ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

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.

Traditional Chinese medicine (TCM) has historically played a pivotal role in the prevention and treatment of warm diseases, establishing a comprehensive theoretical framework that underpins its practices. The distinctive and indispensable contributions of aromatic Chinese herbs in dispelling harmful influences and mitigating the spread of these diseases are well recognized; however, further investigation is warranted to elucidate their systematic properties and regularities, and the theory of aromatic Chinese herbs in preventing and treating warm diseases still needs to be comprehensively summarized. This study employs the principles rooted in TCM, with particular emphasis on the framework for warm diseases. An analysis of the disease mechanisms, transmission dynamics, and preventive strategies is conducted during the early stage of infection, throughout the course of the disease, and in the post-illness phase. Furthermore, the characteristics and applications of aromatic Chinese herbs are integrated with insights drawn from modern pharmacological research to explore their specific roles in the prevention and management of warm diseases. The utilization of aromatic Chinese herbs manifests in a variety of therapeutic effects: aromatic medicinals purging filth and dispelling pathogens for preventing epidemic disease, aromatic medicinals regulation for relieving superficies syndrome and dispersing evils, aromatic medicinals ventilation the lung to relieve cough and asthma, aromatic medicinals resolving the dampness to awaken the spleen and stomach, aromatic medicinals opening the orifices to restore consciousness, aromatic and pungent medicinals to regulate qi, aromatic medicinals dredging the vessels to activate blood circulation and dissipate blood stasis, and aromatic medicinals clearing latent heat from the yin level. These properties facilitate tailored approaches to address the diverse manifestations of warm diseases and their associated symptoms, providing clear guidance for clinical application to achieve pre-disease prevention, active disease treatment, complication prevention, and post-recovery relapse avoidance. The use of aromatic Chinese herbs in preventing and treating warm diseases demonstrates theoretical, practical, systematic, and regular characteristics. The theory of the properties of aromatic Chinese herbs has been expanded and sublimated in clinical practice, and its scientific connotation has been expounded in modern research. Under the guidance of the theory of treatment based on syndrome differentiation, and by taking into account the distinct stages and pathologies of warm diseases, the rational selection of aromatic Chinese herbs can improve the clinical efficacy.

Objective: To investigate the therapeutic effect of patchouli alcohol on mice with lung-heat syndrome based on the phosphoinositide 3-kinase(PI3K)/protein kinase B(Akt)/nuclear factor-kappa B(NF-κB) signaling pathway. Methods: First, network pharmacology was used to predict the potential targets of patchouli alcohol in the treatment of lung-heat syndrome, and a "component-disease-key target" network was constructed for pathway analysis. Then, 40 BALB/c mice were assigned to the normal, lung-heat model, honeysuckle, and low-dose and high-dose patchouli alcohol groups. All groups, except the blank group, were intranasally infected with 50 μL (103 TCID50) of influenza virus solution. After two hours of infection, mice were treated once a day for seven consecutive days. The therapeutic mechanism of patchouli alcohol was explored by measuring pulmonary inflammatory factors, the PI3K/Akt/NF-κB pathway, hypothalamic fever markers (PGE2, cAMP, cGMP levels), rectal temperature, and tissue energy metabolism. Results: Network pharmacology identified 135 target genes related to patchouli alcohol and lung-heat syndrome, with the key targets being STAT3, H1F1A, and NF-κB1. In animal experiments, patchouli alcohol significantly alleviated influenza virus-induced lung inflammatory damage in mice with lung-heat syndrome, inhibited the expression of TNF-α and IL-6 in lung tissues(P<0.01), and suppressed the activation of the PI3K/Akt/NF-κB pathway. It also reduced hypothalamic levels of PGE2 and cAMP(P<0.01), suppressed the increase in rectal temperature, significantly decreased liver glycogen and pyruvate levels(P<0.01), and increased the activities of SDH, LDH, and Na+ -K+ -ATPase in the liver(P<0.01) Conclusion Patchouli alcohol improves the symptoms of lung-heat syndrome in mice by inhibiting the activation of the PI3K/Akt/NF-κB pathway, reducing proinflammatory cytokines and inflammatory damage, and regulating hypothalamic fever markers and energy metabolism.

