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

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.

Purpose To explore the correlation between spectral CT-derived quantitative parameters and different programmed death-ligand 1(PD-L1)expression status in gastric adenocarcinoma,in order to provide additional imaging-based references for immunotherapy regimen selection in patients with this disease.Materials and Methods A total of 45 patients with gastric adenocarcinoma,pathologically confirmed by biopsy,who underwent PD-L1 testing prior to treatment were retrospectively enrolled from Lanzhou University Second Hospital between January 2021 and September 2023.All patients underwent baseline spectral CT scans.Based on PD-L1 expression status,patients were categorized into the higher combined positive score(CPS)group(CPS≥5)and the lower CPS group(CPS<5).From the single-energy images of arterial and venous phases,the CT values(CT40 keV,CT70 keV),effective atomic number,and iodine concentration of the lesions were measured.The standardized iodine concentration and slope of the spectral curve were calculated.Differences in spectral CT parameters between the two groups were compared.Receiver operating characteristic curves were plotted for parameters with statistically significant differences,and the area under the curve was calculated to evaluate the efficacy of each single parameter and combined parameters in distinguishing PD-L1 expression status.Results In the arterial phase,the CT40 keV,CT70 keV,iodine concentration,standardized iodine concentration,effective atomic number,and slope of the spectral curve of lesions in the higher expression group were all significantly higher than those of lesions in the lower expression group(t/Z=-3.115 to-2.725,all P<0.05),whereas no significant differences were observed in all parameters in the venous phase(all P>0.05).For the standardized iodine concentration in the enhanced arterial phase,the area under the curve for distinguishing PD-L1 expression status was 0.755(95%CI 0.612 to 0.898),with a sensitivity of 81.8%and a specificity of 65.2%.No significant differences were found between it and other single spectral CT parameters in the arterial phase(all P>0.05),and its diagnostic efficacy was similar to that of the combined parameters[the area under the curve was 0.755(95%CI 0.613 to 0.897),sensitivity was 90.9%,specificity was 52.2%].Conclusion Quantitative parameters of spectral CT in the arterial phase can predict the PD-L1 expression of gastric adenocarcinoma,which may provide a reference for the screening and immunotherapy evaluation of patients with gastric adenocarcinoma.

To screen and identify a chitosanase with high stability, we cloned the chitosanase gene from Bacillus atrophaeus with a high protease yield from the barren saline-alkali soil and expressed this gene in Escherichia coli. The expressed chitosanase of B. atrophaeus (BA-CSN) was purified by nickel-affinity column chromatography. The properties including optimal temperature, optimal pH, substrate specificity, and kinetic parameters of BA-CSN were characterized. The results showed that BA-CSN had the molecular weight of 31.13 kDa, the optimal temperature of 55 ℃, the optimal pH 5.5, and good stability at temperatures below 45 ℃ and pH 4.0-9.0. BA-CSN also had good stability within 4 h of pH 3.0 and 10.0, be activated by K⁺, Na⁺, Mn²⁺, Ca²⁺, Mg²⁺, and Co²⁺, (especially by Mn²⁺), and be inhibited by Fe³⁺, Cu²⁺, and Ag⁺. BA-CSN showcased the highest relative activity in the hydrolysis of colloidal chitosan, and it had good hydrolysis ability for colloidal chitin. Under the optimal catalytic conditions, BA-CSN demonstrated the Michaelis constant K_m and maximum reaction rate V_max of 9.94 mg/mL and 26.624 μmoL/(mL·min), respectively, for colloidal chitosan. In short, BA-CSN has strong tolerance to acids and alkali, possessing broad industrial application prospects.
Bacillus/genetics* ; Hydrogen-Ion Concentration ; Escherichia coli/metabolism* ; Glycoside Hydrolases/biosynthesis* ; Substrate Specificity ; Enzyme Stability ; Chitosan/metabolism* ; Temperature ; Kinetics ; Cloning, Molecular ; Bacterial Proteins/biosynthesis* ; Recombinant Proteins/genetics*

