ObjectiveTo observe the clinical efficacy and safety of Tangning Tongluo tablets in the treatment of nonproliferative diabetic retinopathy （DR）. MethodsFourteen research centers participated in this study， which spanned a time interval from September 2021 to May 2023. A total of 240 patients with nonproliferative DR were included and randomly assigned into an observation group （120 cases） and a control group （120 cases）. The observation group was treated with Tangning Tongluo tablets， and the control group with calcium dobesilate capsules. Both groups were treated for 24 consecutive weeks. The vision， DR progression rate， retinal microhemangioma， hemorrhage area， exudation area， glycosylated hemoglobin （HbA1c） level， and TCM syndrome score were assessed before and after treatment， and the safety was observed. ResultsThe vision changed in both groups after treatment （P<0.05）， and the observation group showed higher best corrected visual acuity （BCVA） than the control group （P<0.05）. The DR progression was slow with similar rates in the two groups. The fundus hemorrhage area and exudation area did not change significantly after treatment in both groups， while the observation group outperformed the control group in reducing the fundus hemorrhage area and exudation area. There was no significant difference in the number of microhemangiomas between the two groups before treatment. After treatment， the number of microhemangiomas decreased in both the observation group （Z=-1.437， P<0.05） and the control group （Z=-2.238， P<0.05）， and it showed no significant difference between the two groups. As the treatment time prolonged， the number of microhemangiomas gradually decreased in both groups. There was no significant difference in the HbA1c level between the two groups before treatment. After treatment， the decline in the HbA1c level showed no significant difference between the two groups. The TCM syndrome score did not have a statistically significant difference between the two groups before treatment. After treatment， neither the TCM syndrome score nor the response rate had significant difference between the two groups. With the extension of the treatment time， both groups showed amelioration of TCM syndrome compared with the baseline. ConclusionTangning Tongluo tablets are safe and effective in the treatment of nonproliferative DR， being capable of improving vision and reducing hemorrhage and exudation in the fundus.

In this study, RAW264.7 cells were employed to investigate the effects of honey-processed Astragalus on their energy metabolism and polarization, and explore the scientific connotation of the enhanced efficacy of honey-processed Astragalus on invigorating spleen-stomach and replenishing Qi. The medicated sera were prepared by intragastric administration of rats with water extracts of crude and honey-processed Astragalus, and the composition changes of medicated sera of crude and honey-processed Astragalus were analyzed by using LC-MS technology. The cell survival rates were detected and the concentrations of medicated sera were screened through CCK-8 assay. The differences of cell phagocytic rates, ATP energy metabolism, and NO secretion between crude and honey-processed Astragalus were evaluated by using neutral red phagocytosis assay, ATP detection kit, and NO detection kit. The effects of crude and honey-processed Astragalus on TNF-α secretion of RAW264.7 cells were detected by employing ELISA kit. The effects of crude and honey-processed Astragalus on polarization of RAW264.7 cells were evaluated by utilizing flow cytometry. The differential metabolites related to glycolysis in cell lysates and culture media were screened by using LC-MS technology. The experiment was approved by the experimental animal ethics committee from Shanxi University of Chinese Medicine (No. AWE202407352). The results showed that the contents of betaine, amino acids, and ononin in the prepared medicated sera of rats treated by intragastric administration with water extracts of honey-processed Astragalus increased compared to those in crude one. The results of CCK-8 experiment showed that there were no cytotoxic effects on RAW264.7 cells in the medicated sera of crude and honey-processd Astragalus at different concentration. The phagocytic index and ATP yield both increased to varying degrees after administration of the medicated sera to cells. The secretion of NO in normal and inflammatory cells increased and decreased respectively, and the effect of honey-processed Astragalus was better than that of crude one. The results of ELISA kit showed that the medicated sera of both crude and honey-processed Astragalus could promote the secretion of TNF-α in a concentration-dependent manner, and the promoting effect of honey-processed Astragalus was stronger than that of crude one. The results of flow cytometry showed that the medicated sera of both crude and honey-processed Astragalus could promote M1-type polarization and inhibit M2-type polarization of RAW264.7 cells, and the effect of honey-processed Astragalus was better than that of crude one. Compared to crude Astragalus, the metabolites related to glycolysis in the cell lysates and culture media of the medicated sera of honey-processed Astragalus were generally on the rise, indicating that the effect on promoting glycolysis of honey-processed Astragalus was better than that of crude one. In summary, honey-processed Astragalus can promote the polarization and energy metabolism of RAW264.7 cells, and participate in positive immune regulation, which is correlated with its enhanced effect of invigorating spleen-stomach and replenishing Qi.

