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.

The p21-activated kinase 4 (PAK4), a key regulator of malignancy, is negatively correlated with immune infiltration and has become an emergent drug target of cancer therapy. Given the lack of high efficacy PAK4 inhibitors, we herein reported the identification of a novel inhibitor 13 bearing a tetrahydrobenzofuro[2,3-c]pyridine tricyclic core and possessing high potency against MIA PaCa-2 and Pan02 cell lines with IC₅₀ values of 0.38 and 0.50 μmol/L, respectively. This compound directly binds to PAK4 in a non-ATP competitive manner. In the mouse Pan02 model, compound 13 exhibited significant tumor growth inhibition at a dose of 100 mg/kg, accompanied by reduced levels of PAK4 and its phosphorylation together with immune infiltration in mice tumor tissue. Overall, compound 13 is a novel allosteric PAK4 inhibitor with a unique tricyclic structural feature and high potency both in vitro and in vivo, thus making it worthy of further exploration.

To explore the protective effect of Angelica sinensis polysaccharide(ASP)on leptospiro-sis induced by pathogenic Leptospira infection,the golden hamster model of leptospirosis was se-lected for the experiment.The Leptospira and Leptospira+ASP groups were intraperitoneally injected with Leptospira interrogans serovar Lai strain 56601(1 × 10 6 per hamster).After infec-tion,the Leptospira+ASP group was injected intraperitoneally with ASP(50 mg/kg)for three consecutive days,while the Leptospira group was injected intraperitoneally with normal saline for three days.The experiment employed methods such as daily observation of the clinical symptoms of golden hamsters,statistics of the survival status of each group of golden hamsters,pathological damage of liver,kidney,and lung,bacterial load in organs,and the expression of inflammatory cy-tokines(IL-1β and TNF-α).The results indicated that ASP could effectively alleviate the clinical symptoms of the infected hamsters,enhance the survival rate,ameliorate the pathological damage of the body,reduce the bacterial load in various organs,and mitigate tissue inflammation.This study demonstrated for the first time that ASP has a protective effect on leptospirosis,providing medication guidance for the clinical treatment of leptospirosis.

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.

To explore the protective effect of Angelica sinensis polysaccharide(ASP)on leptospiro-sis induced by pathogenic Leptospira infection,the golden hamster model of leptospirosis was se-lected for the experiment.The Leptospira and Leptospira+ASP groups were intraperitoneally injected with Leptospira interrogans serovar Lai strain 56601(1 × 10 6 per hamster).After infec-tion,the Leptospira+ASP group was injected intraperitoneally with ASP(50 mg/kg)for three consecutive days,while the Leptospira group was injected intraperitoneally with normal saline for three days.The experiment employed methods such as daily observation of the clinical symptoms of golden hamsters,statistics of the survival status of each group of golden hamsters,pathological damage of liver,kidney,and lung,bacterial load in organs,and the expression of inflammatory cy-tokines(IL-1β and TNF-α).The results indicated that ASP could effectively alleviate the clinical symptoms of the infected hamsters,enhance the survival rate,ameliorate the pathological damage of the body,reduce the bacterial load in various organs,and mitigate tissue inflammation.This study demonstrated for the first time that ASP has a protective effect on leptospirosis,providing medication guidance for the clinical treatment of leptospirosis.

Background::Carotid intima-media thickness (IMT) and diameter, stiffness, and wave reflections, are independent and important clinical biomarkers and risk predictors for cardiovascular diseases. The purpose of the present study was to establish nationwide reference values of carotid properties for healthy Chinese adults and to explore potential clinical determinants.Methods::A total of 3053 healthy Han Chinese adults (1922 women) aged 18-79 years were enrolled at 28 collaborating tertiary centers throughout China between April 2021 and July 2022. The real-time tracking of common carotid artery walls was achieved by the radio frequency (RF) ultrasound system. The IMT, diameter, compliance coefficient, β stiffness, local pulse wave velocity (PWV), local systolic blood pressure, augmented pressure (AP), and augmentation index (AIx) were then automatically measured and reported. Data were stratified by age groups and sex. The relationships between age and carotid property parameters were analyzed by Jonckheere-Terpstra test and simple linear regressions. The major clinical determinants of carotid properties were identified by Pearson’s correlation, multiple linear regression, and analyses of covariance.Results::All the parameters of carotid properties demonstrated significantly age-related trajectories. Women showed thinner IMT, smaller carotid diameter, larger AP, and AIx than men. The β stiffness and PWV were significantly higher in men than women before forties, but the differences reversed after that. The increase rate of carotid IMT (5.5 μm/year in women and 5.8 μm/year in men) and diameter (0.03 mm/year in both men and women) were similar between men and women. For the stiffness and wave reflections, women showed significantly larger age-related variations than men as demonstrated by steeper regression slopes (all P for age by sex interaction <0.05). The blood pressures, body mass index (BMI), and triglyceride levels were identified as major clinical determinants of carotid properties with adjustment of age and sex. Conclusions::The age- and sex-specific reference values of carotid properties measured by RF ultrasound for healthy Chinese adults were established. The blood pressures, BMI, and triglyceride levels should be considered for clinical application of corresponding reference values.

