Clinical, Genomic, and Transcriptomic Characteristics of Patients with Metastatic Renal Cell Carcinoma Who Developed Thromboembolic Events.

Thromboembolic events (TE) are a common complication in patients with metastatic renal cell carcinoma (mRCC) and are associated with poorer clinical outcomes. However, the incidence of TE and clinical and genomic characteristics of patients with mRCC who develop this complication are poorly understood. Herein, we describe the incidence and clinical features of patients with mRCC with or without TE at our institution, and examine their association with the underlying genomic and transcriptomic characteristics of the tumor. This retrospective study included all consecutive cases of mRCC seen at our institution. A CLIA-certified lab performed tumor genomics and transcriptomics. Patients were classified based on the presence of a TE within the first year of diagnosis. Three hundred and seventy patients with mRCC were included in the study. TE was seen in 11% (42) of the patients. Patients with favorable International mRCC Database Consortium (IMDC) risk were less likely to develop a TE. In contrast, patients receiving combination treatment with a tyrosine kinase inhibitor (TKI) and an immune checkpoint inhibitor were more likely to develop a TE. No difference in overall survival among patients with or without TE was observed (52 vs. 55 months; HR 0.85, 95% CI 0.5574-1.293, p = 0.24). The most upregulated pathways in mRCC with TEs versus those without were the xenobiotic metabolism and mTORC1 signaling pathways. Our findings suggest potential biomarkers that, after external validation, could be used to better select patients who would benefit from prophylactic anticoagulation.

Dehydrogenative cyclization of 2-arylbenzoic acid and 2-arylbenzamide with hydrogen evolution in a photoelectrochemical cell.

This paper describes photoelectrochemical dehydrogenative cyclization of 2-arylbenzoic acid and 2-arylbenzamide in a PEC cell consisting of a mesoporous WO3 photoanode and Pt cathode. The cyclization reaction is effectively driven by this PEC system at room temperature with blue LED irradiation under external oxidant- and metal-free conditions, delivering a series of benzolactones and benzolactams in up to 95% isolated yields. Meanwhile, hydrogen is released as the only byproduct of this process.

The current therapeutic cancer vaccines landscape in non-small cell lung cancer.

Currently, conventional immunotherapies for the treatment of non-small cell lung cancer (NSCLC) have low response rates and benefit only a minority of patients, particularly those with advanced disease, so novel therapeutic strategies are urgent deeded. Therapeutic cancer vaccines, a form of active immunotherapy, harness potential to activate the adaptive immune system against tumor cells via antigen cross-presentation. Cancer vaccines can establish enduring immune memory and guard against recurrences. Vaccine-induced tumor cell death prompts antigen epitope spreading, activating functional T cells and thereby sustaining a cancer-immunity cycle. The success of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rendered cancer vaccines a promising avenue, especially when combined with immunotherapy or chemoradiotherapy for NSCLC. This review delves into the intricate antitumor immune mechanisms underlying therapeutic cancer vaccines, enumerates the tumor antigen spectrum of NSCLC, discusses different cancer vaccines progress and summarizes relevant clinical trials. Additionally, we analyze the combination strategies, current limitations, and future prospects of cancer vaccines in NSCLC treatment, aiming to offer fresh insights for their clinical application in managing NSCLC. Overall, cancer vaccines offer promising potential for NSCLC treatment, particularly combining with chemoradiotherapy or immunotherapy could further improve survival in advanced patients. Exploring inhaled vaccines or prophylactic vaccines represents a crucial research avenue.

Dune Morphology Classification and Dataset Construction Method Based on Unmanned Aerial Vehicle Orthoimagery.

