The role of microorganisms in phosphorus cycling at river-lake confluences: Insights from a study on microbial community dynamics.

River-lake confluences are key zones in the river-lake network, essential for managing contaminant transport and transformation. However, the role of biogeochemical transformations, particularly in phosphorus (P) dynamics, has been underexplored. As a result, this study looks into the dynamics of microbial communities and how important microbes are to the cycling of P. It was revealed that microorganisms contribute differently to phosphorus cycling in different hydraulic regions. Regions with higher-velocity and finer sediment showed increased microbial diversity and enhanced capabilities for organic phosphorus (OP) mineralization and inorganic phosphorus (IP) solubilization due to lower bio-available P (bio-P) concentrations. In areas characterized by flow deflection (FD), flow stagnation (FST), and flow separation (FSE), distinct P fraction distributions were observed: Total phosphorus (TP) and bio-P were found to be more abundant in the FST and FD regions, but residual phosphorus (Res-P) and calcium phosphorus (Ca-P) were more prevalent in the FSE region. Sediment characteristics, including P species like aluminum-phosphorus (Al-P), OP, iron-associate phosphorus (BD-P), and sediment mid-diameter (D50), significantly influence microbial community composition. These results improve our comprehension of the distribution of microbial community distribution and its role in the phosphorus cycle at river-lake confluence, providing useful provide valuable information for managing river-lake confluences and protecting aquatic ecosystems.

Kukoamine A alleviates nephrolithiasis by inhibiting endogenous oxalate synthesis via the IL-6/JAK/STAT3/DAO signaling pathway.

BACKGROUND: The recurrent nature and socioeconomic burden of nephrolithiasis demand effective treatments. Delineating the crosstalk between inflammatory processes and the endogenous oxalate metabolism pathway, which underpins nephrolithiasis pathogenesis, is essential for advancing treatment strategies. PURPOSE: We aim to screen therapeutic Chinese herbal remedies and their bioactive constituents for kidney stone treatment using a fruit fly model, followed by efficacy and mechanism validation in a rodent nephrolithiasis model as well as in vitro human cell culture model. STUDY DESIGN AND METHODS: We developed a fruit fly model to screen for efficient traditional Chinese medicine herbs and their active compounds for kidney stone treatment. Candidate active compounds from efficient herbs were separated and identified by solid-phase chromatography coupled with LC-MS analysis. Fruit fly genetic tools were employed to manipulate the expression of related genes to explore the therapeutic mechanisms of the Lycii Cortex and kukoamine A (KuA). To confirm the therapeutic effects and mechanisms of KuA for mammalian nephrolithiasis, mouse model of glyoxylate-induced kidney stone and human renal tubular cells were used. The therapeutic role of kukoamine A in nephrolithiasis was evaluated by assessing tubular injury, crystal deposition, and adhesion. The level of expression and phosphorylation in cells and mice was assessed using RT-qPCR and western blot. RESULTS: Our findings indicate that Lycii Cortex potently inhibits calcium oxalate stone formation via activation of the JNK/Upd3/JAK/STAT signaling cascade, resulting in diminished endogenous oxalate synthesis by downregulating D-amino acid oxidase (DAO) gene expression, predominantly in fruit fly Malpighian tube stellate cells. KuA was identified as the principal bioactive constituent mediating these effects. Both mouse models and human cell assays confirmed KuA's efficacy in preventing calcium oxalate nephrolithiasis in mammals, through hepatic JAK/STAT3 pathway activation and upregulation of IL-6, culminating in reduced urinary crystal deposition. CONCLUSION: Our research underscores the potential of kukoamine A as a lead compound in treating nephrolithiasis and reveals the interplay between the IL-6/JAK/STAT3 inflammatory pathway and endogenous oxalate metabolism in nephrolithiasis pathogenesis. Additionally, it highlights the utility of the fruit fly model as a powerful tool for deciphering the therapeutic mechanisms of traditional Chinese herbs.

