Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

Objective:To investigate the role of an inflammatory microenvironment induced by Porphyromonasgingivalis ( P. gingivalis) in the occurrence of esophageal squamous cell carcinoma (ESCC) in mice. Methods:A total of 180 C57BL/6 mice were randomly divided into 6 groups, i.e. control group, P. gingivalis group, 4NQO group, 4NQO + P. gingivalis group, 4NQO + P. gingivalis + celecoxib group, and 4NQO + P. gingivalis + antibiotic cocktail (ABC, including metronidazole, neomycin, ampicillin, and vancomycin) group, with 30 mice in each group, using the random number table. All mice were normalized by treatment with ABC in drinking water for 2 weeks. In the following 2 weeks, the mice in the control group and the P. gingivalis group were given drinking water, while the other 4 groups were treated with 30 μg/ml 4NQO in the drinking water. In weeks 11-12, the mice in the P. gingivalis group, the 4NQO + P. gingivalis group, the 4NQO + P. gingivalis + celecoxib group, and the 4NQO + P. gingivalis + ABC group were subjected to ligation of the second molar in oral cavity followed by oral P. gingivalis infection thrice weekly for 24 weeks in weeks 11-34. In weeks 13-34, the mice in 4NQO + P. gingivalis+celecoxib group and 4NQO + P. gingivalis + ABC group were administered with celecoxib and ABC for 22 weeks, respectively. At the end of 34 weeks, gross and microscopic alterations were examined followed by RT-qPCR and immunohistochemistry to examine the expression profiles of inflammatory- and tumor-molecules in esophagi of mice. Results:At 34 weeks, 4NQO treatment alone did not affect the foci of papillary hyperproliferation, diseased area, and the thickness of the esophageal wall, but significantly enhanced the foci of hyperproliferation (median 1.00, P＜0.05) and mild/moderate dysplasia (median 2.00, P＜0.01). In addition, the expression levels of IL-6 [8.35(3.45,8.99)], IL-1β [6.90(2.01,9.72)], TNF-α [12.04(3.31,14.08)], c-myc [2.21(1.80,3.04)], pSTAT3, Ki-67, and pH2AX were higher than those in the control group. The pathological changes of the esophageal mucosa were significantly more overt in the 4NQO + P. gingivalis group in terms of the foci of papillary hyperproliferation (median 2.00), diseased area (median 2.51 mm 2), the thickness of the esophageal wall (median 172.52 μm), the foci of hyperproliferation (median 1.00, P＜0.05), and mild/moderate dysplasia (median 1.00, P＜0.01). In mice of the 4NQO + P. gingivalis group, the expression levels of IL-6 [12.27(5.35,22.08)], IL-1β [13.89(10.04,15.96)], TNF-α [19.56(6.07,20.36)], IFN-γ [11.37(8.23,20.07)], c-myc [2.62(1.51,4.25)], cyclin D1 [4.52(2.68,7.83)], nuclear pSTAT3, COX-2, Ki-67, and pH2AX were significantly increased compared with the mice in the control group. In mice of the 4NQO + P. gingivalis group, the diseased area, invasive malignant foci as well as pSTAT3 and pH2AX expression were significantly blunted by celecoxib. Treatment with ABC markedly reduced the papillary hyperproliferative foci, invasive malignant foci, and pSTAT3 expression but not pH2AX. Conclusions:P. gingivalis promotes the occurrence of esophageal squamous cell carcinoma in mice by inducing an inflammatory microenvironment primed with 4NQO induced DNA damage. Clearance of P. gingivalis with ABC or anti-inflammatory intervention holds promise for prevention of esophageal squamous cell malignant pathogenesis via blockage of IL-6/STAT3 signaling and amelioration of inflammation.

