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.

Forsythia suspensa is a traditional Chinese medicine and a commonly used landscaping plant. Its dried fruit is used in medicine for its functions of clearing heat, removing toxins, reducing swelling, dissipating masses, and dispersing wind and heat. It possesses extremely high medicinal and economic value. However, the genetic differentiation and diversity of its wild populations remain unclear. In this study, chloroplast genome sequences were obtained from 15 wild individuals of F. suspensa using high-throughput sequencing technology. The sequence characteristics and intraspecific variations were analyzed. The results were as follows:(1) The full length of the F. suspensa chloroplast genome ranged from 156 184 to 156 479 bp, comprising a large single-copy region, a small single-copy region, and two inverted repeat regions. The chloroplast genome encoded a total of 132 genes, including 87 protein-coding genes, 37 tRNA genes, and 8 rRNA genes.(2) A total of 166-174 SSR loci, 792 SNV loci, and 63 InDel loci were identified in the F. suspensa chloroplast genome, indicating considerable genetic variation among individuals.(3) Population structure analysis revealed that F. suspensa could be divided into five or six groups. Both the population structure analysis and phylogenetic reconstruction results indicated significant genetic variation within the wild populations of F. suspensa, with no obvious correlation between intraspecific genetic differentiation and geographical distribution. This study provides new insights into the genetic diversity and differentiation within F. suspensa species and offers additional references for the conservation of species diversity and the utilization of germplasm resources in wild F. suspensa.
Genome, Chloroplast ; Forsythia/classification* ; Phylogeny ; Genetic Variation ; Chloroplasts/genetics* ; Microsatellite Repeats

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

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.