Objective:To evaluate the clinical utility of machine learning model based radiomics in predicting high-risk molecular subtypes of lower-grade gliomas(LrGGs).Methods:This was a cross-sectional study. A total of 287 patients diagnosed with LrGGs in the First Hospital of Shanxi Medical University, Shanxi Provincial People′s Hospital, and the Third Hospital of Shanxi Medical University from January 2011 to September 2023 were retrospectively collected, including 166 males and 121 females; 114 cases of high-risk molecular subtypes and 173 cases of non-high-risk molecular subtypes. All patients were divided into 201 cases in the training set and 86 cases in the test set according to 7∶3 in simple randomized grouping method. All patients underwent contrast-enhanced T 1WI (CE-T 1WI) and T 2-weighted fluid-attenuated inversion recovery sequence imaging (T 2-FLAIR), and the imaging features of high-risk and non-high-risk molecular subtypes were analyzed. Analysis of variance, recursive feature elimination, and Kruskal-Wallis were used for radiomics feature screening, and a support vector machine (SVM) classifier was used to construct a radiomics-based classifier model. Univariate and multivariate logistic regression were used to analyze clinical variables independently influencing high-risk molecular subtypes of LrGGs to construct a clinical model; a combined model was developed by integrating radiomics labels and clinical variables. Receiver operating characteristic curve and area under the curve (AUC), calibration curve, and decision curve were used to compare the predictive performance of different models. Results:The patient′s age ( OR=1.042, 95% CI 1.018-1.068, P=0.001), pathological grade ( OR=2.270, 95% CI 1.212-4.311, P=0.011), MGMT methylation status ( OR=0.456, 95% CI 0.238-0.866, P=0.017), and ependymal involvement ( OR=7.335, 95% CI 2.929-18.370, P<0.001) were independent influencing factors for the high-risk molecular subtype of LrGGs, and a clinical model was developed based on these factors. An SVM model was constructed based on 12 radiomics features (3 radiomics features based on CE-T 1WI and 9 radiomics features based on T 2-FLAIR). The radiomics score of the probability output by the SVM model was combined with age, pathological grade, MGMT methylation status, and ependymal involvement to develop a combined model. The AUC values of the SVM model for predicting the high-risk molecular subtype of LrGGs were 0.824 and 0.859 in the training set and test set, respectively; the AUC values of the clinical model in the training set and test set were 0.759 and 0.721, respectively; and the AUC values of the combined model in the training set and test set were 0.823 and 0.815, respectively. The combined model had a high clinical net benefit. Conclusion:The machine learning MRI radiomics model can preoperatively predict high risk molecular subtypes of LGGrs, assist in individualized treatment decisions.

Objective:To evaluate the clinical utility of machine learning model based radiomics in predicting high-risk molecular subtypes of lower-grade gliomas(LrGGs).Methods:This was a cross-sectional study. A total of 287 patients diagnosed with LrGGs in the First Hospital of Shanxi Medical University, Shanxi Provincial People′s Hospital, and the Third Hospital of Shanxi Medical University from January 2011 to September 2023 were retrospectively collected, including 166 males and 121 females; 114 cases of high-risk molecular subtypes and 173 cases of non-high-risk molecular subtypes. All patients were divided into 201 cases in the training set and 86 cases in the test set according to 7∶3 in simple randomized grouping method. All patients underwent contrast-enhanced T 1WI (CE-T 1WI) and T 2-weighted fluid-attenuated inversion recovery sequence imaging (T 2-FLAIR), and the imaging features of high-risk and non-high-risk molecular subtypes were analyzed. Analysis of variance, recursive feature elimination, and Kruskal-Wallis were used for radiomics feature screening, and a support vector machine (SVM) classifier was used to construct a radiomics-based classifier model. Univariate and multivariate logistic regression were used to analyze clinical variables independently influencing high-risk molecular subtypes of LrGGs to construct a clinical model; a combined model was developed by integrating radiomics labels and clinical variables. Receiver operating characteristic curve and area under the curve (AUC), calibration curve, and decision curve were used to compare the predictive performance of different models. Results:The patient′s age ( OR=1.042, 95% CI 1.018-1.068, P=0.001), pathological grade ( OR=2.270, 95% CI 1.212-4.311, P=0.011), MGMT methylation status ( OR=0.456, 95% CI 0.238-0.866, P=0.017), and ependymal involvement ( OR=7.335, 95% CI 2.929-18.370, P<0.001) were independent influencing factors for the high-risk molecular subtype of LrGGs, and a clinical model was developed based on these factors. An SVM model was constructed based on 12 radiomics features (3 radiomics features based on CE-T 1WI and 9 radiomics features based on T 2-FLAIR). The radiomics score of the probability output by the SVM model was combined with age, pathological grade, MGMT methylation status, and ependymal involvement to develop a combined model. The AUC values of the SVM model for predicting the high-risk molecular subtype of LrGGs were 0.824 and 0.859 in the training set and test set, respectively; the AUC values of the clinical model in the training set and test set were 0.759 and 0.721, respectively; and the AUC values of the combined model in the training set and test set were 0.823 and 0.815, respectively. The combined model had a high clinical net benefit. Conclusion:The machine learning MRI radiomics model can preoperatively predict high risk molecular subtypes of LGGrs, assist in individualized treatment decisions.

