OBJECTIVE: To investigate the current status of clinical genetics specialization development and the diagnostic and therapeutic capabilities for hereditary diseases across medical institutions in Shanghai, and to assess the necessity and feasibility of establishing training bases for clinical genetics specialists. METHODS: By employing a cross-sectional survey design, the Clinical Genetics Committee of Shanghai Medical Association has conducted questionnaire surveys from March to April 2025 across 54 healthcare institutions in Shanghai (including 33 tertiary hospitals and 21 secondary hospitals). The survey involved administrative departments and medical personnel from 15 clinical specialties. The survey has covered current genetic disease diagnosis and treatment practices, relevant and specialised disease types, genetic department establishment, testing capabilities, personnel teams, and training requirements. RESULTS: The results revealed that 78.0% of clinical departments surveyed had treated patients with hereditary disorders. Shanghai possesses diagnostic and therapeutic expertise for over 95% of hereditary diseases listed in its rare disease catalogue, reflecting both the practical clinical demand for such conditions and the city's overall diagnostic and therapeutic strengths in this field. Nevertheless, significant disparities exist in the development of genetics departments across different tiers of healthcare institutions. Resources for genetic testing capabilities (including molecular, cellular, and biochemical testing) are also unevenly distributed across different tiers of hospitals. The survey further revealed that only 26.0% of departments believe that their current physician structure fully meets the diagnostic and treatment demands. Over 90% of departments consider standard training for clinical genetic specialists necessary, with 74.0% expressing willingness to participate in establishing training bases. Based on above findings and thorough deliberation, the Clinical Genetics Committee of the Shanghai Medical Association proposes advancing specialist training and discipline development through establishing a standard training system. The committee has drafted a three-year training protocol featuring a "joint training"-centered model, recommending a pilot-first, dynamically optimized strategy for steadily advancing training base development. CONCLUSION Shanghai faces substantial demand for genetic disease diagnosis and treatment, yet exhibits shortcomings in clinical genetics specialization development, resource allocation, and talent pipeline cultivation. To establish a standard training system holds significant practical importance and is underpinned by a broad demand.
Humans ; China ; Surveys and Questionnaires ; Genetic Diseases, Inborn/genetics* ; Cross-Sectional Studies ; Genetics, Medical/education* ; Genetic Testing

OBJECTIVES: To investigate the regulatory role of nucleotide-bound oligomerized domain-like receptor containing pyrin-domain protein 6 (NLRP6) in liver lipid metabolism and non-alcoholic fatty liver disease (NAFLD). METHODS: Mouse models with high-fat diet (HFD) feeding for 16 weeks (n=6) or with methionine choline-deficient diet (MCD) feeding for 8 weeks (n=6) were examined for the development of NAFLD using HE and oil red O staining, and hepatic expressions of NLRP6 were detected with RT-qPCR, Western blotting, and immunohistochemical staining. Cultured human hepatocytes (LO2 cells) with adenovirus-mediated NLRP6 overexpression or knock-down were treated with palmitic acid (PA) in the presence or absence of compound C (an AMPK inhibitor), and the changes in cellular lipid metabolism were examined by measuring triglyceride, ATP and β-hydroxybutyrate levels and using oil red staining, RT-qPCR, and Western blotting. RESULTS: HFD and MCD feeding both resulted in the development of NAFLD in mice, which showed significantly decreased NLRP6 expression in the liver. In PA-treated LO2 cells, NLRP6 overexpression significantly decreased cellular TG content and lipid deposition, while NLRP6 knockdown caused the opposite effects. NLRP6 overexpression in PA-treated LO2 cells also increased mRNA and protein expressions of PGC1A and CPT1A, levels of ATP and β-hydroxybutyrate, and the phosphorylation level of AMPK pathway; the oxidative decomposition of lipids induced by Ad-NLRP6 was inhibited by the use of AMPK inhibitors. CONCLUSIONS NLRP6 overexpression promotes lipid oxidation and decomposition through AMPK/CPT1A/PGC1A to alleviate lipid deposition in hepatocytes.
Non-alcoholic Fatty Liver Disease/metabolism* ; Animals ; Hepatocytes/metabolism* ; Lipid Metabolism ; Mice ; Humans ; Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha ; AMP-Activated Protein Kinases/metabolism* ; Carnitine O-Palmitoyltransferase/metabolism* ; Diet, High-Fat ; Male ; Mice, Inbred C57BL ; Signal Transduction