Objective:To construct and validate a prediction model for intraoperative hypothermia in general anesthesia surgery patients based on machine learning algorithm.Methods:Convenience sampling was used to retrospectively collect data from 1 075 general anesthesia surgery patients in the First Teaching Hospital of Xinjiang Medical University from March to August 2023, which were randomly divided into modeling set and validation set in the ratio of 7∶3. Combining LASSO regression with the random forest algorithm, intraoperative hypothermia risk factors were screened. Models were constructed based on six machine learning algorithms, Logistic regression, decision tree, support vector machine (SVM) , extreme gradient boosting (XGBoost) , multilayer perceptron (MLP) and K-Nearest Neighbors (KNN) , and evaluate the performance of all models. Web-based dynamic nomogram was developed and interpretable analysis of the optimal model was performed using SHAP graph.Results:According to LASSO regression and random forest algorithm, length of anesthesia, intraoperative blood loss, baseline body temperature, age, intraoperative urine volume, and type of surgery were risk factors for intraoperative hypothermia in patients under general anesthesia, and the difference was statistically significant ( P<0.05) . The areas under the receiver operating characteristic curve for Logistic regression, decision tree, XGBoost, KNN, MLP, and SVM models were 0.777, 0.746, 0.793, 0.743, 0.768, 0.793, and the F1 scores were 0.667, 0.719, 0.861, 0.756, 0.820, and 0.842, respectively. Decision curve showed that the net benefit of the XGBoost model for predicting intraoperative hypothermia in patients was high when the threshold probability was between 0 and 1. A web-based dynamic nomogram was developed with good clinical applicability and generalizability. Conclusions:A dynamic nomogram of intraoperative hypothermia in general anesthesia surgery patients constructed and validated based on the machine learning algorithm can assist medical and nursing staff in identifying patients at high risk of intraoperative hypothermia and implementing personalized interventions.

Objective To evaluate the role of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)in the diagnosis of axillary lymph node metastasis(ALNM)in breast cancer patients with low HER-2 expression.Methods A total of 297 breast cancer patients with low HER-2 expression treated at the Affiliated Hospital of Xuzhou Medical University were enrolled and divided into ALNM group(n=71)and non-ALNM group(n=226)according to whether there was ALNM.All patients underwent DCE-MRI,and DCE-MRI derived parameters were collected and analyzed.Logistic regression analysis was used to identify the risk factors for ALNM,and the efficacy of DCE-MRI in diagnosing ALNM was assessed using receiver operating characteristic curve.Results Significant differences were observed between two groups in lesion distribution,TNM staging,vascular invasion,and most DCE-MRI derived parameters including short-to-long axis ratio,Ve,Ktrans,Kep,ADC and SER(all P<0.05).Multivariate regression analysis revealed that the short-to-long axis ratio,Ve,Ktrans,Kep,ADC and SER were significant risk factors for ALNM in breast cancer patients.Comparative analysis demonstrated that the combination of DCE-MRI derived parameters yielded a maximum area under the curve of 0.976,with a sensitivity of 91.5%and a specificity of 92.9%.Conclusion DCE-MRI is an effective tool for determining the presence of ALNM in breast cancer patients with low HER-2 expression,providing significant diagnostic evidence for clinical practice.

Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.