The increase of particle size shifts the biogeochemical cycle functions of mineral-associated microorganisms and weakens the mineral-associated organic carbon sink in mangrove soils.

Mineral-organic matter-microbe interactions greatly impact the biogeochemical processes and biodiversity in soils. An increasing trend of particle size (PS) in mangrove soils has been observed because of the relative sea level rise. However, the impacts of PS increase on the microbial biogeochemical functions and carbon sink in the mineral-associated microcosms are exceedingly nebulous. This work showed a remarkable difference in the communities of mineral-associated microorganisms (MMOs) in various PS fractions. Heavy metal contents and urease activity were the factors that mostly driven the MMO community variation in different PS fractions. Large PS fraction attenuated the stability of MMO communities according to the co-occurrence network characteristics. The PS increase significantly (P < 0.05) lowered the gene abundances for carbon input (e.g., carbon fixation) and raised the gene abundances for carbon loss (e.g., aerobic respiration). Combined with the significant decrease of mineral-associated organic carbon (MOC) in large PS fraction (P < 0.05), this work inferred that the PS increase could weaken the MOC sink partially due to the MMO function shift for carbon cycle. The current work indicated unhealthy changes of MMO communities and MOC storage in mangrove soils, and PS was of significance as an indicator for predicting the carbon sink function, especially for the stable form, such as MOC, in the soils of mangroves under the ecological background of climate migration. IMPORTANCE: Carbon with stable forms, such as mineral-associated organic carbon (MOC), is crucial for the sink capabilities in mangrove soils, and mineral-associated microorganisms (MMOs) are important players for the formation and metabolism of MOC. Therefore, the future successions of the MMO functions and MOC contents under the background of climate change are of value for a deeper understanding of mangrove ecology. The relative sea level rise caused by the global warming results in the increase of mangrove soil particle size (PS), which provides distinct microcosms for MMOs and MOC. However, the responses of MMO functions and MOC content to the PS increase of mangrove soils are unknown. The current study aims to reveal the succession regulations of MMO functions and their potential ecological impacts for the storages of MOC in different PS fractions, therefore widening our knowledge of future function migration and promoting the research development of mangrove.

In-host modeling of dengue virus and non-structural protein 1 and the effects of ivermectin in patients with acute dengue fever.

The increased incidence of dengue poses a substantially global public health challenge. There are no approved antiviral drugs to treat dengue infections. Ivermectin, an old anti-parasitic drug, had no effect on dengue viremia, but reduced the dengue non-structural protein 1 (NS1) in a clinical trial. This is potentially important, as NS1 may play a causal role in the pathogenesis of severe dengue. This study established an in-host model to characterize the plasma kinetics of dengue virus and NS1 with host immunity and evaluated the effects of ivermectin, using a population pharmacokinetic-pharmacodynamic (PK-PD) modeling approach, based on two studies in acute dengue fever: a placebo-controlled ivermectin study in 250 adult patients and an ivermectin PK-PD study in 24 pediatric patients. The proposed model described adequately the observed ivermectin pharmacokinetics, viral load, and NS1 data. Bodyweight was a significant covariate on ivermectin pharmacokinetics. We found that ivermectin reduced NS1 with an EC50 of 67.5 µg/mL. In silico simulations suggested that ivermectin should be dosed within 48 h after fever onset, and that a daily dosage of 800 µg/kg could achieve substantial NS1 reduction. The in-host dengue model is useful to assess the drug effect in antiviral drug development for dengue fever.

Population pharmacokinetics of amodiaquine and piperaquine in African pregnant women with uncomplicated Plasmodium falciparum infections.

