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

OBJECTIVES: To study the epidemiological characteristics of respiratory syncytial virus (RSV) infection in hospitalized children with community-acquired pneumonia (CAP) in Hebei Province. METHODS: Hospitalized children with CAP who tested positive for RSV and were admitted to Hebei Children's Hospital from various cities and counties across Hebei Province between January 2019 and December 2023 were included in the study. Clinical data were collected and analyzed to assess epidemiological characteristics. RESULTS: The clinical data of 43 978 children with CAP were collected, with an overall RSV detection rate of 25.98%. The detection rate was higher during the implementation of non-pharmaceutical interventions (NPIs) (30.60%) than in the non-NPIs period. Winter and spring were the primary epidemic seasons for RSV each year except in 2022. The detection rate in males (26.62%) was higher than in females (25.06%) (P<0.001). The highest detection rate (59.18%) was found in infants aged 29 days to <1 year. Single RSV infection was more common, with rhinovirus being the most frequent co-infection. CONCLUSIONS The overall RSV detection rate in Hebei Province is influenced by NPIs, being higher during their implementation. RSV predominantly circulates in winter and spring. The detection rate of RSV is higher in males and infants. RSV infection is primarily single, most often co-occurring with rhinovirus.
Humans ; Respiratory Syncytial Virus Infections/epidemiology* ; Female ; Male ; Infant ; Child, Preschool ; Seasons ; China/epidemiology* ; Infant, Newborn ; Community-Acquired Infections/epidemiology* ; Child

OBJECTIVE: To analyze the incidence of abnormal hemoglobin (Hb) in neonates in Guangzhou area, as well as the results of quantitative analysis of Hb in neonatal umbilical cord blood and genetic diagnosis of thalassemia in neonates with abnormal Hb; And to explore the hematological phenotypes and clinical characteristics of neonates with abnormal Hb and their parents, providing a reference for eugenics and childcare. METHODS: 650 neonates born at Guangdong Provincial People's Hospital who underwent Hb electrophoresis were included in this study. The results of routine blood test of umbilical cord blood , Hb electrophoresis and α-, β-thalassemia gene detection of the neonates were collected. The genotype distribution of thalassemia in the neonates was analyzed. Additionally, the abnormal Hb content of α and β variants was studied. Furthermore, the differences in hematological parameters between abnormal Hb neonates and normal neonates and α-thalassemia neonates, as well as between the parents of abnormal Hb neonates and normal adults were compared. RESULTS: Among the 650 neonates, 332 (51.08%) were diagnosed with thalassemia, including 235 cases of α-thalassemia (36.15%), 79 cases of β-thalassemia (12.15%), and 18 cases of compound αβ-thalassemia (2.77%). Among all the α-thalassemia genotypes, the most prevalent one was -- ^SEA/αα (48.94%), followed by -α^3.7/αα (20.00%), -α^4.2/αα (11.06%), and αα^CS/αα (8.94%). The four most common genotypes of β-thalassemia were β^CD41-42 (32.91%), β^IVS-Ⅱ-654 (26.58%), β^-28 (21.52%), and β^E (10.13%), respectively. 275 cases of abnormal bands were found in Hb electrophoresis of umbilical cord blood, with a detection rate of 42.31%. The abnormal Hb content of α-variant in the neonates was significantly higher than that of β-variant (P < 0.001). The levels of Hb, MCV, MCH, Hb A, and Hb F in neonates with abnormal Hb were lower than those in normal neonates, while the RDW-CV was higher than that in normal neonates, with statistical significantce (P < 0.05). The levels of RBC and Hb A in neonates with abnormal Hb were lower than those in neonates with α-thalassemia, while the level of MCH was higher than that in neonats with α-thalassemia, with statistical significance (P < 0.05). The levels of Hb, MCV, MCH, and Hb A in parents of neonates with abnormal Hb were lower than those in normal adults, while the RDW-CV was higher than that in normal adults, and the differences were statistically significant (P < 0.05). CONCLUSION The abnormal Hb content of α-variant in the neonates is significantly higher than that of β-variant in the neonates in Guangzhou, which can help to presume whether it is α chain or β chain based on the abnormal Hb content, providing a reference for globin gene sequencing. Meanwhile, analysis of various hematological screening-related indicators in neonates in the early stage is beneficial for early warning of the occurrence of abnormal Hb combined with thalassemia, reducing missed diagnoses to a certain extent.
Humans ; Infant, Newborn ; Genotype ; Hemoglobins, Abnormal/genetics* ; China/epidemiology* ; alpha-Thalassemia/epidemiology* ; beta-Thalassemia/genetics* ; Parents ; Female ; Male ; Fetal Blood

OBJECTIVE: High-altitude hypoxia exposure often damages hippocampus-dependent learning and memory. Nogo-A is an important axonal growth inhibitory factor. However, its function in high-altitude hypoxia and its mechanism of action remain unclear. METHODS: In an in vivo study, a low-pressure oxygen chamber was used to simulate high-altitude hypoxia, and genetic or pharmacological intervention was used to block the Nogo-A/NgR1 signaling pathway. Contextual fear conditioning and Morris water maze behavioral tests were used to assess learning and memory in rats, and synaptic damage in the hippocampus and changes in oxidative stress levels were observed. In vitro, SH-SY5Y cells were used to assess oxidative stress and mitochondrial function with or without Nogo-A knockdown in Oxygen Glucose-Deprivation/Reperfusion (OGD/R) models. RESULTS: Exposure to acute high-altitude hypoxia for 3 or 7 days impaired learning and memory in rats, triggered oxidative stress in the hippocampal tissue, and reduced the dendritic spine density of hippocampal neurons. Blocking the Nogo-A/NgR1 pathway ameliorated oxidative stress, synaptic damage, and the learning and memory impairment induced by high-altitude exposure. CONCLUSION: Our results demonstrate the detrimental role of Nogo-A protein in mediating learning and memory impairment under high-altitude hypoxia and suggest the potential of the Nogo-A/NgR1 signaling pathway as a crucial therapeutic target for alleviating learning and memory dysfunction induced by high-altitude exposure. GRAPHICAL ABSTRACT available in www.besjournal.com.
Animals ; Oxidative Stress ; Hippocampus/metabolism* ; Rats ; Nogo Proteins/genetics* ; Male ; Rats, Sprague-Dawley ; Hypoxia/metabolism* ; Altitude ; Synapses ; Humans ; Altitude Sickness/metabolism*

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