Objective To test the longitudinal mediating mechanism of kinesophobia between family functioning and exercise adherence in elderly patients with postoperative adjacent vertebral re-fracture after osteoporotic vertebral compression fracture(OVCF).Methods Convenience sampling method was used to admit to the Department of Orthopaedics of a tertiary hospital in Xuzhou City with adjacent vertebral re-fracture after OVCF were conveniently selected as the survey subjects,and longitudinal investigation was conducted using the APGAR,Functional Exercise Adherence Scale,and TSK Scale.Unconditional potential growth model,structural equation and Bootstrap method were used for statistical analysis.Results A total of 232 valid questionnaires were collected.Longitudinal mediation modeling revealed that the intercept of family functioning significantly negatively predicted the intercept of kinesophobia(β=-0.456,P＜0.001)and the slope of family functioning significantly negatively predicted the slope of kinesophobia(β=-0.962,P＜0.001).The intercept for kinesophobia significantly negatively predicted the intercept for exercise adherence(β=-0.623,P＜0.001)and significantly negatively predicted the slope for exercise adherence(β=-0.354,P=0.013).Conclusion The initial level and rate of development of kinesophobia play a fully longitudinal mediating role in the development of family functioning on exercise adherence.Medical professionals should assess and manage patients'family functioning and kinesophobia in a timely manner,and rationally utilize their interrelationships to improve the level of exercise adherence as much as possible.

Objective:To explore the value of deep learning models based on ultrafast dynamic contrast-enhanced MRI (UF-DCE MRI) in predicting malignant breast lesions.Methods:The study was a cross-sectional study. Clinical and imaging data of 347 patients with breast lesions who received treatment at Tianjin Medical University Cancer Institute and Hospital from March 2023 to January 2024 were analyzed retrospectively. A total of 347 lesions were observed in the 347 patients, including 75 benign and 272 malignant lesions. The random number method was used to divide into the training set with 243 cases and the validation set with 104 cases in a ratio of 7∶3. All patients underwent breast UF-DCE MRI and conventional dynamic-enhanced MRI (DCE-MRI). A 27-channel model (27-phase enhancement images of input UF-DCE MRI), a 3-channel model (3-phase enhancement images of input DCE-MRI), and a 1-channel model (1st-phase enhancement images of DCE-MRI) were built based on the pre-trained ResNet18 deep learning model on ImageNet. The efficacy of each model in predicting breast malignant lesions was analyzed using receiver operating characteristic curves and area under the curve (AUC). The differences of AUC were compared using DeLong test.Results:In the training and validation sets, the 27-channel model had the highest AUC for diagnosing malignant breast lesions, which were 0.848 (95% CI 0.818-0.877) and 0.784 (95% CI 0.752-0.817), respectively. DeLong test showed no statistically significant difference in the AUC values of the three models in the validation set for the diagnosis of malignant lesions of the breast in a two-by-two comparison ( P>0.05). UF-DCE MRI scans were 27 phases totaling 81 s with a temporal resolution of 3 s/phase; DCE-MRI scans were 3 phases totaling 270 s with a temporal resolution of 90 s/phase. Conclusions:The model combining UF-DCE MRI with deep learning demonstrates comparable efficacy to DCE-MRI deep learning model in diagnosing breast malignant lesions. However the UF-DCE MRI has the advantages of high temporal resolution and short scanning time, which makes this model valuable for precise diagnosis and treatment of breast cancer.

