Nomogram Model to Predict Acute Kidney Injury in Hospitalized Patients with Heart Failure.

Background: Acute kidney injury (AKI) is a common complication of acute heart failure (HF) that can prolong hospitalization time and worsen the prognosis. The objectives of this research were to ascertain independent risk factors of AKI in hospitalized HF patients and validate a nomogram risk prediction model established using those factors. Methods: Finally, 967 patients hospitalized for HF were included. Patients were randomly assigned to the training set (n = 677) or test set (n = 290). Least absolute shrinkage and selection operator (LASSO) regression was performed for variable selection, and multivariate logistic regression analysis was used to search for independent predictors of AKI in hospitalized HF patients. A nomogram prediction model was then developed based on the final identified predictors. The performance of the nomogram was assessed in terms of discriminability, as determined by the area under the receiver operating characteristic (ROC) curve (AUC), and predictive accuracy, as determined by calibration plots. Results: The incidence of AKI in our cohort was 19%. After initial LASSO variable selection, multivariate logistic regression revealed that age, pneumonia, D-dimer, and albumin were independently associated with AKI in hospitalized HF patients. The nomogram prediction model based on these independent predictors had AUCs of 0.760 and 0.744 in the training and test sets, respectively. The calibration plots indicate a strong concordance between the estimated AKI probabilities and the observed probabilities. Conclusions: A nomogram prediction model based on pneumonia, age, D-dimer, and albumin can help clinicians predict the risk of AKI in HF patients with moderate discriminability.

Biocatalytic Desymmetrization for the Atroposelective Synthesis of Axially Chiral Biaryls Using an Engineered Imine Reductase.

The enzymatic atroposelective synthesis of biaryl compounds is relatively rare, despite considerable attention received by biocatalysis in academic and industrial sectors. Imine reductases (IREDs) are an important class of enzymes that have been applied in the asymmetric synthesis of chiral amine building blocks. In this work, two IREDs (IR140 and IR189) were identified to catalyze the efficient desymmetrization of biaryls utilizing various amine donors. Further protein engineering enabled the identification of variants (IR189 M8-M9 and IR189 M13-M14) that are able to catalyze the formation of both (R) and (S) atropisomers in excellent yields and atroposelectivities for up to 24 examples (up to 99% ee and yield). The absolute configuration and rotational barriers were confirmed, and the reactions were readily enlarged to allow isolation of the atropisomeric products in 99% ee and 82% isolated yields. The optically pure biaryl amines were further derivatized into various synthetically useful atropisomers. To shed light on the molecular recognition mechanisms, molecular dynamics (MD) simulations were performed, offering plausible explanations for the improved atroposelectivities and enzymatic activities. The current strategy expands the scope of IRED-catalyzed synthesis of axially chiral biaryl amines, contributing significantly to the field of atroposelective biocatalysis.

Spontaneous assembly of a class of small molecule prodrugs directed by SN38.

Small molecule self-assembling prodrugs (SAPDs) are an emerging class of amphiphilic monomers that can aggregate into supramolecular nanostructures with high drug loading identical to that of the individual prodrug. Despite great progress in creating nanodrugs via nanoprecipitation, the direct self-assembly of small molecule SAPDs in aqueous solution remains challenging, as the proper hydrophilic-hydrophobic balance and intermolecular interactions have to be rationally considered. We report a class of small molecule SAPDs by conjugating the anticancer drug SN38 as the structure-directing component with various hydrophilic auxiliaries (i.e., oligo ethylene glycol (OEG) of different lengths, amino, and carboxyl groups) via a self-immolative disulfanyl-ethyl carbonate linker. Driven by π-π interactions between SN38 units, these SAPDs spontaneously assembled into well-defined fibrous nanostructures. Variations in hydrophilic domains can robustly regulate the hydrophobicity of SAPDs, as well as the morphologies and surface features of supramolecular filaments, subsequently influencing cellular internalization behaviors. Furthermore, our study also reveals that the parent drug can be efficiently and controllably released in the presence of glutathione (GSH), exhibiting high in vitro toxicity against colorectal cancer cells. In this work, we present a delicate platform to design small molecule SAPDs that can spontaneously self-assemble into supramolecular filamentous assemblies directed by aromatic interaction of the parent drugs, providing a new strategy to optimize supramolecular drug delivery systems.

Targeting lipid droplets and lipid droplet-associated proteins: a new perspective on natural compounds against metabolic diseases.

