To summarize the clinical characteristics, antibody spectrum and neuroelectrophysiological features of autoimmune nodopathy(AN), and to explore the phenotypic differences among different antibody-positive subgroups.

Objective:To explore the feasibility of shortening the time of long exercise test (LET) from 120 to 60 minutes by analyzing the positive rate within 60 minutes among periodic paralysis (PP) patients who were positive in 120-minute test.Methods:The data of patients undergoing 120-minute LET from January 2015 to October 2021 in Beijing Tiantan Hospital, Capital Medical University were retrospectively analyzed, with 30%, 33%, and 40% as diagnostic cut-off values, respectively. PP patients with positive results within 120 minutes after exercise were enrolled in the study. The positive rate within 30 minutes and 60 minutes after exercise was calculated. The change rates of compound muscle action potential (CMAP) amplitude and the sensitivity and specificity of LET at 30 minutes, 60 minutes, and 120 minutes after exercise were analyzed. The change rate of CMAP amplitude in PP patients who did not show positive results within 60 minutes was further calculated.Results:A total of 254 patients were examined, including 114 PP patients. With 30%, 33%, and 40% as diagnostic cut-off values, the results showed that there were 88, 88, and 82 positive PP patients, respectively. Under each diagnostic cut-off values, the age of positive PP patients was (32±10) years, with a male proportion of 98% (86/88), 98% (86/88), and 99% (81/82), respectively; the positive rate of PP patients within 30 minutes after exercise was 60% (53/88), 58% (51/88), and 41% (34/82), respectively; the positive rate of PP patients within 60 minutes after exercise was 91% (80/88), 86% (76/88), and 83% (68/82), respectively. At the cut-off values of 30%, 33% and 40%, the change rate of CMAP amplitude at 30 minutes [-36% (-49%, -23%), -36% (-49%, -23%), -37% (-51%, -24%)], 60 minutes [-51% (-66%, -40%), -51% (-66%, -40%), -53% (-66%, -42%)] and 120 minutes [-57% (-67%, -45%), -57% (-67%, -45%), -58% (-67%, -46%)] after exercise showed statistically significant difference among 3 time points ( H=57.764, 57.764, 59.616, respectively, all P<0.001); the further comparison between time points showed that there was statistically significant difference in the change rate of CMAP amplitude between 60 minutes ( Z=5.419, 5.419, 5.531, respectively, all P<0.001), 120 minutes ( Z=7.325, 7.325, 7.431, respectively, all P<0.001) and 30 minutes after exercise, but there was no statistically significant difference in the change rate of CMAP amplitude between 120 minutes and 60 minutes after exercise ( Z=1.906, 1.906, 1.899, respectively, all P>0.05); the sensitivity of LET for the diagnosis of PP at 60 minutes after exercise was 70.2% (80/114), 66.7% (76/114) and 59.6% (68/114), and the specificity of LET for the diagnosis of PP was 77.9% (109/140), 84.3% (118/140) and 91.4%(128/140), respectively. When 30%, 33% and 40% were used as the diagnostic cut-off values, and the change rate of CMAP amplitude at 60 minutes after exercise fell below these cut-off values but showed a decline of ≥20%, ≥22% and ≥24%, respectively, the detection time should be extended to 120 minutes. Conclusions:Whether using 30%, 33%, or 40% as diagnostic cut-off values, it is feasible to shorten the LET time from 120 minutes to 60 minutes. The 60-minute LET has good sensitivity and specificity for the diagnosis of PP. It is recommended to extend the detection time to 120 minutes for patients with a ≥20%, ≥22%, or ≥24% decline in CMAP amplitude at 60 minutes after exercise while falling short of corresponding diagnostic cut-off values when 30%, 33%, and 40% are used as diagnostic cut-off values. This method can not only improve the examination efficiency of LET, but also minimize the missed diagnosis as much as possible.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

The patient, assigned female at birth and aged 1 year and 7 months, presented with clinical manifestations of 46,XY disorders of sex development. The external genitalia exhibited a severely undermasculinized phenotype. Laboratory tests and gonadal biopsy indicated poor Leydig cell function and good Sertoli cell function. Genetic testing revealed compound heterozygous mutations of c.867-2A>C and c.547G>A (p.G183R) in the LHCGR gene. The patient was ultimately diagnosed with type II Leydig cell hypoplasia. Type II Leydig cell hypoplasia presents a broad spectrum of clinical phenotypes, characterized by a lack of parallel function between Leydig cells and Sertoli cells, and significant individual variability in spermatogenesis and gender assignment. This condition should be considered when there is poor Leydig cell function but good development of Wolffian duct derivatives.
Female ; Humans ; Infant ; Disorder of Sex Development, 46,XY/genetics* ; Leydig Cells/pathology* ; Mutation ; Receptors, LH/genetics* ; Testis/abnormalities*

OBJECTIVES: To investigate the clinicopathological characteristics and prognostic factors of pediatric Hodgkin lymphoma (HL). METHODS: A retrospective analysis was conducted on the clinical data of children with newly diagnosed HL from January 2011 to December 2023 at four hospitals: Fujian Medical University Union Hospital, Fujian Medical University Zhangzhou Hospital, First Affiliated Hospital of Xiamen University, and Fujian Children's Hospital. Patients were categorized into low-risk (R1), intermediate-risk (R2), and high-risk (R3) groups based on HL staging and pre-treatment risk factors. The patients received ABVD regimen or Chinese Pediatric HL-2013 regimen chemotherapy. Early treatment response and long-term efficacy were assessed, and prognostic factors were analyzed using the Cox proportional hazards regression model. RESULTS: The overall complete response (CR) rates after 2 and 4 cycles of chemotherapy were 42% and 68%, respectively. Compared with the ABVD regimen group, patients treated with the HL-2013 regimen in the R1 group showed significantly higher CR rates after both 2 and 4 cycles (P<0.05). However, no statistically significant differences in CR rates were observed between the two regimens in the R2 and R3 groups (P>0.05). The 5-year event-free survival (EFS) rate, overall survival rate, and freedom from treatment failure rate were 83%±4%, 97%±2%, and 88%±4%, respectively. Cox analysis indicated that the presence of a large tumor mass at diagnosis and failure to achieve CR after 4 cycles of chemotherapy were independent risk factors for lower EFS rates (P<0.05). CONCLUSIONS Pediatric HL generally has a favorable prognosis. The presence of a large tumor mass at diagnosis and failure to achieve CR after 4 cycles of chemotherapy indicate poor prognosis.
Humans ; Hodgkin Disease/pathology* ; Male ; Child ; Female ; Adolescent ; Retrospective Studies ; Child, Preschool ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Prognosis ; Proportional Hazards Models ; Survival Analysis ; Infant

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

Bone grafting is a primary method for treating bone defects. Among various graft materials, xenogeneic bone substitutes are widely used in clinical practice due to their abundant sources, convenient processing and storage, and avoidance of secondary surgeries. With the advancement of domestic production and the limitations of imported products, an increasing number of bone filling or grafting substitute materials isentering clinical trials. Relevant experts have drafted this consensus to enhance the management of medical device clinical trials, protect the rights of participants, and ensure the scientific and effective execution of trials. It summarizes clinical experience in aspects, such as design principles, participant inclusion/exclusion criteria, observation periods, efficacy evaluation metrics, safety assessment indicators, and quality control, to provide guidance for professionals in the field.
Humans ; Bone Substitutes/therapeutic use* ; Randomized Controlled Trials as Topic/methods* ; Consensus ; Bone Transplantation ; Research Design

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.