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.

The efficacy of Xiangmei Pills on rats with ulcerative colitis(UC) was investigated by characterizing the spectrum of the active chemical components of Xiangmei Pills. Rapid identification and classification of the main chemical components were performed,and the therapeutic effects of Xiangmei Pills on the proteins and intestinal flora of UC rats were analyzed to explore the mechanism of its action in treating UC. Fifty SD rats were acclimatized to feeding for 3 d and randomly divided into blank group, model group,mesalazine group(0. 4 g·kg~(-1)), low-dose group of Xiangmei Pills(1. 89 g·kg~(-1)), and high-dose group of Xiangmei Pills(5. 67 g·kg~(-1)), with 10 rats in each group. 5% dextrose sodium sulfate(DSS) was given by gavage to induce the male SD rat model with UC,and the corresponding medicinal solution was given by gavage after 10 days, respectively. The therapeutic effect of Xiangmei Pills on rats with UC was evaluated according to body mass, disease activity index(DAI), and hematoxylin-eosin(HE) staining, and the histopathological changes in the colon were observed. Ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry(UHPLC-Q-Orbitrap HRMS) technique was used to rapidly and accurately identify the main chemical constituents of Xiangmei Pills. Immunohistochemistry was used to detect the expression of aryl hydrocarbon receptor(AhR),interferon-γ(IFN-γ), mucin-2(MUC-2), and cytochrome P450 1A1(CYP1A1) in colon tissue. Interleukin-22(IL-22) expression in colon tissue was detected by immunofluorescence. The 16S r DNA high-throughput sequencing technique was used to study the modulatory effects of Xiangmei Pills on the intestinal flora structure of rats with UC. Pharmacodynamic results showed that compared with that of the blank group, the colon tissue of the model group was congested, and ulcers were visible in the mucosa; compared with that in the model group, the histopathology of the colon of the rats with UC in the groups of Xiangmei Pills were improved, with scattered ulcers and reduced inflammatory cell infiltration. Chemical analysis showed that a total of 45 components were identified by mass spectrometry information, including 15 phenolic acids, 8 coumarins, 15 organic acids, 3 amino acids, 2 flavonoids, and 2 other components. Compared with those in the blank group, the levels of Ah R, CYP1A1, MUC-2, and IL-22 proteins in the colon tissue of rats in the model group were significantly decreased, and the level of IFN-γ protein was significantly increased; the intestinal flora of rats in the model group was disorganized, with a decrease in the abundance of the flora; the relative abundance of Bacteroidetes,unclassified genera of Ascomycetes, Prevotella of the Prevotella family, and Prevotella decreased significantly, and that of Firmicutes decreased, but the difference was not statistically significant. The relative abundance of Bacteroidetes, Bifidobacterium, and Lactobacillus increased significantly. Compared with those of the model group, the levels of Ah R, CYP1A1, MUC-2, and IL-22proteins in the colonic tissue of the groups of Xiangmei Pills were significantly higher, and the levels of IFN-γ proteins were significantly lower. The recovery of the intestinal flora was accelerated, and the diversity of the intestinal flora was significantly increased. The relative abundance of Bacteroidetes was significantly increased, and that of unclassified genera of Ascomycetes,Lactobacillus, Prevotella of the Prevotella family, and Prevotella was significantly increased. The relative abundance of Bacteroidetes and Bifidobacterium was significantly decreased. This study demonstrated that Xiangmei Pills can effectively treat UC, mainly through the phenolic acid and organic acid components to stimulate the intestinal barrier, regulate protein expression and the relative abundance and diversity of intestinal flora, and play a role in the treatment of UC.
Animals ; Colitis, Ulcerative/metabolism* ; Drugs, Chinese Herbal/chemistry* ; Rats, Sprague-Dawley ; Male ; Rats ; Gastrointestinal Microbiome/genetics* ; Chromatography, High Pressure Liquid ; Humans ; Mass Spectrometry ; RNA, Ribosomal, 16S/genetics* ; Bacteria/drug effects*

