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: 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 evaluate the efficacy and safety of the Chi-BEAM regimen (chidamide combined with carmustine, etoposide, cytarabine, and melphalan) followed by autologous hematopoietic stem cell transplantation (ASCT) in patients with high-risk or relapsed/refractory lymphoma.Methods:This retrospective case series included 78 patients with newly treated high-risk or relapsed/refractory lymphoma who underwent ASCT with the Chi-BEAM conditioning regimen in the Department of Hematology, the First Affiliated Hospital of Nanjing Medical University (Jiangsu Province Hospital), from June 2021 to May 2024. Descriptive statistics were employed to evaluate clinical characteristics, efficacy, and adverse events. The Kaplan-Meier method was applied to calculate cumulative progression-free survival (PFS) and overall survival (OS) rates.Results:The median age of the 78 evaluable patients was 47 years (range 16-68), with 8 patients (10.3%) aged ≥60 years. At the first post-transplant assessment (3 months), the objective response rate was 94.9% (74/78). The median follow-up was 20.1 months (range 2.9-44.9). The median PFS time was 20.1 months (range 1.6-45.1), with a 2-year cumulative PFS rate of 81.8%. The median OS time was 20.6 months (range 3.1-45.1), with a cumulative 2-year OS rate of 93.2%. The regimen was well-tolerated; mild-to-moderate hypocalcemia within 1 week post-infusion and transient mild erythrocyturia on the infusion day were the primary adverse reactions.Conclusion:The Chi-BEAM regimen combined with ASCT demonstrates both safety and clinical benefit in patients with high-risk or relapsed/refractory lymphoma.

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 study the effects of TCM ointment rubbing technique on pain, swelling, and knee joint function in patients with knee osteoarthritis (KOA) after total knee arthroplasty (TKA).Methods:A randomized controlled trial was conducted. In this study, 80 patients with KOA who underwent TKA treatment in the Department of Sports Medicine I, Wangjing Hospital, China Academy of Traditional Chinese Medicine from October 2022 to March 2024 were taken as the study subjects. They were divided into two groups with random number table method, with 40 cases in each group. Both groups were treated with conventional Western medicine + rehabilitation training after surgery, and the observation group was combined with TCM paste mo technique on this basis. VAS score was used to assess the degree of pain at different time points, and skin fold was used to measure the circumference of the upper knee circumference and the circumference of the lower knee circumference, and knee Injury and osteoarthritis outcome score (KOOS) was used to assess the degree of knee joint function recovery.Results:After treatment, the observation group after surgerythe at 7 d (2.57 ± 0.84 vs. 4.00 ± 0.85, t=7.54) and 14 d (0.80 ± 0.93 vs. 2.70 ± 1.04, t=8.56) VAS scores were lower than those in the control group ( P<0.001). After treatment, the observation group after surgerythe at 7 d the superior circumference [(48.32 ± 4.57) cm vs. (50.53 ± 3.97) cm, t=2.32], and inferior circumference [(36.71 ± 2.95) cm vs. (39.21 ± 6.86) cm, t=2.12], at 14 d the superior circumference [(45.68 ± 4.69) cm vs. (47.96 ± 3.89) cm, t=2.37], and inferior circumference [(34.96 ± 2.96) cm vs. (36.70 ± 4.35) cm, t=2.10] were lower than those in the control group ( P<0.05). The observation group after surgerythe at 14 d the the knee joint mobility [(115.32 ± 2.12) ° vs. (113.34 ± 2.16) °, t=4.14] and KOOS scores (85.52 ± 0.82 vs. 80.32 ± 1.13, t=23.56) were higher than those in the control group ( P<0.01). Conclusion:TCM ointment rubbing technique has significant advantages in improving pain, swelling and knee joint mobility after artificial knee arthroplasty, which can effectively restore knee joint function.

Objective:To explore the correlation between renal function and abdominal aortic hemodynamic parameters based on four-dimensional flow(4D Flow) MRI in patients with chronic kidney disease (CKD).Methods:A cross-section prospective study was conducted on 73 patients diagnosed with CKD at First People′s Hospital of Changzhou between March 2021 and May 2023, as well as 13 volunteers without kidney injury. According to the estimated glomerular filtration rate (eGFR), the subjects were divided into CKD 1-3 stage group ( n=34), CKD 4-5 stage group ( n=39), and control group ( n=13). All subjects underwent 4D Flow MRI examination of the abdominal aorta, measuring pulse wave velocity (PWV), peak velocity, and maximum wall shear stress (WSS) at the proximal plane (Plane_1) and the higher renal artery opening plane (Plane_2) of the abdominal aorta. The differences in 4D Flow MRI hemodynamic parameters among the three groups were compared using a one-way analysis of variance or the Kruskal-Wallis test. The correlation between 4D Flow MRI hemodynamic parameters and eGFR was analyzed by using the Spearman correlation coefficient. The independent influencing factors that affect eGFR were analyzed by using multivariate linear regression analysis. Results:There were significant differences in abdominal aortic PWV and maximal WSS of Plane_1 and Plane_2 among the three groups ( H=10.38, P=0.006; F=11.16, P<0.001; F=4.75, P=0.011). There were no significant differences in the peak velocity of Plane_1 and Plane_2 among the three groups (both P>0.05). Abdominal aortic PWV was negatively correlated with eGFR ( r s=-0.30, P=0.005). There was a positive correlation between the maximal WSS of Plane_1 and Plane_2 with eGFR ( r s=0.39, P<0.001; r s=0.29, P=0.006). Abdominal aortic PWV and maximal WSS of Plane_1 were independent influencing factors of eGFR (b=-4.32, P=0.018; b=132.23, P=0.004). Conclusions:There is an independent correlation between renal function and abdominal aortic hemodynamic parameters based on 4D Flow MRI in patients with CKD, and abdominal aortic PWV and maximal WSS of Plane_1 were independent influencing factors of eGFR.