Artemisinin-based combination therapy (ACT) is the first-line recommended treatment for uncomplicated malaria. Pharmacokinetic (PK) properties in pregnant women are often based on small studies and need to be confirmed and validated in larger pregnant patient populations. This study aimed to evaluate the PK properties of amodiaquine and its active metabolite, desethylamodiaquine, and piperaquine in women in their second and third trimester of pregnancy with uncomplicated P. falciparum infections. Eligible pregnant women received either artesunate-amodiaquine (200/540 mg daily, n = 771) or dihydroartemisinin-piperaquine (40/960 mg daily, n = 755) for 3 days (NCT00852423). Population PK properties were evaluated using nonlinear mixed-effects modeling, and effect of gestational age and trimester was evaluated as covariates. 1071 amodiaquine and 1087 desethylamodiaquine plasma concentrations, and 976 piperaquine plasma concentrations, were included in the population PK analysis. Amodiaquine concentrations were described accurately with a one-compartment disposition model followed by a two-compartment disposition model of desethylamodiaquine. The relative bioavailability of amodiaquine increased with gestational age (1.25% per week). The predicted exposure to desethylamodiaquine was 2.8%-32.2% higher in pregnant women than that reported in non-pregnant women, while day 7 concentrations were comparable. Piperaquine concentrations were adequately described by a three-compartment disposition model. Neither gestational age nor trimester had significant impact on the PK of piperaquine. The predicted exposure and day 7 concentrations of piperaquine were similar to that reported in non-pregnant women. In conclusion, the exposure to desethylamodiaquine and piperaquine was similar to that in non-pregnant women. Dose adjustment is not warranted for women in their second and their trimester of pregnancy.

Physiologically based pharmacokinetic modeling in obesity: applications and challenges.

INTRODUCTION: Rising global obesity rates pose a threat to people's health. Obesity causes a series of pathophysiologic changes, making the response of patients with obesity to drugs different from that of nonobese, thus affecting the treatment efficacy and even leading to adverse events. Therefore, understanding obesity's effects on pharmacokinetics is essential for the rational use of drugs in patients with obesity. AREAS COVERED: Articles related to physiologically based pharmacokinetic (PBPK) modeling in patients with obesity from inception to October 2023 were searched in PubMed, Embase, Web of Science and the Cochrane Library. This review outlines PBPK modeling applications in exploring factors influencing obesity's effects on pharmacokinetics, guiding clinical drug development and evaluating and optimizing clinical use of drugs in patients with obesity. EXPERT OPINION: Obesity-induced pathophysiologic alterations impact drug pharmacokinetics and drug-drug interactions (DDIs), altering drug exposure. However, there is a lack of universal body size indices or quantitative pharmacology models to predict the optimal for the patients with obesity. Therefore, dosage regimens for patients with obesity must consider individual physiological and biochemical information, and clinically individualize therapeutic drug monitoring for highly variable drugs to ensure effective drug dosing and avoid adverse effects.

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameters for measuring enzyme catalytic efficiency, are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km and further improve the prediction performance, we propose a universal pretrained multitask deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. Through testing on the same kcat and Km test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting kcat, improving ca. 14.6% and 7.6% compared to the DLKcat and UniKP models, and it achieved the Pearson coefficient of 0.777 for predicting Km, improving ca. 34.9% and 53.3% compared to the Kroll_model and UniKP models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown that MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model, which can be accessed at http://mathtc.nscc-tj.cn/mpek.

Accurate Density Prediction of Sesquiterpenoid HEDFs and the Multiproperty Computing Server SesquiterPre.

Accurate and rapid evaluation of density is crucial for evaluating the packing and combustion characteristics of high-energy-density fuels (HEDFs). This parameter is pivotal in the selection of high-performance HEDFs. Our study leveraged a polycyclic compound density data set and quantum chemical (QC) descriptors to establish a correlation with the target properties using the XGBoost algorithm. We utilized a recursive feature elimination method to simplify the model and developed a concise and interpretable density prediction model incorporating only six QC descriptors. The model demonstrated robust performance, achieving coefficients of determination (R 2) of 0.967 and 0.971 for internal and external test sets, respectively, and root-mean-square errors (RMSE) of 0.031 and 0.027 g/cm3, respectively. Compared to the other two mainstream methods, the marginal discrepancy between the predicted and actual molecular densities underscores the model's superior predictive ability and more usefulness for energy density calculation. Furthermore, we developed a web server (SesquiterPre, https://sespre.cmdrg.com/#/) that can simultaneously calculate the density, enthalpy of combustion, and energy density of sesquiterpenoid HEDFs, which greatly facilitates the use of researchers and is of great significance for accelerating the design and screening of novel sesquiterpenoid HEDFs.

