Objective To explore the potential mechanisms of hypoxic adaptive metabolic changes in the kidneys of plateau pikas at different altitudes using non-targeted metabolomics analysis via ultra-high-performance liquid chromatography coupled with quadrupole electrostatic field orbital trap-mass spectrometry (UHPLC-QE-MS). Methods 10 plateau pikas were captured at an altitude of 4 360 m in Xingxiuhai area, Maduo County, Guoluo Tibetan Autonomous Prefecture, Qinghai Province (MD group), and 10 plateau pikas were captured at an altitude of 2 900 m in Menyuan area, Haibei Tibetan Autonomous Prefecture, Qinghai Province (MY group). After anesthesia, serum samples were collected, and kidney samples were collected after euthanasia. General physiological and biochemical indicators were measured and metabolomics analysis was performed. Part of the serum samples was used for hematology analysis, another part for blood gas analysis, and the remaining part for biochemical indicator detection. Metabolites were extracted from the kidney tissue samples and then analyzed using UHPLC-QE-MS. Differential metabolites were analyzed using metabolomics principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), with screening criteria set as variable importance in projection (VIP)>1.5 and fold change (FC)>1.5, or VIP>1.5 and FC<1/1.5. Correlation analysis heatmaps, significance analysis volcano plots, signaling pathway recognition bubble charts, and rectangular graphs were used for the analysis of differential metabolites and related signaling pathways. Results The red blood cell count, glucose, urea nitrogen, uric acid, and homocysteine levels in the MD group plateau pikas were higher than those in the MY group, while hemoglobin, hematocrit, creatinine, and carbon dioxide combining power were lower than those in the MY group. This indicated a significant difference in the blood oxygen-carrying capacity of plateau pikas at different altitudes. The principal component pattern recognition analyses, and OPLS-DA permutation test showed that the kidney metabolites of the MD and MY groups of plateau pikas had distinct clustering distributions (R²Y=0.930, Q²=0.655). According to the screening criteria and database comparison, 46 differential metabolites were identified in the kidneys of plateau pikas at different altitudes. In the MD group of plateau pikas, the expression levels of bufadienolide, adenosine, adenine, diosgenin, berberine chloride, carnosol, and astaxanthin were significantly increased (VIP>1.5, P<0.05), while the levels of arachidonic acid, histamine, and coumarin were significantly decreased (VIP>1.5, P<0.05). The analysis of related signaling pathways showed that the biosynthetic pathways of valine, leucine, and isoleucine had the largest impact factors (P<0.05), while the biosynthetic pathways of pantothenate and coenzyme A showed the most significant enrichment (P<0.05). Conclusion The differential metabolites of amino acids, pantothenate, and coenzyme A pathways in the kidneys of plateau pikas at different altitudes may be involved in the metabolic mechanisms of plateau pikas' hypoxia adaptation in high-altitude environments.

OBJECTIVES: To explore the effect of quercetin for mitigating HIV-1 gp120-induced astrocyte neurotoxicity and its underlying mechanism. METHODS: Primary rat astrocytes were isolated and treated with quercetin, HIV-1 gp120, or gradient concentrations of quercetin combined with HIV-1 gp120. The formation of stress granules (SGs) in the treated cells was observed with immunofluorescence assay, and the levels of oxidative stress markers and protein expressions were measured using specific assay kits and Western blotting. HIV-1 gp120 transgenic mice were treated with quercetin (50 mg/kg) by gavage for 4 weeks, and the changes in cognitive functions and oxidative stress levels were examined by behavioral assessments, oxidative stress index analysis in serum, and immunohistochemical and Western blotting of the brain tissue. RESULTS: In primary rat astrocytes, treatment with quercetin significantly reduced HIV-1 gp120-induced SG formation, increased the levels of antioxidant indexes, decreased the levels of oxidative substances, and up-regulated protein level associated with SG depolymerization. In the transgenic mouse models, quercetin obviously improved the cognitive function of the rats, reduced oxidative stress levels, and promoted the expression of proteins associate with SG depolymerization in the brain tissues. CONCLUSIONS Quercetin mitigates HIV-1 gp120-induced astrocyte neurotoxicity and cognitive function impairment by inhibiting oxidative stress, enhancing expressions of SG depolymerization-related proteins, and promoting SG disassembly, suggesting the value of quercetin as a potential therapeutic agent for neuroprotection in HIV-associated neurocognitive disorders.
Animals ; Quercetin/pharmacology* ; Astrocytes/metabolism* ; HIV Envelope Protein gp120 ; Oxidative Stress/drug effects* ; Rats ; Stress Granules/drug effects* ; Mice ; Mice, Transgenic ; Rats, Sprague-Dawley ; Cells, Cultured