Dunes are the primary geomorphological type in deserts, and the distribution of dune morphologies is of significant importance for studying regional characteristics, formation mechanisms, and evolutionary processes. Traditional dune morphology classification methods rely on visual interpretation by humans, which is not only time-consuming and inefficient but also subjective in classification judgment. These issues have impeded the intelligent development of dune morphology classification. However, convolutional neural network (CNN) models exhibit robust feature representation capabilities for images and have achieved excellent results in image classification, providing a new method for studying dune morphology classification. Therefore, this paper summarizes five typical dune morphologies in the deserts of western Inner Mongolia, which can be used to define and describe most of the dune types in Chinese deserts. Subsequently, field surveys and the experimental collection of unmanned aerial vehicle (UAV) orthoimages for different dune types were conducted. Five different types of dune morphology datasets were constructed through manual segmentation, automatic rule segmentation, random screening, and data augmentation. Finally, the classification of dune morphologies and the exploration of dataset construction methods were conducted using the VGG16 and VGG19 CNN models. The classification results of dune morphologies were comprehensively analyzed using different evaluation metrics. The experimental results indicate that when the regular segmentation scale of UAV orthoimages is 1024 × 1024 pixels with an overlap of 100 pixels, the classification accuracy, precision, recall, and F1-Score of the VGG16 model reached 97.05%, 96.91%, 96.76%, and 96.82%, respectively. The method for constructing a dune morphology dataset from automatically segmented UAV orthoimages provides a reference value for the study of large-scale dune morphology classification.

PSAR-SR: Patches separation and artifacts removal for improving super-resolution networks.

The success of the ClassSR has led to a strategy of decomposing images being used for large image SR. The decomposed image patches have different recovery difficulties. Therefore, in ClassSR, image patches are reconstructed by different networks to greatly reduce the computational cost. However, in ClassSR, the training of multiple sub-networks inevitably increases the training difficulty. Furthermore, decomposing images with overlapping not only increases the computational cost but also inevitably produces artifacts. To address these challenges, we propose an end-to-end general framework, named patches separation and artifacts removal SR (PSAR-SR). In PSAR-SR, we propose an image information complexity module (IICM) to efficiently determine the difficulty of recovering image patches. Then, we propose a patches classification and separation module (PCSM), which can dynamically select an appropriate SR path for image patches of different recovery difficulties. Moreover, we propose a multi-attention artifacts removal module (MARM) in the network backend, which can not only greatly reduce the computational cost but also solve the artifacts problem well under the overlapping-free decomposition. Further, we propose two loss functions - threshold penalty loss (TP-Loss) and artifacts removal loss (AR-Loss). TP-Loss can better select appropriate SR paths for image patches. AR-Loss can effectively guarantee the reconstruction quality between image patches. Experiments show that compared to the leading methods, PSAR-SR well eliminates artifacts under the overlapping-free decomposition and achieves superior performance on existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN and CAMixerSR). Moreover, PSAR-SR saves 53%-65% FLOPs in computational cost far beyond the leading methods. The code will be made available: https://github.com/dywang95/PSAR-SR.

Construct Validity, Longitudinal Measurement Invariance, Incremental Validity, and Predictive Validity of the Original Grit Scale in Chinese Young Adults.

Although many studies have attempted to validate grit scales because of the construct's popularity, most have considered the shorter rather than the longer Original Grit Scale (Grit-O). We examined the Grit-O's construct validity, longitudinal measurement invariance, incremental validity for academic performance, and longitudinal predictive validity for subjective well-being among young Chinese. We used a cross-sectional sample of 3,322 college students and a longitudinal sample of 1,884 college students, tested twice over 10 months. The first-order factor model fit the data better than other models and showed partial configural and metric measurement invariance over time. Grit and its two facets longitudinally predicted subjective well-being (i.e., life satisfaction, happiness, positive affect, negative affect, and depression) but had negligible incremental validity for two semesters' grades after controlling for conscientiousness. So, while the Grit-O could be a useful construct for young adults, its predictive value overlaps with a better-established construct, conscientiousness.

Unveiling the detrimental vicious cycle linking skeletal muscle and COVID-19: A systematic review and meta-analysis.

