To investigate the impact of preoperative serum follicle-stimulating hormone (FSH) levels on the probability of testicular sperm retrieval, we conducted a study of nonobstructive azoospermic (NOA) men with different testicular volumes (TVs) who underwent microdissection testicular sperm extraction (micro-TESE). A total of 177 NOA patients undergoing micro-TESE for the first time from April 2019 to November 2022 in Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital, Shenzhen, China) were retrospectively reviewed. The subjects were divided into four groups based on average TV quartiles. Serum hormone levels in each TV group were compared between positive and negative sperm retrieval subgroups. Overall sperm retrieval rate was 57.6%. FSH levels (median [interquartile range]) were higher in the positive sperm retrieval subgroup compared with the negative outcome subgroup when average TV was <5 ml (first quartile [Q1: TV <3 ml]: 43.32 [17.92] IU l -1 vs 32.95 [18.56] IU l -1 , P = 0.048; second quartile [Q2: 3 ml ≤ TV <5 ml]: 31.31 [15.37] IU l -1 vs 25.59 [18.40] IU l -1 , P = 0.042). Elevated serum FSH levels were associated with successful micro-TESE sperm retrieval in NOA men whose average TVs were <5 ml (adjusted odds ratio [OR]: 1.06 per unit increase; 95% confidence interval [CI]: 1.01-1.11; P = 0.011). In men with TVs ≥5 ml, larger TVs were associated with lower odds of sperm retrieval (adjusted OR: 0.84 per 1 ml increase; 95% CI: 0.71-0.98; P = 0.029). In conclusion, elevated serum FSH levels were associated with positive sperm retrieval in micro-TESE in NOA men with TVs <5 ml. In men with TV ≥5 ml, increases in average TVs were associated with lower odds of sperm retrieval.
Humans ; Male ; Azoospermia/surgery* ; Sperm Retrieval/statistics & numerical data* ; Adult ; Follicle Stimulating Hormone/blood* ; Retrospective Studies ; Testis/pathology* ; Microdissection ; Organ Size

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Objective To propose a lightweight deep learning network-based segmentation method for automatic segmenta-tion of the skeletal muscle at the third cervical spine(C3)level.Methods Firstly,121 patients with head and neck tumors admitted to the Department of Oncology of Zigong First People's Hospital from January 2019 to December 2022 were selected and randomly divided into a training set,a validation set and a test set in the ratio of 7∶1∶2.Secondly,a lightweight Mamba architecture was introduced into the UNet network and an attention gate(AG)mechanism was added to the skip connection path to construct a MB-UNet network model.Finally,the trained network models were evaluated for segmentation performance on the test set,the MB-UNet network model was compared with manual segmentation over the results of determination of skeletal muscle area(SMA),and with classical network models in terms of parameter scale and computation effort including UNet,Deeplab V3+,U2Net,VMUNet and UltraLight-VMUNet models.The time required by the MB-UNet network model for predicting SMA and that by the physician with the assistance of the model was summarized.Results When used for segmenting the skeletal muscle at C3 level the constructued MB-UNet network model gained advantages over the classical models,with a Dice similarity coefficient of 88.23%,an intersection over union(IoU)ratio of 78.94%,a sensitivity of 91.27%and a 95%Hausdorff distance of 7.13 mm;the SMA determined by manual segementation was basically close to that by the MB-UNet network model;the MB-UNet network model behaved generally better than the classical network models,with the computation effort being 1.88 GFLOPS and the parameter scale being 0.77M;it took the MB-UNet network model 1.93 s for the prediction on the test set,and only 2 min for the physician to obtain satisfactory results with the assistance of the MB-UNet network model,which was significantly shorter than that by munual segmentation(20 min).Conclusion The proposed method contributes to segmenting the skeletal muscle at C3 level precisely and rapidly and calculating SMA accurately,which helps clinicians to quickly diagnose sarcopenia in patients with head and neck tumors and improves the diagnostic efficiency.[Chinese Medical Equipment Journal,2025,46(8):11-17]

