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.

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.

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

OBJECTIVE: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models

OBJECTIVE: This study aimed to reexplore minimum iodine excretion and to build a dietary iodine recommendation for Chinese adults using the obligatory iodine loss hypothesis. METHODS: Data from 171 Chinese adults (19-21 years old) were collected and analyzed based on three balance studies in Shenzhen, Yinchuan, and Changzhi. The single exponential equation was accordingly used to simulate the trajectory of 24 h urinary iodine excretion as the low iodine experimental diets offered (iodine intake: 11-26 μg/day) and to further deduce the dietary reference intakes (DRIs) for iodine, including estimated average requirement (EAR) and recommended nutrient intake (RNI). RESULTS: The minimum iodine excretion was estimated as 57, 58, and 51 μg/day in three balance studies, respectively. Moreover, it was further suggested as 57, 58, and 51 μg/day for iodine EAR, and 80, 81, and 71 μg/day for iodine RNI or expressed as 1.42, 1.41, and 1.20 μg/(day·kg) of body weight. CONCLUSION The iodine DRIs for Chinese adults were established based on the obligatory iodine loss hypothesis, which provides scientific support for the amendment of nutrient requirements.
Humans ; Iodine/administration & dosage* ; Male ; Female ; China ; Young Adult ; Diet ; Adult ; Nutritional Requirements ; East Asian People

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:To compare the efficacy and safety of crisaborole ointment 2% versus pimecrolimus cream 1% in the treatment of mild to moderate atopic dermatitis in children aged 2 years or older.Methods:A multicenter, randomized, open-label, controlled clinical trial was conducted. A total of 120 pediatric patients aged 2 - 17 years with mild to moderate atopic dermatitis were enrolled from departments of dermatology of 8 hospitals in China between March 2022 and February 2023. The participants were randomly assigned in a 1∶1 ratio to the crisaborole group and the pimecrolimus group, and received the treatment with crisaborole ointment 2% and pimecrolimus cream 1% respectively, twice a day for 4 weeks. Visits were scheduled at baseline/on day 1, as well as on days 8, 15, and 29. The primary efficacy outcome was the percentage of patients achieving the Investigator's Static Global Assessment (ISGA) success (defined as clear [0] or almost clear [1] on the ISGA scale, combined with ≥ 2‐grade improvement from baseline) on day 29. The secondary efficacy outcomes included changes in the Eczema Area and Severity Index (EASI) total scores from baseline to day 29, percentages of patients achieving ISGA improvement (defined as clear [0] or almost clear [1] on the ISGA scale), as well as changes in the Peak Pruritus Numerical Rating Scale (NRS) scores, Dermatology Life Quality Index (DLQI) /Infants' Dermatology Life Quality Index (IDLQI) /Children's Dermatology Life Quality Index (CDLQI) scores, and in the Dermatitis Family Impact (DFI) scores. Drug safety was evaluated according to the incidence of adverse events. Categorical data were compared using the chi-square test. Since measurement data did not follow a normal distribution, the rank sum test was used for comparisons of measurement data between groups.Results:A total of 106 children with mild to moderate atopic dermatitis were included in the per-protocol analysis set, with 52 in the crisaborole group (26 males and 26 females) and 54 in the pimecrolimus group (27 males and 27 females). There were no significant differences in age, disease duration, ISGA and EASI scores at baseline between the two groups (all P > 0.05). On day 29, 22 patients (42.31%) in the crisaborole group and 25 (46.30%) in the pimecrolimus group achieved ISGA success, with no significant difference between the two groups ( χ2 = 0.17, P = 0.68) ; 35 patients (67.31%) in the crisaborole group and 45 (83.33%) in the pimecrolimus group achieved ISGA improvement, also with no significant difference between the two groups ( χ2 = 3.68, P = 0.06) ; additionally, there were no significant differences in the EASI, pruritus NRS, DLQI/IDLQI/CDLQI, or DFI scores between the two groups (all P > 0.05). Adverse reactions to the two topical agents were mainly local reactions such as mild to moderate pain, itching, or worsening of itching, and no obvious systemic adverse reactions occurred. The incidence of drug-related adverse reactions was 46.15% (24 cases) in the crisaborole group and 37.04% (20 cases) in the pimecrolimus group, with no significant difference between the two groups ( χ2 = 0.91, P = 0.34) . Conclusion:The efficacy of crisaborole ointment 2% was comparable to that of pimecrolimus cream 1% in the treatment of mild to moderate atopic dermatitis in children aged ≥ 2 years, and it yielded early and rapid improvement in the quality of life of patients and their families, with good safety and tolerability profiles.

Objective To evaluate the performance and clinical application value of EasyNAT nucleic acid test kits for influenza A virus(Flu A),influenza B virus(Flu B),and Mycoplasma pneumoniae(MP)based on cross-primed amplification(CPA)technology.Methods In compliance with relevant standards,the detection limits,cross-reactivity,and anti-interference abilities of the EasyNAT Flu and MP nucleic acid test kits were validated using influenza(Flu)and MP reference materials.Additionally,a total of 811 suspected Flu or MP infection patients who visited the hospital from January 1 to July 5,2024 were enrolled in this study.Their oropharyngeal swabs were collected and tested using reverse transcription quantitative polymerase chain reac-tion(RT-qPCR)as the gold standard to evaluate the concordance and compliance of the EasyNAT Flu and MP nucleic acid test kits,thus assessing their clinical application value.Results The EasyNAT Flu and MP nucleic acid test kits yielded positive results in 8 repeated tests at the detection limit concentrations of the ref-erence materials.No pathogen-specific peaks were detected in Flu and MP negative samples that had been spiked with common cross-reacting pathogens.Furthermore,the detection results of Flu or MP positive sam-ples were unaffected by the presence of interfering substances.Among the 811 clinical samples,the sensitivity of the EasyNAT Flu A test was 97.33%,and the sensitivity for Flu B was 98.47%,both with a specificity of 98.04%.The EasyNAT MP nucleic acid test showed a sensitivity of 97.95%and specificity of 99.03%.The results demonstrated a high degree of agreement with RT-qPCR(Kappa values were over 0.900).Further-more,detection rates of the EasyNAT Flu test kits showed no difference in positive samples with different Ct values(Flu A:χ2=4.20,P=0.08;Flu B:χ2=2.22,P=0.31)and MP test kits showed significant differences in positive samples with different cycle threshold values(χ2=11.84,P<0.01).Conclusion The EasyNAT Flu and MP nucleic acid test kits exhibit excellent detection performance with high sensitivity and specificity.Moreover,the products are easy to operate,offer rapid detection,and provide accurate results.These features make them suitable for primary healthcare settings,with significant potential for clinical application.