Objective To screen antidepressant-active compounds from Polygonati Rhizoma and explore their effects and possible mechanisms against depression induced by simulated weightlessness.Methods A systems pharmacology approach was used to screen potential antidepressant-active compounds and their targets from Polygonati Rhizoma.The hindlimb unloading(HLU)rat model was employed for the study.Twenty-four healthy male Sprague-Dawley rats were randomly divided into three groups:control group(administered 0.5%carboxymethylcellulose by gavage),HLU group(hindlimb unloading),and HLU+treatment group(hindlimb unloading+active compound gavage),with 8 rats in each group.After 28 days of hindlimb unloading,depressive-like behaviors in rats were evaluated using the forced swimming test and tail suspension test.Hippocampal morphology was examined with H&E staining,and GO and KEGG enrichment analyses were conducted on the targets of active compounds.Results A total of 38 active compounds were screened from Polygonati Rhizoma,among which apigenin had an oral bioavailability of 23.06%and a drug-likeness score of 0.21.Compound-target network analysis indicated that apigenin had the highest degree and betweenness centrality values,suggesting it might be the key active component with antidepressant potential in Polygonati Rhizoma.In the forced swimming and tail suspension tests,rats in the HLU group showed a significant increase in immobility time compared to the control group,indicating successful establishment of the depression model.However,compared to the HLU group,rats in the HLU plus apigenin group exhibited significantly reduced immobility time.The H&E staining results of hippocampal tissue showed a significant reduction in the number of hippocampal neurons,along with numerous shrunken neurons and small vacuoles in nerve fibers in the HLU group.In contrast,the treatment group exhibited an increased number of hippocampal neurons,with improved cellular morphology.Target enrichment analysis indicated that apigenin targets were mainly involved in the regulation of apoptosis and cancer-related signaling pathways.Conclusion Apigenin significantly improved depressive-like behaviors in rats subjected to hindlimb unloading,and it has a protective effect on hippocampal tissue.It may provide a new natural active compound for the treatment of depression caused by spaceflight-induced weightlessness.

Acute-on-chronic liver failure （ACLF） refers to severe liver dysfunction that occurs on the basis of chronic liver diseases， and it is characterized by rapid disease progression， poor prognosis， and high mortality rate. In recent years， inflammatory markers have become a research hotspot due to their significant role in assessing the prognosis of ACLF. This article reviews the advances in the application of inflammatory markers in assessing the prognosis of ACLF， such as systemic immune inflammatory index， lymphocyte-white blood cell ratio， neutrophil-lymphocyte ratio， and C-reactive protein， and discusses their clinical value and future research directions， in order to provide a theoretical basis for the early intervention and prognosis management of ACLF patients.

Objective To test the longitudinal mediating mechanism of kinesophobia between family functioning and exercise adherence in elderly patients with postoperative adjacent vertebral re-fracture after osteoporotic vertebral compression fracture(OVCF).Methods Convenience sampling method was used to admit to the Department of Orthopaedics of a tertiary hospital in Xuzhou City with adjacent vertebral re-fracture after OVCF were conveniently selected as the survey subjects,and longitudinal investigation was conducted using the APGAR,Functional Exercise Adherence Scale,and TSK Scale.Unconditional potential growth model,structural equation and Bootstrap method were used for statistical analysis.Results A total of 232 valid questionnaires were collected.Longitudinal mediation modeling revealed that the intercept of family functioning significantly negatively predicted the intercept of kinesophobia(β=-0.456,P＜0.001)and the slope of family functioning significantly negatively predicted the slope of kinesophobia(β=-0.962,P＜0.001).The intercept for kinesophobia significantly negatively predicted the intercept for exercise adherence(β=-0.623,P＜0.001)and significantly negatively predicted the slope for exercise adherence(β=-0.354,P=0.013).Conclusion The initial level and rate of development of kinesophobia play a fully longitudinal mediating role in the development of family functioning on exercise adherence.Medical professionals should assess and manage patients'family functioning and kinesophobia in a timely manner,and rationally utilize their interrelationships to improve the level of exercise adherence as much as possible.

