Objective:To explore the clinical application value of convolutional neural network（CNN）based on deep learning in the automatic measurement of dynamic pelvic floor ultrasound video parameters and the diagnosis and classification of cystocele.Methods:A retrospective analysis was conducted on dynamic pelvic floor ultrasound videos from 398 postpartum women who underwent examinations at the First People's Hospital of Foshan between June 2020 and June 2022. The lowest point of the posterior bladder wall（PWB），urethral rotation angle（URA），and retrovesical angle（RVA）were manually measured by a senior radiologist（R1）and a junior radiologist（R2），and cystocele was classified according to the Green standard. The CNN model was employed to automatically extract the above parameters and to diagnose and classify cystocele. Using R1 measurements as a reference，intraclass correlation coefficient（ICC）was used to evaluate the consistency between the CNN model and R1，as well as between R2 and R1. The Kappa value was used to assess the agreement between the CNN model，R2，and R1 in the diagnosis and classification of cystocele. Additionally，the time consumption of the three measurement methods was compared.Results:The CNN model showed good consistency with R1 in measuring PWB and URA（ICC = 0.983，0.894），while its consistency in measuring RVA was moderate（ICC = 0.614）. The ICC between R2 and R1 in measuring PWB，URA，and RVA was 0.979，0.815，and 0.627，respectively. In the measurement of PWB and URA，the consistency between the CNN model and R1 was superior to that between R2 and R1. For cystocele diagnosis，the Kappa value between the CNN model and R1 was 0.924，which was higher than that between R2 and R1（0.904）. In cystocele classification，the Kappa value between the CNN model and R1 was 0.503，also higher than that between R2 and R1（0.426）. The CNN model processed a single video in 2.5（0.6）s，significantly faster than R1［59.9（16.9）s］and R2［56.8（11.2）s］（all P < 0.001）. Conclusions:The CNN model demonstrates high accuracy and efficiency in the measurement，diagnosis，and classification of cystocele，outperforming a junior radiologist and showing potential for clinical application.

Human carboxylesterase 2A (hCES2A) plays pivotal roles in prodrug activation and hydrolytic metabolism of ester-bearing chemicals. Targeted inhibition of intestinal hCES2A represents a feasible strategy to mitigate irinotecan-triggered gut toxicity (ITGT), but the orally active, selective, and efficacious hCES2A inhibitors are rarely reported. Here, a novel drug-like hCES2A inhibitor was developed via three rounds of structure-based drug design (SBDD) and structural optimization. Initially, donepezil was identified as a moderate hCES2A inhibitor from 2000 US Food and Drug Administration (FDA)-approved drugs. Following two rounds of SBDD and structural optimization, a donepezil derivative (B7) was identified as a strong reversible hCES2A inhibitor. Subsequently, nine B7 carbamates were rationally designed, synthesized and biologically assayed. Among all synthesized carbamates, C3 showed the most potent time-dependent inhibition on hCES2A (IC₅₀ = 0.56 nmol/L), excellent specificity and favorable drug-like properties. C3 could covalently modify the catalytic serine of hCES2A with high selectivity, while this agent also showed favorable safety profiles, high intestinal exposure, and impressive effects for ameliorating ITGT in both human intestinal organoids and tumor-bearing mice. Collectively, this study showcases a rational strategy for developing drug-like and serine-targeting covalent inhibitors against target serine hydrolase(s), while C3 emerges as a promising orally active drug candidate for ameliorating ITGT.