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.

ObjectiveTo analyze the epidemiological trends and characteristics of brucellosis in humans (hereinafter referred to as brucellosis) in Tangshan City, Hebei Province from 2016 to 2023, and to provide a scientific basis for formulating brucellosis prevention and control strategies in the region. MethodsThe incidence data of human brucellosis in Tangshan City from 2016 to 2023 were collected from the China Disease Prevention and Control Information System. The diagnosis time, infection route, and clinical characteristics of the cases were obtained from the case investigation reports. Descriptive epidemiological methods were used to analyze the temporal, spatial, demographic distributions, and clinical characteristics of human brucellosis. Brucella species were identified using agglutination tests with bacterial suspension and A/M antigen-positive serum. ResultsA total of 2 193 cases of human brucellosis were confirmed and clinically diagnosed in Tangshan City from 2016 to 2023, with the peak incidence occured from March to August, and which exhibited distinct geographic distribution patterns. The highest incidence rate was found in people aged 60‒<70 years. The occupation of cases were primarily farmers. The incidence rate in males (528/100 000) was higher than that in females (184/100 000). All cases had confirmed exposure to infected animals or contaminated animal products. ConclusionThe epidemic of human brucellosis in Tangshan exhibited an overall steady downward trend from 2016 to 2023, except for a slight increase in 2016 and 2021, with the incidence rate controlled at 289/100 000‒335/100 000. The prevention and control situation of human brucellosis still remains severe, with the highest incidence rate in the eastern region of Tangshan, which are characterized by the breeding, slaughtering, and processing of cattle and sheep. Therefore, it it is necessary to enhance the prevention and control of human brucellosis among the personnel engaged in these industries in the eastern areas.

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

By employing the analytic hierarchy process(AHP), the CRITIC method(a weight determination method based on indicator correlations), and the AHP-CRITIC hybrid weighting method, the weight coefficients of evaluation indicators were determined, followed by a comprehensive score comparison. The grey correlation analysis was then performed to analyze the results calculated using the hybrid weighting method. Subsequently, a backpropagation-artificial neural network(BP-ANN) model was constructed to predict the extraction process parameters and optimize the extraction process for Shenxiong Huanglian Jiedu Granules(SHJG). In the extraction process, an L_9(3~4) orthogonal experiment was designed to optimize three factors at three levels, including extraction frequency, water addition amount, and extraction time. The evaluation indicators included geniposide, berberine, ginsenoside Rg_1 + Re, ginsenoside Rb_1, ferulic acid, and extract yield. Finally, the optimal extraction results obtained by the orthogonal experiment, grey correlation analysis, and BP-ANN method were compared, and validation experiments were conducted. The results showed that the optimal extraction process involved two rounds of aqueous extraction, each lasting one hour; the first extraction used ten times the amount of added water, while the second extraction used eight times the amount. In the validation experiments, the average content of each indicator component was higher than the average content obtained in the orthogonal experiment, with a higher comprehensive score. The optimized extraction process parameters were reliable and stable, making them suitable for subsequent preparation process research.
Drugs, Chinese Herbal/analysis* ; Neural Networks, Computer