Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Humans ; Aged ; Lung Diseases ; Pneumonia/drug therapy* ; Adrenal Cortex Hormones/therapeutic use* ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Administration, Inhalation ; Community-Acquired Infections/drug therapy*

Objective To compare the effects of 20 mg qd and 10 mg bidadministration of iprrazole enteric-coated tablets on the control of gastric acid in healthy subjects.Methods A randomized,single-center,parallel controlled trial was designed to include 8 healthy subjects.Randomly divided into 2 groups,20 mg qd administration group:20 mg enteric-coated tablets of iprrazole in the morning;10 mg bid administration group:10 mg enteric-coated tablets of iprrazole in the morning and 10 mg in the evening.The pH values in the stomach of the subjects before and 24 h after administration were monitored by pH meter.The plasma concentration of iprazole after administration was determined by HPLC-MS/MS.The main pharmacokinetic parameters were calculated by Phoenix WinNonlin(V8.0)software.Results The PK parameters of iprrazole enteric-coated tablets and reference preparations in fasting group were as follows:The Cmax of 20 mg qd group and 10 mg bid group were(595.75±131.15)and(283.50±96.98)ng·mL-1;AUC0-t were(5 531.94±784.35)and(4 686.67±898.23)h·ng·mL-1;AUC0-∞ were(6 003.19±538.59)and(7 361.48±1 816.77)h·ng·mL-1,respectively.The mean time percentage of gastric pH＞3 after 20 mg qd and 10 mg bid were 82.64％and 61.92％,and the median gastric pH within 24 h were 6.25±1.49 and 3.53±2.05,respectively.The mean gastric pH values within 24 h were 5.71±1.36 and 4.23±1.45,respectively.The correlation analysis of pharmacokinetic/pharmacodynamics showed that there was no significant correlation between the peak concentration of drug in plasma and the inhibitory effect of acid.Conclusion Compared with the 20 mg qd group and the 10 mg bid group,the acid inhibition effect is better,the administration times are less,and the safety of the two administration regimes is good.

Objective To compare the pharmacokinetic profiles of the two teriflunomide tablets in healthy Chinese subjects under fasting and fed conditions and to evaluate their bioequivalence and safety.Methods A randomized,open,single-dose,parallel trial design was used to enroll 31 and 32 healthy Chinese male subjects in the fasting and fed groups,who were randomized to a single oral dose of 14 mg of either reference or test preparation of teriflunomide tablets.The plasma concentrations of teriflunomide were determined using liquid chromatography-tandem mass spectrometry method,and Phoenix WinNonlin 8.1 software was used to calculate pharmacokinetic parameters and perform bioequivalence analysis.Results Subjects received a single oral dose of the reference and test formulations of teriflunomide.The main pharmacokinetic parameters of teriflunomide in the fasting group were as follows:Cmax were(2.14±0.27)and(2.27±0.33)μg·mL-1,AUC0-72h were(105.70±11.20)and(107.72±11.77)μg·mL-1·h,tmax was 1.49 and 0.99 h;the main pharmacokinetic parameters of teriflunomide in the fed group were as follows:Cmaxwere(1.83±0.17)and(1.75±0.22)μg·mL-1,AUC0-72h were(102.66±9.18)and(101.57±13.01)μg·mL-1·h,tmax was 4.01 and 4.99 h.The 90％confidence intervals for the geometric means of Cmax and AUC0-72h for reference and test preparations in the fasting and fed groups were in the range of 80％to 125％.Conclusion The pharmacokinetic characteristics of the 2 formulations were similar under fasting and fed administration conditions,with good bioequivalence and safety;Postprandial administration may delay the time to peak of the drug.

Objective To investigate the effect of paeoniflorin on aerobic glycolysis of macrophages induced by resiquimod.Methods THP-1 cells were treated with phorbol ester(PM A)to differentiate into macrophages.The cells were divided into control group,model group and low,medium,high dose experimental group.The cells in the control group were cultured normally;in the model group,2 μg·mL-1 resiquimod was used to stimulate macrophages for 24 h to induce aerobic glycolysis.The low,medium and high dose experimental groups were treated with 1,10 and 100 μmol·L-1 paeoniflorin for 24 h on the basis of the model group.Cell activity was detected by cell counting kit-8(CCK-8)method.Lactate and glucose determination kit were used to detect lactate secretion and glucose consumption of cells in each group.The protein and mRNA expression levels of(PKM2)and(LDHA)were detected by Western blot and real-time fluorescence quantitative polynucleotide chain reaction(q-PCR)respectively.Immunofluorescence method was used to compare the fluorescence intensity of PKM2 in each group.Results After 24 h stimulation of THP-1 cells with 2 μg·mL-1 resiquimod,the glucose contents in cell culture supernatants of control group,model group and low,medium and high dose experimental groups were(14.70±0.44),(9.83±0.43),(10.68±0.29),(11.79±0.33)and(13.63±0.74)mmol·L-1;the lactate secreted by cells were(6.17±0.48),(11.94±0.55),(9.08±0.55),(7.79±0.66)and(6.50±0.55)mmol·L-1;the protein expression levels of PKM2 in cells were 1.00±0.00,1.33±0.18,1.02±0.17,0.74±0.17 and 0.73±0.18;the protein expression levels of LDHA were 1.00±0.00,1.20±0.09,0.90±0.14,0.76±0.12 and 0.78±0.17;the PKM2 mRNA levels were 1.00±0.09,2.11±0.23,1.98±0.31,1.38±0.25 and 0.93±0.32;the LDHA mRNA levels were 1.00±0.13,1.85±0.25,1.44±0.21,0.91±0.24 and 0.96±0.14;the average fluorescence intensities of PKM2 were 136.41±33.63,217.94±5.33,210.27±1.03,204.14±3.27 and 186.79±14.03.Compared with control group,the above indicators in model group showed statistically significant differences(P＜0.05,P＜0.01);compared with model group,the differences in the above indicators in medium and high dose experimental group were all statistically significant(P＜0.05,P＜0.01).Conclusion Paeoniflorin can inhibit the aerobic glycolysis of macrophages induced by resiquimod.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dysfunctions related to thinking, learning, and memory of the brain. AD has multiple pathological characteristics with complicated causes, constructing a suitable pathological model is crucial for the research of AD. Microfluidic chip technology integrates multiple functional units on a chip, which can realize microenvironmental control similar to the physiological environment. It is well applied in the construction of pathological model, early diagnosis as well as drug screening of AD. This paper focuses on the construction of AD microfluidic chips model from the perspective of cell type, culture formats and the chips structure as well as the research progress of microfluidic chips in AD application based on the pathological characteristics of AD, which will provide a reference for further elucidation of AD mechanism and drug development.

Biosensor analysis technology is a kind of technology with high specificity that can convert biological reactions into optical and electrical signals. In the development of drugs for Alzheimer's disease (AD), according to different disease hypotheses and targets, this technology plays an important role in confirming targets and screening active compounds. This paper briefly describes the pathogenesis of AD and the current situation of therapeutic drugs, introduces three biosensor analysis techniques commonly used in the discovery of AD drugs, such as surface plasmon resonance (SPR), biolayer interferometry (BLI) and fluorescence analysis technology, explains its basic principle and application progress, and summarizes their advantages and limitations respectively.