Objective The aim of this study was to investigate the correlation between the interaction of uric acid and inflammatory factors and hyperuricemia in overweight/obese patients. Methods The personnel with hyperuricemia who underwent physical examination in our hospital from September 2021 to September 2022 were selected as the study subjects, and they were divided into 100 cases of overweight group and 90 cases of obese group according to the BMI index; 120 cases of healthy and non-hyperuricemic personnel were randomly selected as the control group; venous blood of the three groups was collected in 5 mL after 8 h of fasting, and were tested respectively for serum uric acid, lipid indexes and inflammatory factors: IL-6, IL-2, IFN-γ, TNF-α, IL-4, IL-10. Results Glucose, triglycerides, total cholesterol, and LDL were significantly higher in the obese group versus the overweight group (P<0.001), while HDL was significantly lower than the control group (P<0.001), and these changes were more pronounced in the obese group (P<0.001).The Pearson correlation coefficient pointed out that the levels of serum uric acid in patients with hyperuricosuric acid were significantly associated with the pro-inflammatory factors IL- 6, IL-2, IFN-γ, and TNF-α were significantly positively correlated (P<0.001), whereas they were significantly negatively correlated with the anti-inflammatory factors IL-4, IL-10 (P<0.001). Conclusion High uric acid levels in overweight/obese patients can cause enhanced inflammatory responses and reduced expression levels of anti-inflammatory factors, and the interaction between uric acid and pro-inflammatory factors aggravates the condition of patients with hyperuricemia.

ObjectiveThis paper aims to investigate the effects and mechanism of Mimenghua prescription in modulating the mitogen-activated protein kinase kinase （MEK）/rat sarcoma viral oncogene homolog （Ras）/rapidly accelerated fibrosarcoma kinase （Raf）/extracellular signal-regulated kinase （ERK） signaling pathway to inhibit inflammatory responses in a dry eye animal model. MethodsA total of 60 C57BL/6J mice （eight weeks old， half male and half female） were used in the experiment. Ten mice were randomly selected as the blank control group， while the remaining 50 were exposed to a controlled dry system and received instillation of 0.2% benzalkonium chloride （BAC） into the eyes for four weeks to establish a dry eye mouse model. After successful modeling， the mice were randomly divided into five groups： Model group， sodium hyaluronate group， and Mimenghua prescription groups with low dose （4.83 g·kg^-1）， medium dose （9.67 g·kg^-1）， and high dose （19.34 g·kg^-1）. The mice in the model group received an equal volume of normal saline via gavage for four weeks. The mice in the sodium hyaluronate group received instillation of sodium hyaluronate eye drops twice daily for 14 consecutive days. The tear secretion volume， tear film break-up time （TBUT）， and corneal fluorescein staining were evaluated once every two weeks. After four weeks of administration， mice were euthanized， and their lacrimal gland tissues and corneas were harvested. Hematoxylin-eosin （HE） staining was used to assess histopathological morphology. Western blot was performed to detect the protein expression levels of MEK， Ras， Raf， and ERK. Enzyme-linked immunosorbent assay （ELISA） was used to measure the contents and expressions of MEK， Ras， Raf， ERK， and interleukin （IL）-1β in lacrimal gland and corneal tissues of the mice in each group. Quantitative real-time polymerase chain reaction （Real-time PCR） was employed to determine mRNA expression levels of MEK， Ras， Raf， and ERK. ResultsThe Mimenghua prescription groups and the sodium hyaluronate group exhibited significantly increased tear secretion volume （P<0.05） and prolonged TBUT （P<0.05） after treatment. Ocular surface damage of mice was visibly recovered. Western blot results indicated that protein expression levels of MEK， Ras， Raf， and ERK in the lacrimal gland and corneal tissues were significantly downregulated in the sodium hyaluronate group and Mimenghua prescription group with high dose （P<0.05）. ELISA results showed that IL-1β levels were highest in the model group but significantly reduced in the sodium hyaluronate group and Mimenghua prescription groups （P<0.05）. Both ELISA and Real-time PCR results demonstrated that the expression levels of MEK， Ras， Raf， and ERK in the lacrimal glands and corneal tissues were significantly elevated in the model group （P<0.05）， but markedly downregulated in the sodium hyaluronate group and Mimenghua prescription groups （P<0.05）， suggesting that Mimenghua prescription can decrease the expressions of MEK， Ras， Raf， and ERK in the lacrimal glands and corneal tissues. ConclusionMimenghua prescription can reduce inflammatory responses， increase tear secretion， prolong TBUT， and promote corneal recovery by inhibiting the MEK， Ras， Raf， and ERK signaling pathways in lacrimal gland and corneal tissues.