Nanocluster-agminated amorphous cobalt nanofilms for highly selective electroreduction of nitrate to ammonia.

Developing highly-efficient electrocatalysts for the nitrate reduction reaction (NITRR) is a persistent challenge. Here, we present the successful synthesis of 14 amorphous/low crystallinity metal nanofilms on three-dimensional carbon fibers (M-NFs/CP), including Al, Ti, Mn, Fe, Co, Ni, Cu, Zn, Ag, In, Sn, Pb, Au, or Bi, using rapid thermal evaporation. Among these samples, our study identifies the amorphous Co nanofilm with fine agglomerated Co clusters as the optimal electrocatalyst for NITRR in a neutral medium. The resulting Co-NFs/CP exhibits a remarkable Faradaic efficiency (FENH3) of 91.15 % at - 0.9 V vs RHE, surpassing commercial Co foil (39 %) and Co powder (20 %), despite sharing the same metal composition. Furthermore, during the electrochemical NITRR, the key intermediates on the surface of the Co-NFs/CP catalyst were detected by in situ Fourier-transform infrared (FTIR) spectroscopy, and the possible reaction ways were probed by Density functional theory (DFT) calculations. Theoretical calculations illustrate that the abundant low-coordinate Co atoms of Co-NFs/CP could enhances the adsorption of *NO3 intermediates compared to crystalline Co. Additionally, the amorphous Co structure lowers the energy barrier for the rate-determining step (*NH2→*NH3). This work opens a new avenue for the controllable synthesis of amorphous/low crystallinity metal nano-catalysts for various electrocatalysis reaction applications.

Small extracellular vesicle CA1 as a promising diagnostic biomarker for nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC), a malignant cancer originating from the epithelial cells of the nasopharynx, presents diagnostic challenges with current methods such as plasma Epstein-Barr virus (EBV) DNA testing showing limited efficacy. This study focused on identifying small extracellular vesicle (sEV) proteins as potential noninvasive biomarkers to enhance NPC diagnostic accuracy. We isolated sEVs from plasma and utilized 4D label-free proteomics to identify differentially expressed proteins (DEPs) among healthy controls (NC = 10), early-stage NPC (E-NPC = 10), and late-stage NPC (L-NPC = 10). Eighteen sEV proteins were identified as potential biomarkers. Subsequently, parallel reaction monitoring (PRM) proteomic analysis preliminarily confirmed sEV carbonic anhydrase 1 (CA1) as a highly promising biomarker for NPC, particularly in early-stage diagnosis (NC = 15; E-NPC = 10; L-NPC = 15). To facilitate this, we developed an automated, high-throughput and highly sensitive CA1 immune-chemiluminescence chip technology characterized by a broad linear detection range and robust controls. Further validation in an independent retrospective cohort (NC = 89; E-NPC = 39; L-NPC = 172) using this technology confirmed sEV CA1 as a reliable diagnostic biomarker for NPC (AUC = 0.9809) and E-NPC (AUC = 0.9893), independent of EBV-DNA testing. Notably, sEV CA1 exhibited superior diagnostic performance compared to EBV-DNA, with a significant incremental net reclassification improvement of 27.61 % for NPC and 72.11 % for E-NPC detection. Thus, this study identifies sEV CA1 as an innovative diagnostic biomarker for NPC and E-NPC independent of EBV-DNA. Additionally, it establishes an immune-chemiluminescence chip technology for the detection of sEV CA1 protein, paving the way for further validation and clinical application.

QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning.

Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/ .

Long-term prognosis of patients with gallbladder carcinoma after curative-intent resection based on changes in the ratio of carbohydrate antigen 19-9 to total bilirubin (CA19-9/TB): a multicenter retrospective cohort study.