OBJECTIVE: Skeletal muscle catabolism supports multiple organs and systems during severe trauma and infection, but its role in COVID-19 remains unclear. This study investigates the interactions between skeletal muscle and COVID-19. METHODS: The PubMed, EMbase, and The Cochrane Library databases were systematically searched from January 2020 to August 2023 for cohort studies focusing on the impact of skeletal muscle on COVID-19 prevalence and outcomes, and longitudinal studies examining skeletal muscle changes caused by COVID-19. Skeletal muscle quantity (SMQN) and quality (SMQL) were assessed separately. The random-effect model was predominantly utilized for statistical analysis. RESULTS: Seventy studies with moderate to high quality were included. Low SMQN/SMQL was associated with an increased risk of COVID-19 infection (OR = 1.62, p < 0.001). Both the low SMQN and SMQL predicted COVID-19-related mortality (OR = 1.53, p = 0.016; OR = 2.18, p = 0.001, respectively). Mortality risk decreased with increasing SMQN (OR = 0.979, p = 0.009) and SMQL (OR = 0.972, p = 0.034). Low SMQN and SMQL were also linked to the need for intensive care unit/mechanical ventilation, increased COVID-19 severity, and longer hospital stays. Significant skeletal muscle wasting, characterized by reduced volume and strength, was observed during COVID-19 infection and the pandemic. CONCLUSIONS: This study reveals a detrimental vicious circle between skeletal muscle and COVID-19. Effective management of skeletal muscle could be beneficial for treating COVID-19 infections and addressing the broader pandemic. These findings have important implications for the management of future virus pandemics. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023395476.

Model selection of GLMMs in the analysis of count data in single-case studies: A Monte Carlo simulation.

Generalized linear mixed models (GLMMs) have great potential to deal with count data in single-case experimental designs (SCEDs). However, applied researchers have faced challenges in making various statistical decisions when using such advanced statistical techniques in their own research. This study focused on a critical issue by investigating the selection of an appropriate distribution to handle different types of count data in SCEDs due to overdispersion and/or zero-inflation. To achieve this, I proposed two model selection frameworks, one based on calculating information criteria (AIC and BIC) and another based on utilizing a multistage-model selection procedure. Four data scenarios were simulated including Poisson, negative binominal (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB). The same set of models (i.e., Poisson, NB, ZIP, and ZINB) were fitted for each scenario. In the simulation, I evaluated 10 model selection strategies within the two frameworks by assessing the model selection bias and its consequences on the accuracy of the treatment effect estimates and inferential statistics. Based on the simulation results and previous work, I provide recommendations regarding which model selection methods should be adopted in different scenarios. The implications, limitations, and future research directions are also discussed.

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder.

BACKGROUND: Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD. METHODS: Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. FINDINGS: Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability. INTERPRETATION: These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD. FUNDING: National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Genome-wide identified VvOFP genes family and VvOFP4 functional characterization provide insight into fruit shape in grape.

Ovate Family Proteins (OFPs) are emerging as novel transcriptional regulators of fruit shape. Despite their established role in various species, their involvement in regulating grape fruit shape remains understudied. This study encompassed a comprehensive evaluation of 16 grape OFP genes in total at the whole genome level. Phylogenetic and synteny analyses established a close relationship between grape VvOFP genes and their tomato counterparts. Expression profiling post-treatment with gibberellic acid (GA3) and thidiazuron (TDZ) revealed that certain OFP genes responded to these regulators, with VvOFP4 showing peak expression three days post-anthesis. Functional assays via overexpression of VvOFP4 in tobacco and tomato altered the morphology of both vegetative and reproductive organs, including leaves, stamens, and fruits/pods. Paraffin sections of transgenic tobacco stems and tomato fruits demonstrated that VvOFP4 overexpression modifies cell dimensions, leading to changes in organ morphology. Additionally, treatments with GA3 and TDZ similarly influenced the shape of grape pulp cells and thereby the overall fruit morphology. These findings suggest that the VvOFP4 gene plays a crucial role in fruit shape determination by modulating cell shape and presents a potential target for future grape breeding programs aimed at diversifying fruit shapes.

High efficiency Hg(II) electrochemical detection based on the number of defect engineering on MoS2: Insight in synergistic action of sulfur vacancies and undercoordinated Mo.

