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*

OBJECTIVE: To examine the mechanism of action of tanshinone II A (Tan II A) in promoting chemosensitization of osteosarcoma cells to cisplatin (DDP). METHODS: The effects of different concentrations of Tan II A (0-80 µ mol/L) and DDP (0-2 µ mol/L) on the proliferation of osteosarcoma cell lines (U2R, U2OS, 143B, and HOS) at different times were examined using the cell counting kit-8 and colony formation assays. Migration and invasion of U2R and U2OS cells were detected after 24 h treatment with 30 µ mol/L Tan II A, 0.5 µ mol/L DDP alone, and a combination of 10 µ mol/L Tan II A and 0.25 µ mol/L DDP using the transwell assay. After 48 h of treatment of U2R and U2OS cells with predetermined concentrations of each group of drugs, the cell cycle was analyzed using a cell cycle detection kit and flow cytometry. After 48 h treatment, apoptosis of U2R and U2OS cells was detected using annexin V-FITC apoptosis detection kit and flow cytometry. U2R cells were inoculated into the unilateral axilla of nude mice and then the mice were randomly divided into 4 groups of 6 nude mice each. The 4 groups were treated with equal volume of Tan II A (15 mg/kg), DDP (3 mg/kg), Tan II A (7.5 mg/kg) + DDP (1.5 mg/kg), and normal saline, respectively. The body weight of the nude mice was weighed, and the tumor volume and weight were measured. Cell-related gene and signaling pathway expression were detected by RNA sequencing and Kyoto Encyclopedia of Genes and Genomes pathway analysis. p38 MAPK signaling pathway proteins and apoptotic protein expressions were detected by Western blot. RESULTS: In vitro studies have shown that Tan II A, DDP and the combination of Tan II A and DDP inhibit the proliferation, migration and invasion of osteosarcoma cells. The inhibitory effect was more pronounced in the Tan II A and DDP combined treatment group (P<0.05 or P<0.01). Osteosarcoma cells underwent significantly cell-cycle arrest and cell apoptosis by Tan II A-DDP combination treatment (P<0.05 or P<0.01). In vivo studies demonstrated that the Tan II A-DD combination treatment group significantly inhibited tumor growth compared to the Tan II A and DDP single drug group (P<0.01). Additionally, we found that the combination of Tan II A and DDP treatment enhanced the p38 MAPK signaling pathway. Western blot assays showed higher p-p38, cleaved caspase-3, and Bax and lower caspase-3, and Bcl-2 expressions with the combination of Tan II A and DDP treatment compared to the single drug treatment (P<0.01). CONCLUSION Tan II A synergizes with DDP by activating the p38/MAPK pathway to upregulate cleaved caspase-3 and Bax pro-apoptotic gene expressions, and downregulate caspase-3 and Bcl-2 inhibitory apoptotic gene expressions, thereby enhancing the chemosensitivity of osteosarcoma cells to DDP.
Abietanes/therapeutic use* ; Osteosarcoma/enzymology* ; Cisplatin/therapeutic use* ; Humans ; Cell Line, Tumor ; Animals ; Apoptosis/drug effects* ; Mice, Nude ; Cell Proliferation/drug effects* ; Cell Movement/drug effects* ; p38 Mitogen-Activated Protein Kinases/metabolism* ; MAP Kinase Signaling System/drug effects* ; Bone Neoplasms/enzymology* ; Cell Cycle/drug effects* ; Xenograft Model Antitumor Assays ; Mice ; Drug Resistance, Neoplasm/drug effects* ; Neoplasm Invasiveness ; Mice, Inbred BALB C

