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.

This study aims to investigate the mechanism by which resveratrol(RES) alleviates cerebral vascular endothelial damage in sepsis-associated encephalopathy(SAE) through network pharmacology and animal experiments. By using network pharmacology, the study identified common targets and genes associated with RES and SAE and constructed a protein-protein interaction( PPI) network. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed to pinpoint key signaling pathways, followed by molecular docking validation. In the animal experiments, a cecum ligation and puncture(CLP) method was employed to induce SAE in mice. The mice were randomly assigned to the sham group, CLP group, and medium-dose and high-dose groups of RES. The sham group underwent open surgery without CLP, and the CLP group received an intraperitoneal injection of 0. 9% sodium chloride solution after surgery. The medium-dose and high-dose groups of RES were injected intraperitoneally with 40 mg·kg-1 and 60 mg·kg~(-1) of RES after modeling, respectively, and samples were collected 12 hours later. Neurological function scores were assessed, and the wet-dry weight ratio of brain tissue was detected. Serum superoxide dismutase(SOD), catalase( CAT) activity, and malondialdehyde( MDA) content were measured by oxidative stress kit. Histopathological changes in brain tissue were examined using hematoxylin-eosin(HE) staining. Transmission electron microscopy was employed to evaluate tight cell junctions and mitochondrial ultrastructure changes in cerebral vascular endothelium. Western blot analysis was performed to detect the expression of zonula occludens1( ZO-1), occludin, claudins-5, optic atrophy 1( OPA1), mitofusin 2(Mfn2), dynamin-related protein 1(Drp1), fission 1(Fis1), and hypoxia-inducible factor-1α(HIF-1α). Network pharmacology identified 76 intersecting targets for RES and SAE, with the top five core targets being EGFR, PTGS2, ESR1, HIF-1α, and APP. GO enrichment analysis showed that RES participated in the SAE mechanism through oxidative stress reaction. KEGG enrichment analysis indicated that RES participated in SAE therapy through HIF-1α, Rap1, and other signaling pathways. Molecular docking results showed favorable docking activity between RES and key targets such as HIF-1α. Animal experiment results demonstrated that compared to the sham group, the CLP group exhibited reduced nervous reflexes, decreased water content in brain tissue, as well as serum SOD and CAT activity, and increased MDA content. In addition, the CLP group exhibited disrupted tight junctions in cerebral vascular endothelium and abnormal mitochondrial morphology. The protein expression levels of Drp1, Fis1, and HIF-1α in brain tissue were increased, while those of ZO-1, occludin, claudin-5, Mfn2, and OPA1 were decreased. In contrast, the medium-dose and high-dose groups of RES showed improved neurological function, increased water content in brain tissue and SOD and CAT activity, and decreased MDA content. Cell morphology in brain tissue, tight junctions between endothelial cells, and mitochondrial structure were improved. The protein expressions of Drp1, Fis1, and HIF-1α were decreased, while those of ZO-1, occludin, claudin-5, Mfn2, and OPA1 were increased. This study suggested that RES could ameliorate cerebrovascular endothelial barrier function and maintain mitochondrial homeostasis by inhibiting oxidative stress after SAE damage, potentially through modulation of the HIF-1α signaling pathway.
Animals ; Mice ; Network Pharmacology ; Resveratrol/administration & dosage* ; Male ; Sepsis-Associated Encephalopathy/genetics* ; Signal Transduction/drug effects* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Endothelium, Vascular/metabolism* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Humans ; Sepsis/complications* ; Oxidative Stress/drug effects*

Descurainiae Semen Lepidii Semen, also known as Tinglizi, originates from Brassicaceae plants Descurainia sophia or Lepidium apetalum. The former is commonly referred to as "Southern Tinglizi(Descurainiae Semen)", while the latter is known as "Northern Tinglizi(Lepidii Semen)". To scientifically and accurately identify the origin of Tinglizi medicinal materials and traditional Chinese medicine products, this study developed a specific DNA mini-barcode based on chloroplast genome sequences. By combining the DNA mini-barcode with DNA metabarcoding technology, a method for the qualitative and quantitative identification of Tinglizi medicinal materials and Chinese patent medicines was established. In this study, chloroplast genomes of Southern Tinglizi and Northern Tinglizi and seven commonly encountered counterfeit products were downloaded from the GenBank database. Suitable polymorphic regions were identified to differentiate these species, enabling the development of the DNA mini-barcode. Using DNA metabarcoding technology, medicinal material mixtures of Southern and Northern Tinglizi, as well as the most common counterfeit product, Capsella bursa-pastoris seeds, were analyzed to validate the qualitative and quantitative capabilities of the mini-barcode and determine its minimum detection limit. Additionally, the mini-barcode was applied to Chinese patent medicines containing Tinglizi to authenticate their botanical origin. The results showed that the developed mini-barcode(psbB) exhibited high accuracy and specificity, effectively distinguishing between the two authentic origins of Tinglizi and commonly encountered counterfeit products. The analysis of mixtures demonstrated that the mini-barcode had excellent qualitative and quantitative capabilities, accurately identifying the composition of Chinese medicinal materials in mixed samples with varying proportions. Furthermore, the analysis of Chinese patent medicines revealed the presence of the adulterant species(Capsella bursa-pastoris) in addition to the authentic species(Southern and Northern Tinglizi), indicating the occurrence of adulteration in commercially available Tinglizi-containing products. This study developed a method for the qualitative and quantitative identification of multi-origin Chinese medicinal materials and related products, providing a model for research on other multi-origin Chinese medicinal materials.
DNA Barcoding, Taxonomic/methods* ; Drugs, Chinese Herbal/chemistry* ; Drug Contamination ; Genome, Chloroplast ; Medicine, Chinese Traditional