BACKGROUND: Lipid droplet (LD) is a metabolically active organelle, which changes dynamically with the metabolic state and energy requirements of cells. Proteins that either insert into the LD phospholipid monolayer or are present in the cytoplasm, playing a crucial role in lipid homeostasis and signaling regulation, are known as LD-associated proteins. METHODS: The keywords "lipid droplets" and "metabolic diseases" were used to obtain literature on LD metabolism and pathological mechanism. After searching databases including Scopus, OVID, Web of Science, and PubMed from 2013 to 2024 using terms like "lipid droplets", "lipid droplet-associated proteins", "fatty liver disease", "diabetes", "diabetic kidney disease", "obesity", "atherosclerosis", "hyperlipidemia", "natural drug monomers" and "natural compounds", the most common natural compounds were identified in about 954 articles. Eventually, a total of 91 studies of 10 natural compounds reporting in vitro or in vivo studies were refined and summarized. RESULTS: The most frequently used natural compounds include Berberine, Mangostin, Capsaicin, Caffeine, Genistein, Epigallocatechin-3-gallate, Chlorogenic acid, Betaine, Ginsenoside, Resveratrol. These natural compounds interact with LD-associated proteins and help ameliorate abnormal LDs in various metabolic diseases. CONCLUSION: Natural compounds involved in the regulation of LDs and LD-associated proteins hold promise for treating metabolic diseases. Further research into these interactions may lead to new therapeutic applications.

Quantum chemical studies of the reaction mechanisms of enzymatic CO2 conversion.

Enzymatic capture and conversion of carbon dioxide (CO2) into value-added chemicals are of great interest in the field of biocatalysis and have a positive impact on climate change. The quantum chemical methods, recognized as valuable tools for studying reaction mechanisms, have been widely employed in investigating the reaction mechanisms of the enzymes involved in CO2 utilization. In this perspective, we review the mechanistic studies of representative enzymes that are either currently used or have the potential for converting CO2, utilizing the quantum chemical cluster approach and the quantum mechanical/molecular mechanical (QM/MM) method. We begin by summarizing current trends in enzymatic CO2 conversion, followed by a brief description of the computational details of quantum chemical methods. Then, a series of representative examples of the computational modeling of biocatalytic CO2 conversion are presented, including the reduction of CO2 to C1 species (carbon monoxide and formate), and the fixation of CO2 to form aliphatic and aromatic carboxylic acids. The microscopic views of reaction mechanisms obtained from these studies are helpful in guiding the rational design of current enzymes and the discovery of novel enzymes with enhanced performance in converting CO2. Additionally, they provide key information for the de novo design of new-to-nature enzymes. To conclude, we present a perspective on the potential combination of machine learning with quantum description in the study of enzymatic conversion of CO2.

Unspecific peroxygenase enabled formation of azoxy compounds.

Enzymes are making a significant impact on chemical synthesis. However, the range of chemical products achievable through biocatalysis is still limited compared to the vast array of products possible with organic synthesis. For instance, azoxy products have rarely been synthesized using enzyme catalysts. In this study, we discovered that fungal unspecific peroxygenases are promising catalysts for synthesizing azoxy products from simple aniline starting materials. The catalytic features (up to 48,450 turnovers and a turnover frequency of 6.7 s-1) and substrate transformations (up to 99% conversion with 98% chemoselectivity) highlight the synthetic potential. We propose a mechanism where peroxygenase-derived hydroxylamine and nitroso compounds spontaneously (non-enzymatically) form the desired azoxy products. This work expands the reactivity repertoire of biocatalytic transformations in the underexplored field of azoxy compound formation reactions.

Enhancing reductive dechlorination of trichloroethylene in bioelectrochemical systems with conductive materials.

The incorporation of conductive materials to enhance electron transfer in bioelectrochemical systems (BES) is considered a promising approach. However, the specific effects and mechanisms of these materials on trichloroethylene (TCE) reductive dechlorination in BES remains are not fully understood. This study investigated the use of magnetite nanoparticles (MNP) and biochars (BC) as coatings on biocathodes for TCE reduction. Results demonstrated that the average dechlorination rates of MNP-Biocathode (122.89 µM Cl·d-1) and BC-Biocathode (102.88 µM Cl·d-1) were greatly higher than that of Biocathode (78.17 µM Cl·d-1). Based on MATLAB calculation, the dechlorination rate exhibited a more significantly increase in TCE-to-DCE step than the other dechlorination steps. Microbial community analyses revealed an increase in the relative abundance of electroactive and dechlorinating populations (e.g., Pseudomonas, Geobacter, and Desulfovibrio) in MNP-Biocathode and BC-Biocathode. Functional gene analysis via RT-qPCR showed the expression of dehalogenase (RDase) and direct electron transfer (DET) related genes was upregulated with the addition of MNP and BC. These findings suggest that conductive materials might accelerate reductive dechlorination by enhancing DET. The difference of physicochemical characteristics (e.g. particle size and specific surface area), electron transfer enhancement mechanism between MNP and BC as well as the reduction of Fe(III) by hydrogen may explain the superior dechlorination rate observed with MNP-Biocathode.

