With the rapid development of sequencing technologies, the detection of alternative polyadenylation (APA) in mammals has become more precise. APA precisely regulates gene expression by altering the length and position of the poly(A) tail, and is involved in various biological processes such as disease occurrence and embryonic development. The research on APA in mammals mainly focuses on the following aspects:(1) identifying APA based on transcriptome data and elucidating their characteristics; (2) investigating the relationship between APA and gene expression regulation to reveal its important role in life regulation;(3) exploring the intrinsic connections between APA and disease occurrence, embryonic development, differentiation, and other life processes to provide new perspectives and methods for disease diagnosis and treatment, as well as uncovering embryonic development regulatory mechanisms. In this review, the classification, mechanisms and functions of APA were elaborated in detail and the methods for APA identifying and APA data resources based on various transcriptome data were systematically summarized. Moreover, we epitomized and provided an outlook on research on APA, emphasizing the role of sequencing technologies in driving studies on APA in mammals. In the future, with the further development of sequencing technology, the regulatory mechanisms of APA in mammals will become clearer.

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.

Aim To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.

Objective To investigate the role of magnesium ion(Mg2+)in cisplatin-induced acute kidney injury(Cis-AKI)in kidney organoids and HK-2 cells,as well as the potential mechanism.Methods Initially,we utilized human-derived induced pluripotent stem cells(iPSCs)to construct kidney organoids,and then built a Cis-AKI model based on kidney organoids.HE staining was used to observe the structure of kidney organoids,and immunofluorescence staining was used to observe the localization of markers and the expression of cleaved caspase-3.qRT-PCR was conducted to detect mRNA levels of tubular and glomerular markers,as well as inflammatory factors.Subsequently,the kidney organoids were randomly divided into control group,cisplatin group(Cis group),and Mg2+pretreatment group(Cis+Mg2+group).CCK-8 and ATP content assays were employed to evaluate the cell viability of renal tubular epithelial cells.TUNEL staining was performed to detect the apoptosis of renal tubular epithelial cells.Western blot was utilized to detect the expression of apoptosis-associated proteins(Bcl-2,Bax,cleaved caspase-3)and organic cation transporter 2(OCT2).Immunofluorescence was used to detect the localization and expression of OCT2.Results On the 10th day,the tubular structure in kidney organoids was visible,with abundant expression of renal markers.Treatment with 10 μmol/L cisplatin resulted in structural damage to kidney organoids,significantly increased expression of cleaved caspase-3 and mRNA levels of inflammatory factors,and significantly decreased ATP content.Compared with the Cis group,the Cis+Mg2+group showed increased ATP content in kidney organoids,reduced number of TUNEL-positive cells,significantly decreased expression of apoptosis-associated proteins,and significantly decreased expression of OCT2.However,there was no significant improvement in HK-2 cell viability,the number of TUNEL-positive cells,or apoptosis-associated proteins in the Cis+Mg2+group,and HK-2 cells did not express OCT2.Conclusion Kidney organoid is an ideal in vitro model to study the pathogenesis and treatment of Cis-AKI.Mg2+pretreatment can significantly reduce the damage of kidney organoids induced by cisplatin,and the mechanism may be related to the downregulation of OCT2.