The purpose of this study was to clarify the effect of Huotan Jiedu Tongluo Decoction on atherosclerosis(AS) injury in ApoE~(-/-) mice by regulating the ferroptosis pathway. Seventy-five ApoE~(-/-) mice were randomly divided into model group, low-, medium-, and high-dose of Huotan Jiedu Tongluo Decoction groups, and evolocumab group(n=15), and 15 C57BL/6J mice were selected as the blank group. Mice in the blank group were fed with a normal diet, and those in the other groups were fed with a high-fat diet to induce AS. From the 9th week, mice in Huotan Jiedu Tongluo Decoction groups were administrated with Huotan Jiedu Tongluo Decoction at corresponding doses by gavage, and those in the blank group and the model group were given an equal volume of distilled water. Mice in the evolocumab group were treated with evolocumab 18.2 mg·kg~(-1 )by subcutaneous injection every 2 weeks. After 8 weeks of continuous intervention, oil red O staining and hematoxylin-eosin(HE) staining were employed to observe the lipid deposition and plaque formation in the aortic root. Masson staining was used to evaluate the collagen content in the aortic root. The serum levels of total cholesterol(TC), triglycerides(TG), high-density lipoprotein cholesterol(HDL-C), and low-density lipoprotein cholesterol(LDL-C) were determined by biochemical kits. The levels of Fe~(2+), superoxide dismutase(SOD), malondialdehyde(MDA), and glutathione(GSH) in the aorta were measured by colorimetry. The protein and mRNA levels of nuclear factor erythroid 2-related factor 2(Nrf2), glutathione peroxidase 4(GPX4), solute carrier family 7 member 11(SLC7A11), and acyl-CoA synthetase long chain family member 4(ACSL4) in the aorta were detected by Western blot and RT-qPCR, respectively. The expression of Nrf2, GPX4, and SLC7A11 was localized by immunofluorescence. The results showed that low-, medium-, and high-dose Huotan Jiedu Tongluo Decoction reduced the plaque formation of aortic root and increased the collagen content in AS mice. At the same time, Huotan Jiedu Tongluo Decoction improved the lipid metabolism by lowering the levels of TC, LDL-C, and TG and elevating the level of HDL-C in the serum. Huotan Jiedu Tongluo Decoction enhanced the antioxidant capacity by elevating the levels of GSH and SOD and lowering the level of MDA in the aorta and inhibiting the accumulation of Fe~(2+) in the aorta. In addition, Huotan Jiedu Tongluo Decoction up-regulated the protein and mRNA levels of Nrf2, GPX4, and SLC7A11, while down-regulating the protein and mRNA levels of ACSL4. In summary, Huotan Jiedu Tongluo Decoction can effectively alleviate AS lesions in ApoE~(-/-) mice by activating the Nrf2/GPX4 pathway, reducing lipid peroxidation, and inhibiting ferroptosis.
Animals ; Ferroptosis/drug effects* ; Atherosclerosis/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; NF-E2-Related Factor 2/genetics* ; Mice ; Mice, Inbred C57BL ; Apolipoproteins E/metabolism* ; Male ; Phospholipid Hydroperoxide Glutathione Peroxidase/genetics* ; Signal Transduction/drug effects* ; Humans ; Mice, Knockout