Olfactory receptors (ORs) form the largest superfamily of G protein-coupled receptors (GPCRs). Traditionally recognized for their role in the nasal olfactory epithelium, where they mediate the sense of smell, accumulating evidence has firmly established their ectopic expression in non-olfactory tissues, including the intestine, lungs, and kidneys. The intestine, as the primary site for nutrient digestion and absorption, harbors a highly complex chemical environment. To adapt to this environment, the gut employs a sophisticated network of “chemosensors” to monitor luminal contents and maintain homeostasis. Among these sensors, intestinal ORs have emerged as crucial functional components, serving as a molecular bridge that connects environmental chemical signals—such as food-derived odorants—to specific physiological responses. This discovery has significantly deepened our understanding of how dietary flavors and compounds influence intestinal physiology at the molecular level. This review systematically summarizes the expression profiles, ligand classification, and biological functions of ORs within the gastrointestinal tract. Studies indicate that intestinal ORs exhibit distinct spatial distribution patterns across different gut segments and display cell-type specificity, particularly within enterocytes and enteroendocrine cells. These receptors function as versatile sensors capable of recognizing a wide variety of ligands, including exogenous dietary components, gut microbiota metabolites such as short-chain fatty acids, and endogenous small molecules like azelaic acid. Upon activation by specific ligands, intestinal ORs trigger intracellular signaling cascades, primarily involving the AC-cAMP-PKA pathway or calcium influx channels. A major focus of this review is to elucidate the molecular mechanisms by which these receptors regulate the secretion of gut hormones. Activation of specific ORs in enteroendocrine cells has been shown to stimulate the release of hormones such as glucagon-like peptide-1 (GLP-1), peptide YY (PYY), and serotonin (5-HT), thereby modulating systemic energy metabolism, glucose homeostasis, and gastrointestinal motility. Furthermore, the review addresses the critical roles of ORs in immune regulation and pathology. Evidence suggests that specific ORs contribute to the maintenance of intestinal immune homeostasis and may offer protection against inflammation. Beyond their involvement in inflammatory responses, ORs such as Olfr78 have been shown to regulate the differentiation and function of intestinal endocrine cells. Similarly, Olfr544 has been demonstrated to alleviate intestinal inflammation by remodeling the gut microbiome and metabolome. These findings collectively suggest that specific ORs hold promise as therapeutic targets for mitigating intestinal inflammation and maintaining gut homeostasis. Additionally, the review explores the emerging role of ORs in cancer. Although OR expression is often downregulated in tumor tissues compared to normal mucosa, activation of specific ORs by certain ligands can inhibit tumor cell proliferation and migration and induce apoptosis via pathways such as MEK/ERK and p38 MAPK. Conversely, other receptors, such as OR7C1, may serve as biomarkers for cancer-initiating cells. In conclusion, intestinal ORs represent a vital component of the gut’s sensory network. The review also discusses the translational potential of these findings. By elucidating the precise pairing relationships between dietary components and specific ORs, novel therapeutic strategies could be developed. Intestinal ORs may thus emerge as promising targets for nutritional and pharmacological interventions in metabolic diseases, inflammatory bowel diseases, and malignancies.

The circadian clock plays a critical role in regulating various physiological processes, including gene expression, metabolic regulation, immune response, and the sleep-wake cycle in living organisms. Post-translational modifications (PTMs) are crucial regulatory mechanisms to maintain the precise oscillation of the circadian clock. By modulating the stability, activity, cell localization and protein-protein interactions of core clock proteins, PTMs enable these proteins to respond dynamically to environmental and intracellular changes, thereby sustaining the periodic oscillations of the circadian clock. Different types of PTMs exert their effects through distincting molecular mechanisms, collectively ensuring the proper function of the circadian system. This review systematically summarized several major types of PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation and oxidative modification, and overviewed their roles in regulating the core clock proteins and the associated pathways, with the goals of providing a theoretical foundation for the deeper understanding of clock mechanisms and the treatment of diseases associated with circadian disruption.
Protein Processing, Post-Translational/physiology* ; Circadian Rhythm/physiology* ; Humans ; Animals ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Phosphorylation ; Acetylation ; Ubiquitination ; Sumoylation

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.

Objective:To identify the factors influencing the diagnostic accuracy of endoscopic ultrasonography (EUS) for colorectal submucosal tumors (SMT).Methods:A retrospective analysis was conducted on 330 colorectal SMT lesions (from 323 patients) diagnosed by EUS at Shenzhen Hospital of Southern Medical University from December 2015 to October 2023. Pathological diagnosis were confirmed through endoscopic resection, EUS-guided fine needle aspiration (EUS-FNA) or surgical resection. Diagnostic accuracy was calculated for each type of colorectal SMT. Univariate and multivariate logistic regression analysis were performed to identify factors affecting EUS diagnostic accuracy.Results:The overall diagnostic accuracy of EUS for colorectal SMT was 73.6% (243/330). Among 19 SMT subtypes enrolled, neuroendocrine neoplasms (51.2%, 169/330) and lipomas (15.5%, 51/330) were most prevalent, while 17 rare subtypes each accounted for <6%. Seven rare SMT (mucosal chronic inflammation, colorectal schwannoma, xanthogranulomatous inflammation, capillary hemangioma, colonic xanthoma, lymphadenoid complex, and angiomyolipoma) showed 0% diagnostic accuracy. Seven other subtypes (granular cell tumor, leiomyoma, rectal tonsil, intestinal schistosomiasis, fibrous tissue hyperplasia, gastrointestinal stromal tumor, and lymphangioma) showed accuracy <30%, whereas five subtypes (cyst, bowel endometriosis, neuroendocrine neoplasm, lipoma, and pneumatosis cystoides intestinalis) achieved >60% accuracy. Multivariate logistic regression analysis confirmed that the lesion location (left colon VS rectum: OR=0.06, 95% CI: 0.02-0.17, P<0.001; right colon VS rectum: OR=0.04, 95% CI: 0.01-0.13, P<0.001; ileocecal valve VS rectum: OR=0.09, 95% CI: 0.02-0.42, P=0.002); echogenicity (anechoic VS hypoechoic: OR=6.26, 95% CI: 1.31-29.97, P=0.022; hyperechoic VS hypoechoic: OR=13.39, 95% CI: 4.16-43.09, P<0.001) and ultrasonic layer (layer 4 VS layer 3: OR=0.22, 95% CI: 0.06-0.81, P=0.023) were independent influencing factors of EUS diagnostic accuracy for colorectal SMT. Conclusion:Neuroendocrine neoplasms and lipomas represent the most common colorectal SMT, whereas rare and uncommon SMT exhibit low EUS diagnostic accuracy. Lesion location, echogenicity, and ultrasonic layer significantly influence EUS diagnostic accuracy for colorectal SMT.

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.