The health of women and children serves as the cornerstone of comprehensive public health and represents a crucial barometer for measuring social progress and civilization. It directly impacts family well-being and social harmony. As a national-level guidance center for complex disease diagnosis and treatment, Beijing's critical maternal care center, and the designated consultation hospital for pregnancy complicated by rheumatic immune and endocrine disorders, Peking Union Medical College Hospital bears significant responsibilities in managing complex medical cases and implementing counterpart assistance programs. Confronted with diverse and complicated maternal sources, particularly emergency cases lacking standardized prenatal care, the hospital has established a multi-tiered, whole-process, and comprehensive maternal-infant safety management system. Through implementing high-risk maternal management protocols, optimizing critical care procedures, promoting informatization development, and enhancing multidisciplinary collaboration, Peking Union Medical College Hospital has substantially improved the efficiency of critical maternal care and maternal-infant safety assurance. This paper systematically summarizes the institutional development experience in maternal management and explores potential optimization approaches, aiming to provide valuable references for enhancing maternal-infant safety management systems in domestic healthcare institutions.

Objective:To explore the clinical application value of convolutional neural network（CNN）based on deep learning in the automatic measurement of dynamic pelvic floor ultrasound video parameters and the diagnosis and classification of cystocele.Methods:A retrospective analysis was conducted on dynamic pelvic floor ultrasound videos from 398 postpartum women who underwent examinations at the First People's Hospital of Foshan between June 2020 and June 2022. The lowest point of the posterior bladder wall（PWB），urethral rotation angle（URA），and retrovesical angle（RVA）were manually measured by a senior radiologist（R1）and a junior radiologist（R2），and cystocele was classified according to the Green standard. The CNN model was employed to automatically extract the above parameters and to diagnose and classify cystocele. Using R1 measurements as a reference，intraclass correlation coefficient（ICC）was used to evaluate the consistency between the CNN model and R1，as well as between R2 and R1. The Kappa value was used to assess the agreement between the CNN model，R2，and R1 in the diagnosis and classification of cystocele. Additionally，the time consumption of the three measurement methods was compared.Results:The CNN model showed good consistency with R1 in measuring PWB and URA（ICC = 0.983，0.894），while its consistency in measuring RVA was moderate（ICC = 0.614）. The ICC between R2 and R1 in measuring PWB，URA，and RVA was 0.979，0.815，and 0.627，respectively. In the measurement of PWB and URA，the consistency between the CNN model and R1 was superior to that between R2 and R1. For cystocele diagnosis，the Kappa value between the CNN model and R1 was 0.924，which was higher than that between R2 and R1（0.904）. In cystocele classification，the Kappa value between the CNN model and R1 was 0.503，also higher than that between R2 and R1（0.426）. The CNN model processed a single video in 2.5（0.6）s，significantly faster than R1［59.9（16.9）s］and R2［56.8（11.2）s］（all P < 0.001）. Conclusions:The CNN model demonstrates high accuracy and efficiency in the measurement，diagnosis，and classification of cystocele，outperforming a junior radiologist and showing potential for clinical application.

Purpose/Significance To analyze relevant patented technologies,and to provide experience for medical and commercial health insurance institutions to break through the data interaction blocking point and construct a multi-level medical security system.Method/Process The patentometrics method is used to analyze the data interaction patent technology between medical and health insurance institutions from four aspects:time trend,regional distribution,category distribution and text clustering.Result/Conclusion China should pay attention to the role of data interaction in deepening cooperation between medical and insurance institutions,improve the declaration,authorization,application and protection of high-quality patents,pay attention to cross-border integration and technology-driven,and innovate patent layout to meet technological development and social needs.

Central medial thalamic nucleus (CM) is a nucleus located in the medullary plate of the midline thalamus. It belongs to the midline nucleus group of the thalamus and serves as a non-specific projection system. CM has extensive anatomical connections with other brain areas and acts as a relay and integration center for brain functions. Recent research has demonstrated that CM is involved in various behaviors such as general anesthesia and arousal, pain, itch, emotions, and cognition. This paper reviews the anatomical structure, morphology, fiber connection, electrophysiological characteristics, and other related functions of CM to provide references for future research.