The scientific research and innovation capabilities of medical students are intrinsically linked to the sustained and high-quality development of national healthcare initiatives.Cultivating outstanding medi-cal students with independent scientific capabilities and innovative consciousness is a critical component in the education and training of high-level medical professionals.Our investigation revealed that within the imperfections of the cultivating model,some faculty and students at medical schools have an insufficient understanding of scientific research and innovation and lack motivation for engaging in such activities,which hinder the progression of scientific research activities.Consequently,we initiated a teaching practice and exploratory study on the"tutorial system"aimed at fostering medical students'scientific research and innovation abilities.Based on the principle of"research informing teaching,teaching and research advan-cing together,"this study implements a"tutorial system"coordinated by tutors,supplemented by graduate and undergraduate student mentors,to cultivate innovative thinking,stimulate interest in scientific re-search,and enhance practical and research skills among medical students.Through collaborative efforts within"scientific research innovation teams,"various educational methods—including preliminary re-search,in-class and extracurricular activities,intra-group and inter-group interactions,and theoretical and practical applications—are employed to improve and strengthen the cultivation of medical students'scientif-ic research and innovation abilities.This study aims to provide valuable references for optimizing medical education management systems and enhancing the quality of medical student training.

Objective To propose a lightweight deep learning network-based segmentation method for automatic segmenta-tion of the skeletal muscle at the third cervical spine(C3)level.Methods Firstly,121 patients with head and neck tumors admitted to the Department of Oncology of Zigong First People's Hospital from January 2019 to December 2022 were selected and randomly divided into a training set,a validation set and a test set in the ratio of 7∶1∶2.Secondly,a lightweight Mamba architecture was introduced into the UNet network and an attention gate(AG)mechanism was added to the skip connection path to construct a MB-UNet network model.Finally,the trained network models were evaluated for segmentation performance on the test set,the MB-UNet network model was compared with manual segmentation over the results of determination of skeletal muscle area(SMA),and with classical network models in terms of parameter scale and computation effort including UNet,Deeplab V3+,U2Net,VMUNet and UltraLight-VMUNet models.The time required by the MB-UNet network model for predicting SMA and that by the physician with the assistance of the model was summarized.Results When used for segmenting the skeletal muscle at C3 level the constructued MB-UNet network model gained advantages over the classical models,with a Dice similarity coefficient of 88.23%,an intersection over union(IoU)ratio of 78.94%,a sensitivity of 91.27%and a 95%Hausdorff distance of 7.13 mm;the SMA determined by manual segementation was basically close to that by the MB-UNet network model;the MB-UNet network model behaved generally better than the classical network models,with the computation effort being 1.88 GFLOPS and the parameter scale being 0.77M;it took the MB-UNet network model 1.93 s for the prediction on the test set,and only 2 min for the physician to obtain satisfactory results with the assistance of the MB-UNet network model,which was significantly shorter than that by munual segmentation(20 min).Conclusion The proposed method contributes to segmenting the skeletal muscle at C3 level precisely and rapidly and calculating SMA accurately,which helps clinicians to quickly diagnose sarcopenia in patients with head and neck tumors and improves the diagnostic efficiency.[Chinese Medical Equipment Journal,2025,46(8):11-17]

The scientific research and innovation capabilities of medical students are intrinsically linked to the sustained and high-quality development of national healthcare initiatives.Cultivating outstanding medi-cal students with independent scientific capabilities and innovative consciousness is a critical component in the education and training of high-level medical professionals.Our investigation revealed that within the imperfections of the cultivating model,some faculty and students at medical schools have an insufficient understanding of scientific research and innovation and lack motivation for engaging in such activities,which hinder the progression of scientific research activities.Consequently,we initiated a teaching practice and exploratory study on the"tutorial system"aimed at fostering medical students'scientific research and innovation abilities.Based on the principle of"research informing teaching,teaching and research advan-cing together,"this study implements a"tutorial system"coordinated by tutors,supplemented by graduate and undergraduate student mentors,to cultivate innovative thinking,stimulate interest in scientific re-search,and enhance practical and research skills among medical students.Through collaborative efforts within"scientific research innovation teams,"various educational methods—including preliminary re-search,in-class and extracurricular activities,intra-group and inter-group interactions,and theoretical and practical applications—are employed to improve and strengthen the cultivation of medical students'scientif-ic research and innovation abilities.This study aims to provide valuable references for optimizing medical education management systems and enhancing the quality of medical student training.

The recording and analysis of activities of calcium signals in neurons is of critical importance in the field of neuroscience.Over the past three decades,various fluorescent calcium imaging techniques not only have been used in the imaging study of functional activities of neuronal communities,but also can be combined with specific markers to record the functional activities of specific types of neuronal communities.To analyze neural activities at the cellular level,a series of preprocessing such as motion correction,cell body recognition,calcium signal extraction and peak deconvolution is required for the collected video.However,current methods for manual preprocessing are time-consuming and laborious,so computer automatic analysis technology is urgently needed to quickly repair the jitter in the video,identify the position and outline of a single cell,extract its activity trajectory and infer the action potential peak.In this paper,the methods of calcium imaging data processing used in recent years are summarized,and the future developments are predicted.