Defects on nanomaterials can effectively enhance the performance of electrochemical detection, but an excessive number of defects may have an adverse effect. In this study, MoS2 nanosheets were synthesized using a hydrothermal synthesis method. By controlling the calcination temperature, MoS2-7H, calcined at 700 °C under H2/Ar2, exhibited an optimal ratio of "point" defects to "plane" defects, resulting in excellent detection performance for mercury ions (Hg(II)). In general, the sulfur vacancies (SV) and undercoordinated Mo generated after calcination of MoS2 significantly promotes the adsorption process and redox of Hg(II) by increasing surface chemical activity, providing additional adsorption sites and adjusting surface charge status to accelerate the catalytic redox of Hg(II). The prepared MoS2-7H-modified electrode showed a sensitivity of 18.25 µA µM-1 and a low limit of detection (LOD) of 6.60 nM towards Hg(II). MoS2-7H also demonstrated a good anti-interference, stability, and exhibited a strong current response in real water samples. The modulation to obtain appropriate number of defects in MoS2 holds promise as a prospective electrode modification material for the electroanalysis.

Design and Evaluation of a Rapid Monolithic Manufacturing Technique for a Novel Vision-Based Tactile Sensor: C-Sight.

Tactile sensing has become indispensable for contact-rich dynamic robotic manipulation tasks. It provides robots with a better understanding of the physical environment, which is a vital supplement to robotic vision perception. Compared with other existing tactile sensors, vision-based tactile sensors (VBTSs) stand out for augmenting the tactile perception capabilities of robotic systems, owing to superior spatial resolution and cost-effectiveness. Despite their advantages, VBTS production faces challenges due to the lack of standardised manufacturing techniques and heavy reliance on manual labour. This limitation impedes scalability and widespread adoption. This paper introduces a rapid monolithic manufacturing technique and evaluates its performance quantitatively. We further develop and assess C-Sight, a novel VBTS sensor manufactured using this technique, focusing on its tactile reconstruction capabilities. Experimental results demonstrate that the monolithic manufacturing technique enhances VBTS production efficiency significantly. Also, the fabricated C-Sight sensor exhibits its reliable tactile perception and reconstruction capabilities, proofing the validity and feasibility of the monolithic manufacturing method.

Calcium(II)-Mediated Three-Component Selenylation of gem-Difluoroalkenes: Access to α,α-Difluoroalkyl-ß-selenides.

A calcium-mediated three-component selenylation of gem-difluoroalkenes using alcohols as nucleophiles and N-(phenylseleno)phthalimide as the selenylation agent has been developed for the efficient synthesis of various α,α-difluoroalkyl-ß-selenides. This selenylation reaction exhibits broad substrate and functional group tolerance, along with high levels of chemo- and regioselectivity. Additionally, the synthetic utility of the developed transformation in the late-stage functionalization of drug molecules was demonstrated.

Highly Efficient and Reversible Carbon Dioxide Capture by Carbanion-Functionalized Ionic Liquids.

Due to the active unstable nature of carbon anions, it is challenging to develop carbanion-functionalized ionic liquids (ILs) for efficient and reversible carbon dioxide (CO2) capture. Here, a series of carbanion-based ILs with large conjugated structures were designed and a promising system was achieved through tuning the nucleophilicity of carbanions and screening the cation. The ideal carbanion-functionalized IL trihexyl(tetradecyl)phosphonium N,N-diethycyanoacetoamide ([P66614][DECA]) showed equimolar chemisorption of CO2 (up to 0.98 mol CO2 /mol IL) under ambient pressure and excellent absorption rate. What's more, the combined CO2 can be released easily, leading to excellent reversibility due to high stability of anion conjugated structures. More importantly, the presence of water had negligible effect on the absorption capacity, which makes it potential to be applied to the CO2 capture in industrial flue gas. The chemisorption mechanism of the carbanion and CO2 was confirmed by spectroscopic investigations and DFT calculations, where carboxylic acid product was formed through proton transfer after the carbanions reacted with CO2. Considering that high capacity, quick rate as well as excellent reversibility, these carbanion-functionalized IL should certainly represent competitive candidates for further scale up and practical application in CO2 capture.

