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.

eToyBox is a learning management system for preschool teachers to improve their health literacy, which ultimately aims to improve children’s obesity-related behaviour. As part of the development process of eToyBox, assessment on digital literacy, acceptance of digitization of education materials, and perceived barriers in adopting online learning is needed. Fifty-four preschool teachers under the Community Development Department (KEMAS) in Kuala Lumpur, Selangor, and Sarawak, who participated in ToyBox Study Malaysia intervention in 2018, took part in this cross-sectional study. An online self-administered questionnaire was used to assess sociodemographic background, use of communication tools and media, and teacher’s views on adapting the ToyBox modules to digital education materials. Respondents were contacted, and questionnaire link was shared through WhatsApp messages. Most participants (74.0%) were Malay females aged 31 to 40 years old. Most participants had internet access (94.4%) and owned at least a smart phone, laptop or tablet (94.4%). Participants perceived their computer skills to be average (75.0%). Majority of respondents (65.0%) reported advanced and higher abilities in word processing and email, but only 22.0% in spreadsheet skills. The main barrier to accessing online material was unstable internet connection (74.1%). Most respondents (90.0%) agree that adapting effective modules to online learning will be beneficial for professional development and teaching practices. In conclusion, most participants supported digitizing Toybox Study Malaysia educational content and were comfortable 72 with its implementation via an online learning platform. The findings from this study can advise future development of online learning materials for preschool teachers in Malaysia.

Purpose To explore the diagnostic value of beak sign in fetal annular pancreas by analyzing the ultrasonographic features of fetal annular pancreas.Materials and Methods The ultrasound images and clinical data of 13 cases of fetal annular pancreas diagnosed by prenatal ultrasound in Shandong Provincial Maternal and Child Health Hospital from September 2019 to December 2021 and confirmed by surgery after birth were retrospectively analyzed.The degree of duodenal stenosis at the obstruction site was observed,especially whether the angle formed by the intestinal wall could identify the fetal annular pancreas,and the ultrasonic characteristics were summarized and analyzed.Results A total of 13 fetuses with annular pancreas showed double bubble sign,3 cases showed clamp sign,and 7 cases showed beak sign at the end of duodenal dilatation.All the 13 cases underwent surgical treatment after birth,including 2 cases with duodenal atresia and 1 case with atypical intestinal malrotation.All the children had good prognosis after operation.Conclusion By observing the dilated end of duodenum and the relationship with pancreatic head,prenatal ultrasound combined with beak sign and double bubble sign could improve the diagnostic accuracy of fetal annular pancreas,which has significant value in prenatal diagnosis of fetal annular pancreas.

Isocitrate dehydrogenase 1 (IDH1) R132H is the most common mutated gene in grade II-III gliomas and oligodendrogliomas. Instead of activating telomerase (a reverse transcriptase which using RNA as a template to extend telomere length), the majority of IDH1^R132H mutant glioma maintain telomere length through an alternative mechanism that relies on homologous recombination (HR), which is known as alterative lengthening of telomere (ALT).The phenotype of ALT mechanism include: ALT associated promyelocytic leukemia protein (PML) bodies (APBs); extrachromosomal telomeric DNA repeats such as C- and T-loops; telomeric sister chromatid exchange (T-SCE), etc. The mechanism of ALT activation is not fully understood. Recent studies have shown that mutation IDH1 contributes to ALT phenotype in glioma cells in at least three key ways. Firstly, the IDH1^R132H mutation mediates RAP1 down-regulation leading to telomere dysfunction, thus ensuring persistent endogenous telomeric DNA damage, which is important for ALT activation. Spontaneous DNA damage at telomeres may provide a substrate for mutation break-induced replication (BIR)‑mediated ALT telomere lengthening, and it has been demonstrated that RAP1 inhibits telomeric repeat-containing RNA, transcribed from telomeric DNA repeat sequences (TERRA) transcription to down-regulate ALT telomere DNA replication stress and telomeric DNA damage, thereby inhibiting ALT telomere synthesis. Similarly, in ALT cells, knockdown of telomere-specific RNaseH1 nuclease triggers TERRA accumulation, which leads to increased replication pressure. Overexpression of RNaseH1, on the other hand, attenuates the recombination capacity of ALT telomeres, leading to telomere depletion, suggesting that RAP1 can regulate the level of replication pressure and thus ALT activity by controlling TERRA expression. Secondly, the IDH1^R132H also alters the preference of the telomere damage repair pathway by down-regulating XRCC1, which inhibits the alternative non-homologous end joining (A-NHEJ) pathway at telomeres and alters cellular preference for the HR pathway to promote ALT. Finally, the IDH1^R132H has a decreased affinity for isocitric acid and NADP+ and an increased affinity for α ketoglutarate (α‑KG) and NADPH, so that the mutant IDH1^R132H catalyzes the hydrogenation of α‑KG to produce 2-hydroxyglutarate (2-HG)in a NADPH-dependent manner. Because 2-HG is structurally similar to α‑KG, which maintains the trimethylation level of H3k9me3 by competitively inhibiting the activity of the α‑KG-dependent histone demethylase KDM4B, and recruits heterochromatin protein HP1α to heterochromatinize telomeres, and promote ALT phenotypes in cooperation with the inactivating of ATRX. In addition, it has been shown that APBs contain telomeric chromatin, which is essentially heterochromatin, and HP1α is directly involved in the formation of APBs. Based on these studies, this article reviews the mechanism of IDH1^R132H mediated telomere dysfunction and the preference of DNA repair pathway at telomeres in cooperate with ATRX loss to promote ALT, which may provide references for clinical targeted therapy of IDH1^R132H mutant glioma.

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.