Objective: To investigate the status of condom use and its influencing factors among men who have sex with men (MSM), so as to provide a basis for improving condom utilization rates and AIDS prevention and control in this population. Methods: From May to October 2024, a snowball sampling method was employed to recruit MSM in Songjiang District, Shanghai Municipality. Self-administered questionnaires were used to collect data on demographic characteristics, AIDS-related knowledge, sexual behaviors, pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP), and condom use in the past six months. Multivariable logistic regression model was used to analyze the influencing factors for consistent condom use. Results: A total of 921 MSM were surveyed, with a median age of 29.00 (interquartile range, 9.00) years. Among them, 697 (75.68%) were aware of AIDS-related knowledge, 826 (89.69%) expressed willingness to use PrEP, and 835 (90.66%) were willing to use PEP. Additionally, 787 (85.45%) MSM reported their age at first homosexual intercourse as ≥18 years, while 519 (56.35%) reported consistent condom use in the past six months. Multivariable logistic regression analysis revealed that MSM who were aware of AIDS-related knowledge (OR=0.582, 95% CI: 0.423-0.801), willing to use PrEP (OR =0.611, 95% CI: 0.385-0.969), and whose age at first homosexual intercourse was <18 years (OR=0.480, 95% CI: 0.330-0.700) were less likely to consistent use condoms. Conclusion The proportion of consistent condom use among the MSM remains relatively low, which is primarily associated with AIDS-related knowledge, willingness to use PrEP, and the age at first homosexual intercourse.

OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

ObjectiveTo explore the role of the histone deacetylase Sirtuin-1 （SIRT1）/p53 signaling pathway in promoting apoptosis of non-small cell lung cancer cells （NSCLC） induced by the co-stimulatory molecule B7 homolog 3 （B7-H3）. MethodsThe GEPIA 2 platform was used for survival analysis of NSCLC patients based on B7⁃H3 gene expression levels. The Gene Enrichment Analysis （GSEA） method was used to analyze the enrichment characteristics of B7⁃H3 molecules in the gene set of cell apoptosis. In the non-small cell lung cancer A549 cell line， B7⁃H3 was knocked down， and the protein expression levels of SIRT1 and p53 were detected by Western blot. B7⁃H3 was overexpressed in A549 cells and the apoptosis rate was analyzed by flow cytometry after Annexin V/PI double staining. Overexpression of B7⁃H3 and knockdown of SIRT1 were performed in A549 cell line. The expression levels of p53 and apoptosis-related proteins B-cell lymphoma/leukemia-2 （Bcl-2） and Bcl-2-associated X protein （Bax） were detected respectively by Western blot. Cell apoptosis rate was analyzed by flow cytometry after Annexin V/PI double staining. ResultsThe overall survival of the B7-H3 high-expression group was significantly lower than that of the low-expression group （P<0.01）. B7-H3 was significantly enriched in the cell apoptosis signaling pathway and the p53 signaling pathway （P<0.05）. Compared with the control group， the expression of SIRT1 was significantly downregulated， and p53 was significantly upregulated in the B7⁃H3 knockdown group （both P<0.001）. Overexpression of B7-H3 significantly up-regulated SIRT1 protein expression （P<0.05）， down-regulated p53 expression （P<0.01）， and markedly increased the Bcl-2/Bax ratio of apoptosis-related proteins （P<0.001）. The results of Annexin V/PI double staining showed that the apoptosis rate of A549 cells with overexpressed B7⁃H3 decreased （the apoptosis rate of the control group was 26.72%±4.13%， while that of the B7⁃H3 overexpression group was 13.87%±0.82%； P<0.01）. In B7-H3-overexpressing cell lines， SIRT1 knockdown significantly reversed apoptosis （P<0.05）， up-regulated p53 protein expression （P<0.001）， and markedly reduced the Bcl-2/Bax ratio （P<0.001）. ConclusionB7-H3 molecule inhibits the apoptosis of non-small cell lung cancer cells via the SIRT1/p53 signaling pathway.