Immobile polyanionic backbone enables a 900-µm-thick electrode for compact energy storage with unprecedented areal capacitance.

Thickening of electrodes is crucial for maximizing the proportion of active components and thus improving the energy density of practical energy storage cells. Nevertheless, trade-offs between electrode thickness and electrochemical performance persist because of the considerably increased ion transport resistance of thick electrodes. Herein, we propose accelerating ion transport through thick and dense electrodes by establishing an immobile polyanionic backbone within the electrode pores; and as a proof of concept, gel polyacrylic electrolytes as such a backbone are in situ synthesized for supercapacitors. During charge and discharge, protons rapidly hop among RCOO- sites for oriented transport, fundamentally reducing the effects of electrode tortuosity and polarization resulting from concentration gradients. Consequently, nearly constant ion transport resistance per unit thickness is achieved, even in the case of a 900-µm-thick dense electrode, leading to unprecedented areal capacitances of 14.85 F cm-2 at 1 mA cm-2 and 4.26 F cm-2 at 100 mA cm-2. This study provides an efficient method for accelerating ion transport through thick and dense electrodes, indicating a significant solution for achieving high energy density in energy storage devices, including but not limited to supercapacitors.

[Mechanisms of oxidative stress in myocardial ischemia-reperfusion injury and protective effects of traditional Chinese medicines].

Myocardial ischemia is a disease characterized by high morbidity and mortality rates, restoring blood supply to the ischemic area through reperfusion is an effective intervention method. However, numerous studies have shown that reperfusion may cause severe myocardial damage, resulting in myocardial systolic and diastolic dysfunction and seriously affecting myocardial function. This phenomenon is called myocardial ischemia reperfusion injury(MIRI). The physiological and pathological mechanisms of MIRI include oxidative stress, calcium overload, autophagy, pyrolysis, endoplasmic reticulum stress, apoptosis, etc. Oxidative stress plays an important role in MIRI-related cell death and is considered to be the main mechanism of MIRI. The occurrence of oxidative stress is mainly due to the excessive production of reactive oxygen species(ROS), which disrupts the balance of the redox system of the body or tissue. A large number of highly reactive ROS exceed the antioxidant defense capacity of cardiomyocytes, causing modifications in biological macromolecules such as DNA and proteins and resulting in severe reactions like DNA damage, protein dysfunction, cell damage or death, and local inflammation. Oxidative stress mediates apoptosis, autophagy, and inflammatory injury through various pathways, resulting in irreversible cardiomyocyte injury and myocardial dysfunction, which brings significant challenges for clinical treatment and prognosis. In recent years, remarkable progress has been made in understanding oxidative stress in ischemia reperfusion(I/R) injury of different organs and tissue. However, the injury mechanism caused by oxidative stress in restoring blood supply to the ischemic area and the protective effect of TCM remain largely unexplored. This article reviewed the role of oxidative stress in MIRI, the main production pathways of ROS, and the protective effects of TCM on oxidative stress injury during ischemic myocardial reperfusion, so as to provide a reference for future research and clinical treatment in this field.

Deep-sea microorganisms-driven V5+ and Cd2+ removal from vanadium smelting wastewater: Bacterial screening, performance and mechanism.