Objective To build a neural network based on the Unet infrastructure for recognition and segmentation of two-dimensional calcium imaging fluorescence images.Methods The in vivo miniaturized two-photon microscope(mTPM)was used for brain calcium imaging in freely moving mice.The imaging data was motion corrected using the NoRMCorre algorithm and processed using ImageJ software to obtain the original images after correction,and the labels were produced using the Labelme software.The neural network HDCGUnet was built using the original images and labels for training,and optimized to improve the model structure according to the training effect.Finally,the evaluation indexes were selected and compared with those of other models to verify the utility of this model.Results The HDCGUnet model,which was collected and made on our own,performed best in the two-photon calcium imaging dataset compared to other models,and performed well on the BBBC dataset either.Conclusion The HDCGUnet model provides a novel alternative for the recognition and segmentation of two-photon calcium imaging images.

AIM To establish a UPLC-MS/MS method for the simultaneous content determination of liquiritin apioside,alibiflorin,swertiamarin,methyl gallate,benzoylpaeoniflorin,sweroside,6′-O-β-D-glucosylgentiopicroside,isoliquiritigenin,loganic acid,liquiritigenin,gallic acid,paeoniflorin,oxypaeoniflorin,gentiopicroside,glycyrrhizic acid,isoliquiritoside and liquiritin in Yangxue Ruanjian Capsules.METHODS The analysis was performed on a 40℃thermostatic Waters BEH C18column(2.1 mm×100 mm,1.7 μm),with the mobile phase comprising of 2 mmol/L ammonium acetate(containing 0.1%formic acid)-acetonitrile flowing at 0.3 mL/min in a gradient elution manner,and electron spray ionization source was adopted in negative ion scanning with multiple reaction monitoring mode.RESULTS Seventeen constituents showed good linear relationships within their own ranges(r>0.999 6),whose average recoveries were 91.33%-104.03%with the RSDs of 1.58%-3.50%.CONCLUSION This rapid,accurate and stable method can be used for the quality control of Yangxue Ruanjian Capsules.

Purpose::Minimal data exist on brain injury in patients with exertional heatstroke (EHS) in developing country. In this study, we explored the risk factors for brain injury induced by EHS 90-day after onset.Methods::A retrospective cohort study of patients with EHS was conducted in the intensive care unit of the General Hospital of Southern Theater Command of PLA in China from April 2014 to June 2019. Patients were divided into non-brain injury (fully recovered) and brain injury groups (comprising deceased patients or those with neurological sequelae). The brain injury group was further subdivided into a death group and a sequela group for detailed analysis. General information, neurological performance and information on important organ injuries in the acute stage were recorded and analysed. Multivariable logistic regression was used to identify risk factors for brain injury after EHS and mortality risk factors for brain injury, and Kaplan-Meier survival curve was used to evaluate the effect of the neurological dysfunction on survival.Results::Out of the 147 EHS patients, 117 were enrolled, of which 96 (82.1%) recovered, 13 (11.1%) died, and 8 (6.8%) experienced neurological sequelae. Statistically significant differences were found between non-brain injury and brain injury groups in age, hypotension, duration of consciousness disorders, time to drop core body temperature below 38.5°C, lymphocyte counts, platelet counts, procalcitonin, alanine aminotransferase, aspartate aminotransferase, creatinine, cystatin C, coagulation parameters, international normalized ratio, acute physiology and chronic health evaluation II scores, sequential organ failure assessment (SOFA) scores, and Glasgow coma scale scores (all p < 0.05). Multivariate logistic regression showed that age ( OR = 1.090, 95% CI: 1.02 - 1.17, p = 0.008), time to drop core temperature ( OR = 8.223, 95% CI: 2.30 - 29.40, p = 0.001), and SOFA scores ( OR= 1.676, 95% CI: 1.29 - 2.18, p < 0.001) are independent risk factors for brain injury induced by EHS. The Kaplan-Meier curves suggest significantly prolonged survival ( p < 0.001) in patients with early Glasgow coma scale score > 8 and duration of consciousness disorders ≤ 24 h. Conclusions::Advanced age, delayed cooling, and higher SOFA scores significantly increase the risk of brain injury post-EHS. These findings underscore the importance of rapid cooling and early assessment of organ failure to improve outcomes in EHS patients.