Objective:To understand the major cognition, major choice motivation and the relationship between the two of medical students, and provide references and suggestions for the selection of talents in various majors of medical schools and the effective development of enrollment work.Methods:This study selected undergraduates of Batch 2019 from Peking University Health Science Center as the survey objects, conducted a questionnaire survey on their major cognition, major choice motivation and influencing factors, and used principal component analysis and Spearman rank correlation analysis.Results:The study found that the major cognition scores of 640 undergraduates of Batch 2019 from Peking University Health Science Center were clinical medicine (3.24±0.89) > stomatology (2.89±1.00) > basic medicine (2.66±1.02) > pharmacy (2.54±0.97) > preventive medicine (2.29±0.93) > nursing medicine (2.21±0.99) > medical laboratory (1.98±0.95) > medical English (1.95±0.93). Six major motivation factors for professional choice were school and professional strength, professional learning and job prospects, own factors, Peking University sentiments and the influence of others, medical factors, school policies, and the contribution rates were 34.60%, 12.97%, 7.42%, 6.00%, 5.59% and 5.37%, respectively. Major cognition scores and major choice motivation factors were positively correlated with each other to some extent.Conclusions:At present, students' major cognition level of medical majors still has a large room for improvement, and the motivational factors of major choice are more complicated, among which "the school and professional strength" and "the prospects of study and work" are important factors. Medical schools should focus on strengthening major publicity, improving students' major cognition, attracting aspiring students to apply for medical majors from many aspects, and improving the training quality of medical professionals.

Objective To identify clinical risk factors for early major adverse cardiovascular events (MACEs) following surgical correction of supravalvar aortic stenosis (SVAS). Methods Patients who underwent SVAS surgical correction between 2002 and 2019 in Beijing and Yunnan Fuwai Cardiovascular Hospitals were included. The patients were divided into a MACEs group and a non-MACEs group based on whether MACEs concurring during postoperative hospitalization or within 30 days following surgical correction for SVAS. Their preoperative, intraoperative, and postoperative clinical data were collected for multivariate logistic regression. Results This study included 302 patients. There were 199 males and 103 females, with a median age of 63.0 (29.2, 131.2) months. The incidence of early postoperative MACEs was 7.0% (21/302). The multivariate logistic regression model identified independent risk factors for early postoperative MACEs, including ICU duration (OR=1.01, 95%CI 1.00-1.01, P=0.032), intraoperative cardiopulmonary bypass (CPB) time (OR=1.02, 95%CI 1.01-1.04, P=0.014), aortic annulus diameter (OR=0.65, 95%CI 0.43-0.97, P=0.035), aortic sinus inner diameter (OR=0.75, 95%CI 0.57-0.98, P=0.037), and diameter of the stenosis (OR=0.56, 95%CI 0.35-0.90, P=0.016). Conclusion The independent risk factors for early postoperative MACEs include ICU duration, intraoperative CPB time, aortic annulus diameter, aortic sinus inner diameter, and diameter of the stenosis. Early identification of high-risk populations for MACEs is beneficial for the development of clinical treatment strategies.

