new biomarkers and therapeutic targets for HCC.

Fragile X-associated tremor/ataxia syndrome(FXTAS)is a neurodegenerative disorder caused by a pre-mutationof the fragile X mental retardation protein 1(FMR1)gene on the X chromosome,and its low incidence rate and complex clinical manifestations often lead to misdiagnosis and missed diagnosis,which requires improving the awareness of this disease among clinicians.This article reviews the recent research advances in the pathogenesis,clinical manifesta-tions,pathological features,and diagnostic criteria of FXTAS.

Prefoldin (PFDN) acts as a co-companion protein to stabilize newly synthesized peptides and prevent protein misfolding and aggregation. PFDN plays an important role in cytoskeleton formation. PFDN is upregulated in glioma, breast cancer, lung cancer, gastric cancer, liver cancer, colorectal cancer and other tumor tissues and is closely linked with the proliferation and migration of tumor cells. A deeper understanding of the role of PFDN in tumorigenesis and development may provide new ideas and insights for PFDN in preventing or reversing tumor progression.

Abstract On 18 May 2021, the Center for Disease Control and Prevention (CDC) of X District in P City, Z Province received a co-investigation of a suspected case of intentional HIV transmission from the public security branch, and conducted epidemiological investigations on Zhao and Wang (both males). Wang was confirmed HIV-positive in 2019. Zhao had unprotected sexual encounters several times with Wang in March 2021 without being informed of Wang's HIV infection. Zhao developed fever, sore throat and other symptoms of acute infection phase on 28 March, and were confirmed HIV positive by the CDC of P City on 11 May. Zhao did not have sex with anyone else before or after having sex with Wang. In addition, Zhao had no history of surgery, blood transfusions, drug use or any other history of HIV exposure. Laboratory tests conducted by the CDC of Z Province showed that the HIV nucleic acid sequences between the samples of Zhao and Wang had a high degree of homology. Combined with the epidemiological investigation, laboratory testing and the evidence from the public security branch, it was concluded that Wang intentionally transmitted HIV to Zhao through unprotected anal sex without disclosing his HIV infection status.

Purpose: Tamoxifen showed individual differences in efficacy under different CYP2D6*10 genotypes. Our study evaluated the prognosis of tamoxifen or toremifene in hormone receptor (HR)–positive breast cancer patients under different genotypes. Materials and Methods: CYP2D6*10 genotypes of HR-positive breast cancer patients were determined by Sanger sequencing, and all the patients were divided into tamoxifen group or toremifene group. Results: A total of 268 patients with HR-positive breast cancer were studied. The median follow-up time was 72.0 months (range, 5.0 to 88.0 months). Of these, 88 (32.9%), 114 (42.5%), and 66 (24.6%) patients had C/C, C/T, and T/T genotypes, respectively. Among patients who received tamoxifen (n=176), the 5-year disease-free survival (DFS) rate in patients with C/C and C/T genotype was better than that in patients with T/T genotype, and the difference was statistically significant (p < 0.001 and p=0.030, respectively). In patients receiving toremifene, CYP2D6*10 genotype was not significantly associated with DFS (p=0.325). Regardless of genotypes, the 5-year DFS rate was higher in patients treated with toremifene than in patients with tamoxifen (91.3% vs. 80.0%, p=0.011). Compared with tamoxifen, toremifene remained an independent prognostic marker of DFS in multivariate analysis (hazard ratio, 0.422; p=0.021). For all the 180 patients with CYP2D6*10 C/T and T/T genotypes, the 5-year DFS rate was significantly higher in the toremifene group than in the tamoxifen group (90.8% vs. 70.1%, p=0.003). Conclusion Toremifene may be an alternative adjuvant endocrine therapy for patients with CYP2D6*10 mutant genotypes.

Primary liver cancer is a major global public health issue and currently the second leading cause of cancer death worldwide. The continuous changes in early cancer detection and treatment strategies have improved the overall survival rate of patients. Emerging organoid technologies have had a dramatic impact on cancer research in recent years, and tumor organoids are widely used in basic and translational cancer research. Tumor organoid culture technology will bridge the gap among molecular genetics, biology and clinical treatment. In this review, we discussed the recent different applications of liver organoids in liver cancer biology and clinical translation, involving liver cancer molecular studies, microenvironment and metastasis studies and drug screening trials, in order to provide a reference for clinical practice.

In recent years, artificial intelligence (AI) has been widely applied in the field of drug discovery and development.In particular, natural language processing technology has been significantly improved after the emergence of the pre-training model.On this basis, the introduction of graph neural network has also made drug development more accurate and efficient.In order to help drug developers more systematically and comprehensively understand the application of artificial intelligence in drug discovery, this article introduces cutting-edge algorithms in AI, and elaborates on the various applications of AI in drug development, including drug small molecule design, virtual screening, drug repurposing, and drug property prediction, finally discusses the opportunities and challenges of AI in future drug development.