Objective:To investigate the role of an inflammatory microenvironment induced by Porphyromonasgingivalis ( P. gingivalis) in the occurrence of esophageal squamous cell carcinoma (ESCC) in mice. Methods:A total of 180 C57BL/6 mice were randomly divided into 6 groups, i.e. control group, P. gingivalis group, 4NQO group, 4NQO + P. gingivalis group, 4NQO + P. gingivalis + celecoxib group, and 4NQO + P. gingivalis + antibiotic cocktail (ABC, including metronidazole, neomycin, ampicillin, and vancomycin) group, with 30 mice in each group, using the random number table. All mice were normalized by treatment with ABC in drinking water for 2 weeks. In the following 2 weeks, the mice in the control group and the P. gingivalis group were given drinking water, while the other 4 groups were treated with 30 μg/ml 4NQO in the drinking water. In weeks 11-12, the mice in the P. gingivalis group, the 4NQO + P. gingivalis group, the 4NQO + P. gingivalis + celecoxib group, and the 4NQO + P. gingivalis + ABC group were subjected to ligation of the second molar in oral cavity followed by oral P. gingivalis infection thrice weekly for 24 weeks in weeks 11-34. In weeks 13-34, the mice in 4NQO + P. gingivalis+celecoxib group and 4NQO + P. gingivalis + ABC group were administered with celecoxib and ABC for 22 weeks, respectively. At the end of 34 weeks, gross and microscopic alterations were examined followed by RT-qPCR and immunohistochemistry to examine the expression profiles of inflammatory- and tumor-molecules in esophagi of mice. Results:At 34 weeks, 4NQO treatment alone did not affect the foci of papillary hyperproliferation, diseased area, and the thickness of the esophageal wall, but significantly enhanced the foci of hyperproliferation (median 1.00, P＜0.05) and mild/moderate dysplasia (median 2.00, P＜0.01). In addition, the expression levels of IL-6 [8.35(3.45,8.99)], IL-1β [6.90(2.01,9.72)], TNF-α [12.04(3.31,14.08)], c-myc [2.21(1.80,3.04)], pSTAT3, Ki-67, and pH2AX were higher than those in the control group. The pathological changes of the esophageal mucosa were significantly more overt in the 4NQO + P. gingivalis group in terms of the foci of papillary hyperproliferation (median 2.00), diseased area (median 2.51 mm 2), the thickness of the esophageal wall (median 172.52 μm), the foci of hyperproliferation (median 1.00, P＜0.05), and mild/moderate dysplasia (median 1.00, P＜0.01). In mice of the 4NQO + P. gingivalis group, the expression levels of IL-6 [12.27(5.35,22.08)], IL-1β [13.89(10.04,15.96)], TNF-α [19.56(6.07,20.36)], IFN-γ [11.37(8.23,20.07)], c-myc [2.62(1.51,4.25)], cyclin D1 [4.52(2.68,7.83)], nuclear pSTAT3, COX-2, Ki-67, and pH2AX were significantly increased compared with the mice in the control group. In mice of the 4NQO + P. gingivalis group, the diseased area, invasive malignant foci as well as pSTAT3 and pH2AX expression were significantly blunted by celecoxib. Treatment with ABC markedly reduced the papillary hyperproliferative foci, invasive malignant foci, and pSTAT3 expression but not pH2AX. Conclusions:P. gingivalis promotes the occurrence of esophageal squamous cell carcinoma in mice by inducing an inflammatory microenvironment primed with 4NQO induced DNA damage. Clearance of P. gingivalis with ABC or anti-inflammatory intervention holds promise for prevention of esophageal squamous cell malignant pathogenesis via blockage of IL-6/STAT3 signaling and amelioration of inflammation.

Purpose/Significance To explore the establishment of a remote maternal and fetal monitoring system based on 5 G tech-nology in the obstetrics and gynecology hospital,and to provide references for the medical system to improve telemedicine and smart medi-cal care based on 5 G technology.Method/Process By utilizing the advantages of 5 G technology such as fast speed,wide spectrum and low delay,and combining services such as maternal and fetal monitoring,online education,remote consultation,artificial intelligence(AI),health data management,and medical green channel,the maternal and fetal monitoring database and the AI model of maternal and fetal monitoring are constructed,the remote maternal and fetal monitoring system is constructed,and fetal heart monitoring process is op-timized.Result/Conclusion It realizes the combination of maternal and fetal monitoring application in hospital and outside hospital,medical alliance applications,internet hospital applications and ambulance transfer process applications.

