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.

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.

Objective: To investigate the prognostic value of preoperative inflammatory and nutritional condition detection in the postoperative survival, and establish a prognostic model for predicting the survival of patients with gastric cancer. Methods: The clinicopathological data of 1123 patients with gastric cancer who had undergone radical gastrectomy in Tianjin Medical University Cancer Institute & Hospital from January 2005 to December 2014 were retrospectively analyzed. Patients with history of other malignancy, with history of gastrectomy, who had received preoperative treatment, who died during the initial hospital stay or first postoperative month, and missing clinical and pathological information were excluded. Cox univariate and multivariate analyses were used to identify independent clinicopathological factors associated with the survival of these gastric cancer patients. Cox univariate analysis was used to identify preoperative inflammatory and nutritional indexes related to the survival of patients with gastric cancer after radical gastrectomy. Moreover, the Cox proportional regression model for multivariate survival analysis (forward stepwise regression method based on maximum likelihood estimation) was used. The independent clinicopathological factors that affect survival were incorporated into the following three new prognostic models: (1) an inflammatory model: significant preoperative inflammatory indexes identified through clinical and univariate analysis; (2) a nutritional model: significant preoperative nutritional indexes identified through clinical and univariate analysis; and (3) combined inflammatory/nutritional model: significant preoperative inflammatory and nutritional indexes identified through clinical and univariate analysis. A model that comprised only pT and pN stages in tumor TNM staging was used as a control model. The integrated area under the receiver operating characteristic curve (iAUC) and C-index were used to evaluate the discrimination of the model. Model fitting was evaluated by Akaike information criterion analysis. Calibration curves were used to assess agreement between the predicted probabilities and actual probabilities at 3-year or 5-year overall survival (OS). Results: The study cohort comprised 1 123 patients with gastric cancer. The mean age was 58.9±11.6 years, and 783 were males. According to univariate analysis, age, surgical procedure, extent of lymph node dissection, tumor location, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, and nerve invasion were associated with 5-year OS after radical gastrectomy for gastric cancer (all P<0.050). Multivariate analysis further identified age (HR: 1.18, 95%CI: 1.03-1.36, P=0.019), maximum tumor size (HR: 1.19, 95%CI: 1.03-1.38, P=0.022), number of examined lymph nodes (HR: 0.79, 95%CI: 0.68-0.92, P=0.003), pT stage (HR: 1.40, 95%CI: 1.26-1.55, P<0.001) and pN stage (HR: 1.28, 95%CI: 1.21-1.35, P<0.001) as independent prognostic factors for OS of gastric cancer patients. Additionally, according to univariate survival analysis, the preoperative inflammatory markers of neutrophil count, percentage of neutrophils, neutrophil/lymphocyte ratio, platelet/neutrophil ratio and preoperative nutritional indicators of serum albumin and body mass index were potential prognostic factors for gastric cancer (all P<0.05). On the basis of the above results, three models for prediction of prognosis were constructed. Variables included in the three models are as follows. (1) Inflammatory model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, percentage of neutrophils, and neutrophil-lymphocyte ratio; (2) nutritional model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, and serum albumin; and (3) combined inflammatory/nutritional model: age, maximum tumor size, number of examined lymph nodes, pT stage, pN stage, percentage of neutrophils, neutrophil-lymphocyte ratio, and serum albumin. We found that the predictive accuracy of the combined inflammatory/nutritional model, which incorporates both inflammatory indicators and nutrition indicators (iAUC: 0.676, 95% CI: 0.650-0.719, C-index: 0.698),was superior to that of the inflammation model (iAUC: 0.662, 95% CI: 0.673-0.706;C-index: 0.675), nutritional model (iAUC: 0.666, 95% CI: 0.642-0.698, C-index: 0.672), and TNM staging control model (iAUC: 0.676, 95% CI: 0.650-0.719, C-index: 0.658). Furthermore, the combined inflammatory/nutritional model had better fitting performance (AIC: 10 762) than the inflammatory model (AIC: 10 834), nutritional model (AIC: 10 810), and TNM staging control model (AIC: 10 974). Conclusions: Preoperative percentage of neutrophils, NLR, and BMI have predictive value for the prognosis of gastric cancer patients. The inflammatory / nutritional model can be used to predict the survival and prognosis of gastric cancer patients on an individualized basis.
Male ; Humans ; Middle Aged ; Aged ; Female ; Prognosis ; Retrospective Studies ; Stomach Neoplasms/pathology* ; Neoplasm Staging ; Gastrectomy ; Serum Albumin

