Association between triglyceride-glucose index and chronic kidney disease: results from NHANES 1999-2020.

AIMS: Examining the connection between the triglyceride-glucose (TyG) index and chronic kidney disease (CKD) was the aim of this investigation. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) covering the years 1999-2020 were analyzed in this study. The TyG index was calculated as Ln (triglycerides (mg/dl) * fasting glucose (mg/dl)/2). The two criteria used to diagnose CKD were low estimated glomerular filtration rate (eGFR) (eGFR < 60 mL/min/1.73m2) or albuminuria (urine albumin-to-creatinine ratio (ACR) ≥ 30 mg/g). To look into the independent associations between TyG index levels with CKD, albuminuria, and low-eGFR, weighted multivariable logistic regression and generalized additive models were employed. To assess and contrast the diagnostic ability, receiver operating characteristic (ROC) curves were employed. RESULTS: Out of 18,078 total participants recruited, 48.54% were male. 8.48 + 0.68 was the mean value of the TyG index. CKD, albuminuria, and low-eGFR were common, with respective prevalences of 17.06%, 11.26%, and 8.03%, respectively. The TyG index and CKD were observed to positively correlate (OR = 4.03; 95% CI 1.81, 8.96). In US adults between the ages of 41 and 60, a J-shaped connection was found between the two. Furthermore, a higher TyG index is associated with a higher prevalence of albuminuria (OR = 6.11; 95% CI 2.64, 14.14). Subgroup analyses and interaction tests revealed that different stratifications did not significantly affect the relationship between TyG index and CKD, albuminuria, and low-eGFR. Comparing the TyG index to other indicators [lipid accumulation product (LAP), Visceral adiposity index (VAI), and the triglyceride glucose-body mass index (TyG-BMI)], it may be more accurate and discriminative in predicting CKD and albuminuria. CONCLUSION: When predicting CKD and albuminuria, the TyG index may be a more useful marker when compared to other markers (LAP, VAI, and TyG-BMI index). In addition, in American adults aged 41-60, the TyG index shows a J-shaped relationship with CKD. As a result, when assessing the kidney health of US adults, we must pay close attention to the significance of the TyG index.

AGPAT3 deficiency impairs adipocyte differentiation and leads to a lean phenotype in mice.

Acylglycerophosphate acyltransferases (AGPATs) catalyze the de novo formation of phosphatidic acid to synthesize glycerophospholipids and triglycerides. AGPATs demonstrate unique physiological roles despite a similar biochemical function. AGPAT3 is highly expressed in the testis, kidney, and liver, with intermediate expression in adipose tissue. Loss of AGPAT3 is associated with reproductive abnormalities and visual dysfunction. However, the role of AGPAT3 in adipose tissue and whole body metabolism has not been investigated. We found that male Agpat3 knockout (KO) mice exhibited reduced body weights with decreased white and brown adipose tissue mass. Such changes were less pronounced in the female Agpat3-KO mice. Agpat3-KO mice have reduced plasma insulin growth factor 1 (IGF1) and insulin levels and diminished circulating lipid metabolites. They manifested intact glucose homeostasis and insulin sensitivity despite a lean phenotype. Agpat3-KO mice maintained an energy balance with normal food intake, energy expenditure, and physical activity, except for increased water intake. Their adaptive thermogenesis was also normal despite reduced brown adipose mass and triglyceride content. Mechanistically, Agpat3 was elevated during mouse and human adipogenesis and enriched in adipocytes. Agpat3-knockdown 3T3-L1 cells and Agpat3-deficient mouse embryonic fibroblasts (MEFs) have impaired adipogenesis in vitro. Interestingly, pioglitazone treatment rescued the adipogenic deficiency in Agpat3-deficient cells. We conclude that AGPAT3 regulates adipogenesis and adipose development. It is possible that adipogenic impairment in Agpat3-deficient cells potentially leads to reduced adipose mass. Findings from this work support the unique role of AGPAT3 in adipose tissue.NEW & NOTEWORTHY AGPAT3 deficiency results in male-specific growth retardation. It reduces adipose tissue mass but does not significantly impact glucose homeostasis or energy balance, except for influencing water intake in mice. Like AGPAT2, AGPAT3 is upregulated during adipogenesis, potentially by peroxisome proliferator-activated receptor gamma (PPARγ). Loss of AGPAT3 impairs adipocyte differentiation, which could be rescued by pioglitazone. Overall, AGPAT3 plays a significant role in regulating adipose tissue mass, partially involving its influence on adipocyte differentiation.

Improvement strategy of citrate and biochar assisted nano-palladium/iron composite for effective dechlorination of 2,4-dichlorophenol.