Objective To investigate the distribution of the traditional Chinese medicine(TCM)constitution types in the patients with nonspecific low back pain(NLBP)and to explore the correlation of the thickness of ligamentum flavum with the age,body mass index(BMI),gender,the presence of diabetes mellitus,and the grading of hypertension.Methods Sixty patients with NLBP admitted to Guangdong Second Traditional Chinese Medicine Hospital from January 2023 to June 2023 were selected as the study subjects.The TCM constitution types of the patients were identified,the thickness of the ligamentum flavum at lumbar vertebrae 4/5 segment(L4/5)disc level was measured by computerized tomography(CT)scanning,and the patients'age,genders,TCM constitution types,BMI,the presence or absence of diabetes mellitus,and hypertension grading were recorded.Correlation analysis and linear regression analysis were used for the exploration of the relevant influencing factors for the thickness of the ligamentum flavum of patients with NLBP.Results(1)The average thickness of ligamentum flavum in the 60 patients with NLBP was(2.60±0.72)mm.(2)The TCM constitutions of NLBP patients were classified into four types,of which blood stasis constitution was the most common,accounting for 21 cases(35.0%),followed by 19 cases(31.7%)of damp-heat constitution,12 cases(20.0%)of phlegm-damp constitution,and 8 cases(13.3%)of qi deficiency constitution.(3)The results of correlation analysis showed that BMI,gender,TCM constitution type and the presence or absence of diabetes mellitus had no influence on the thickness of ligamentum flavum in NLBP patients(P＞0.05),while the age and hypertension grading had an influence on the thickness of ligamentum flavum(P＜0.01).(4)The results of linear regression analysis showed that the age had an influence on the thickness of the ligamentum flavum(b = 0.034,t = 6.282,P＜0.01),while the influence of the hypertension grading had no influence on the thickness of the ligamentum flavum(P＞0.05).Conclusion The TCM constitution type of NLBP patients is predominated by blood stasis constitution,the thickness of ligamentum flavum is significantly affected by the age,and hypertension may be a potential factor affecting the thickness of ligamentum flavum.

Objective To observe the clinical efficacy of"triple-posture positive bone-setting"chiropractic manipulation combined with Tongluo Huoxue Formula for the treatment of lumbar spinal stenosis(LSS)with qi deficiency and blood stasis syndrome.Methods Sixty patients with LSS of qi deficiency and blood stasis type were randomly divided into trial group and control group,with 30 cases in each group.The trial group was treated with"triple-posture positive bone-setting"chiropractic manipulation(a chiropractic manipulation performed under the positive cooperation of the patients at three postures)combined with Tongluo Huoxue Formula,while the control group was treated with"triple-posture positive bone-setting"chiropractic manipulation combined with conventional western medicine.The course of treatment for the two groups covered 4 weeks.Before and after treatment,the patients of the two groups were observed in the changes of pain visual analogue scale(VAS)score,Japanese Orthopedic Association(JOA)score of lumbar function,Oswestry Disability Index(ODI)score,straight-leg raising test results and serum interleukin 6(IL-6)and C-reactive protein(CRP)levels.After treatment,the clinical efficacy and safety of the two groups were evaluated.Results(1)After 4 weeks of treatment,the total effective rate of the trial group was 96.67％(29/30)and that of the control group was 63.33％(19/30).The intergroup comparison(tested by Fisher's exact test)showed that the clinical efficacy of the trial group was significantly superior to that of the control group(P＜0.05).(2)After treatment,the lumbar function indicators of pain VAS scores and ODI scores in the trial group were significantly lower(P＜0.05),and the JOA scores were significantly higher than those before treatment(P＜0.05),while in the control group,only the ODI scores were significantly lower than those before treatment(P＜0.05).The intergroup comparison showed that the decrease of VAS and ODI scores and the increase of JOA scores in the trial group were significantly superior to those in the control group(P＜0.05 or P＜0.01).(3)After treatment,the Laseque s sign of the trial group was significantly improved compared with that before treatment(P＜0.05),while no significant improvement was presented in the control group(P＞0.05).The intergroup comparison showed that the improvement of Laseque's sign in the trial group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the levels of serum inflammatory factors of IL-6 and CRP in the two groups were lower than those before treatment(P＜0.05),and the decrease of serum IL-6 level in the trial group was significantly superior to that in the control group(P＜0.05),but CRP level in the two groups after treatment did not differ from that before treatment,no statistically significant difference was shown between the two groups after treatment,either(P＞0.05).(5)The incidence of adverse reactions in the trial group was 6.67％(2/30)and that in the control group was 13.33％(4/30),and the intergroup comparison(by Fisher's exact test)showed that there was no significant difference between the two groups(P＞0.05).Conclusion The therapeutic effect of"triple-posture positive bone-setting"chiropractic manipulation combined with Tongluo Huoxue Formula exert certain effect for the treatment of LSS patients with qi deficiency and blood stasis syndrome,and it has more obvious advantages in improving the lumbar function,promoting the rehabilitation of the patients,and lowering the level of serum inflammatory factors than"triple-posture positive bone-setting"chiropractic manipulation combined with conventional western medication.