BACKGROUND: The prognostic value of carbohydrate antigen 19-9 (CA19-9) is known to be affected by elevated bilirubin levels in patients with gallbladder carcinoma (GBC). The clinical significance of changes in the ratio of CA19-9 levels to total bilirubin (TB) levels in patients with GBC after curative-intent resection remains unknown. The aim of this study was to determine the prognostic value of changes in preoperative and postoperative CA19-9/TB ratio in these patients. METHODS: Prospectively collected data on consecutive patients who underwent curative-intent resection for GBC between January 2015 and December 2020 stored in a multicenter database from 10 hospitals were analyzed in this retrospective cohort study. Based on the adjusted CA19-9 defined as the ratio of CA19-9 to TB, and using 2×10 3  U/µmol as the upper normal value, patients were divided into a normal group (with normal preoperative and postoperative adjusted CA19-9), a normalization group (with abnormal preoperative but normal postoperative adjusted CA19-9), and a non-normalization group (with abnormal postoperative adjusted CA19-9). The primary outcomes were overall survival (OS) and recurrence-free survival (RFS). The log-rank test was used to compare OS and RFS among the groups. The Cox regression model was used to determine factors independently associated with OS and RFS. RESULTS: The normal group ( n =179 patients) and the normalization group ( n =73 patients) had better OS and RFS than the non-normalization group ( n =65 patients) (the 3-year OS rates 72.0%, 58.4% and 24.2%, respectively; the RFS rates 54.5%, 25.5% and 11.8%, respectively; both P <0.001). There were no significant differences between the normal and the normalization groups in OS and RFS (OS, P =0.255; RFS, P =0.130). Cox regression analysis confirmed that the non-normalization group was independently associated with worse OS and RFS. Subgroup analysis revealed that the non-normalization group of patients who received adjuvant therapy had significantly improved OS and RFS as compared to those who did not receive adjuvant therapy (OS, P =0.025; RFS, P =0.003). CONCLUSIONS: Patients with GBC who underwent curative-intent surgical resection with postoperative abnormal levels of adjusted CA19-9 (the CA19-9/TB ratio) were associated with poorer long-term survival outcomes. Adjuvant therapy after surgery improved the long-term outcomes of these patients.

Water masses influence the variation of microbial communities in the Yangtze River Estuary and its adjacent waters.

The Yangtze River estuary (YRE) are strongly influenced by the Kuroshio and terrigenous input from rivers, leading to the formation of distinct water masses, however, there remains a limited understanding of the full extent of this influence. Here the variation of water masses and bacterial communities of 58 seawater samples from the YRE and its adjacent waters were investigated. Our findings suggested that there were 5 water masses in the studied area: Black stream (BS), coastal water in the East China Sea (CW), nearshore mixed water (NM), mixed water in the middle and deep layers of the East China Sea (MM), and deep water blocks in the middle of the East China Sea (DM). The CW mass harbors the highest alpha diversity across all layers, whereas the NM mass exhibits higher diversity in the surface layer but lower in the middle layers. Proteobacteria was the most abundant taxa in all water masses, apart from that, in the surface layer masses, Cyanobacterium, Bacteroidota, and Actinobacteriota were the highest proportion in CW, while Bacteroidota and Actinobacteriota were the highest proportion in NM and BS; in the middle layer, Bacteroidota and Actinobacteriota were dominant phylum in CW and BS masses, but Cyanobacterium was main phylum in NM mass; in the bottom layer, Bacteroidota and Actinobacteriota were the dominant phylum in CW, while Marininimicrobia was the dominated phylum in DM and MM masses. Network analysis suggests water masses have obvious influence on community topological characteristics, moreover, community assembly across masses also differ greatly. Taken together, these results emphasized the significant impact of water masses on the bacterial composition, topological characteristics and assembly process, which may provide a theoretical foundation for predicting alterations in microbial communities within estuarine ecosystems under the influence of water masses.