Rapid passivation and aggregation of nanoscale zero-valent iron (nZVI) seriously limit its performance in the remediation of different contaminants from wastewater. To overcome such issues, in the present study, nano-palladium/iron (nPd/Fe) was simultaneously improved by biochar (BC) prepared from discarded peanut shells and green complexing agent sodium citrate (SC). For this purpose, a composite (SC-nPd/Fe@BC) was successfully synthesized to remove 2,4-dichlorophenol (2,4-DCP) from wastewater. In the SC-nPd/Fe@BC, BC acts as a carrier with dispersed nPd/Fe particles to effectively prevent its agglomeration, and increased the specific surface area of the composite, thereby improving the reactivity and stability of nPd/Fe. Characterization results demonstrated that the SC-nPd/Fe@BC composites were well dispersed, and the agglomeration was weakened. The formation of the passivation layer on the surface of the particles was inhibited, and the mechanism of SC and BC improving the reactivity of nPd/Fe was clarified. Different factors were found to influence the reductive dichlorination of 2,4-DCP, including Pd loading, Fe:C, SC addition, temperature, initial pH, and initial pollutant concentration. The dechlorination results revealed that the synergistic effect of the BC and SC made the removal efficiency and dechlorination rate of 2,4-DCP by SC-nPd/Fe@BC reached to 96.0 and 95.6%, respectively, which was better than that of nPd/Fe (removal: 46.2%, dechlorination: 45.3%). Kinetic studies explained that the dechlorination reaction of 2,4-DCP and the data were better represented by the pseudo-first-order kinetic model. The reaction rate constants followed the order of SC-nPd/Fe@BC (0.0264 min-1) > nPd/Fe@BC (0.0089 min-1) > SC-nPd/Fe (0.0081 min-1) > nPd/Fe (0.0043 min-1). Thus, SC-nPd/Fe@BC was capable of efficiently reducing 2,4-DCP and the dechlorination efficiency of BC and SC synergistically assisted composite on 2,4-DCP was much better than that of SC-nPd/Fe, nPd/Fe@BC and nPd/Fe. Findings suggested that SC-nPd/Fe@BC can be promising for efficient treatment of chlorinated pollutants.

Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening.

In modern drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing and refinement due to its cost-effective nature for large compound libraries. For decades, efforts have been devoted to developing VLS methods with high accuracy. These include the state-of-the-art FINDSITE suite of approaches FINDSITEcomb2.0, FRAGSITE, and FRAGSITE2 and the meta version FRAGSITEcomb that were developed in our lab. These methods combine ligand homology modeling (LHM), traditional ligand similarity methods, and more recently machine learning approaches to rank ligands and have proven to be superior to most recent deep learning and large language model-based approaches. Here, we describe further improvements to our previous best methods by combining the Morgan fingerprint (MF) with the originally used PubChem fingerprint and FP2 fingerprint. We then benchmarked FINDSITEcomb2.0M, FRAGSITEM, FRAGSITE2M, and the composite meta-approach FRAGSITEcombM. On the 102 target DUD-E set, the 1% enrichment factor (EF1%) and area under the precision-recall curve (AUPR) of FRAGSITEcomb increased from 42.0/0.59 to 47.6/0.72. This 0.72 AUPR is significantly better than that of the state-of-the-art deep learning-based method DenseFS's AUPR of 0.443. An independent test on the 81 targets DEKOIS2.0 set shows that EF1%/AUPR increases from 18.3/0.520 to 23.1/0.683. An ablation investigation shows that the MF contributes to most of the improvement of all four approaches. Thus, the MF is a useful addition to structure-based VLS.

Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study.

Introduction: Monocyte-to-lymphocyte ratio (MLR) is a convenient and noninvasive inflammatory biomarker, and inflammation has been reported to be associated with prostate cancer (PCa). Our objective was to ascertain any possible correlation between PCa and MLR. Methods: We utilized data from the 1999-2020 cycles of the National Health and Nutrition Examination Survey (NHANES) regarding MLR and PCa. The independent associations of MLR and other inflammatory biomarkers (platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), system inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI)) with PCa was investigated using weighted multivariate logistic regression and generalized additive models. Receiver operating characteristic (ROC) curves were conducted to evaluate and contrast their diagnostic capabilities. Results: The analysis we conducted comprised 25,367 persons in total. The mean MLR was 0.31 ± 0.14. The prevalence of PCa was 3.1%. A positive association was found between MLR and PCa (OR = 2.28; 95% CI: 1.44, 3.62). According to the interaction tests, age, body mass index (BMI), hypertension, diabetes, and smoking status did not significantly impact the relationship between MLR and PCa (all p for interaction >0.05). ROC analysis showed that MLR had a stronger discriminative ability and accuracy in predicting PCa than other inflammatory biomarkers (NLR, SII, AISI, PLR, and SIRI). Conclusion: MLR might be better than other inflammatory biomarkers (NLR, SIRI, AISI, PLR, and SII) in predicting PCa. American adults who have elevated levels of MLR, NLR, PLR, SII, and AISI should be aware that they have a greater risk of PCa.

circPVT1 Inhibits the Proliferation and Aids in Prediction of the Prognosis of Bladder Cancer.