BACKGROUND: LY01005 (Goserelin acetate sustained-release microsphere injection) is a modified gonadotropin-releasing hormone (GnRH) agonist injected monthly. This phase III trial study aimed to evaluated the efficacy and safety of LY01005 in Chinese patients with prostate cancer. METHODS: We conducted a randomized controlled, open-label, non-inferiority trial across 49 sites in China. This study included 290 patients with prostate cancer who received either LY01005 or goserelin implants every 28 days for three injections. The primary efficacy endpoints were the percentage of patients with testosterone suppression ≤50 ng/dL at day 29 and the cumulative probability of testosterone ≤50 ng/dL from day 29 to 85. Non-inferiority was prespecified at a margin of -10%. Secondary endpoints included significant castration (≤20 ng/dL), testosterone surge within 72 h following repeated dosing, and changes in luteinizing hormone, follicle-stimulating hormone, and prostate specific antigen levels. RESULTS: On day 29, in the LY01005 and goserelin implant groups, testosterone concentrations fell below medical-castration levels in 99.3% (142/143) and 100% (140/140) of patients, respectively, with a difference of -0.7% (95% confidence interval [CI], -3.9% to 2.0%) between the two groups. The cumulative probabilities of maintaining castration from days 29 to 85 were 99.3% and 97.8%, respectively, with a between-group difference of 1.5% (95% CI, -1.3% to 4.4%). Both results met the criterion for non-inferiority. Secondary endpoints were similar between groups. Both treatments were well-tolerated. LY01005 was associated with fewer injection-site reactions than the goserelin implant (0% vs . 1.4% [2/145]). CONCLUSION: LY01005 is as effective as goserelin implants in reducing testosterone to castration levels, with a similar safety profile. TRIAL REGISTRATION ClinicalTrials.gov, NCT04563936.
Humans ; Male ; Antineoplastic Agents, Hormonal/therapeutic use* ; East Asian People ; Gonadotropin-Releasing Hormone/agonists* ; Goserelin/therapeutic use* ; Prostate-Specific Antigen ; Prostatic Neoplasms/drug therapy* ; Testosterone

Type 2 diabetes (T2D) is often accompanied with an induction of retinaldehyde dehydrogenase 1 (RALDH1 or ALDH1A1) expression and a consequent decrease in hepatic retinaldehyde (Rald) levels. However, the role of hepatic Rald deficiency in T2D progression remains unclear. In this study, we demonstrated that reversing T2D-mediated hepatic Rald deficiency by Rald or citral treatments, or liver-specific Raldh1 silencing substantially lowered fasting glycemia levels, inhibited hepatic glucogenesis, and downregulated phosphoenolpyruvate carboxykinase 1 (PCK1) and glucose-6-phosphatase (G6PC) expression in diabetic db/db mice. Fasting glycemia and Pck1/G6pc mRNA expression levels were strongly negatively correlated with hepatic Rald levels, indicating the involvement of hepatic Rald depletion in T2D deterioration. A similar result that liver-specific Raldh1 silencing improved glucose metabolism was also observed in high-fat diet-fed mice. In primary human hepatocytes and oleic acid-treated HepG2 cells, Rald or Rald + RALDH1 silencing resulted in decreased glucose production and downregulated PCK1/G6PC mRNA and protein expression. Mechanistically, Rald downregulated direct repeat 1-mediated PCK1 and G6PC expression by antagonizing retinoid X receptor α, as confirmed by luciferase reporter assays and molecular docking. These results highlight the link between hepatic Rald deficiency, glucose dyshomeostasis, and the progression of T2D, whilst also suggesting RALDH1 as a potential therapeutic target for T2D.

Body is equipped with organic cation transporters (OCTs). These OCTs mediate drug transport and are also involved in some disease process. We aimed to investigate whether liver failure alters intestinal, hepatic and renal Oct expressions using bile duct ligation (BDL) rats. Pharmacokinetic analysis demonstrates that BDL decreases plasma metformin exposure, associated with decreased intestinal absorption and increased urinary excretion. Western blot shows that BDL significantly downregulates intestinal Oct2 and hepatic Oct1 but upregulates renal and hepatic Oct2. In vitro cell experiments show that chenodeoxycholic acid (CDCA), bilirubin and farnesoid X receptor (FXR) agonist GW4064 increase OCT2/Oct2 but decrease OCT1/Oct1, which are remarkably attenuated by glycine-β-muricholic acid and silencing FXR. Significantly lowered intestinal CDCA and increased plasma bilirubin levels contribute to different Octs regulation by BDL, which are confirmed using CDCA-treated and bilirubin-treated rats. A disease-based physiologically based pharmacokinetic model characterizing intestinal, hepatic and renal Octs was successfully developed to predict metformin pharmacokinetics in rats. In conclusion, BDL remarkably downregulates expressions of intestinal Oct2 and hepatic Oct1 protein while upregulates expressions of renal and hepatic Oct2 protein in rats, finally, decreasing plasma exposure and impairing hypoglycemic effects of metformin. BDL differently regulates Oct expressions via Fxr activation by CDCA and bilirubin.