OBJECTIVES: Shelter hospital was an alternative way to provide large-scale medical isolation and treatment for people with mild coronavirus disease 2019 (COVID-19). Due to various reasons, patients admitted to the large shelter hospital was reported high level of psychological distress, so did the healthcare workers. This study aims to introduce a comprehensive and multifaceted psychosocial crisis intervention model. METHODS: The psychosocial crisis intervention model was provided to 200 patients and 240 healthcare workers in Wuhan Wuchang shelter hospital. Patient volunteers and organized peer support, client-centered culturally sensitive supportive care, timely delivery of scientific information about COVID-19 and its complications, mental health knowledge acquisition of non-psychiatric healthcare workers, group activities, counseling and education, virtualization of psychological intervention, consultation and liaison were exhibited respectively in the model. Pre-service survey was done in 38 patients and 49 healthcare workers using the Generalized Anxiety Disorder 7-item (GAD-7) scale, the Patient Health Questionnaire 2-item (PHQ-2) scale, and the Primary Care PTSD screen for the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (PC-PTSD-5). Forty-eight healthcare workers gave feedback after the intervention. RESULTS: The psychosocial crisis intervention model was successfully implemented by 10 mental health professionals and was well-accepted by both patients and healthcare workers in the shelter hospital. In pre-service survey, 15.8% of 38 patients were with anxiety, 55.3% were with stress, and 15.8% were with depression; 16.3% of 49 healthcare workers were with anxiety, 26.5% were with stress, and 22.4% were with depression. In post-service survey, 62.5% of 48 healthcare workers thought it was very practical, 37.5% thought more practical; 37.5% of them thought it was very helpful to relief anxiety and insomnia, and 27.1% thought much helpful; 37.5% of them thought it was very helpful to recognize patients with anxiety and insomnia, and 29.2% thought much helpful; 35.4% of them thought it was very helpful to deal with patients' anxiety and insomnia, and 37.5% thought much helpful. CONCLUSIONS Psychological crisis intervention is feasible, acceptable, and associated with positive outcomes. Future tastings of this model in larger population and different settings are warranted.
Humans ; COVID-19 ; Sleep Initiation and Maintenance Disorders ; Crisis Intervention ; Psychosocial Intervention ; SARS-CoV-2 ; Mental Health ; Depression/epidemiology* ; Health Personnel/psychology* ; Anxiety/etiology*

"The Expert Group on Tumor Ablation Therapy of Chinese Medical Doctor Association, The Tumor Ablation Committee of Chinese College of Interventionalists, The Society of Tumor Ablation Therapy of Chinese Anti-Cancer Association and The Ablation Expert Committee of the Chinese Society of Clinical Oncology" have organized multidisciplinary experts to formulate the consensus for thermal ablation of pulmonary subsolid nodules or ground-glass nodule (GGN). The expert consensus reviews current literatures and provides clinical practices for thermal ablation of GGN. The main contents include: (1) clinical evaluation of GGN, (2) procedures, indications, contraindications, outcomes evaluation and related complications of thermal ablation for GGN and (3) future development directions.
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Objective To evaluate clinical utility of the personalized drug delivery decision support system, Smart Dose, applied to the monitoring of therapeutic drugs in Chinese population. Methods Use Smart Dose system to predict the trough concentration of vancomycin in patients, analyze the difference between the predicted value and the measured value of the trough concentration, and to evaluate the prediction performance of the system for vancomycin blood concentration. Results Smart Dose adjusts the difference between the predicted value of concentration and the measured value, the average percentage error, and the average absolute percentage error is less than the difference between the predicted value of initial concentration and the measured value. The difference between the initial concentration prediction value and the measured value of the neurosurgery group was smaller than that of the non-neurosurgery group, and the prediction efficiency was better than that of the non-neurosurgery group. The predicted initial concentration of the high trough concentration group and the low-age group (<59 years old) are closer to the measured value. The predictive performance of different BMI for the initial concentration is similar. Conclusion Smart Dose system is more suitable for predicting the adjusted concentration of vancomycin; When used for initial concentration prediction, the prediction values of neurosurgery group, high trough concentration, and low age group are more accurate. Different BMI has similar performance in predicting initial concentration.

Objective:To evaluate the clinical efficacy of sepsis in children by dynamic monitoring of serum procalcitonin (PCT) and hypersensitive C-reactive protein (hs-CRP).Methods:Seventy-five children with sepsis hospitalized in Taihe County People′s Hospital from January 2015 to April 2019 were selected as the sepsis group, and 75 healthy children in the same period were selected as the control group. The values of PCT and hs-CRP in two groups were detected by chemiluminescence and immunoturbidimetry respectively, and the levels of serum PCT and hs-CRP were compared between the two groups. According to the PCT, hs-CRP value and APACHE Ⅱ score at 72 h after treatment, children in sepsis group can be divided into two groups: good curative effect group (APACHE Ⅱ <27 scores)and poor curative effect group with (APACHE Ⅱ ≥27 scores).The changes of serum PCT, hs-CRP and Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score in two group were monitored dynamically at different time points, and data were analyzed by SPSS 18.0 software.Results:The levels of PCT and hs-CRP in sepsis group were significantly higher than those in control group [(5.37 ± 1.56) μg/L vs. (0.32 ± 0.10) μg/L, (64.6 ± 9.5) mg/L vs. (4.2 ± 1.2) mg/L] ( P<0.01). At 1, 3 and 5 d after treatment, the levels of PCT, hs-CRP value and APACHE Ⅱ scores in good curative effect group were significantly reduced. However, the levels of PCT, hs-CRP and APACHE Ⅱ scores in poor curative effect group did not decrease significantly or even continued to increase, and the levels of PCT, hs-CRP and APACHE Ⅱ scores in good curative effect group at different time point were significantly higher than those in poor curative effect group ( P<0.01). In sepsis group, the level of PCT had positive correlation with hs-CRP and APACHE Ⅱ ( r=0.846, P<0.05; r=0.531, P<0.05), and the level of hs-CRP had positive correlation with APACHEⅡ( r=0.558, P<0.05). The sensitivity and specificity of PCT combined with hs-CRP in diagnosis of sepsis was 98.7% and 93.5%. Conclusions:PCT combined with hs-CRP detection is helpful for the early diagnosis of sepsis, and through the dynamic monitoring of PCT and hs-CRP values, the therapeutic efficacy of children can be evaluated.