Objective: To evaluate the prognostic value of left ventricular ejection fraction (LVEF) reserve assessed by gated SPECT myocardial perfusion imaging (SPECT G-MPI) for major adverse cardiovascular event (MACE) in patients with coronary artery disease. Methods: This is a retrospective cohort study. From January 2017 to December 2019, patients with coronary artery disease and confirmed myocardial ischemia by stress and rest SPECT G-MPI, and underwent coronary angiography within 3 months were enrolled. The sum stress score (SSS) and sum resting score (SRS) were analyzed by the standard 17-segment model, and the sum difference score (SDS, SDS=SSS-SRS) was calculated. The LVEF at stress and rest were analyzed by 4DM software. The LVEF reserve (ΔLVEF) was calculated (ΔLVEF=stress LVEF-rest LVEF). The primary endpoint was MACE, which was obtained by reviewing the medical record system or by telephone follow-up once every twelve months. Patients were divided into MACE-free and MACE groups. Spearman correlation analysis was used to analyze the correlation between ΔLVEF and all MPI parameters. Cox regression analysis was used to analyze the independent factors of MACE, and the optimal SDS cutoff value for predicting MACE was determined by receiver operating characteristic curve (ROC). Kaplan-Meier survival curves were plotted to compare the difference in the incidence of MACE between different SDS groups and different ΔLVEF groups. Results: A total of 164 patients with coronary artery disease [120 male; age (58.6±10.7) years] were included. The average follow-up time was (26.5±10.4) months, and a total of 30 MACE were recorded during follow-up. Multivariate Cox regression analysis showed that SDS (HR=1.069, 95%CI: 1.005-1.137, P=0.035) and ΔLVEF (HR=0.935, 95%CI: 0.878-0.995, P=0.034) were independent predictors of MACE. According to ROC curve analysis, the optimal cut-off to predict MACE was a SDS of 5.5 with an area under the curve of 0.63 (P=0.022). Survival analysis showed that the incidence of MACE was significantly higher in the SDS≥5.5 group than in the SDS<5.5 group (27.6% vs. 13.2%, P=0.019), but the incidence of MACE was significantly lower in the ΔLVEF≥0 group than in theΔLVEF<0 group (11.0% vs. 25.6%, P=0.022). Conclusions: LVEF reserve (ΔLVEF) assessed by SPECT G-MPI serves as an independent protective factor for MACE, while SDS is an independent risk predictor in patients with coronary artery disease. SPECT G-MPI is valuable for risk stratification by assessing myocardial ischemia and LVEF.
Humans ; Male ; Middle Aged ; Aged ; Coronary Artery Disease/diagnostic imaging* ; Stroke Volume ; Myocardial Perfusion Imaging ; Retrospective Studies ; Ventricular Function, Left ; Myocardial Ischemia

This study explored the feasibility of mineral element content and ratios of nitrogen isotopes to discriminate the cultivation mode of Dendrobium nobile in order to provide theoretical support for the discrimination of the cultivation mode of D. nobile. The content of 11 mineral elements(N, K, Ca, P, Mg, Na, Fe, Cu, Zn, Mn, and B) and nitrogen isotope ratios in D. nobile and its substrate samples in three cultivation methods(greenhouse cultivation, tree-attached cultivation, and stone-attached cultivation) were determined. According to the analysis of variance, principal component analysis, and stepwise discriminant analysis, the samples of different cultivation types were classified. The results showed that the nitrogen isotope ratios and the content of elements except for Zn were significantly different among different cultivation types of D. nobile(P<0.05). The results of correlation analysis showed that the nitrogen isotope ratios, mineral element content, and effective component content in D. nobile were correlated with the nitrogen isotope ratio and mineral element content in the corresponding substrate samples to varying degrees. Principal component analysis can preliminarily classify the samples of D. nobile, but some samples overlapped. Through stepwise discriminant analysis, six indicators, including δ~(15)N, K, Cu, P, Na, and Ca, were screened out, which could be used to establish the discriminant model of D. nobile cultivation methods, and the overall correct discrimination rates after back-substitution test, cross-check, and external validation were all 100%. Therefore, nitrogen isotope ratios and mineral element fingerprints combined with multivariate statistical analysis could effectively discriminate the cultivation types of D. nobile. The results of this study provide a new method for the identification of the cultivation type and production area of D. nobile and an experimental basis for the quality evaluation and quality control of D. nobile.
Dendrobium ; Minerals ; Discriminant Analysis ; Multivariate Analysis ; Nitrogen Isotopes

