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.

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.

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.

Meridian-tendon is a central concept in meridian theory of TCM, and its basic research has been increasingly emphasized. While there is no unified understanding of the essence of meridian-tendon, the concept that function of fascia could partially reflect the functions of meridian-tendons has reached consensus in the academic community. This article suggests that under the guidance of meridian-tendon theory, based on previous research foundation of fascia, focusing on adopting fascia research methods, the mechanisms of tender point hyperalgesia and abnormal proliferation related to meridian lesions should be adopted to explain yitong weishu (taking the worst painful sites of muscle spasm as the points), and the mechanisms of meridian intervention efficacy should be adopted to explain yizhi weishu (feelings from patients and acupuncture operators). Furthermore, this article provides an analysis of the future trends in basic research of meridian tendons.
Humans ; Meridians ; Acupuncture Therapy ; Acupuncture ; Tendons ; Pain ; Research Design ; Acupuncture Points

Objective: To study the incidence of bloodstream infections, pathogen distribution, and antibiotic resistance profile in patients with hematological malignancies. Methods: From January 2018 to December 2021, we retrospectively analyzed the clinical characteristics, pathogen distribution, and antibiotic resistance profiles of patients with malignant hematological diseases and bloodstream infections in the Department of Hematology, Nanfang Hospital, Southern Medical University. Results: A total of 582 incidences of bloodstream infections occurred in 22,717 inpatients. From 2018 to 2021, the incidence rates of bloodstream infections were 2.79%, 2.99%, 2.79%, and 2.02%, respectively. Five hundred ninety-nine types of bacteria were recovered from blood cultures, with 487 (81.3%) gram-negative bacteria, such as Klebsiella pneumonia, Escherichia coli, and Pseudomonas aeruginosa. Eighty-one (13.5%) were gram-positive bacteria, primarily Staphylococcus aureus, Staphylococcus epidermidis, and Enterococcus faecium, whereas the remaining 31 (5.2%) were fungi. Enterobacteriaceae resistance to carbapenems, piperacillin/tazobactam, cefoperazone sodium/sulbactam, and tigecycline were 11.0%, 15.3%, 15.4%, and 3.3%, with a descending trend year on year. Non-fermenters tolerated piperacillin/tazobactam, cefoperazone sodium/sulbactam, and quinolones at 29.6%, 13.3%, and 21.7%, respectively. However, only two gram-positive bacteria isolates were shown to be resistant to glycopeptide antibiotics. Conclusions: Bloodstream pathogens in hematological malignancies were broadly dispersed, most of which were gram-negative bacteria. Antibiotic resistance rates vary greatly between species. Our research serves as a valuable resource for the selection of empirical antibiotics.
Humans ; Bacteremia/epidemiology* ; Cefoperazone ; Sulbactam ; Retrospective Studies ; Drug Resistance, Bacterial ; Microbial Sensitivity Tests ; Hematologic Neoplasms ; Sepsis ; Anti-Bacterial Agents/pharmacology* ; Gram-Negative Bacteria ; Gram-Positive Bacteria ; Piperacillin, Tazobactam Drug Combination ; Escherichia coli