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: 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.

In this study, lipopolysaccharide(LPS), ovalbumin(OVA), and compound 48/80(C48/80) were administered to establish non-infectious pneumonia models under simulated clinical conditions, and the correlation between their pathological characteristics and traditional Chinese medicine(TCM) syndromes was compared, providing the basis for the selection of appropriate animal models for TCM efficacy evaluation. An acute pneumonia model was established by nasal instillation of LPS combined with intraperitoneal injection for intensive stimulation. Three doses of OVA mixed with aluminum hydroxide adjuvant were injected intraperitoneally on days one, three, and five and OVA was administered via endotracheal drip for excitation on days 14-18 to establish an OVA-induced allergic pneumonia model. A single intravenous injection of three doses of C48/80 was adopted to establish a C48/80-induced pneumonia model. By detecting the changes in peripheral blood leukocyte classification, lung tissue and plasma cytokines, immunoglobulins(Ig), histamine levels, and arachidonic acid metabolites, the multi-dimensional analysis was carried out based on pathological evaluation. The results showed that the three models could cause pulmonary edema, increased wet weight in the lung, and obvious exudative inflammation in lung tissue pathology, especially for LPS. A number of pyrogenic cytokines, inclading interleukin(IL)-6, interferon(IFN)-γ, IL-1β, and IL-4 were significantly elevated in the LPS pneumonia model. Significantly increased levels of prostacyclin analogs such as prostaglandin E2(PGE2) and PGD2, which cause increased vascular permeability, and neutrophils in peripheral blood were significantly elevated. The model could partly reflect the clinical characteristics of phlegm heat accumulating in the lung or dampness toxin obstructing the lung. The OVA model showed that the sensitization mediators IgE and leukotriene E4(LTE4) were increased, and the anti-inflammatory prostacyclin 6-keto-PGF2α was decreased. Immune cells(lymphocytes and monocytes) were decreased, and inflammatory cells(neutrophils and basophils) were increased, reflecting the characteristics of "deficiency", "phlegm", or "dampness". Lymphocytes, monocytes, and basophils were significantly increased in the C48/80 model. The phenotype of the model was that the content of histamine, a large number of prostacyclins(6-keto-PGE1, PGF2α, 15-keto-PGF2α, 6-keto-PGF1α, 13,14-D-15-keto-PGE2, PGD2, PGE2, and PGH2), LTE4, and 5-hydroxyeicosatetraenoic acid(5S-HETE) was significantly increased, and these indicators were associated with vascular expansion and increased vascular permeability. The pyrogenic inflammatory cytokines were not increased. The C48/80 model reflected the characteristics of cold and damp accumulation. In the study, three non-infectious pneumonia models were constructed. The LPS model exhibited neutrophil infiltration and elevated inflammatory factors, which was suitable for the efficacy study of TCM for clearing heat, detoxifying, removing dampness, and eliminating phlegm. The OVA model, which took allergic inflammation as an index, was suitable for the efficacy study of Yiqi Gubiao formulas. The C48/80 model exhibited increased vasoactive substances(histamine, PGs, and LTE4), which was suitable for the efficacy study and evaluation of TCM for warming the lung, dispersing cold, drying dampness, and resolving phlegm. The study provides a theoretical basis for model selection for the efficacy evaluation of TCM in the treatment of pneumonia.
Animals ; Disease Models, Animal ; Mice ; Pneumonia/genetics* ; Medicine, Chinese Traditional ; Male ; Humans ; Cytokines/immunology* ; Female ; Lipopolysaccharides/adverse effects* ; Lung/drug effects* ; Drugs, Chinese Herbal ; Ovalbumin ; Mice, Inbred BALB C