Lilii Bulbus is a commonly used Chinese herbal medicine with both medicinal and edible values, while the market products usually has the problem of sulfur fumigation. Therefore, the quality and safety of Lilii Bulbus products deserve attention. In this study, ultra-high performance liquid chromatography-time of flight-tandem mass spectrometry(UPLC-Q-TOF-MS/MS) was combined with principal component analysis(PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA) to analyze the differential components of Lilii Bulbus before and after sulfur fumigation. We identified ten markers generated after sulfur fumigation, summarized their mass fragmentation and transformation patterns, and verified the structures of phenylacrylic acid markers of sulfur fumigation. At the same time, the cytotoxicity of the aqueous extracts of Lilii Bulbus before and after sulfur fumigation was evaluated. The results showed that in the concentration range of 0-800 mg·L~(-1), the aqueous extract of Lilii Bulbus after sulfur fumigation had no significant effect on the viability of human liver LO2 cells, human renal proximal tubular HK-2 cells, and rat adrenal pheochromocytoma PC-12 cells. Moreover, the viability of the cells exposed to the aqueous extract of Lilii Bulbus before and after sulfur fumigation showed no significant difference. This study identified phenylacrylic acid and furostanol saponins as markers of sulfur-fumigated Lilii Bulbus for the first time, and made clear that proper sulfur fumigation of Lilii Bulbus would not produce cytotoxicity, providing a theoretical basis for the rapid identification and quality and safety control of sulfur-fumigated Lilii Bulbus.
Humans ; Animals ; Rats ; Fumigation ; Tandem Mass Spectrometry ; Chromatography, High Pressure Liquid ; Epithelial Cells ; Sulfur

Objective: To explore the efficacy of chemotherapy re-challenge in the third-line setting for patients with metastatic colorectal cancer (mCRC) in the real world. Methods: The clinicopathological data, treatment information, recent treatment efficacy, adverse events and survival data of mCRC patients who had disease progression after treatment with oxaliplatin-based and/or irinotecan-based chemotherapy and received third-line chemotherapy re-challenge from January 2013 to December 2020 at Tianjin Medical University Cancer Institute and Hospital were retrospectively collected. Survival curves were plotted with the Kaplan-Meier method, and the Cox proportional hazard model was used to analyze the prognostic factors. Results: A total of 95 mCRC patients were included. Among them, 32 patients (33.7%) received chemotherapy alone and 63 patients (66.3%) received chemotherapy combined with targeted drugs. Eighty-three patients were treated with dual-drug chemotherapy (87.4%), including oxaliplatin re-challenge in 35 patients and irinotecan re-challenge in 48 patients. The remaining 12 patients were treated with triplet chemotherapy regimens (12.6%). Among them, as 5 patients had sequential application of oxaliplatin and irinotecan in front-line treatments, their third-line therapy re-challenged both oxaliplatin and irinotecan; 7 patients only had oxaliplatin prescription before, and these patients re-challenged oxaliplatin in the third-line treatment. The overall response rate (ORR) and disease control rate (DCR) reached 8.6% (8/93) and 61.3% (57/93), respectively. The median progression free survival (mPFS) and median overall survival (mOS) were 4.9 months and 13.0 months, respectively. The most common adverse events were leukopenia (34.7%) and neutropenia (34.7%), followed by gastrointestinal adverse reactions such as nausea (32.6%) and vomiting (31.6%). Grade 3-4 adverse events were mostly hematological toxicity. Cox multivariate analysis showed that gender (HR=1.609, 95% CI: 1.016-2.548) and the PFS of front-line treatments (HR=0.598, 95% CI: 0.378-0.947) were independent prognostic factors. Conclusion: The results suggested that it is safe and effective for mCRC patients to choose third-line chemotherapy re-challenge, especially for patients with a PFS of more than one year in front-line treatments.
Humans ; Irinotecan/therapeutic use* ; Oxaliplatin/therapeutic use* ; Colorectal Neoplasms/pathology* ; Retrospective Studies ; Fluorouracil ; Colonic Neoplasms/chemically induced* ; Rectal Neoplasms/drug therapy* ; Antineoplastic Combined Chemotherapy Protocols/adverse effects* ; Camptothecin/adverse effects*