Objective To summarize the experience of multi-disciplinary team(MDT)in the pediatric department of Qujing Maternal and Child Health Hospital,and to evaluate the effectiveness of MDT on neonatal brain injury.Methods The clinical data of children with brain injury and treated in the pediatrics department of Qujing Maternal and Child Health Hospital from November 2019 to April 2023 were collected.The children with brain injury and treated from October 2019 to June 2020 were regarded as the non-MDT group,and the children with brain injury and treated from July 2020 to April 2023 were regarded as the MDT group for comparative analysis.Chi-square test/t-test was used to compare and analyze the clinical data of the two groups.Results Among the 890 cases of pediatric brain injury,there were 519 males and 371 females.The median and quartiles of the age distribution for the two groups were as follows:MDT group 2.00(0.82,5.00)years and non-MDT group 1.00(1.00,4.00)years.Craniocerebral injury was the main type of brain injury in both groups,in addition,among children with craniocerebral injury and intracranial hemorrhage,the cure rate of MDT group was higher than that of non-MDT group,and the difference was statistically significant(P<0.05).Among the 405 children in MDT group,154(38.0%)underwent the surgery,while among the 485 children in non-MDT group,121(24.9%)underwent the surgery.The difference was statistically significant(P<0.05).23.2% of children in MDT group were in critical condition during the hospitalization,which was significantly lower than that in non-MDT group(30.5%),and the difference was statistically significant(P<0.05).The unhealed rate of MDT group(2.0%)was also significantly lower than that of non-MDT group(5.6%),the cure rate of MDT group(40.5%)was significantly higher than that of non-MDT group(34.4%),and there was a statistically significant difference(P<0.05).The expense of treatment,medicine and sanitary materials in MDT group were lower than those in non-MDT group,and the differences were statistically significant(P<0.05).The multivariate Logistic regression model analysis of the cure rate of children with brain injury showed that the MDT model could effectively improve the cure rate of children with brain injury(RR = 1.513,95% CI = 1.134-2.020).The results of multiple linear regression model analysis showed that there was no statistical difference in the effect of MDT on the actual hospitalization days of children(P>0.05).Conclusion Using MDT model to diagnose and treat children with brain injury is helpful to improve the cure rate,reduce the risk of children's disease aggravation,and achieve the significant therapeutic effects in children with brain injury.MDT model is worth popularizing and applying in children with brain injury.

Humans ; Female ; Deep Learning ; Ovarian Neoplasms/genetics* ; Carcinoma, Ovarian Epithelial/genetics* ; Neural Networks, Computer ; RNA

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults and is poorly controlled. Previous studies have shown that both macrophages and angiogenesis play significant roles in GBM progression, and co-targeting of CSF1R and VEGFR is likely to be an effective strategy for GBM treatment. Therefore, this study developed a novel and selective inhibitor of CSF1R and VEGFR, SYHA1813, possessing potent antitumor activity against GBM. SYHA1813 inhibited VEGFR and CSF1R kinase activities with high potency and selectivity and thus blocked the cell viability of HUVECs and macrophages and exhibited anti-angiogenetic effects both in vitro and in vivo. SYHA1813 also displayed potent in vivo antitumor activity against GBM in immune-competent and immune-deficient mouse models, including temozolomide (TMZ) insensitive tumors. Notably, SYHA1813 could penetrate the blood-brain barrier (BBB) and prolong the survival time of mice bearing intracranial GBM xenografts. Moreover, SYHA1813 treatment resulted in a synergistic antitumor efficacy in combination with the PD-1 antibody. As a clinical proof of concept, SYHA1813 achieved confirmed responses in patients with recurrent GBM in an ongoing first-in-human phase I trial. The data of this study support the rationale for an ongoing phase I clinical study (ChiCTR2100045380).