A Phase 1, Randomized, Double-Blind, Placebo-Controlled, Single Ascending Dose Study to Evaluate the Pharmacokinetics, Immunogenicity, Safety, and Tolerability After Subcutaneous Administration of Tozorakimab in Healthy Chinese Participants.

Tozorakimab is a high-affinity human immunoglobulin G1 monoclonal antibody that neutralizes interleukin (IL)-33, an IL-1 family cytokine. This phase 1, single-center, randomized, double-blind, placebo-controlled, single ascending dose study (NCT05070312) evaluated tozorakimab in a healthy Chinese population. Outcomes included the characterization of the pharmacokinetic (PK) profile and immunogenicity of tozorakimab. Safety outcomes included treatment-emergent adverse events (TEAEs) and clinical laboratory, electrocardiogram, and vital sign parameters. Healthy, non-smoking, male, and female Chinese participants aged 18-45 years with a body mass index 19-24 kg/m2 were enrolled. In total, 36 participants across 2 cohorts of 18 participants were randomized 2:1 to receive a single subcutaneous dose of tozorakimab (300 mg [2 mL] or 600 mg [4 mL]) or matching placebo (2 or 4 mL). Tozorakimab showed dose-dependent serum PK concentrations with an approximate monophasic distribution in serum over time and a maximum observed peak concentration of 20.1 and 33.7 µg/mL in the 300- and 600-mg cohorts, respectively. No treatment-emergent anti-drug antibodies for tozorakimab were observed in any of the participants. There were no clinically relevant trends in the occurrence of TEAEs across the treatment groups. There were no clinically relevant trends over time in clinical laboratory (hematology, clinical chemistry, and urinalysis), electrocardiogram, or vital sign parameters in any treatment group. Overall, tozorakimab demonstrated dose-dependent systemic exposure in healthy Chinese participants and was well tolerated, with no safety concerns identified in this study.

Medication adherence framework: A population-based pharmacokinetic approach and its application in antimalarial treatment assessments.

We reported here on the development of a pharmacometric framework to assess patient adherence, by using two population-based approaches - the percentile and the Bayesian method. Three different dosing strategies were investigated in patients prescribed a total of three doses; (1) non-observed therapy, (2) directly observed administration of the first dose, and (3) directly observed administration of the first two doses. The percentile approach used population-based simulations to derive optimal concentration percentile cutoff values from the distribution of simulated drug concentrations at a specific time. This was done for each adherence scenario and compared to full adherence. The Bayesian approach calculated the posterior probability of each adherence scenario at a given drug concentration. The predictive performance (i.e., Youden index, receiver operating characteristic [ROC] curve) of both approaches were highly influenced by sample collection time (early was better) and interindividual variability (smaller was better). The complexity of the structural model and the half-life had a minimal impact on the predictive performance of these methods. The impact of the assay limitation (LOQ) on the predictive performance was relatively small if the fraction of LOQ data was less than 20%. Overall, the percentile method performed similar or better for adherence predictions compared to the Bayesian approach, with the latter showing slightly better results when investigating the adherence to the last dose only. The percentile approach showed acceptable adherence predictions (area under ROC curve > 0.74) when sampling the antimalarial drugs piperaquine at day 7 postdose and lumefantrine at day 3 postdose (i.e., 12 h after the last dose). This could be a highly useful approach when evaluating programmatic implementations of preventive and curative antimalarial treatment programs in endemic areas.

Realizing ampere-level CO2 electrolysis at low voltage over a woven network of few-atom-layer ultralong silverene nanobelts with ultrahigh aspect ratio by pairing with formaldehyde oxidation.