Objective: To explore the efficacy of chemotherapy re-challenge in the third-line setting for patients with metastatic colorectal cancer (mCRC) in the real world. Methods: The clinicopathological data, treatment information, recent treatment efficacy, adverse events and survival data of mCRC patients who had disease progression after treatment with oxaliplatin-based and/or irinotecan-based chemotherapy and received third-line chemotherapy re-challenge from January 2013 to December 2020 at Tianjin Medical University Cancer Institute and Hospital were retrospectively collected. Survival curves were plotted with the Kaplan-Meier method, and the Cox proportional hazard model was used to analyze the prognostic factors. Results: A total of 95 mCRC patients were included. Among them, 32 patients (33.7%) received chemotherapy alone and 63 patients (66.3%) received chemotherapy combined with targeted drugs. Eighty-three patients were treated with dual-drug chemotherapy (87.4%), including oxaliplatin re-challenge in 35 patients and irinotecan re-challenge in 48 patients. The remaining 12 patients were treated with triplet chemotherapy regimens (12.6%). Among them, as 5 patients had sequential application of oxaliplatin and irinotecan in front-line treatments, their third-line therapy re-challenged both oxaliplatin and irinotecan; 7 patients only had oxaliplatin prescription before, and these patients re-challenged oxaliplatin in the third-line treatment. The overall response rate (ORR) and disease control rate (DCR) reached 8.6% (8/93) and 61.3% (57/93), respectively. The median progression free survival (mPFS) and median overall survival (mOS) were 4.9 months and 13.0 months, respectively. The most common adverse events were leukopenia (34.7%) and neutropenia (34.7%), followed by gastrointestinal adverse reactions such as nausea (32.6%) and vomiting (31.6%). Grade 3-4 adverse events were mostly hematological toxicity. Cox multivariate analysis showed that gender (HR=1.609, 95% CI: 1.016-2.548) and the PFS of front-line treatments (HR=0.598, 95% CI: 0.378-0.947) were independent prognostic factors. Conclusion: The results suggested that it is safe and effective for mCRC patients to choose third-line chemotherapy re-challenge, especially for patients with a PFS of more than one year in front-line treatments.
Humans ; Irinotecan/therapeutic use* ; Oxaliplatin/therapeutic use* ; Colorectal Neoplasms/pathology* ; Retrospective Studies ; Fluorouracil ; Colonic Neoplasms/chemically induced* ; Rectal Neoplasms/drug therapy* ; Antineoplastic Combined Chemotherapy Protocols/adverse effects* ; Camptothecin/adverse effects*

This study analyzed the molecular mechanism of Huangjing Qianshi Decoction(HQD) in the treatment of prediabetes based on network pharmacology and molecular docking. The active components of HQD were identified and screened based on Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP, http://Lsp.nwu.edu.cn/tcmsp.php) and then the targets of the components and the genes related to prediabetes were retrieved, followed by identifying the common targets of the decoction and the disease. The medicinal component-target network was constructed by Cytoscape to screen key components. The protein-protein interaction(PPI) network was established by STRING and hub genes were identified by Cytoscape-CytoNCA, followed by Gene Ontology(GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) of the hub genes with R-clusterProfi-ler. Thereby, the possible signaling pathways were predicted and the molecular mechanism was deduced. A total of 79 active components of HQD and 785 diabetes-related targets of the components were screened out. The hub genes mainly involved the GO terms of tricarboxylic acid cycle, peptide binding, amide binding, hydrolase activity, and kinase activity regulation, and the KEGG pathways of AGE-RAGE signaling pathway, TNF signaling pathway, AMPK signaling pathway, IL-17 signaling pathway, and insulin signaling pathway. Western blot result showed that HQD-containing serum significantly reduced the expression of AKT1, AGE, and RAGE proteins in insulin resistance model cells. HQD's treatment of prediabetes is characterized by multiple pathways, multiple targets, and multiple levels. The main mechanism is that the components zhonghualiaoine, baicalein, kaempferol, and luteolin act on AKT1 and inhibit the AGE-RAGE axis.
Drugs, Chinese Herbal/pharmacology* ; Humans ; Medicine, Chinese Traditional ; Molecular Docking Simulation ; Network Pharmacology ; Prediabetic State/genetics*