Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Aim To study the effect of evodiamine(EVO)regulating forkhead box protein Ml(FOXM1)on the proliferation and apoptosis of colorectal cancer-resistant cells HCT8/5-FU.Methods CCK-8 assay and EdU assay were used to detect the effect of EVO on cell proliferation ability.Clone formation assay was employed to detect the effect of EVO on the clone for-mation ability of cells.Flow cytometric counting was applied to detect apoptosis.Western blot was utilized to detect the expression of cellular Bcl-2,Bax,FOXM1,β-catenin,c-MYC,and CyclinD1;Molecular docking was used to explore the EVO-FOXM1 interac-tion.Nude mouse transplant tumor model was estab-lished to validate the effect of EVO on HCT8/5-FU cells in vivo.Results CCK-8 assay showed that EVO inhibited the proliferation of HCT8/5-FU cells in a time-and concentration-dependent manner.EdU assay found that the newly proliferated cells in the EVO-trea-ted group were significantly reduced.The results of the clone formation assay showed that EVO inhibited the clone-forming ability of HCT8/5-FU cells.Flow cyto-metric counting found that apoptosis rate of the cells in the EVO group significantly increased.Western blot showed that FOXM1 and β-catenin were significantly highly expressed in HCT8/5-FU cells,and EVO down-regulated the expression of FOXM1,β-cateniin,c-MYC,CyclinD1,and Bcl-2,and up-regulated the ex-pression of Bax.Molecular docking revealed strong in-teractions between EVO and FOXM1.The in vivo ex-perimental results demonstrated that EVO exerted a substantial inhibitory effect on the growth of subcutane-ously implanted HCT8/5-FU xenograft tumors and regulated the expression of related proteins.HE stai-ning revealed significant nuclear consolidation and fragmentation of tumor cells in the EVO group.Con-clusions The findings suggest that EVO could sup-press the activation of the Wnt signaling pathway through a mechanism involving the downregulation of FOXM1 protein expression,thus inhibiting the prolifer-ation of HCT8/5-FU cells and induce their apoptosis.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Aim To investigate the effects of the fangchinoline derivative LYY-32 on the biological prop-erties of the BLM642-1290 DNA helicase,in order to lay a foundation for further research on its antitumor activity.Methods Fluorescence polarization assay,malachite green-phosphate and ammonium molybdate colorime-try,and fluorescein-labeled DNA gel electrophoresis experiments were conducted to study the effects of fangchinoline derivative LYY-32 on the DNA binding activity,ATPase activity,and DNA unwinding activity of BLM642-1290 DNA helicase.The effects of LYY-32 on the DNA unwinding activity of DNA helicase in cells were studied using fluorescent techniques and time-lapse microscopy.Ultraviolet spectral scanning was used to investigate the effects of LYY-32 on the confor-mation of the BLM642-1290 DNA helicase.Results At a concentration of 10 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to dsDNA was 53.17％.At a concentration of 5 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to ssDNA was 88.49％.The inhibition rate of LYY-32 on the ATPase activity of BLM642-1290 DNA he-licase was 89.3％at a concentration of 50 μmol·L-1.When the concentration of LYY-32 exceeded 5μmol·L-1,its inhibition rate on the DNA unwinding activity of BLM642-1290 DNA helicase was 100％.LYY-32 also significantly inhibited the DNA unwinding ac-tivity of DNA helicase in cells.However,LYY-32 had no effect on the conformation of BLM642-1290 DNA heli-case.Conclusion The DNA binding activity,AT-Pase activity,and DNA unwinding activity of BLM642-1290 DNA helicase could be significantly inhibi-ted by the fangchinoline derivative LYY-32.

Aim To investigate the effects of the fangchinoline derivative LYY-32 on the biological prop-erties of the BLM642-1290 DNA helicase,in order to lay a foundation for further research on its antitumor activity.Methods Fluorescence polarization assay,malachite green-phosphate and ammonium molybdate colorime-try,and fluorescein-labeled DNA gel electrophoresis experiments were conducted to study the effects of fangchinoline derivative LYY-32 on the DNA binding activity,ATPase activity,and DNA unwinding activity of BLM642-1290 DNA helicase.The effects of LYY-32 on the DNA unwinding activity of DNA helicase in cells were studied using fluorescent techniques and time-lapse microscopy.Ultraviolet spectral scanning was used to investigate the effects of LYY-32 on the confor-mation of the BLM642-1290 DNA helicase.Results At a concentration of 10 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to dsDNA was 53.17％.At a concentration of 5 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to ssDNA was 88.49％.The inhibition rate of LYY-32 on the ATPase activity of BLM642-1290 DNA he-licase was 89.3％at a concentration of 50 μmol·L-1.When the concentration of LYY-32 exceeded 5μmol·L-1,its inhibition rate on the DNA unwinding activity of BLM642-1290 DNA helicase was 100％.LYY-32 also significantly inhibited the DNA unwinding ac-tivity of DNA helicase in cells.However,LYY-32 had no effect on the conformation of BLM642-1290 DNA heli-case.Conclusion The DNA binding activity,AT-Pase activity,and DNA unwinding activity of BLM642-1290 DNA helicase could be significantly inhibi-ted by the fangchinoline derivative LYY-32.