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 correlation between complement C3 and urine protein level and proteinuria remission status in patients with primary membranous nephropathy (PMN), and better guide individualized clinical treatment.Methods:It was a single-center retrospective study. The clinical data of PMN patients who underwent renal biopsy in the First Affiliated Hospital of Nanjing Medical University from January 2017 to June 2022 were collected. Patients with 24 h urinary protein ≥ 3.5 g were followed up after receiving standard treatment, and the last outpatient or inpatient review was used as the end point of follow-up. 24 h urine protein was collected to evaluate the remission status of proteinuria. Kaplan-Meier method was used to analyze the correlation between serum and renal complements and proteinuria remission. Cox regression analysis method was used to analyze the correlation between serum C3 level and renal tissue C3 deposition and proteinuria remission.Results:This study included 507 PMN patients with 312 (61.54%) males, aged 54 (43, 64) years old. Compared with 24 h urinary protein < 3.5 g group, proportion of males ( χ2=22.479, P<0.001), age ( Z=-2.521, P=0.012), systolic blood pressure ( Z=-4.148, P<0.001), diastolic blood pressure ( Z=-4.084, P<0.001), serum anti-phospholipase A2 receptor (PLA2R) antibody titer ( Z=-7.019, P<0.001), total cholesterol ( Z=-8.796, P<0.001), triglyceride ( Z=-6.158, P<0.001), low density lipoprotein cholesterol ( Z=-8.716, P<0.001), serum creatinine ( Z=-7.368, P<0.001), serum C3 ( Z=-3.663, P<0.001), serum C4 ( Z=-6.560, P<0.001), proportion of glucocorticoid use ( χ2=116.417, P<0.001) and proportion of immunosuppressant use ( χ2=53.839, P<0.001) were all higher, while serum albumin ( Z=12.518, P<0.001), estimated glomerular filtration rate ( Z=6.345, P<0.001) and serum IgG ( Z=7.321, P<0.001) were all lower in 24 h urinary protein ≥3.5 g group. There were 268 patients included in the follow-up cohort with baseline 24 h urinary protein of 7.15 (5.14, 10.24) g, serum anti-PLA2R antibody titer of 61.44 (14.35, 193.24) RU/ml, serum C3 of 1.005 (0.864, 1.150) g/L, and serum C4 of 0.260 (0.214, 0.317) g/L. Kaplan-Meier survival curve showed that the incomplete remission rate of proteinuria in serum C3 > 1.005 g/L group was lower than that in serum C3 ≤ 1.005 g/L group (log-rank χ2=4.757, P=0.029). There was no significant difference in the incomplete remission rate of proteinuria between serum C4 ≤ 0.260 g/L group and serum C4 > 0.260 g/L group (log-rank χ2=3.543, P=0.060). Renal C1q (log-rank χ2=0.167, P=0.683) and C4 (log-rank χ2=1.927, P=0.165) deposition had no significant effects on proteinuria remission in PMN patients. The incomplete remission rate of proteinuria in patients with renal C3 deposition was higher than that in patients without renal C3 deposition (log-rank χ2=7.018, P=0.008). Univariate Cox regression analysis showed that serum C3 level and C3 deposition in renal tissues were influencing factors of incomplete remission of proteinuria (both P<0.05), while adjusting for gender, age, mean arterial pressure, serum anti-PLA2R antibody, serum albumin and 24 h urinary protein, serum C3 ≤ 1.005 g/L ( HR=1.374, 95% CI 1.021-1.849, P=0.036), C3 deposition in renal tissues ( HR=1.949, 95% CI 1.098-3.460, P=0.023), and serum C3 ≤ 1.005 g/L combined with C3 deposition in renal tissues ( HR=1.472, 95% CI 1.093-1.983, P=0.011) were independent influencing factors of incomplete remission of proteinuria. Conclusions:The serum C3 level and C3 deposition in renal tissues are closely related to urinary protein level and proteinuria remission status in PMN patients. The patients with higher urinary protein have higher serum C3. For patients with massive proteinuria, serum C3 ≤ 1.005 g/L, C3 deposition in renal tissues, serum C3 ≤ 1.005 g/L combined with C3 deposition in renal tissues are independent risk factors of incomplete remission of proteinuria.

At present,there are many difficulties in the diagnosis and treatment of acute vestibular syndrome(AVS).For example,complex and difficult identification of the cause of disease,uneven diagnosis and treatment levels of clinical doctors,weak humanistic care awareness,lack of communication skills,intrinsic affinity and other reasons,which make it difficult for AVS patients in the process of diagnosis and treatment,and cannot receive timely and effective treatment,resulting in an exacerbation of doctor-patient conflicts.Therefore,it is recommended to explore new paths of AVS diagnosis and treatment work using the humanistic care concept,respect each other between doctors and patients,build a team of medical staff with the value orientation of"humanistic care",and promote the organic unity of theory and practice of"humanistic care",with a view to better promoting the implementation of AVS diagnosis and treatment work,helping more patients rebuild confidence,enhancing quality of life,and improving the doctor-patient relationship.

Objective To observe the reproducibility of radiomics feature(RF)extracted from virtual monoenergetic image(VMI)of rabbit VX2 hepatoma models obtained with 3 different dual-energy CT(DECT)systems,and to explore relationship of reproducibility and diagnostic performance of RF.Methods Fifteen rabbits with VX2 hepatoma were randomly divided into 3 groups(each n=5).Contrast-enhanced abdominal CT scanning under volume CT dose index(CTDIvol)levels of 6,9 and 12 mGy were performed with dual-source DECT(dsDECT),rapid kV switching DECT(rsDECT)and dual-layer detector DECT(dlDECT),respectively.VMI were reconstructed at 10 keV increments from 40 to 140 keV.RF were extracted from VMI,the reproducibility was assessed using intra-class correlation coefficient(ICC),and those with ICC≥0.8 were considered as reproducible RF.The percentage of reproducible features(denoted by R)were compared among different scanner pairings and different CTDIvol levels.Within each CTDIvol group,the reconstruction energy levels yielding the maximum number(denoted by N)of common RF across different scanner pairings were identified.The receiver operating characteristic(ROC)curve was drawn,the area under the curve(AUC)was calculated,and the diagnostic efficacies of reproducible RF and other RF were compared under optimal reproducible conditions.Spearman correlation coefficient between ICC and the corresponding AUC of RF were calculated.Results RrsDECT-dsDECT(6.45％,95％CI[2.36％,8.87％])was higher than RdlDECT-dsDECT(0.72％,95％CI[0.15％,1.79％])and RrsDECT-dlDECT(1.43％,95％CI[0.60％,4.06％])(all adjusted P＜0.05),R9mGy(3.70％,95％CI[1.31％,5.73％])and R12mGy(2.63％,95％CI[0.60％,6.69％])were higher than R6mGy(1.31％,95％CI[0.12％,1.55％])(all adjusted P＜0.05).The optimal reproducible reconstruction energy levels of RF under CTDIvol of 6,9 and 12 mGy concentrated at 50-70 keV.AUC of reproducible RFs were higher than of other RF(all adjusted P＜0.05)and had certain correlation with the reproducibility(rs=0.102-0.516,P＜0.05).Conclusion The reproducibility of RF extracted from contrast-enhanced VMI CT images of rabbit VX2 hepatoma models associated with DECT scanner,CTDIvol level and reconstruction energy level.RF with higher reproducibility might have better diagnostic performance.