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:To explore the role and mechanism of serum response factor (SRF) and lncRNA FGD5-AS1 in lung adenocarcinoma (LUAD).Methods:The plasma and tissue wax of LUAD patients treated in Tangshan People's Hospital from 2020 to 2022 and the plasma of healthy people were collected. The expression of SRF in LUAD tissues and cells, and the expression of lncRNA FGD5-AS1 in LUAD tissues, plasma and cells were detected by quantitative real-time polymerase chain reaction (qRT-PCR). The expression levels of SRF and lncRNA FGD5-AS1 in LUAD tissue microarray were detected by immunohistochemistry and in situ hybridization. LUAD cells A549, H1299 and H1975 were cultured in vitro and divided into si-NC and si-SRF groups, si-NC and si-lncRNA FGD5-AS1 groups, pcDNA3.1 and lncRNA FGD5-AS1 groups, si-NC+pcDNA3.1/si-SRF+pcDNA3.1/si-SRF+lncRNA FGD5-AS1 groups. The effects of the above groups on the proliferation, invasion and migration of LUAD cells were detected by CCK-8, cloning formation, EdU, Transwell and scratch test. The JASPAR database was used to predict the downstream lncRNA FGD5-AS1 that can be regulated by SRF; double luciferase experiment, chromatin Immunoprecipitation (CHIP) and electrophoretic mobility shift assay (EMSA) experiment were used to verify the regulatory effect between SRF and lncRNA FGD5-AS1, and the subcutaneous tumorigenesis experiment in nude mice was used to detect the effects of cells that stably knock down SRF and stably overexpress lncRNA FGD5-AS1 on the growth of transplanted tumors. Results:The results of immunohistochemistry showed that the mean optical density of SRF in LUAD tissues (1.49±0.33) was higher than that in adjacent tissues (1.00±0.00, P＜0.001). The expression level of SRF in paraffin tissues of LUAD patients was higher than that in normal tissues adjacent to cancer ( P=0.037). CCK-8, cloning, scratch and Transwell experiments showed that knockdown SRF could inhibit the proliferation, migration and invasion of A549 and H1299 cells, respectively. [For A549 cells: The clone formation count, migration count, invasion count, and 48-h migration distance ratio were (233.70±18.50), (808.70±6.11), (489.70±53.00), and 1.00±0.03, respectively, in the si-NC group; and (131.30±22.50), (403.00±9.54), (372.70±26.27), and 2.14±0.09, respectively, in the si-SRF group. For H1299 cells: The clone formation count, migration count, invasion count, and 48-h migration distance ratio were (194.30±20.98), (988.70±64.52), (907.70±67.02), and 1.00±0.05, respectively, in the si-NC group; and (137.70±7.77), (665.70±157.10), (565.70±67.01), and 1.52±0.03, respectively, in the si-SRF group. All comparisons showed statistically significant differences ( P＜0.05)] JASPAR database prediction shows that SRF and lncRNA FGD5-AS1 have binding site. The double luciferase experiment, CHIP and EMSA experiments showed that SRF could regulate lncRNA FGD5-AS1. In situ hybridization showed that the mean optical density of lncRNA FGD5-AS1 in tissue microarray of LUAD patients (1.28±0.31) was higher than that in adjacent tissues (1.00±0.00, P＜0.001). The results of qRT-PCR experiment showed that the expression level of lncRNA FGD5-AS1 in wax tissues of LUAD patients was higher than that in normal tissues adjacent to cancer ( P=0.017). The expression level of lncRNA FGD5-AS1 in plasma of LUAD patients (3.48±2.62) was higher than that of healthy people (1.02±0.03, P＜0.001). CCK-8, cloning, EDU, scratch and Transwell experiments showed that overexpression of lncRNA FGD5-AS1 could promote cell proliferation [For A549 cells: The clone formation count, EdU-positive cell count, invasion count, and 48-h migration distance ratio were (22.67±5.86), (1.00±0.09), (135.70±13.20), and 0.35±0.02, respectively, in the pcDNA3.1 group; and (46.33±9.07), (1.65±0.10), (205.00±13.23), and 0.20±0.01, respectively, in the FGD5-AS1-overexpressing group. All comparisons showed statistically significant differences ( P＜0.05)], migration and invasion and vice versa [For H1975 cells: The clone formation count, EdU-positive cell count, invasion count, and 48-h migration distance ratio were (75.33±4.16), (1.00±0.02), (258.70±45.79), and 0.18±0.01, respectively, in the NC group; and (37.00±4.00), (0.52±0.07), (130.70±9.07), and 0.53±0.04, respectively, in the lncRNA FGD5-AS1 knockdown group (si-lncRNA FGD5-AS1 group). All comparisons showed statistically significant differences ( P＜0.05)]. Overexpression of lncRNA FGD5-AS1 could rescue the effect of knockdown SRF on the proliferation, migration and invasion of A549 and H1299 cells. The results of subcutaneous tumorigenesis experiment in nude mice indicated that the tumorigenicity of LUAD cells stably knockdown SRF was weakened and vice versa. Conclusion:SRF can promote the progress of LUAD by regulating lncRNA FGD5-AS1.

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.

Numerous studies have shown that depression is main-ly associated with the abnormal expression of connexin 43(Cx43)in astrocytes(Astro)and its mediated dysfunction of gap junction(GJ).However,the molecular mechanism of post-translational modifications targeting Cx43 to regulate neuroin-flammation-associated depression is still unclear.Post-transla-tional modifications of Cx43 mainly include phosphorylation of specific amino acid sites by PKC,PKA,PKG,MAPK and PTK,and protein degradation of Cx43 through the K48/K63 polyubiq-uitylation and deubiquitination pathways,which ultimately lead to protein degradation through K48/K63 polyubiquitination and deubiquitination.These modifications are ultimately involved in the regulation of neuroinflammatory responses through the associ-ation of GJ function.In this paper,we systematically review the role of Cx43 post-translational modifications in neuroinflamma-tion,with the aim of further exploring the potential application of targeting these modifications to modulate the inflammatory re-sponse mechanism in improving depressive symptoms.