Purpose To explore the correlation between spectral CT-derived quantitative parameters and different programmed death-ligand 1(PD-L1)expression status in gastric adenocarcinoma,in order to provide additional imaging-based references for immunotherapy regimen selection in patients with this disease.Materials and Methods A total of 45 patients with gastric adenocarcinoma,pathologically confirmed by biopsy,who underwent PD-L1 testing prior to treatment were retrospectively enrolled from Lanzhou University Second Hospital between January 2021 and September 2023.All patients underwent baseline spectral CT scans.Based on PD-L1 expression status,patients were categorized into the higher combined positive score(CPS)group(CPS≥5)and the lower CPS group(CPS<5).From the single-energy images of arterial and venous phases,the CT values(CT40 keV,CT70 keV),effective atomic number,and iodine concentration of the lesions were measured.The standardized iodine concentration and slope of the spectral curve were calculated.Differences in spectral CT parameters between the two groups were compared.Receiver operating characteristic curves were plotted for parameters with statistically significant differences,and the area under the curve was calculated to evaluate the efficacy of each single parameter and combined parameters in distinguishing PD-L1 expression status.Results In the arterial phase,the CT40 keV,CT70 keV,iodine concentration,standardized iodine concentration,effective atomic number,and slope of the spectral curve of lesions in the higher expression group were all significantly higher than those of lesions in the lower expression group(t/Z=-3.115 to-2.725,all P<0.05),whereas no significant differences were observed in all parameters in the venous phase(all P>0.05).For the standardized iodine concentration in the enhanced arterial phase,the area under the curve for distinguishing PD-L1 expression status was 0.755(95%CI 0.612 to 0.898),with a sensitivity of 81.8%and a specificity of 65.2%.No significant differences were found between it and other single spectral CT parameters in the arterial phase(all P>0.05),and its diagnostic efficacy was similar to that of the combined parameters[the area under the curve was 0.755(95%CI 0.613 to 0.897),sensitivity was 90.9%,specificity was 52.2%].Conclusion Quantitative parameters of spectral CT in the arterial phase can predict the PD-L1 expression of gastric adenocarcinoma,which may provide a reference for the screening and immunotherapy evaluation of patients with gastric adenocarcinoma.

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 investigate the association between photodynamic diagnosis(PDD)and treatment efficacy of 5-aminolevu-linic acid(ALA)photodynamic therapy(PDT)in patients with cutaneous squamous cell carcinoma in situ(cSCCis).Methods:A pro-spective cohort study was conducted among the patients with cSCCis who underwent ALA-PDT in Department of Dermatology,Huashan Hospital,Fudan University,from January 2020 to November 2024.All patients were diagnosed with cSCCis based on biopsy,and invasive squamous cell carcinoma was excluded.Clinical information was collected and PDD was performed before treatment,and the patients were divided into moderate fluorescence group and strong fluorescence groups based on fluorescence intensity.All patients received six sessions of PDT treatment at an interval of 1-2 weeks.The primary endpoint was the initial complete clearance rate at 3 months after the last treatment session,and secondary endpoints were the sustained complete clearance rate at 12 months after the last treatment session and treatment failure rate.The multivariate regression analysis,the survival curves,and the Cox regression analysis were used to investigate the association between PDD fluorescence intensity and treatment efficacy,as well as other influencing factors for treatment efficacy.Results:Compared with the strong fluorescence group,the moderate fluorescence group had significantly lower initial complete clearance rate[57.14%(8/14)vs.93.33%(28/30),odds ratio OR=0.100,P=0.010]and sustained complete clearance rate[42.86%(6/14)vs.76.67%(23/30),OR=0.230,P=0.030)].The multivariate regression analysis showed that moderate fluores-cence was an independent risk factor for initial complete clearance(OR=0.030,P=0.030).The moderate fluorescence group had a significantly higher treatment failure rate than the strong fluorescence group[57.14%(8/14)vs.23.33%(7/30),P=0.030].The survival analysis and the Cox regression analysis showed that moderate fluo-rescence was an independent risk factor for PDT treatment failure(hazard ratio=3.040,P=0.048).There were no significant differences in adverse reactions between the two groups.Conclusion:PDD fluorescence intensity can predict the efficacy of ALA-PDT in patients with cSCCis.Moderate fluorescence indicates a higher risk of treatment failure,which can help to guide clinicians in develop-ing individualized treatment strategies.