Background: Circular RNA PVT1 (circPVT1) is aberrantly expressed in several cancers, but its functional role and clinical relevance in bladder urothelial carcinoma (BLCA) remain unknown. This study aimed to identify the expression level of circPVT1 in BLCA and investigated its functional relevance with BLCA progression both in vitro and in vivo. Methods: GEPIA, UALCAN, and OncoLnc were referred to presented data. Quantitative real-time PCR (qPCR) was used for the measurement of transnational expression of genes in BLCA specimens and cell lines. Immunohistochemistry (IHC) and fluorescence in situ hybridization analysis (FISH) assays were performed to detect HER2 amplification, Pearson's correlation analysis to analyze the correlation between circPVT1 expression and clinical characteristics, Cox regression and K-M survival analyses to analyze prognostic factors. A nomogram was constructed for predicting prognosis. The proliferation of cells was measured by CCK-8 and colony formation assay, and the proliferation in vivo was evaluated using nude mouse models. qPCR was used to detect the expression of proliferation-related genes. Results: circPVT1 was but mRNA PVT1 was not significantly overexpressed in BLCA. A high circPVT1 expression was associated with a better survival and negative HER2, but not with age, gender, and T stage. circPVT1 was an independent prognostic factor for the overall survival of BLCA patients. Knocking down circPVT1 promoted BLCA proliferation in vitro and in vivo. Knocking down circPVT1 upregulated ERBB2, MKI67, and PCNA expression and downregulated TP53 expression, but exerted no influence on CCND1 and CCNB1 expression. Conclusion: circPVT1 is a tumor suppressor and novel prognostic biomarker for BLCA.

FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.

Protein function annotation and drug discovery often involve finding small molecule binders. In the early stages of drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing. While our recent ligand homology modeling (LHM)-machine learning VLS method FRAGSITE outperformed approaches that combined traditional docking to generate protein-ligand poses and deep learning scoring functions to rank ligands, a more robust approach that could identify a more diverse set of binding ligands is needed. Here, we describe FRAGSITE2 that shows significant improvement on protein targets lacking known small molecule binders and no confident LHM identified template ligands when benchmarked on two commonly used VLS datasets: For both the DUD-E set and DEKOIS2.0 set and ligands having a Tanimoto coefficient (TC) < 0.7 to the template ligands, the 1% enrichment factor (EF1% ) of FRAGSITE2 is significantly better than those for FINDSITEcomb2.0 , an earlier LHM algorithm. For the DUD-E set, FRAGSITE2 also shows better ROC enrichment factor and AUPR (area under the precision-recall curve) than the deep learning DenseFS scoring function. Comparison with the RF-score-VS on the 76 target subset of DEKOIS2.0 and a TC < 0.99 to training DUD-E ligands, FRAGSITE2 has double the EF1% . Its boosted tree regression method provides for more robust performance than a deep learning multiple layer perceptron method. When compared with the pretrained language model for protein target features, FRAGSITE2 also shows much better performance. Thus, FRAGSITE2 is a promising approach that can discover novel hits for protein targets. FRAGSITE2's web service is freely available to academic users at http://sites.gatech.edu/cssb/FRAGSITE2.

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study.

Aims: This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Methods: Adults with T2DM who were included in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2020 were the subjects of the current cross-sectional investigation. Low estimated glomerular filtration rate (eGFR) (< 60 mL/min/1.73 m2) or albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g) in T2DM patients were the diagnostic criteria for DKD. Weighted multivariable logistic regression models and generalized additive models were used to investigate the independent relationships between NLR levels with DKD, albuminuria, and low-eGFR. Additionally, we examined the relationships between DKD, albuminuria, and low-eGFR with other inflammatory markers, such as the aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), system inflammation response index (SIRI), and platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR). Their diagnostic capabilities were evaluated and contrasted using receiver operating characteristic (ROC) curves. Results: 44.65% of the 7,153 participants who were recruited for this study were males. DKD, albuminuria, and low-eGFR were prevalent in 31.76%, 23.08%, and 14.55% of cases, respectively. Positive correlations were seen between the NLR with the prevalences of DKD, albuminuria, and low-eGFR. Subgroup analysis and interaction tests revealed that the associations of NLR with DKD, albuminuria, and low-eGFR were not significantly different across populations. In addition, MLR, SII and SIRI showed positive associations with the prevalence of DKD. ROC analysis discovered that when compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may demonstrate more discriminatory power and accuracy in assessing the risk of DKD, albuminuria, and low-eGFR. Conclusion: Compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may serve as the more effective potential inflammatory marker for identifying the risk of DKD, albuminuria, and low-eGFR in US T2DM patients. T2DM patients with elevated levels of NLR, MLR, SII, and SIRI should be closely monitored for their potential risk to renal function.