OBJECTIVE: To investigate the effect of zedoarondiol on neovascularization of atherosclerotic (AS) plaque by exosomes experiment. METHODS: ApoE^-/- mice were fed with high-fat diet to establish AS model and treated with high- and low-dose (10, 5 mg/kg daily) of zedoarondiol, respectively. After 14 weeks, the expressions of anti-angiogenic protein thrombospondin 1 (THBS-1) and its receptor CD36 in plaques, as well as platelet activation rate and exosome-derived miR-let-7a were detected. Then, zedoarondiol was used to intervene in platelets in vitro, and miR-let-7a was detected in platelet-derived exosomes (Pexo). Finally, human umbilical vein endothelial cells (HUVECs) were transfected with miR-let-7a mimics and treated with Pexo to observe the effect of miR-let-7a in Pexo on tube formation. RESULTS: Animal experiments showed that after treating with zedoarondiol, the neovascularization density in plaques of AS mice was significantly reduced, THBS-1 and CD36 increased, the platelet activation rate was markedly reduced, and the miR-let-7a level in Pexo was reduced (P<0.01). In vitro experiments, the platelet activation rate and miR-let-7a levels in Pexo were significantly reduced after zedoarondiol's intervention. Cell experiments showed that after Pexo's intervention, the tube length increased, and the transfection of miR-let-7a minics further increased the tube length of cells, while reducing the expressions of THBS-1 and CD36. CONCLUSION Zedoarondiol has the effect of inhibiting neovascularization within plaque in AS mice, and its mechanism may be potentially related to inhibiting platelet activation and reducing the Pexo-derived miRNA-let-7a level.
Animals ; MicroRNAs/genetics* ; Exosomes/drug effects* ; Plaque, Atherosclerotic/genetics* ; Neovascularization, Pathologic/genetics* ; Human Umbilical Vein Endothelial Cells/metabolism* ; Humans ; Blood Platelets/drug effects* ; Apolipoproteins E/deficiency* ; Thrombospondin 1/metabolism* ; CD36 Antigens/metabolism* ; Platelet Activation/drug effects* ; Male ; Mice ; Mice, Inbred C57BL

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.

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 effects of autologous platelet-rich plasma (aPRP) collected using a continuous blood cell separator on blood conservation and prognosis in patients with type A aortic dissection. Methods: The clinical data of patients who underwent emergency aortic replacement for acute type A aortic dissection at our hospital from January 2020 to December 2023 were respectively analyzed. Patients were divided into two groups based on whether they received aPRP collection before surgery for subsequent reinfusion: the aPRP group (n=32) and the control group (n=35). The volume of aPRP collected and the platelet concentration in the aPRP were recorded. The volumes of allogeneic blood and blood products transfused, and the associated costs during hospitalization were compared between two groups. Intraoperative blood loss, perioperative laboratory parameter changes, 24-hour postoperative drainage volume, duration of ICU stay and mechanical ventilation, length of hospital stay, and mortality rate of the two groups were also compared. Results: The platelet concentration in aPRP was (491.5±85.4)×10 /L, accounting for (24.1±9.6)% of the patient's total platelet count. The volume of aPRP collected accounted for (23.0±6.3)% of the patient's total plasma volume. Compared with the control group, the aPRP group demonstrated significantly reduced transfusion volumes of allogeneic red blood cells, plasma, and platelets (P<0.05), along with significantly lower blood-related costs during hospitalization (P<0.05). Postoperative coagulation parameters (APTT, PT, INR, and TEG) were significantly improved (P<0.05), and platelet counts were markedly increased (P<0.05) in aPRP group as compared with the control group. No statistically significant differences were observed in postoperative use of prothrombin complex concentrate and fibrinogen between the two groups. Similarly, there were no significant differences in postoperative 24-hour drainage volume, 24-hour extubation rate, ICU length of stay, duration of mechanical ventilation, or total hospital length of stay. The incidence of complications and mortality did not differ significantly between the two groups. Conclusion: The administration of aPRP significantly reduces the requirement for perioperative allogeneic blood transfusion in patients undergoing surgery for type A aortic dissection. Furthermore, it enhances coagulation function and reduces associated transfusion costs, thereby establishing itself as an effective and safe strategy for blood conservation.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.