ObjectiveQi deficiency and phlegm dampness syndrome is a common type of clinical traditional Chinese medicine（TCM） syndrome. However， there is no standard， scientific， and accurate report on the construction of animal models of Qi deficiency and phlegm dampness syndrome. This study aims to construct a mouse model of Qi deficiency and phlegm dampness syndrome by using a multi-factor composite modeling method and to evaluate the model. MethodsTwenty-one C57BL/6 mice were randomly divided into three groups with seven mice in each group， which were the normal group， model group， and Shenling Baizhusan （SLBZ） group. The control group was fed with ordinary diet and kept in a normal environment. The model group and SLBZ group were fed with a high-fat diet in a high-humidity environment. Swimming with heavy weights until exhaustion and gavage with cold water or lard were used to establish the mouse model of Qi deficiency and phlegm dampness syndrome. In order to test the syndrome by prescription， mice in the SLBZ group were treated with SLBZ for 14 days after model construction. The exhaustive swimming time， body weight， serum lipid levels， tongue changes， "Qi deficiency and phlegm dampness" assessment scale score， and cecal index of mice in each group were measured. The feces of each group of mice were sent for metagenomics and metabolome sequencing， and the changes in intestinal flora and metabolites were analyzed. ResultsAfter the modeling of Qi deficiency and phlegm dampness syndrome， the exhaustive swimming time of mice was obviously shortened （P<0.01）. The serum total cholesterol， low density lipoprotein cholesterol， and non-high density lipoprotein cholesterol of mice were significantly increased （all P<0.01）. The tongue of mice was significantly different from that of the normal group， and the score of the assessment scale was significantly higher than that of the control group （P<0.01）. Cecal index decreased significantly （P<0.01）. The serum lipid level， tongue image， assessment scale score， and cecal index were reversed in the SLBZ group. Metagenomic and metabolome sequencing results showed that intestinal flora and fecal metabolites were significantly changed in mice with Qi deficiency and phlegm dampness syndrome. Akkermansia_muciniphila， Faecalibaculum_rodentium， Eubacterium_plexicaudatum， Eubacterium sp 14_2， Candida glabrata， Romboutsia_ilealis， Turicibacter sp TS3， and other bacteria had significant changes， and the expressions of intestinal metabolites such as chenodeoxycholic acid， choline， L-phenylalanine betaine， and 2-phenylbutyric acid were significantly changed. Related metabolic pathways such as linoleic acid metabolism， primary bile acid biosynthesis， lysine degradation， arginine biosynthesis， and alpha-linolenic acid metabolism were affected. ConclusionThe Qi deficiency and phlegm dampness model of mice can be constructed by the multi-factor composite modeling method of high-fat diet feeding， high-humidity environment feeding， exhaustive swimming with heavy weight， and intragastric administration with cold water or lard. The blood lipid level， tongue change， score of "Qi deficiency and phlegm dampness assessment scale"， cecal index， and changes in related intestinal flora and metabolites of mice can be used as key indicators for model evaluation.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveTo evaluate the effects of Tongnaoyin on the blood-brain barrier status and neurological impairment in acute ischemic stroke （AIS） patients with the syndrome of phlegm-stasis blocking collaterals by dynamic contrast-enhanced magnetic resonance imaging （DCE-MRI）. MethodsA total of 63 patients diagnosed with AIS in the Jiangsu Province Hospital of Chinese Medicine from October 2022 to December 2023 were enrolled in this study. According to random number table method，the patients were assigned into a control group （32 cases） and an observation group （31 cases）. The control group received conventional Western medical treatment，and the observation group took 200 mL Tongnaoyin after meals，twice a day from day 2 of admission on the basis of the treatment in the control group. After 7 days of treatment，the patients were examined by DCE-MRI. The baseline data for two groups of patients before treatment were compared. The National Institute of Health Stroke Scale （NIHSS） score and modified Rankin Scale （mRS） score were recorded before treatment and after 90 days of treatment for both groups. The rKtrans，rKep，and rVe values were obtained from the region of interest （ROI） of the infarct zone/mirror area and compared between the two groups. ResultsThere was no significant difference in the NIHSS or mRS score between the two groups before treatment. After 90 days of treatment，the NIHSS and mRS scores declined in both groups，and the observation group had lower scores than the control group （P<0.05）. After treatment，the rKtrans and rVe in the observation group were lower than those in the control group （P<0.01）. ConclusionCompared with conventional Western medical treatment alone，conventional Western medical treatment combined with Tongnaoyin accelerates the repair of the blood-brain barrier in AIS patients，thereby ameliorating neurological impairment after AIS to improve the prognosis.

Objective To investigate the molecular mechanism by which Lichong Xiaozheng Granules(LCXZ)sensitize ovarian cancer to cisplatin(DDP)treatment.Methods LC-MS analysis was used to identify the blood components of LCXZ after its administration in mice via gavage.In a BALB/c mouse model bearing subcutaneous ovarian cancer xenografts,the effects of daily gavage of distilled water(control group),intraperitoneal injection of DDP(5 mg/kg)once a week,or both DDP injection and daily LCXZK gavage(15 g/kg)on tumor growth were evaluated.Histopathological changes in the xenografts and kidneys were assessed with HE staining.RNA-seq was performed to identify the differentially expressed genes followed by KEGG pathway analysis.The changes in mitochondrial ultrastructure and expressions of mitochondrial apoptosis-related were examined with transmission electron microscopy and Western blotting.Results A total of 218 blood-borne components of LCXZ were detected by LC-MS.In the tumor-bearing mice,treatments with DDP and DDP combined with LCXZ redcued the tumor volume by 60.3%and 72.6%compared with that in the control group,respectively.Transcriptomic analysis revealed significantly upregulated ANT3 expression in both the two treatment groups.Molecular docking indicated that the main active components of LCXZ were capable of binding to adenine nucleotide translocator 3(ANT3)with binding energies below-6 kcal/mol.Transmission electron microscopy showed obvious mitochondrial swelling and outer-membrane damage in the tumor cells in DDP-treated mice,and these changes were more pronounced in the combined treatment group.The expression levels of BAX,ANT3,cleaved caspase-3 and cleaved caspase-9 were increased,whereas BCL-2 expression was decreased significantly in the tumor cells in both the DDP and DDP+LCXZ groups.Conclusion LCXZ enhances the therapeutic efficacy of cisplatin against ovarian cancer xenografts in mice by promoting mitochondrial dysfunction and activating apoptotic signaling pathways via upregulating ANT3.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

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.