The development of advanced multi-functional electrocatalysts and their industrial operation on paired electrocatalysis systems presents a promising avenue for the gradual penetration of renewable energy into practical production. Herein, a self-supported conductive network of silverene nanobelts (Ag-ene NBs) was delicately assembled (Ag-NB-NWs), in which ultralong and few-atom-layer Ag-ene NBs with a high edge-to-facet ratio were interconnected, serving as "superreactors" for electron transfer and mass transport during the reaction. Such superstructures as electrocatalysts delivered an unparalleled performance toward the CO2-to-CO conversion with exclusively high faradaic efficiency (FE) and partial current densities of up to 1 A cm-2. Remarkably, the membrane electrode assembly (MEA) cell with Ag-NB-NWs as the cathode was capable of ultrastable and continuous operation for over 240 h at 0.4 A with ∼100% selectivity. More importantly, by further using Ag-NB-NWs as a bifunctional electrocatalyst, a record-low voltage overall CO2 electrolysis system coupling cathodic CO2 reduction with anodic formaldehyde oxidation in MEA cell was performed to achieve concurrent feed gas generation and formate production, substantially improving electrochemical techno-economic feasibility.

Distinct Assembly Processes Structure Planktonic Bacterial Communities Among Near- and Offshore Ecosystems in the Yangtze River Estuary.

The estuarine system functions as natural filters due to its ability to facilitate material transformation, planktonic bacteria play a crucial role in the cycling of complex nutrients and pollutants within estuaries, and understanding the community composition and assembly therein is crucial for comprehending bacterial ecology within estuaries. Despite extensive investigations into the composition and community assembly of two bacterial fractions (free-living, FLB; particle-attached, PAB), the process by which bacterioplankton communities in these two habitats assemble in the nearshore and offshore zones of estuarine ecosystems remains poorly understood. In this study, we conducted sampling in the Yangtze River Estuary (YRE) to investigate potential variations in the composition and community assembly of FLB and PAB in nearshore and offshore regions. We collected 90 samples of surface, middle, and bottom water from 16 sampling stations and performed 16S rRNA gene amplicon analysis along with environmental factor measurements. The results unveiled that the nearshore communities demonstrated significantly greater species richness and Chao1 indices compared to the offshore communities. In contrast, the nearshore communities had lower values of Shannon and Simpson indices. When compared to the FLB, the PAB exhibit a higher level of biodiversity and abundance. However, no distinct alpha and beta diversity differences were observed between the bottom, middle, and surface water layers. The community assembly analysis indicated that nearshore communities are predominantly shaped by deterministic processes, particularly due to heterogeneous selection of PAB; In contrast, offshore communities are governed more by stochastic processes, largely due to homogenizing dispersal of FLB. Consequently, the findings of this study demonstrate that nearshore and PAB communities exhibit higher levels of species diversity, while stochastic and deterministic processes exert distinct influences on communities among near- and offshore regions. This study further sheds new light on our understanding of the mechanisms governing bacterial communities in estuarine ecosystems.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics.

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Quantitative Predictive Studies of Multiple Biological Activities of TRPV1 Modulators.

TRPV1 channel agonists and antagonists, which have powerful analgesic effects without the addictive qualities associated with traditional analgesics, have become a focus area for the development of novel analgesics. In this study, quantitative structure-activity relationship (QSAR) models for three bioactive endpoints (Ki, IC50, and EC50) were successfully constructed using four machine learning algorithms: SVM, Bagging, GBDT, and XGBoost. These models were based on 2922 TRPV1 modulators and incorporated four types of molecular descriptors: Daylight, E-state, ECFP4, and MACCS. After the rigorous five-fold cross-validation and external test set validation, the optimal models for the three endpoints were obtained. For the Ki endpoint, the Bagging-ECFP4 model had a Q2 value of 0.778 and an R2 value of 0.780. For the IC50 endpoint, the XGBoost-ECFP4 model had a Q2 value of 0.806 and an R2 value of 0.784. For the EC50 endpoint, the SVM-Daylight model had a Q2 value of 0.784 and an R2 value of 0.809. These results demonstrate that the constructed models exhibit good predictive performance. In addition, based on the model feature importance analysis, the influence between substructure and biological activity was also explored, which can provide important theoretical guidance for the efficient virtual screening and structural optimization of novel TRPV1 analgesics. And subsequent studies on novel TRPV1 modulators will be based on the feature substructures of the three endpoints.