OBJECTIVES: To explore the effect of A. muciniphila gavage on intestinal microbiota and gut-brain interaction disorders (DGBIs) in gp120tg transgenic mouse models of HIV-associated neurocognitive disorder (HAND). METHODS: Intestinal microbiota was detected by 16S rRNA gene sequencing in 6-, 9-, and 12-month-old wild-type (WT) mice and gp120tg transgenic mice. The 12-month-old WT and transgenic mice were divided into 2 groups for daily treatment with PBS or A.muciniphila gavage (2×10⁸ CFU/mouse) for 6 weeks. After the treatment, immunohistochemistry, ELISA and qPCR were used to detect changes in colonic expression levels of glycosylated mucins, MBP and IL-1β, eosinophil infiltration, serum lipopolysaccharide (LPS) levels, and colonic expressions of occludin, ZO-1, IL-10, TNF-α and INF-γ mRNA. Morris water maze test and immunofluorescence assay were used to assess learning and spatial memory abilities and neuronal damage of the mice. RESULTS: Compared with WT mice, the transgenic mice exhibited significantly lowered Simpson's diversity of the intestinal microbiota with reduced abundance of Akkermansia genus, increased serum LPS levels and decreased colonic expression of glycosylated mucin. A.muciniphila gavage obviously ameliorated the reduction of glycosylated mucin in the transgenic mice without causing significant changes in body weight. The 12-month-old gp120tg mice had significantly decreased cdonic expressions of Occludin and ZO-1 with increased eosinophil infiltration and TNF-β, INF-γ and IL-1β levels and obviously lowered IL-10 level; all these changes were significantly mitigated by A.muciniphila gavage, which also improved cognitive impairment and neuronal loss in the hippocampus and cortex of the transgenic mice. CONCLUSIONS The gp120tg mice have lower intestinal microbiota richness and diversity than WT mice. The 12-month-old gp120tg mice have significantly reduced Akkermansia abundance with distinct DGBIs-related indexes, and A. muciniphila gavage can reduce intestinal barrier injury, colonic inflammation and eosinophil activation, cognitive impairment and brain neuron injury in these mice.
Animals ; Mice, Transgenic ; Gastrointestinal Microbiome ; Mice ; Brain ; HIV Envelope Protein gp120/genetics* ; Akkermansia ; Disease Models, Animal

OBJECTIVES: To explore the molecular mechanism of Jiangzhi Huaban Decoction (JZHBD) for improving atherosclerosis through the "qi meridian-blood channels" pathway. METHODS: ApoE^-/- mouse models of atherosclerosis were established by high-fat diet feeding for 8 weeks, with C57BL/6 mice on a normal diet as the controls. Forty ApoE^-/- mouse models were randomized into model group, low-, medium-, and high-dose JZHBD treatment groups, and atorvastatin treatment group (n=8) for their respective treatments for 8 weeks. The changes in body weight and overall condition of the mice were monitored weekly. After the treatments, serum levels of TC, TG, HDL-C, LDL-C, TBA, ALT, and AST of the mice were measured, pathological changes in the liver and aortic root plaques were examined with HE staining, and lipid accumulation in the liver and aortic wall was assessed using Oil Red O staining. The core molecular mechanism was studied through transcriptomics, and the expressions of the key pathway proteins were confirmed using Western blotting and immunohistochemistry. RESULTS: Treatment with JZHBD significantly reduced blood lipid and total bile acid levels, improved liver function and hepatic steatosis, and decreased aortic lipid deposition and plaque area in the mouse models of atherosclerosis. Transcriptomic analysis suggested that the therapeutic mechanism of JZHBD involved reverse cholesterol transport, PPAR signaling, and the inflammatory pathways. In atherosclerotic mice, JZHBD treatment obviously up-regulated hepatic expressions of PPARγ, LXRα, ABCA1, ABCG1, and CYP7A1, down-regulated hepatic expressions of p-p65/p65, IL-6, IL1β in the liver, increased ABCG5 and ABCG8 expressions in the intestines, and decreased ICAM-1 and VCAM-1 expressions in the aortic plaques. CONCLUSIONS JZHBD improves atherosclerotic vascular damage and plaque formation possibly by regulating hepatic reverse cholesterol transport and inflammation via modulating the hepatic PPARγ/LXRα/NF-κB signaling pathway.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Mice, Inbred C57BL ; Plaque, Atherosclerotic/metabolism* ; Liver/metabolism* ; Mice ; Atherosclerosis/metabolism* ; Cholesterol/metabolism* ; PPAR gamma/metabolism* ; Male ; Diet, High-Fat ; Biological Transport

Male ; Humans ; Liquid Chromatography-Mass Spectrometry ; Chromatography, Liquid ; Tandem Mass Spectrometry ; Varicocele/diagnosis* ; Biomarkers

OBJECTIVE To investigate and analyze the operational costs and current charging policies of pharmacy intravenous admixture service （PIVAS） in China， and provide a reference for promoting high-quality and sustainable development of PIVAS. METHODS Questionnaires were distributed in 30 provinces， autonomous regions， and municipalities across the country through the “Wenjuanxing” platform from May 6th to July 1st， 2022. The operational costs， charging status and suggestions of PIVAS were investigated and analyzed. RESULTS A total of 761 PIVAS participated in the survey nationwide， including 666 tertiary medical institutions， 93 secondary medical institutions， and 2 primary medical institutions. Approximately 60.58% of PIVAS had implemented a charging system that allowed charges. Among them， most PIVAS required inspection and evaluation before charging. The annual operating cost of PIVAS in China was approximately 2 098 100 yuan， with the integrated operating cost comprising 89.36% of the total， while the dispensing cost accounted for only 10.64%. Human costs emerged as the highest annual consumption （74.20%）， followed by decoration and facility maintenance costs （4.77%） and equipment acquisition costs （3.44%）. Regarding charges for different drugs nationwide， common drugs had an average charge standard of 4.39 yuan per bag while antibacterial drugs averaged 5.01 yuan per bag； hazardous drugs had an average charge of 23.17 yuan per bag， whereas parenteral nutrition solutions averaged 38.75 yuan per bag. However， the recommended average charges of the four drugs mentioned above were 6.71， 9.63， 38.35 and 44.03 yuan per bag， respectively. CONCLUSIONS At present， there is no unified inspection and evaluation standard and charging standard in China. Moreover， the current charging standard is lower than the recommended standard. It is necessary to combine operational costs and develop more reasonable and fair charging standards.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.