This study aims to reveal the effect and mechanism of Gentianella turkestanorum total extract(GTI) in ameliorating non-alcoholic steatohepatitis(NASH). UPLC-Q-TOF-MS was employed to identify the chemical components in GTI. SwissTarget-Prediction, GeneCards, OMIM, and TTD were utilized to screen the targets of GTI components and NASH. The common targets shared by GTI components and NASH were filtered through the STRING database and Cytoscape 3.9.0 to identify core targets, followed by GO and KEGG enrichment analysis. AutoDock was used for molecular docking of key components with core targets. A mouse model of NASH was established with a methionine-choline-deficient high-fat diet. A 4-week drug intervention was conducted, during which mouse weight was monitored, and the liver-to-brain ratio was measured at the end. Hematoxylin-eosin staining, Sirius red staining, and oil red O staining were employed to observe the pathological changes in the liver tissue. The levels of various biomarkers, including aspartate aminotransferase(AST), alanine aminotransferase(ALT), hydroxyproline(HYP), total cholesterol(TC), triglycerides(TG), low-density lipoprotein cholesterol(LDL-C), high-density lipoprotein cholesterol(HDL-C), malondialdehyde(MDA), superoxide dismutase(SOD), and glutathione(GSH), in the serum and liver tissue were determined. RT-qPCR was conducted to measure the mRNA levels of interleukin 1β(IL-1β), interleukin 6(IL-6), tumor necrosis factor α(TNF-α), collagen type I α1 chain(COL1A1), and α-smooth muscle actin(α-SMA). Western blotting was conducted to determine the protein levels of IL-1β, IL-6, TNF-α, and potential drug targets identified through network pharmacology. UPLC-Q-TOF/MS identified 581 chemical components of GTI, and 534 targets of GTI and 1 157 targets of NASH were screened out. The topological analysis of the common targets shared by GTI and NASH identified core targets such as IL-1β, IL-6, protein kinase B(AKT), TNF, and peroxisome proliferator activated receptor gamma(PPARG). GO and KEGG analyses indicated that the ameliorating effect of GTI on NASH was related to inflammatory responses and the phosphoinositide 3-kinase(PI3K)/AKT pathway. The staining results demonstrated that GTI ameliorated hepatocyte vacuolation, swelling, ballooning, and lipid accumulation in NASH mice. Compared with the model group, high doses of GTI reduced the AST, ALT, HYP, TC, and TG levels(P<0.01) while increasing the HDL-C, SOD, and GSH levels(P<0.01). RT-qPCR results showed that GTI down-regulated the mRNA levels of IL-1β, IL-6, TNF-α, COL1A1, and α-SMA(P<0.01). Western blot results indicated that GTI down-regulated the protein levels of IL-1β, IL-6, TNF-α, phosphorylated PI3K(p-PI3K), phosphorylated AKT(p-AKT), phosphorylated inhibitor of nuclear factor kappa B alpha(p-IκBα), and nuclear factor kappa B(NF-κB)(P<0.01). In summary, GTI ameliorates inflammation, dyslipidemia, and oxidative stress associated with NASH by regulating the PI3K/AKT/NF-κB signaling pathway.
Animals ; Non-alcoholic Fatty Liver Disease/genetics* ; Mice ; Network Pharmacology ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Chromatography, High Pressure Liquid ; Liver/metabolism* ; Mice, Inbred C57BL ; Humans ; Mass Spectrometry ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation

Objective To explore the value of inflammatory markers in predicting paroxysmal sympathetic hyperactivity(PSH)after traumatic brain injury(TBI).Methods A total of 84 TBI patients who were admitted to The Second Affiliated Hospital of Naval Medical University(Second Military Medical University)from Dec.2016 to Nov.2020 were retrospectively analyzed.They were classified into PSH group(n=41)and non-PSH group(n=43)according to whether PSH occurred during hospitalization.The baseline data and laboratory results of the 2 groups were collected and compared.Kendall correlation analysis was used to analyze the correlation between inflammatory markers and the occurrence of PSH after TBI,and receiver operating characteristic(ROC)curve was used to analyze the predictive value of inflammatory markers to PSH.Results There were no significant differences in baseline data,including age,gender,or Glasgow coma scale score,between the 2 groups(all P＞0.05).Compared with patients in the non-PSH group,the neutrophil to lymphocyte ratio(NLR),platelet to lymphocyte ratio(PLR),systemic immune-inflammation index(SII),neutrophils and leukocytes in the PSH group were significantly increased(all P＜0.05).NLR,SII and neutrophil were positively correlated with PSH(r=0.360,0.308,0.289;all P＜0.01),with the corresponding ROC area under curve values being 0.752,0.716 and 0.702,respectively.Conclusion NLR,SII and neutrophils have a value in predicting the occurrence of PSH after TBI.

Temporomandibular joint osteoarthritis (TMJ-OA) is a common disease often accompanied by pain, seriously affecting physical and mental health of patients. Abnormal innervation at the osteochondral junction has been considered as a predominant origin of arthralgia, while the specific mechanism mediating pain remains unclear. To investigate the underlying mechanism of TMJ-OA pain, an abnormal joint loading model was used to induce TMJ-OA pain. We found that during the development of TMJ-OA, the increased innervation of sympathetic nerve of subchondral bone precedes that of sensory nerves. Furthermore, these two types of nerves are spatially closely associated. Additionally, it was discovered that activation of sympathetic neural signals promotes osteoarthritic pain in mice, whereas blocking these signals effectively alleviates pain. In vitro experiments also confirmed that norepinephrine released by sympathetic neurons promotes the activation and axonal growth of sensory neurons. Moreover, we also discovered that through releasing norepinephrine, regional sympathetic nerves of subchondral bone were found to regulate growth and activation of local sensory nerves synergistically with other pain regulators. This study identified the role of regional sympathetic nerves in mediating pain in TMJ-OA. It sheds light on a new mechanism of abnormal innervation at the osteochondral junction and the regional crosstalk between peripheral nerves, providing a potential target for treating TMJ-OA pain.
Animals ; Osteoarthritis/physiopathology* ; Mice ; Sympathetic Nervous System/physiopathology* ; Temporomandibular Joint Disorders/physiopathology* ; Arthralgia ; Sensory Receptor Cells ; Disease Models, Animal ; Norepinephrine ; Male ; Temporomandibular Joint/physiopathology* ; Pain Measurement

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.