With the completion of the "Human Genome Project" and the smooth progress of the "Herbal Genome Project", the research wave of RNAomics is gradually advancing, opening the research gateway for the modernization of traditional Chinese medicine (TCM) and initiating the post-genome era of medicinal plant RNA research. Therefore, this article proposes for the first time the concept of HerbRNomes, which involves constructing databases of medicinal plant, medicinal fungus, and medicinal animal RNA at different stages, from different origins, and in different organs. This research aims to explore the role of HerbRNA in self-genetic information transmission, functional regulation, as well as cross-species regulation functional mechanisms and key technologies. It also investigates application scenarios, providing a theoretical basis and research ideas for the resistance of TCM or medicinal plants to adversity and stress, molecular assistant breeding, and the development of small nucleic acid drugs. This article reviews recent research progress in elucidating the molecular mechanisms of the transmission and expression of genetic information, self-regulation and cross-species regulation of herbs at the RNA level, along with key technologies. It proposes a development strategy for small nucleic acid drugs based on HerbRNomes, providing theoretical support and guidance for the modernization of TCM based on HerbRNomes research.

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: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome

This study aimed to investigate the effect and mechanism of Buyang Huanwu Decoction in regulating endoplasmic reticulum stress via the inositol-requiring enzyme 1α(IRE1α)/apoptosis signal-regulating kinase 1(ASK1)/c-Jun N-terminal kinase(JNK) pathway to improve neurological function in rats with cerebral ischemia/reperfusion injury(CIRI). SPF-grade male sprague-dawley(SD) rats were randomly divided into Sham group, model group, Buyang Huanwu Decoction group, and edaravone group. Except for the Sham group, the other groups were subjected to the modified suture method to establish a middle cerebral artery occlusion/reperfusion(MCAO/R) model. After treatment, neurological function was assessed using the Zea Longa scoring system. Gait analysis was used to detect the motor function. Detection of relative infarct area in brain tissue using 2,3,5-triphenyltetrazolium chloride(TTC) staining. Nissl staining was used to observe the structure of neuronal cells. Western blot and real-time fluorescence quantitative PCR(RT-qPCR) were used to detect IRE1α, ASK1, JNK, B cell lymphoma-2(Bcl-2), Bcl-2 related X protein(Bax), and Caspase-3 in the brain tissue. Immunohistochemistry was used to detect the positive expression of IRE1α, ASK1, and JNK. Immunofluorescence was used to detect the fluorescence expression levels of Bax, Bcl-2, and Caspase-3. The results showed that compared with the Sham group, the model group exhibited increased neurological scores(P<0.01), increased ratio of ground contact area and strength in both forelimbs(P<0.01), enlarged relative infarct area of brain tissue(P<0.05), and a reduced number of Nissl staining-positive cells(P<0.01). The protein and mRNA expression levels of IRE1α, ASK1, JNK, Bax, and Caspase-3 in brain tissue were significantly elevated, while those of Bcl-2 were decreased(P<0.05). Compared with the model group, both the Buyang Huanwu Decoction group and edaravone group showed reduced neurological scores(P<0.05), decreased ratio of ground contact area and strength in both forelimbs(P<0.05), smaller relative infarct area(P<0.05), alleviated neuronal damage, and increased number of Nissl staining-positive cells(P<0.05). The expression levels of IRE1α, ASK1, JNK, Bax, and Caspase-3 protein and mRNA in brain tissue were significantly reduced, while those of Bcl-2 were significantly increased(P<0.05). The results indicated that Buyang Huanwu Decoction can effectively improve brain injury in CIRI rats, and its mechanism of action may be related to regulating the endoplasmic reticulum stress IRE1α/ASK1/JNK signaling pathway.
Animals ; Male ; Rats, Sprague-Dawley ; Protein Serine-Threonine Kinases/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; MAP Kinase Kinase Kinase 5/genetics* ; Ischemic Stroke/physiopathology* ; Humans ; MAP Kinase Signaling System/drug effects* ; Apoptosis/drug effects* ; Endoribonucleases/genetics* ; JNK Mitogen-Activated Protein Kinases/genetics* ; Endoplasmic Reticulum Stress/drug effects* ; Multienzyme Complexes

