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.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

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.

Background::The potential impact of pre-existing coronary artery stenosis (CAS) on acute pulmonary embolism (PE) episodes remains underexplored. This study aimed to investigate the association between pre-existing CAS and the elevation of high-sensitivity cardiac troponin I (hs-cTnI) levels in patients with PE.Methods::In this multicenter, prospective case-control study, 88 cases and 163 controls matched for age, sex, and study center were enrolled. Cases were patients with PE with elevated hs-cTnI. Controls were patients with PE with normal hs-cTnI. Coronary artery assessment utilized coronary computed tomographic angiography or invasive coronary angiography. CAS was defined as ≥50% stenosis of the lumen diameter in any coronary vessel >2.0 mm in diameter. Conditional logistic regression was used to evaluate the association between CAS and hs-cTnI elevation.Results::The percentage of CAS was higher in the case group compared to the control group (44.3% [39/88] vs. 30.1% [49/163]; P = 0.024). In multivariable conditional logistic regression model 1, CAS (adjusted odds ratio [OR], 2.680; 95% confidence interval [CI], 1.243–5.779), heart rate >75 beats/min (OR, 2.306; 95% CI, 1.056–5.036) and N-terminal pro-B type natriuretic peptide (NT-proBNP) >420 pg/mL (OR, 12.169; 95% CI, 4.792–30.900) were independently associated with elevated hs-cTnI. In model 2, right CAS (OR, 3.615; 95% CI, 1.467–8.909) and NT-proBNP >420 pg/mL (OR, 13.890; 95% CI, 5.288–36.484) were independently associated with elevated hs-cTnI. Conclusions::CAS was independently associated with myocardial injury in patients with PE. Vigilance towards CAS is warranted in patients with PE with elevated cardiac troponin levels.

AIM To establish a UPLC-MS/MS method for the simultaneous content determination of gallic acid,protocatechuic acid,neomangiferin,catechin,caffeic acid,mangiferin,isomangiferin,albiflorin,paeoniflorin,vitexin,liquiritin,scutellarin,baicalin,liquiritigenin,timosaponin BⅡ,quercetin,wogonoside,benzoylpaeoniflorin,isoliquiritigenin,honokiol,magnolol,norarecaidine,arecaidine,arecoline,epicatechin,baicalein,glycyrrhizinate and wogonin in Dayuan Drink.METHODS The analysis was performed on a 35℃thermostatic Syncronis C18 column(100 mm×2.1 mm,1.7 μm),with the mobile phase comprising of 0.1%formic acid-acetonitrile flowing at 0.3 mL/min in a gradient elution manner,and electron spray inoization source was adopted in positive and negative ion scanning with select reaction monitoring mode.RESULTS Twenty-eight constituents showed good linear relationships within their own ranges(R2≥0.991 0),whose average recoveries were 95.60%-103.53%with the RSDs of 0.60%-5.45%.CONCLUSION This rapid,simple,selective,accurate and reliable method can be used for the quality control of Dayuan Drink.

Abstract Allergic diseases can occur in all systems of the body, covering the whole life cycle, from children to adults and to old age, can be lifelong onset and even fatal in severe cases. Children account for the largest proportion of the victims of allergic disease, Children s allergies start from scratch, ranging from mild to severe, from less to more, from single to multiple systems and systemic performance, so the prevention and treatment of allergic diseases in children is of great importance, which can not only prevent high risk allergic conditions from developing into allergic diseases, but also further block the process of allergy. At present, there is no consensus on the management system of allergic children in kindergartens and primary schools. The "Consensus on Allergy Management and Prevention in Kindergartens and Primary Schools", which includes the organizational structure, system construction and management of allergic children, provides evidence informed recommendations for the long term comprehensive management of allergic children in kindergartens and primary schools, and provides a basis for the establishment of the prevention system for allergic children.

