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.

Psoraleae Fructus （PF） is a non-toxic Chinese herbal medicine， while the liver injury caused by PF has aroused wide concern in recent years. At present， animal experiments and in vitro studies have been carried out to explore the mechanism， targets， and toxic components of PF in inducing liver injury， which， however， have differences compared with the actual conditions in clinical practice， and there are still some potential hepatotoxic components and targets of PF that have not been discovered. With the continuous progress in systems biology， establishing the drug-induced liver injury model and the liver injury prediction model based on network toxicology can reduce the cost of animal experiments， improve the toxicity prediction efficiency， and provide new tools for predicting toxic components and targets. To systematically explain the characteristics of liver injury in the application of PF and explore the potential hepatotoxic components and targets of PF， we reviewed the related articles published by China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP， and PubMed from 1962 to 2021 and analyzed the characteristics and influencing factors of liver injury caused by PF in the patients. Furthermore， we summarized the chemical components of PF and the components entering blood. By reviewing the mechanism， targets， and components of PF in inducing liver injury that were discovered by in vivo and in vitro experiments， we summarized the known compounds in PF that may cause liver injury. Finally， the current methods for building the prediction model of PF-induced liver injury were summarized， and the predicted toxic components and targets were introduced. The possible factors of PF in causing liver injury were explained from three aspects： clinical characteristics， preclinical studies， and computer-assisted network prediction， which provide a reference for predicting the risk of PF-induced liver injury.

Objective:To evaluate the optical performance of two aspheric intraocular lenses (IOL) AcrySof IQ SN60WF and Proming A1-UV with identical negative spherical aberration values, using the optical bench OptiSpheric IOL R&D through an in vitro study. Methods:The optical performance of + 20.0 D blue-light filtering SN60WF and monofocal high-order aspheric non blue-light filtering A1-UV IOL was evaluated through cornea models with the spherical aberration of 0 μm (ISO-1) and + 0.28 μm (ISO-2) under apertures of 3.0 mm and 4.5 mm via the optical bench OptiSpheric IOL R&D.The modulation transfer function (MTF) and USAF 1951 resolution test chart were employed to measure the IOL with centering, decentration of 0.3, 0.5, 0.7, 0.9 and 1.1 mm, as well as tilt of 3°, 5°, 7°, 9° and 11°.The spectral transmittance of IOL was measured with the UV-3300 UV-VIS spectrophotometer.Results:Compared with the A1-UV IOL, the spectral transmittance of SN60WF for blue light with wavelengths of 400-500 nm was significantly reduced, which effectively reduced the passage of blue light.At an aperture of 3.0 mm, the MTF values at 100 lp/mm spatial frequency for the centered SN60WF and A1-UV were 0.576 and 0.598 under ISO-1 corneal measurement conditions, 0.564 and 0.563 under ISO-2 conditions.At an aperture of 4.5 mm, the MTF values were 0.238 and 0.404 under ISO-1 corneal measurement conditions, and 0.438 and 0.339 under ISO-2 conditions.The MTF values of A1-UV and SN60WF at 3.0 mm aperture and 100 lp/mm spatial frequency under ISO-1 corneal measurement conditions were larger than those under ISO-2 corneal measurement conditions.Under ISO-1 corneal measurement conditions with a 3.0 mm aperture, A1-UV had a better optical quality compared to SN60WF, whereas under ISO-2 corneal measurement conditions, the optical quality of both IOLs was similar.Under the 3.0 mm aperture, the MTF values of SN60WF and A1-UV at a decentration of 0.3 mm and 100 lp/mm spatial frequency were 0.414 and 0.571 under ISO-1 corneal measurement conditions, 0.438 and 0.512 under ISO-2 corneal measurement conditions, respectively.The MTF values of SN60WF and A1-UV at a tilt of 3° were 0.522 and 0.597 under ISO-1 corneal measurement conditions, and 0.532 and 0.531 under ISO-2 corneal measurement conditions.The MTF values and USAF resolution test chart of A1-UV had no significant change between the two corneal measurement conditions.When subjected to equal degrees of decentration or tilting, except for the ISO-1 corneal measurement conditions at a 4.5 mm aperture, the MTF values of A1-UV showed a gradual decline across various spatial frequencies compared to SN60WF.With the increase in aperture size, the impact of IOL decentration or tilting on MTF values and USAF 1951 resolution test chart became more notable for A1-UV relative to SN60WF.Conclusions:The SN60WF IOL effectively filters blue light within the wavelength range of 400-500 nm.However, when both IOL experience decentration greater than 0.3 mm or tilting beyond 3°, the optical quality of the IOL will decline.A1-UV has a distinct advantage over SN60WF in terms of resistance to both decentration and tilting-induced optical performance degradation in vitro.

Surgery is currently the only effective treatment for cataract.As the standard of living improves, people's demand for postoperative visual quality increases, and a variety of functional artificial lenses (IOL) have been continuously introduced.The in vitro optical quality testing system is used for the design and optimization of new IOL and for the preliminary clinical study of IOL to evaluate the effects of influencing factors such as IOL material, design, decentration, tilt, rotation, incident light wavelength and pupil diameter on the optical quality of IOL.It is helpful for doctors to fully understand and correctly select IOL. In vitro optical quality test systems mainly include optical testing platform and optical design software.The former can experimentally measure IOL, while the latter can perform optical numerical simulation of IOL. In vitro optical quality test systems have received increasing attention in China in recent years.This article reviews the in vitro optical quality test system of IOL and its clinical application.This article reviews the commonly used in vitro optical quality test systems and their clinical applications, including the measurement and evaluation indicators of in vitro optical quality, the construction of optical test platforms (OptiSpheric ? IOL PRO, Badal Optometer, PMTF, and NIMO) and the measurement principles of optical design software (ZEMAX, OSLO, and VirtualLab), as well as their applications in IOL optical quality evaluation and the limitations of in vitro optical testing.

Objective:To explore the genetic characteristics of a child with comorbid 16p11.2 microdeletion syndrome and Rett syndrome (RTT).Methods:A male infant who was admitted to Gansu Provincial Maternity and Child Health Care Hospital in May 2020 was selected as the study subject. Clinical data of the infant was collected. Genomic DNA was extracted from peripheral blood samples from the infant and his parents, and subjected to whole exome sequencing (WES). Candidate variant was verified by Sanger sequencing.Result:The patient, a 4-day-old male infant, had presented with poor response, poor intake, feeding difficulties, and deceased at 8 months after birth. WES revealed that he has harbored a 0.643 Mb deletion in the 16p11.2 region, which encompassed key genes of the 16p11.2 microdeletion syndrome such as ALDOA, CORO1A, KIFF22, PRRT2 and TBX6. His father has carried the same deletion, but was phenotypically normal. The deletion was predicted to be pathogenic. The child was also found to harbor a maternally derived c. 763C>T (p.R255X) hemizygous variant of the MECP2 gene, which was also predicted to be pathogenic (PVS1+ PS4+ PM2_Supporting). Conclusion:The 16p11.2 deletion and the MECP2: c.763C>T (p.R255X) variant probably underlay the pathogenesis in this infant.