Objective: To investigate the biological effects of carvacrol on rats with stomach-heat and stomach-cold and its regulation on transient receptor potential(TRP) channels in rats with stomach-heat, and to study the cold and heat properties of carvacrol and its possible mechanism. Methods: According to the random number method, 100 SD rats were divided into stomach-heat blank group, stomach-heat model group, Coptidis Rhizoma group, stomach-heat low-dose and high-dose carvacrol group, stomach-cold blank group, stomach-cold model group, Baked ginger group, stomach-cold low-dose group and high-dose carvacrol group, 10 rats in each group. The rat model of stomach-heat was established by intragastric administration of pepper aqueous solution (0.80 g/kg) and anhydrous ethanol, and the rat model of stomach-cold was established by intragastric administration of water extract of Anemarrhena asphodeloides and sodium hydroxide (10.40 g/kg). On the day of modeling, the rats in the Baked ginger group were given Baked ginger decoction (0.78 g/kg), and the rats in the Coptidis Rhizoma group were given Coptidis Rhizoma decoction (0.43 g/kg).The stomach-cold and stomach-heat low-dose group of carvacrol was given carvacrol emulsion (40 mg/kg), high-dose group was given carvacrol emulsion (80 mg/kg).All rats of the blank and model groups were given the equal volume of emulsion prepared by 5% dimethyl sulfoxide, 1% Tween 80, 1% polyethylene glycol 400, and 93% normal saline, once a day, for 7 days. The general condition of rats was observed and the body mass was recorded. The pathological morphology of gastric tissue was observed by hematoxylin-eosin staining. The changes of material and energy metabolism, cyclic nucleotide (cAMP), thyroid hormone and gastrointestinal hormone in each group were determined by enzyme-linked immunosorbent assay. The expression levels of transient receptor potential vanilloid subtype 1 (TRPV1), transient receptor potential channel M8 (TRPM8) and uncoupling protein-1 (UCP1) in rats with gastric fever were detected by Western blotting. Results: Compared with the stomach-heat blank group, the body mass of rats in the stomach-heat model group decreased at the fifth and seventh day (P<0.05). The contents (or ratio) of hepatic glycogen (HGlyc), total cholesterol (TC), triglyceride (TG), and vasoactive intestinal peptide (VIP) were decreased (P<0.05), and Na+ -K+ -ATPase, Ca2+ -Mg2+ -ATPase, cytochrome C oxidase (COX), NADH dehydrogenase (ND), cyclic adenosine phosphate (cAMP), cAMP/cyclic guanosine phosphate (cGMP), triiodothyronine (T3), thyroxine (T4), gastrin (GAS), motilin (MTL), and α-amylase (α-AMS) all increased (P<0.05). Compared with the stomach-heat model group, the body mass of rats in the Coptidis Rhizoma group decreased at the third, fifth, and seventh day, the contents (or ratio) of HGlyc, TC, TG, VIP and α-AMS were increased, and Na+ -K+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, and GAS all decreased (P<0.05). The body mass of rats in the stomach-heat low-dose carvacrol group decreased at the seventh day. The contents (or ratio) of HGlyc, TC, and VIP were increased, Na+ -K+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, and MTL all decreased, the expression of TRPV1 and UCP1 in gastric tissue decreased, while TRPM8 increased (P<0.05) in rats of the stomach-heat low-dose and high-dose carvacrol groups. Compared with the stomach-cold blank group, the body mass of rats in the stomach-cold model group decreased at the third, fifth, and seventh day, the contents (or ratio) of HGlyc, TC, TG, α-AMS, and VIP all increased, while Na+ -K+ -ATPase, Ca2+ -Mg2+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, GAS, and MTL all decreased (P<0.05). Compared with the stomach-cold model group, the body mass of rats in the Baked ginger group was increased at the seventh day, and the contents (or ratio) of HGlyc, VIP, and α-AMS all decreased, while Na+ -K+ -ATPase, COX, ND, cAMP/cGMP, T3, T4, GAS, and MTL all increased (P<0.05). The contents of HGlyc, cAMP, α-AMS, and VIP of rats in the stomach-cold low and high-dose carvacrol group all decreased (P<0.05). TG in the stomach-cold low-dose carvacrol group was increased. TC, Ca2+ -Mg2+ -ATPase, and cGMP all increased, while cAMP/cGMP decreased (P<0.05) in the high-dose carvacrol group. Conclusion In this study, the rat model of stomach-cold and stomach-heat were successfully established by using cold and heat factors. The result showed that carvacrol had a certain inhibitory effect on body mass, material energy metabolism, cyclic nucleotide level, thyroid hormone and gastrointestinal function in rats with stomach-heat, indicating that the drug was cold. Carvacrol′s cold medicinal property could be biologically explained by TRPV1 activation, UCP1 induction, and TRPM8 suppression.

OBJECTIVE: To determine the role of Tripterygium wilfordii multiglycoside (TGW) in the treatment of psoriatic dermatitis from a cellular immunological perspective. METHODS: Mouse models of psoriatic dermatitis were established by imiquimod (IMQ). Twelve male BALB/c mice were assigned to IMQ or IMQ₊TGW groups according to a random number table. Histopathological changes in vivo were assessed by hematoxylin and eosin staining. Ratios of immune cells and cytokines in mice, as well as PAM212 cell proliferation in vitro were assessed by flow cytometry. Pro-inflammatory cytokine expression was determined using reverse transcription quantitative polymerase chain reaction. RESULTS: TGW significantly ameliorated the severity of IMQ-induced psoriasis-like mouse skin lesions and restrained the activation of CD45⁺ cells, neutrophils and T lymphocytes (all P<0.01). Moreover, TGW significantly attenuated keratinocytes (KCs) proliferation and downregulated the mRNA levels of inflammatory cytokines including interleukin (IL)-17A, IL-23, tumor necrosis factor α, and chemokine (C-X-C motif) ligand 1 (P<0.01 or P<0.05). Furthermore, it reduced the number of γ δ T17 cells in skin lesion of mice and draining lymph nodes (P<0.01). CONCLUSIONS TGW improved psoriasis-like inflammation by inhibiting KCs proliferation, as well as the associated immune cells and cytokine expression. It inhibited IL-17 secretion from γ δ T cells, which improved the immune-inflammatory microenvironment of psoriasis.
Male ; Animals ; Mice ; Tripterygium ; Psoriasis/drug therapy* ; Keratinocytes ; Skin Diseases/metabolism* ; Cytokines/metabolism* ; Imiquimod/metabolism* ; Dermatitis/pathology* ; Disease Models, Animal ; Mice, Inbred BALB C ; Skin/metabolism*