Objective:To explore the application and value of metagenomic next-generation sequencing (mNGS) in refractory infection after organ transplantation.Methods:A case report discussed about a patient with lumbar spine infection after kidney transplantation and the relevant literature was reviewed. The recipient was a 63-year-old man with low back pain after kidney transplantation. Lumbar spine magnetic resonance imaging showed lumbar spine infection. Multiple operations plus antibacterial and antituberculosis treatments were ineffective. Before and after treatment, numerous tests of traditional pathogenic microorganisms failed to detect any positive bacteria.Results:The detection of lumbar secretion by mNGS suggested aspergillus infection. The symptoms improved after dosing of voriconazole.Conclusions:The incidence of fungal infection of lumbar spine is low. The imaging manifestations are non-typical so that it is easy to misdiagnose. mNGS helps to timely diagnose and guide treatment. With a review of the literature, mNGS has some application value for some difficult and rare infectious diseases.

Objective: To analyze epidemiological and clinical characteristics of laboratory confirmed epidemic cerebrospinal meningitis cases. Methods: Epidemiological and clinical informations and cerebrospinal fluid (CSF) and blood specimens of AMES (acute meningitis/encephalitis syndrome) cases were collected in the six sentinel hospitals from 2007 to 2016. neisseria meningitides (Nm) species and serogroup identification were detected by the methods of real-time fluorescent quantitative polymerase chain reaction (Real-time PCR) and bacterial culture, and epidemiological and clinical characteristics of laboratory confirmed epidemic cerebrospinal meningitis cases were analyzed. Results: 6 809 AMES cases were reported from 2007 to 2016. Total 4 422 cases were detected, and 90 cases were Nm positive. Through the methods of Real-time PCR, bacterial blood culture and CSF culture, the numbers of Nm positive cases were 90, 2 and 1 respectively. Twenty-two Nm cases were identified from 2007 to 2011 (4 cases were ungrouped), which with the highest incidence in serogroup C cases (17/18), and one cases was ungroupable Nm. Nm laboratory confirmed cases (68 cases) were increased dramatically and mainly occurred in serogroup B cases (43/67, 64.2%) from 2012 to 2016, with serogroup C cases highly decreased (5/67, 7.5%) and ungroupable Nm cases increased (13/67, 19.4%) meanwhile. Serogroup W135 and X cases were first detected at 2012 and 2014, and serogroup A remaining a low level which only detected one case at 2013. The morbidity of epidemic cerebrospinal meningitis was occured in the whole year, and mainly in winter and spring. The ratio of Nm laboratory confirmed cases to AMES cases during November to May (3.5%, 67/1 920) was higher than that during June to October (0.9%, 23/2 502) (χ²=34.45, P<0.001). Most Nm cases were children, students and farmers, and account for 30.0% (27/90), 31.1% (28/90), 18.9% (17/90), respectively. The majority of Nm cases were under 20 years old (60/90, 66.67%), and serogroup C cases (17/22, 77.3%) mainly occurred in over 12 years old population, while serogroup B (24/43, 55.8%) and ungroupable (6/14) cases mainly occurred in under 12 years old population. The main clinical symptoms of epidemic cerebrospinal meningitis cases were fever (78/90, 86.7%), headache (59/90, 65.6%) and vomiting (51/90, 56.7%). Misdiagnosis rate of admitting diagnosis was up to 87.8% (79/90) for the reason of atypical features in specific symptoms and blood or CSF positive index. The well-healed ratio in correct diagnosed group (7/11) was higher than that in misdiagnosed group (2.5%, 2/79) (χ²=40.61, P<0.001). Conclusion The clinical symptoms of epidemic cerebrospinal meningitis cases were atypical, and the diagnosed sensitivity and accuracy would be improved by enhanced molecular biology detection. The predominant epidemic serogroup of Nm switched from serogroup C to B, and the key work was surveaylance of serogroup transition.