The disorderly discharge of industrial wastewater containing heavy metals has caused serious water pollution and ecological environmental risks, ultimately threatening human life and health. Biological treatment methods have obvious advantages, but the existing microorganisms exhibit issues such as poor resistance, adaptability, colonization ability, and low activity. However, a wide variety of microorganisms in deep-sea hydrothermal vent areas are tolerant to heavy metals, possessing the potential for efficient treatment of heavy metal wastewater. Based on this, the study obtained a group of deep-sea microbial communities dominated by Burkholderia-Caballeronia-Paraburkholderia through shake flask experiments from the sediments of deep-sea hydrothermal vents, which can simultaneously achieve the synchronous removal of vanadium and cadmium heavy metals through bioreduction, biosorption, and biomineralization. Through SEM-EDS, XRD, XPS, and FT-IR analyses, it was found that V(V) was reduced to V(IV) through a reduction process and subsequently precipitated. Glucose oxidation accelerated this process. Cd(II) underwent biomineralization to form precipitates such as cadmium hydroxide and cadmium carbonate. Functional groups on the microbial cell surface, such as -CH2, C=O, N-H, -COOH, phosphate groups, amino groups, and M-O moieties, participated in the bioadsorption processes of V(V) and Cd(II) heavy metals. Under optimal conditions, namely a temperature of 40 °C, pH value of 7.5, inoculation amount of 10%, salinity of 4%, COD concentration of 600 mg/L, V5+ concentration of 300 mg/L, and Cd2+ concentration of 40 mg/L, the OD600 can reach its highest at 72 h, with the removal efficiency of V5+, Cd2+, and COD in simulated vanadium smelting wastewater reaching 86.32%, 59.13%, and 61.63%, respectively. This study provides theoretical insights and practical evidence for understanding the dynamic changes in microbial community structure under heavy metal stress, as well as the resistance mechanisms of microbial treatment of industrial heavy metal wastewater.

Optical in situ deciphering of the surface reconstruction-assistant multielectron transfer event of single Co3O4 nanoparticles.

Surface reconstruction determines the fate of catalytic sites on the near-surface during the oxygen evolution reaction. However, deciphering the conversion mechanism of various intermediate-states during surface reconstruction remains a challenge. Herein, we employed an optical imaging technique to draw the landscape of dynamic surface reconstruction on individual Co3O4 nanoparticles. By regulating the surface states of Co3O4 nanoparticles, we explored dynamic growth of the CoOx(OH)y sublayer on single Co3O4 nanoparticles and directly identified the conversion between two dynamics. Rich oxygen vacancies induced more active sites on the surface and prolonged surface reconstruction, which enhanced electrochemical redox and oxygen evolution. These results were further verified by in situ electrochemical extinction spectroscopy of single Co3O4 nanoparticles. We elucidate the heterogeneous evolution of surface reconstruction on individual Co3O4 nanoparticles and present a unique perspective to understand the fate of catalytic species on the nanosurface, which is of enduring significance for investigating the heterogeneity of multielectron-transfer events.

Toxicokinetics and bioavailability of indoxacarb enantiomers and their new metabolites in rats.

Indoxacarb is a chiral insecticide that consists of two enantiomers, S-(+)-indoxacarb and R-(-)-indoxacarb, of which only S-(+)-indoxacarb has insecticidal activity. Previous enantioselective toxicology studies of indoxacarb focused mostly on simple environmental model organisms. The lack of a toxicology evaluation of indoxacarb conducted in a mammalian system could mean that the extent of the potential health risk posed by the insecticide to humans is not adequately known. In this study, we reported on a new pair of enantiomers, S-IN-RM294 and R-IN-RM294, derived from the metabolic breakdown of S-(+)-indoxacarb and R-(-)-indoxacarb, respectively, in rats. The toxicokinetics of S-(+)-indoxacarb, R-(-)-indoxacarb, S-IN-RM294, and R-IN-RM294 in rats were evaluated to provide a more comprehensive risk assessment of these molecules. The bioavailability and excretion rates of both S-(+)-indoxacarb and R-(-)-indoxacarb were relatively low, which may be due to their faster metabolism and accumulation in the tissues. In addition, there were significant differences in the metabolism and distribution between the two indoxacarb enantiomers and their metabolites in vivo. S-(+)-Indoxacarb was found to be more easily metabolized in the blood compared with R-(-)-indoxacarb, as shown by the differences in pharmacokinetic parameters between oral and intravenous administration. Analysis of their tissue distribution showed that S-(+)-indoxacarb was less likely to accumulate in most tissues. The results obtained for the two metabolites were consistent with those of the two parent compounds. S-IN-RM294 was more readily cleared from the blood and less likely to accumulate in the tissues compared with R-IN-RM294. Therefore, whether from the perspective of insecticidal activity or from the perspective of mammalian and environmental friendliness, the application of optically pure S-(+)-indoxacarb in agriculture may be a more efficient and safer strategy.