Jiao-tai-wan and its effective component-coptisine alleviate cognitive impairment in db/db mice through the JAK2/STAT3 signaling pathway.

BACKGROUND: Cognitive impairment (CI) is now well-accepted as a complication and comorbidity of diabetes mellitus (DM), becoming a serious medical and social problem. Jiao-tai-wan (JTW), one of noted traditional Chinese medicine (TCM), showed dual therapeutic effects on DM and CI. Nevertheless, the potential mechanism is unclear. PURPOSE: This study sought to investigate the mechanism how JTW protected against DM and CI and screen the active component in JTW. METHODS: Db/db mice were used as mouse models. Mice were treated by gavage with 0.9 % saline (0.1 mL/10g/d), low dose of JTW (2.4 g/kg/d) or high dose of JTW (4.8 g/kg/d) for 8 weeks separately. To access the effects of JTW, the levels of OGTT, HOMA-IR, blood lipids, inflammatory cytokines in serum and hippocampus were measured, behavioral tests were conducted, and histopathological changes were observed. The mechanism exploration was performed via network pharmacology, RT-qPCR, western blot, and immunofluorescence staining (IF). The impact and mechanism of coptisine in vitro were investigated using BV2 cells induced by LPS as cellular models. In vitro experiments were conducted in two parts. The first part comprised four groups: Control group, LPS group, LPS+LCOP group and LPS+HCOP group. The second part consisted of four groups: Control group, LPS group, LPS+HCOP group, and LPS+ Fed group. The western blot and RT-qPCR methods were used to examine the changes in biomarkers of the JAK2/STAT3 signaling pathways in BV2 cells. RESULTS: The results demonstrated that JTW could improve OGTT and HOMA-IR, reduce the serum levels of LDL-C, HDL-C, TG, and TC, restore neuronal dysfunction and synaptic plasticity, and decrease the deposition of Aß in the hippocampus. The findings from ELISA, IF, and RT-qPCR revealed that JTW could alleviate microglial activation and inflammatory status in vivo and coptisine could play the same role in vitro. Moreover, the changes of the JAK2/STAT3 signaling pathway in LPS-induced BV2 cells or hippocampus of db/db mice were distinctly reversed by coptisine or JTW, respectively. CONCLUSION: Our study suggested that JTW and its effective component coptisine could alleviate diabetes mellitus-related cognitive impairment, closely linked to the JAK2/STAT3 signaling pathway.

Establish TIIC signature score based the machine learning fusion in bladder cancer.

BACKGROUND: Bladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging. METHODS: We analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients. RESULTS: The study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations. CONCLUSION: This study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.

Association between composite dietary antioxidant index and epilepsy in American population: a cross-sectional study from NHANES.

BACKGROUND: Epilepsy is a major global health challenge, affecting approximately 50 million people across the globe and resulting in significant economic impacts on individuals and society. Oxidative stress is implicated in the pathogenesis of epilepsy, highlighting the potential of antioxidant-rich dietary patterns in offering preventive and protective benefits by mitigating oxidative stress. The Composite Dietary Antioxidant Index (CDAI) provides a measure for assessing dietary antioxidant intake, yet its link to epilepsy remains unexplored. METHODS: Our analysis utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning 2013 to 2018, including 20,180 screened participants. Weighted logistic regression models were employed to examine the association between the CDAI and epilepsy prevalence. Non-linear associations were explored through restricted cubic splines (RCS), and the relationships between individual antioxidant components within the CDAI and epilepsy were also assessed. RESULTS: After adjusting for potential confounders, a negative association between the CDAI and epilepsy was suggested (OR = 0.991; p = 0.087, 95% CI [0.819,1.014]). Stratification of CDAI into quartiles revealed a significantly reduced risk of epilepsy in higher CDAI quartiles (Q3 and Q4) compared to the lowest quartile (Q1) (Q3: OR = 0.419; p = 0.030, 95% CI [0.192, 0.914]; Q4: OR = 0.421; p = 0.004, 95% CI [0.239, 0.742]), with a significant trend observed across quartiles (p for trend = 0.013). RCS analysis suggested a nonlinear association between CDAI levels and epilepsy (non-linear p = 0.049), which, however, was not statistically significant after full adjustment (non-linear p = 0.103). Additionally, significant negative correlations with epilepsy were observed for vitamin A and zinc (Vitamin A: OR = 0.999; p = 0.012, 95% CI [0.998, 1.000]; Zinc: OR = 0.931; p = 0.042, 95% CI [0.869, 0.997]). CONCLUSIONS: Our research indicates a correlation where higher CDAI levels correspond to a reduced risk of epilepsy. Therefore, embracing a diet rich in antioxidants could be beneficial in preventing epilepsy. This finding holds considerable potential for shaping future strategies in both epilepsy prevention and treatment.