Aim To study the effect of evodiamine(EVO)regulating forkhead box protein Ml(FOXM1)on the proliferation and apoptosis of colorectal cancer-resistant cells HCT8/5-FU.Methods CCK-8 assay and EdU assay were used to detect the effect of EVO on cell proliferation ability.Clone formation assay was employed to detect the effect of EVO on the clone for-mation ability of cells.Flow cytometric counting was applied to detect apoptosis.Western blot was utilized to detect the expression of cellular Bcl-2,Bax,FOXM1,β-catenin,c-MYC,and CyclinD1;Molecular docking was used to explore the EVO-FOXM1 interac-tion.Nude mouse transplant tumor model was estab-lished to validate the effect of EVO on HCT8/5-FU cells in vivo.Results CCK-8 assay showed that EVO inhibited the proliferation of HCT8/5-FU cells in a time-and concentration-dependent manner.EdU assay found that the newly proliferated cells in the EVO-trea-ted group were significantly reduced.The results of the clone formation assay showed that EVO inhibited the clone-forming ability of HCT8/5-FU cells.Flow cyto-metric counting found that apoptosis rate of the cells in the EVO group significantly increased.Western blot showed that FOXM1 and β-catenin were significantly highly expressed in HCT8/5-FU cells,and EVO down-regulated the expression of FOXM1,β-cateniin,c-MYC,CyclinD1,and Bcl-2,and up-regulated the ex-pression of Bax.Molecular docking revealed strong in-teractions between EVO and FOXM1.The in vivo ex-perimental results demonstrated that EVO exerted a substantial inhibitory effect on the growth of subcutane-ously implanted HCT8/5-FU xenograft tumors and regulated the expression of related proteins.HE stai-ning revealed significant nuclear consolidation and fragmentation of tumor cells in the EVO group.Con-clusions The findings suggest that EVO could sup-press the activation of the Wnt signaling pathway through a mechanism involving the downregulation of FOXM1 protein expression,thus inhibiting the prolifer-ation of HCT8/5-FU cells and induce their apoptosis.

Chronic pelvic inflammatory disease(CPID)is a common chronic inflammatory disease in women,and has a long course and is easy to relapse.Danggui Shaoyao Powder is from Jin Gui Yao Lve(Synopsis of the Golden Chamber),which was a commonly-used formula for the treatment of women's abdominal pain in ancient medical records.It is now often used in the treatment of CPID and has achieved satisfactory therapeutic effect.The article summarizes and analyzes the achievements in the clinical research and experimental study of Danggui Shaoyao Powder in the treatment of CPID over the past 10 years,and invesigates the clinical efficacy of Danggui Shaoyao Powder in the treatment of CPID and its therapeutic mechanism.In the field of clinical studies,Danggui Shaoyao Powder for the treatment of CPID was used by modification,or alone,or in combination with antibiotics and Chinese medicine external treatment,and its combined use was effective on significantly improving the indicators of inflammatory response and immune function,alleviating the clinical signs and symptoms such as pain,and did not increase the incidence of adverse reactions compared with the application of western medicines alone.In the field of experimental studies,Danggui Shaoyao Powder played the therapeutic role in CPID by decreasing the adhesion of endothelial cells,regulating the degradation of extracellular matrix,improving the level of inflammatory factors,and down-regulating the expression of proteins related to the nuclear factor κB(NF-κB)pathway.

Aim To discover the potential active compounds and possible mechanisms in rheumatoid arthritis (RA) treatment with Zhi-Huang-Zhi-Tong powder (ZHZTP) by using network pharmacology and in vitro study. Methods The active ingredient targets and disease targets of Zhihuang Zhitong Powder were searched and screened by database; they intersected to get a common target; and the "drug-component-target" relationship network diagram was constructed for GO and KEGG enrichment analysis of the overlapping genes; then the core components were docked with the core targets. Finally, based on the inflammation model of HUVECs in vitro, the efficacy and mechanism of Zhihuang Zhitong powder were verified by MTT method, plate scratch test and Western blot. Results Active compounds involved in RA treatment were screened in the present study, and the top two were ursolic acid and emodin, all playing crucial roles in RA treatment with ZHZTP. Additionally, the key target was AKTA, TNF and IL-6. GO and KEGG enrichment analysis revealed that ZHZTP regulated BP, MF and CC, and also focused on regulating AKTA, TNF and IL-6 signaling pathway. Molecular docking showed that interactions between key active compounds and key targets were stable. In vitro ZHZTP significantly inhibited cell viability and migration of TNF-a-stimulated HUVECs, and the involved mechanism may be associated with PI3K/AKT/m-TOR signaling. Conclusions The present study reveals that the potential active compounds of ZHZTP are ursolic acid and emodin, and moreover, the involved mechanisms of ZHZTP for RA treatment are associated with PI3 K/AKT/m-TOR signaling.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.