Objective:To explore the value of deep learning models based on ultrafast dynamic contrast-enhanced MRI (UF-DCE MRI) in predicting malignant breast lesions.Methods:The study was a cross-sectional study. Clinical and imaging data of 347 patients with breast lesions who received treatment at Tianjin Medical University Cancer Institute and Hospital from March 2023 to January 2024 were analyzed retrospectively. A total of 347 lesions were observed in the 347 patients, including 75 benign and 272 malignant lesions. The random number method was used to divide into the training set with 243 cases and the validation set with 104 cases in a ratio of 7∶3. All patients underwent breast UF-DCE MRI and conventional dynamic-enhanced MRI (DCE-MRI). A 27-channel model (27-phase enhancement images of input UF-DCE MRI), a 3-channel model (3-phase enhancement images of input DCE-MRI), and a 1-channel model (1st-phase enhancement images of DCE-MRI) were built based on the pre-trained ResNet18 deep learning model on ImageNet. The efficacy of each model in predicting breast malignant lesions was analyzed using receiver operating characteristic curves and area under the curve (AUC). The differences of AUC were compared using DeLong test.Results:In the training and validation sets, the 27-channel model had the highest AUC for diagnosing malignant breast lesions, which were 0.848 (95% CI 0.818-0.877) and 0.784 (95% CI 0.752-0.817), respectively. DeLong test showed no statistically significant difference in the AUC values of the three models in the validation set for the diagnosis of malignant lesions of the breast in a two-by-two comparison ( P>0.05). UF-DCE MRI scans were 27 phases totaling 81 s with a temporal resolution of 3 s/phase; DCE-MRI scans were 3 phases totaling 270 s with a temporal resolution of 90 s/phase. Conclusions:The model combining UF-DCE MRI with deep learning demonstrates comparable efficacy to DCE-MRI deep learning model in diagnosing breast malignant lesions. However the UF-DCE MRI has the advantages of high temporal resolution and short scanning time, which makes this model valuable for precise diagnosis and treatment of breast cancer.

Objective:To investigate the correlation between digital breast tomosynthesis(DBT)image features,pathological features,and the results of breast-conserving surgery(BCS)in early stage breast cancer.Methods:We retrospectively analyzed 422 cases of BCS resulting in successful breast preservation and 211 BCS cases that were followed by mastectomy.All of the patients underwent surgery at Tianjin Medical University Cancer Institute&Hospital between January 2019 and December 2023.Preoperative DBT images and clinicopathological features were assessed.A univariate Logistic regression analysis was performed to screen out the characteristics associated with BCS surgic-al results,after which multivariate Logistic regression analysis was performed to screen out the characteristics.Results:Univariate Logistic regression analysis showed that age(P=0.020),architectural distortion(P<0.001),breast composition(extremely dense,P=0.001),and mo-lecular subtype(Her2,P=0.001)were statistically different between the two groups.Multivariate Logistic regression analysis showed that ar-chitectural distortion(P<0.001),breast composition(extremely dense breast,P=0.003),and molecular subtype(Her2,P<0.001)differed signi-ficantly between the two groups.Conclusions:Breast composition,architectural distortion,and molecular subtype correlated with BCS res-ults.Extremely dense breast composition,architectural distortion,and Her2 subtype are associated with a higher possibility of conversion to mastectomy.These factors serve as effective predictive indicators of BCS results and thus aid clinicians in deciding the appropriate surgical strategies in the treatment of breast cancer.

Objective To explore the value of deep learning models in predicting axillary lymph node(ALN)metastasis of breast cancer based on intratumoral and peritumoral dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI).Methods A retrospective analysis was conducted on cases from Tianjin Medical University Cancer Hospital and Laoshan Branch of Affiliated Hospital of Qingdao University,involving a total of 850 lesions in 850 patients.The region of interest within the tumor was delineated at the largest area of the lesion on the first enhancement images and automatically expanded by 3 mm and 6 mm in a conformal fashion.Deep learning prediction models based on ResNet50 were developed via intratumoral,peritumoral,and intratumoral combined peritumoral models,respectively,and a comprehensive prediction model was developed by integrating semantic features of imaging reports.Cases from Tianjin Medical University Cancer Hospital were randomly divided into training and test cohorts in a 7∶3 ratio,while cases from Laoshan Branch of Affiliated Hospital of Qingdao University served as the external validation cohort.The area under the curve(AUC),accuracy,sensitivity,specificity,F1-score,and Brier-score were calculated,respectively.Results The model incorporating intratumoral,peritumoral(3 mm),and semantic features demonstrated the highest performance,with AUC of 0.801[95％confidence interval(CI)0.765-0.845],0.781(95％CI 0.745-0.817),and 0.752(95％CI 0.700-0.793)in the training cohort,test cohort,and external validation cohort,respectively,and there was no significant difference in AUC between combined model and intratumoral/peritumoral model,respectively,but it demonstrated the higher sensitivity and F1-score,and the lower Brier-score.Conclusion Incorporating peritumoral images into the conventional model based on intratumoral images enhanced the predictive ability of ALN metastasis in breast cancer.