Based on spleen deficiency-phlegm dampness as the core pathogenesis of obesity， and integrating recent advances in modern medicine regarding the key role of immune inflammation in obesity， this paper proposes a multidimensional pathogenic network of "obesity-spleen deficiency-phlegm dampness-immune imbalance". Various traditional Chinese medicine （TCM） herbs that strengthen the spleen， regulate qi， and resolve phlegm and dampness can treat obesity by improving spleen-stomach transport and transformation， promoting water-damp metabolism， and regulating immune homeostasis. This highlights immune inflammation as an important entry point to elucidate the TCM concepts of "spleen deficiency-phlegm dampness" and the therapeutic principle of "strengthening the spleen and eliminating dampness to treat obesity". By systematically analyzing the intrinsic connection between "spleen deficiency generating dampness， internal accumulation of phlegm dampness" and immune dysregulation in obesity， this paper aims to provide theoretical support for TCM treatment of obesity based on dampness.

Alzheimer’s disease (AD) is a prevalent neurodegenerative condition characterized by progressive cognitive decline and memory loss. As the incidence of AD continues to rise annually, researchers have shown keen interest in the active components found in natural plants and their neuroprotective effects against AD. Quercetin, a flavonol widely present in fruits and vegetables, has multiple biological effects including anticancer, anti-inflammatory, and antioxidant. Oxidative stress plays a central role in the pathogenesis of AD, and the antioxidant properties of quercetin are essential for its neuroprotective function. Quercetin can modulate multiple signaling pathways related to AD, such as Nrf2-ARE, JNK, p38 MAPK, PON2, PI3K/Akt, and PKC, all of which are closely related to oxidative stress. Furthermore, quercetin is capable of inhibiting the aggregation of β‑amyloid protein (Aβ) and the phosphorylation of tau protein, as well as the activity of β‑secretase 1 and acetylcholinesterase, thus slowing down the progression of the disease.The review also provides insights into the pharmacokinetic properties of quercetin, including its absorption, metabolism, and excretion, as well as its bioavailability challenges and clinical applications. To improve the bioavailability and enhance the targeting of quercetin, the potential of quercetin nanomedicine delivery systems in the treatment of AD is also discussed. In summary, the multifaceted mechanisms of quercetin against AD provide a new perspective for drug development. However, translating these findings into clinical practice requires overcoming current limitations and ongoing research. In this way, its therapeutic potential in the treatment of AD can be fully utilized.

Background: Nasopharyngeal carcinoma (NPC) is characterized by high programmed death-ligand 1 (PD-L1) expression and abundant infiltration of non-malignant lymphocytes, which renders patients potentially suitable candidates for immune checkpoint blockade therapies. Palate, lung, and nasal epithelium clone (PLUNC) inhibit the growth of NPC cells and enhance cellular apoptosis and differentiation. Currently, the relationship between PLUNC (as a tumor-suppressor) and PD-L1 in NPC is unclear. Methods: We collected clinical samples of NPC to verify the relationship between PLUNC and PD-L1. PLUNC plasmid was transfected into NPC cells, and the variation of PD-L1 was verified by western blot and immunofluorescence. In NPC cells, we verified the relationship of PD-L1, activating transcription factor 3 (ATF3), and β-catenin by western blot and immunofluorescence. Later, we further verified that PLUNC regulates PD-L1 through β-catenin. Finally, the effect of PLUNC on β-catenin was verified by co-immunoprecipitation (Co-IP). Results: We found that PLUNC expression was lower in NPC tissues than in paracancer tissues. PD-L1 expression was opposite to that of PLUNC. Western blot and immunofluorescence showed that β-catenin could upregulate ATF3 and PD-L1, while PLUNC could downregulate ATF3/PD-L1 by inhibiting the expression of β-catenin. PLUNC inhibits the entry of β-catenin into the nucleus. Co-IP experiments demonstrated that PLUNC inhibited the interaction of DEAD-box helicase 17 (DDX17) and β-catenin. Conclusions PLUNC downregulates the expression of PD-L1 by inhibiting the interaction of DDX17/β-catenin in NPC.

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: Nasopharyngeal carcinoma (NPC) is characterized by high programmed death-ligand 1 (PD-L1) expression and abundant infiltration of non-malignant lymphocytes, which renders patients potentially suitable candidates for immune checkpoint blockade therapies. Palate, lung, and nasal epithelium clone (PLUNC) inhibit the growth of NPC cells and enhance cellular apoptosis and differentiation. Currently, the relationship between PLUNC (as a tumor-suppressor) and PD-L1 in NPC is unclear. Methods: We collected clinical samples of NPC to verify the relationship between PLUNC and PD-L1. PLUNC plasmid was transfected into NPC cells, and the variation of PD-L1 was verified by western blot and immunofluorescence. In NPC cells, we verified the relationship of PD-L1, activating transcription factor 3 (ATF3), and β-catenin by western blot and immunofluorescence. Later, we further verified that PLUNC regulates PD-L1 through β-catenin. Finally, the effect of PLUNC on β-catenin was verified by co-immunoprecipitation (Co-IP). Results: We found that PLUNC expression was lower in NPC tissues than in paracancer tissues. PD-L1 expression was opposite to that of PLUNC. Western blot and immunofluorescence showed that β-catenin could upregulate ATF3 and PD-L1, while PLUNC could downregulate ATF3/PD-L1 by inhibiting the expression of β-catenin. PLUNC inhibits the entry of β-catenin into the nucleus. Co-IP experiments demonstrated that PLUNC inhibited the interaction of DEAD-box helicase 17 (DDX17) and β-catenin. Conclusions PLUNC downregulates the expression of PD-L1 by inhibiting the interaction of DDX17/β-catenin in NPC.

Background: Nasopharyngeal carcinoma (NPC) is characterized by high programmed death-ligand 1 (PD-L1) expression and abundant infiltration of non-malignant lymphocytes, which renders patients potentially suitable candidates for immune checkpoint blockade therapies. Palate, lung, and nasal epithelium clone (PLUNC) inhibit the growth of NPC cells and enhance cellular apoptosis and differentiation. Currently, the relationship between PLUNC (as a tumor-suppressor) and PD-L1 in NPC is unclear. Methods: We collected clinical samples of NPC to verify the relationship between PLUNC and PD-L1. PLUNC plasmid was transfected into NPC cells, and the variation of PD-L1 was verified by western blot and immunofluorescence. In NPC cells, we verified the relationship of PD-L1, activating transcription factor 3 (ATF3), and β-catenin by western blot and immunofluorescence. Later, we further verified that PLUNC regulates PD-L1 through β-catenin. Finally, the effect of PLUNC on β-catenin was verified by co-immunoprecipitation (Co-IP). Results: We found that PLUNC expression was lower in NPC tissues than in paracancer tissues. PD-L1 expression was opposite to that of PLUNC. Western blot and immunofluorescence showed that β-catenin could upregulate ATF3 and PD-L1, while PLUNC could downregulate ATF3/PD-L1 by inhibiting the expression of β-catenin. PLUNC inhibits the entry of β-catenin into the nucleus. Co-IP experiments demonstrated that PLUNC inhibited the interaction of DEAD-box helicase 17 (DDX17) and β-catenin. Conclusions PLUNC downregulates the expression of PD-L1 by inhibiting the interaction of DDX17/β-catenin in NPC.

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.