The optimal protocol for neuromodulation by transcranial direct current stimulation (tDCS) remains unclear. Using the rotarod paradigm, we found that mouse motor learning was enhanced by anodal tDCS (3.2 mA/cm²) during but not before or after the performance of a task. Dual-task experiments showed that motor learning enhancement was specific to the task accompanied by anodal tDCS. Studies using a mouse model of stroke induced by middle cerebral artery occlusion showed that concurrent anodal tDCS restored motor learning capability in a task-specific manner. Transcranial in vivo Ca²⁺ imaging further showed that anodal tDCS elevated and cathodal tDCS suppressed neuronal activity in the primary motor cortex (M1). Anodal tDCS specifically promoted the activity of task-related M1 neurons during task performance, suggesting that elevated Hebbian synaptic potentiation in task-activated circuits accounts for the motor learning enhancement. Thus, application of tDCS concurrent with the targeted behavioral dysfunction could be an effective approach to treating brain disorders.
Transcranial Direct Current Stimulation/methods* ; Motor Cortex/physiology* ; Neurons ; Transcranial Magnetic Stimulation

Objective:To investigate the significance of clinical factors combined with transvaginal ultrasound and contrast-enhanced ultrasound(CEUS) in guiding the choice of treatment plan for cesarean scar pregnancy(CSP).Methods:The clinical and transvaginal ultrasound and CEUS data of 120 patients with CSP from January 2016 to June 2021 in the First People′s Hospital of Foshan were retrospectively analyzed, and they were divided into ultrasound-guided curettage/ hysteroscopic group (Group A, 91 cases) and laparoscopic group (Group B, 29 cases) according to treatment option, and the differences in clinical and ultrasound factors between the two groups were compared, and to determine the relevant clinical and ultrasound indicators for the choice of treatment option.Results:There were statistical differences between the 2 groups in comparison of whether the gestational sac/mass protruded toward the plasma membrane, gestational sac/mass diameter, the main blood supply site of the gestational sac/mass, the site of the chorion/early placenta and scar thickness (all P<0.05). Logistic regression analysis indicated that CEUS showing major blood supply site of the gestational sac/mass ( OR=6.029, P=0.003) and uterine scar thickness ( OR=12.998, P=0.002) were independent risk factors for minimally invasive surgery for CSP. Conclusions:Ultrasound combined with clinical factors have a certain value in the selection of treatment options for CPS, and the thickness of the uterine scar and the main blood supply site of the gestational sac/mass showed in CEUS may be key factors affecting the minimally invasive surgical treatment of CSP.

Objective: To investigate the feasibility and accuracy of transperineal real-time three-dimensional ultrasound combined with clinical factors in predicting the risk of female stress urinary incontinence(SUI). Methods: Three hundred and forty-eight female patients with SUI diagnosed were selected as the case group, and 102 healthy people in the same period were selected as the control group. All subjects underwent transperineal real-time three-dimensional ultrasound. The ultrasonic parameters of resting state, contraction and Valsalva were measured, and the clinical parameters such as age, height, weight, history of pregnancy and childbirth were collected. According to the time sequence, all the subjcets were divided into derivation cohort and verification cohort inproportion to 2∶1, single factor screening and logistic multiple regression analysis were carried out on 24 factors, and the risk model was established. The cut-off value of the disease probability P was determined by the ROC curve of the subjects, and then the accuracy of the cut-off value in predicting SUI was verified in the verification group. Results: Single factor analysis showed that 13 parameters were associated with SUI(all P<0.05). Logit P=2.014+ 1.870×Z₁ was established by multivariate logistic regression analysis. The cut-off value of the disease probability P determined by ROC curve was 0.823. The predictive sensitivity of the model was 68.1% (95%CI: 59.6%-76.6%), specificity was 91.2% (95%CI: 86.0%-96.4%), positive predictive value was 64.3% (95%CI: 55.6%-73.0%) and negative predictive value was 92.5% (95%CI: 86.2%-98.8%). Conclusions It is feasible to predict the risk of female stress urinary incontinence by transperineal real-time three-dimensional ultrasound combined with clinical factors. Although, some limitations with the prediction model, it has accuracy in predicting SUI with obvious symptoms.