OBJECTIVE: To explore the early clinical efficacy of a three-stage external treatment with traditional Chinese medicine (TCM) in the treatment of talar bone contusion caused by acute ankle sprain. METHODS: A retrospective analysis was performed on 360 patients with primary lateral ankle sprain admitted from September 2021 to July 2024. Patients with talar bone contusion were selected based on MRI examination, and 73 cases were finally included. According to different treatment methods, they were divided into the observation group and the control group. The observation group consisted of 35 cases, including 16 males and 19 females, aged 24 to 37 years old with an average of (30.34±2.68) years old, and received the three-stage external TCM treatment combined with the "POLICE" protocol. The control group included 38 cases, including 18 males and 20 females, aged 24 to 35 years old with an average of (29.87±2.57) years old, and was treated with the "POLICE" protocol alone. The volume of bone marrow edema (BME) area shown by MRI before treatment and 6 weeks after treatment was measured using 3D Slicer software, and the BME improvement rate was calculated. The "Figure of 8" measurement method was used to assess ankle swelling before treatment and at 1 and 3 weeks after treatment. The visual analogue scale (VAS) was used to evaluate ankle pain before treatment and at 1 and 6 weeks after treatment. At 6 weeks after treatment, the American Orthopaedic Foot and Ankle Society (AOFAS) ankle-hindfoot score and Karlsson ankle function score system were used to evaluate the improvement of ankle function. RESULTS: A total of 73 patients with talar bone contusion caused by ankle sprain completed the 6-week follow-up. At 6 weeks after treatment, the BME improvement rate in the observation group was (39.18±0.06)%, which was higher than (26.75±0.03)% in the control group, with a statistically significant difference (P<0.05). After 1 week of treatment, the VAS score in the observation group was (2.89±0.72) points, lower than (3.37±0.79) points in the control group, and the difference was statistically significant (P<0.05). The ankle swelling degree in the observation group was (50.20±3.19) cm, lower than (52.00±3.60) cm in the control group, with a statistically significant difference (P<0.05). After 3 weeks of treatment, there was no statistically significant difference in ankle swelling between the two groups. At 6 weeks after treatment, there was no statistically significant difference in VAS scores between the two groups. At 6 weeks after treatment, the AOFAS ankle-hindfoot score and Karlsson score in the observation group were (87.43±4.18) and (82.77±5.93) points, respectively, which were higher than (82.92±4.87) and (76.45±6.85) points in the control group, with statistically significant differences (P<0.05). According to the AOFAS ankle-hindfoot score, 8 cases were excellent and 27 cases were good in the observation group;2 cases were excellent, 33 cases were good, and 3 cases were fair in the control group. The difference between the two groups was statistically significant (χ²=7.089, P=0.029). CONCLUSION The three-stage external TCM treatment combined with the "POLICE" protocol has a significant early clinical efficacy. It can significantly reduce ankle pain and swelling in patients with bone contusion caused by acute lateral ankle sprain, promote the absorption of bone marrow edema, and accelerate the recovery of ankle function.
Ankle Injuries/drug therapy* ; Drugs, Chinese Herbal/administration & dosage* ; Talus/injuries* ; Retrospective Studies ; Administration, Cutaneous ; Magnetic Resonance Imaging ; Humans ; Male ; Female ; Young Adult ; Adult ; Contusions/etiology* ; Visual Analog Scale ; Musculoskeletal Pain/etiology* ; Recovery of Function/drug effects* ; Treatment Outcome ; Follow-Up Studies

OBJECTIVE: To investigate the effects and underlying mechanism of ionizing radiation on the adipogenic of mesenchymal stem cells (MSCs). METHODS: Mouse MSCs were cultured in vitro and treated with 2 Gy and 6 Gy radiation with ⁶⁰Co, and the radiation dose rate was 0.98 Gy/min. Bulk RNA-seq was performed on control and irradiated MSCs. The changes of adipogenic differentiation and oxidative stress pathways of MSC were revealed by bioinformatics analysis. Oil Red O staining was used to detect the adipogenic differentiation ability of MSCs in vitro, and real-time fluorescence quantitative PCR (qPCR) was used to detect the expression differences of key regulatory factors Cebpa, Lpl and Pparg after radiation treatment. At the same time, qPCR and Western blot were used to detect the effect of inhibition of Nrf2, a key factor of antioxidant stress pathway, on the expression of key regulatory factors of adipogenesis. Moreover, the species conservation of the irradiation response of human bone marrow MSCs and mouse MSC was determined by qPCR. RESULTS: Bulk RNA-seq suggested that ionizing radiation promotes adipogenic differentiation of MSCs and up-regulation of oxidative stress-related genes and pathways. The results of Oil Red O staining and qPCR showed that ionizing radiation promoted the adipogenesis of MSCs, with high expression of Cebpa, Lpl and Pparg, as well as oxidative stress-related gene Nrf2. Nrf2 pathway inhibitors could further enhance the adipogenesis of MSCs in bone marrow after radiation. Notably, the similar regulation of oxidative pathways and enhanced adipogenesis post irradiation were observed in human bone marrow MSCs. In addition, irradiation exposure led to up-regulated mRNA expression of interleukin-6 and down-regulated mRNA expression of colony stimulating factor 2 in human bone marrow MSCs. CONCLUSION Ionizing radiation promotes adipogenesis of MSCs in mice, and oxidative stress pathway participates in this effect, blocking Nrf2 further promotes the adipogenesis of MSCs. Additionally, irradiation activates oxidative pathways and promotes adipogenic differentiation of human bone marrow MSCs.
Mesenchymal Stem Cells/cytology* ; Oxidative Stress/radiation effects* ; Animals ; Adipogenesis/radiation effects* ; Mice ; Radiation, Ionizing ; Cell Differentiation/radiation effects* ; Humans ; NF-E2-Related Factor 2/metabolism* ; PPAR gamma ; Cells, Cultured