Kui was first recorded in The Rites of Zhou and is the earliest domesticated wild vegetable in China. In the Qi Min Yao Shu, Kui was called “the master of all vegetables” and has a long history of application in China. As a medicine, Kuizi was first recorded in Shen Nong’s Herbal Classic, which has a history of more than 2 000 years of medicinal use and a long history of clinical application. By researching the ancient and modern herbal literature, the first herbs texts of Kui were examined, various recorded texts, confused products and the history of the original medicinal use were clarified. It was concluded that the ancient herbal texts recorded the base plant of Kui as Malva verticillata L. belonging to family Malvaceae, which provided scientific basis for the development and utilization of Kui.

With the aging of the global population， osteoporosis （OP） is becoming a major public health concern worldwide. Currently， the commonly used anti-osteoporosis drugs in clinical practice have limited application due to many side effects. Therefore， developing more effective and safer strategies for the prevention and treatment of OP has become a research focus in this field. In recent years， the clinical efficacy and advantages of traditional Chinese medicine （TCM） in treating OP have been gradually recognized. With the deepening pharmacological research on TCM for OP prevention and treatment， it is found that the active ingredients of Scutellaria baicalensis can promote bone formation or inhibit bone resorption by regulating signaling pathways， including Wnt/β-catenin， osteoprotegerin （OB）/receptor activator of nuclear factor-κB ligand （RANKL）/RANK （OPG/RANKL/RANK）， and bone morphogenetic protein 2 （BMP-2）/Smad， mitogen-activated protein kinase （MAPK）， and mammalian target of rapamycin （mTOR）. However， existing research on active ingredients of S. baicalensis for OP treatment is scattered， making it difficult for scholars to gain a systematic understanding of its research and application. This review summarized the literature on the active ingredients of S. baicalensis in OP treatment worldwide， clarified their mechanisms of action， and explored some issues， providing references for the integration of TCM in OP prevention and treatment.

With the aging of the global population， osteoporosis （OP） is becoming a major public health concern worldwide. Currently， the commonly used anti-osteoporosis drugs in clinical practice have limited application due to many side effects. Therefore， developing more effective and safer strategies for the prevention and treatment of OP has become a research focus in this field. In recent years， the clinical efficacy and advantages of traditional Chinese medicine （TCM） in treating OP have been gradually recognized. With the deepening pharmacological research on TCM for OP prevention and treatment， it is found that the active ingredients of Scutellaria baicalensis can promote bone formation or inhibit bone resorption by regulating signaling pathways， including Wnt/β-catenin， osteoprotegerin （OB）/receptor activator of nuclear factor-κB ligand （RANKL）/RANK （OPG/RANKL/RANK）， and bone morphogenetic protein 2 （BMP-2）/Smad， mitogen-activated protein kinase （MAPK）， and mammalian target of rapamycin （mTOR）. However， existing research on active ingredients of S. baicalensis for OP treatment is scattered， making it difficult for scholars to gain a systematic understanding of its research and application. This review summarized the literature on the active ingredients of S. baicalensis in OP treatment worldwide， clarified their mechanisms of action， and explored some issues， providing references for the integration of TCM in OP prevention and treatment.

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.

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy of the digestive tract that poses a significant threat to human health, with an incidence rate that continues to rise globally. Increasing research highlights the crucial role of non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), in regulating ferroptosis and contributing to the malignant progression of ESCC. These ncRNAs influence the proliferation, apoptosis, and invasion capabilities of ESCC cells by modulating iron metabolism and redox balance. miRNAs can regulate cellular iron accumulation and oxidative stress by targeting ferroptosis-related genes; lncRNAs may indirectly affect iron metabolic pathways by competitively binding to miRNAs; circRNAs, through a sponge effect, may regulate the activity of miRNAs. This review systematically summarizes the mechanisms of ncRNAs-mediated regulation of ferroptosis in ESCC, focusing on molecular mechanisms, regulatory networks, and their specific roles in the ferroptosis process. Additionally, the potential of ncRNAs in ESCC diagnosis, prognosis assessment, and therapeutic intervention is discussed, aiming to provide new insights and targets for ferroptosis-based tumor therapy.
Ferroptosis/genetics* ; Humans ; Esophageal Neoplasms/physiopathology* ; Esophageal Squamous Cell Carcinoma ; MicroRNAs/physiology* ; RNA, Long Noncoding/physiology* ; RNA, Circular ; RNA, Untranslated/physiology*

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.