Objective: To investigate the epidemiology and hospitalization costs of pediatric community-acquired pneumonia (CAP) in Shanghai. Methods: A retrospective case summary was conducted on 63 614 hospitalized children with CAP in 59 public hospitals in Shanghai from January 2018 to December 2020. These children's medical records, including their basic information, diagnosis, procedures, and costs, were extracted. According to the medical institutions they were admitted, the patients were divided into the children's hospital group, the tertiary general hospital group and the secondary hospital group; according to the age, they were divided into <1 year old group, 1-<3 years old group, 3-<6 years old group, 6-<12 years old group and 12-18 years old group; according to the CAP severity, they were divided into severe pneumonia group and non-severe pneumonia group; according to whether an operation was conducted, the patients were divided into the operation group and the non-operation group. The epidemiological characteristics and hospitalization costs were compared among the groups. The χ² test or Wilcoxon rank sum test was used for the comparisons between two groups as appropriate, and the Kruskal-Wallis H test was conducted for comparisons among multiple groups. Results: A total of 63 614 hospitalized children with CAP were enrolled, including 34 243 males and 29 371 females. Their visiting age was 4 (2, 6) years. The length of stay was 6 (5, 8) days. There were 17 974 cases(28.3%) in the secondary hospital group, 35 331 cases (55.5%) in the tertiary general hospital group and 10 309 cases (16.2%) in the children's hospital group. Compared with the hospitalizations cases in 2018 (27 943), the cases in 2019 (29 009) increased by 3.8% (1 066/27 943), while sharply declined by 76.2% (21 281/27 943) in 2020 (6 662). There were significant differences in the proportion of patients from other provinces and severe pneumonia cases, and the hospitalization costs among the children's hospital, secondary hospital and tertiary general hospital (7 146 cases(69.3%) vs. 2 202 cases (12.3%) vs. 9 598 cases (27.2%), 6 929 cases (67.2%) vs. 2 270 cases (12.6%) vs. 9 397 cases (26.6%), 8 304 (6 261, 11 219) vs. 1 882 (1 304, 2 796) vs. 3 195 (2 364, 4 352) CNY, χ²=10 462.50, 9 702.26, 28 037.23, all P<0.001). The annual total hospitalization costs of pediatric CAP from 2018 to 2020 were 110 million CNY, 130 million CNY and 40 million CNY, respectively. And the cost for each hospitalization increased year by year, which was 2 940 (1 939, 4 438), 3 215 (2 126, 5 011) and 3 673 (2 274, 6 975) CNY, respectively. There were also significant differences in the hospitalization expenses in the different age groups of <1 year old, 1-<3 years old, 3-<6 years old, 6-<12 years old and 12-18 years old (5 941 (2 787, 9 247) vs. 2 793 (1 803, 4 336) vs. 3 013 (2 070, 4 329) vs. 3 473 (2 400, 5 097) vs. 4 290 (2 837, 7 314) CNY, χ²=3 462.39, P<0.001). The hospitalization cost of severe pneumonia was significantly higher than that of non-severe cases (5 076 (3 250, 8 364) vs. 2 685 (1 780, 3 843) CNY, Z=109.77, P<0.001). The cost of patients who received operation was significantly higher than that of whom did not (10 040 (4 583, 14 308) vs. 3 083 (2 025, 4 747) CNY, Z=44.46, P<0.001). Conclusions: The number of children hospitalized with CAP in Shanghai decreased significantly in 2020 was significantly lower than that in 2018 and 2019.The proportion of patients from other provinces and with severe pneumonia are mainly admitted in children's hospitals. Hospitalization costs are higher in children's hospitals, and also for children younger than 1 year old, severe cases and patients undergoing operations.
Infant ; Female ; Male ; Humans ; Child ; Retrospective Studies ; China/epidemiology* ; Hospitalization ; Community-Acquired Infections/therapy* ; Hospitals, Pediatric ; Pneumonia/therapy*