Association between carotenoid intake and periodontitis in diabetic patients.

This study aimed to evaluate the association between dietary carotenoid intake and periodontitis in diabetic patients. Data on diabetic patients were collected from the National Health and Nutrition Examination Survey (NHANES) 2009-2014 for this cross-sectional study. Dietary intake of carotenoids was assessed through the first 24-hour dietary recall interview. Full-mouth periodontal examinations were conducted by trained dental examiners. Subgroup analysis was conducted in terms of age, gender, the number of missing teeth, cardiovascular disease, smoking, and anti-diabetic drugs. Totally 1914 diabetic patients were included, with 1281 (66.93%) in the periodontitis group. After adjusting for age, gender, race, education, smoking, dental implants, hepatitis, and the number of missing teeth, α-carotene intake ≥55.82 mcg was associated with lower odds of periodontitis than α-carotene intake <55.82 mcg [OR = 0.70, 95% CI: 0.53-0.91, P = 0.010]; lutein and zeaxanthin intake ≥795.95 mcg was associated with decreased odds of periodontitis than lutein and zeaxanthin intake <795.95 mcg (OR = 0.75, 95%CI: 0.57-0.98, P = 0.039). The association between carotenoid intake and periodontitis varied across different subpopulations. In diabetes, dietary intake of α-carotene and lutein and zeaxanthin was inversely associated with the odds of periodontitis, which may facilitate clinical periodontitis management.

Erratum to "PGAM5-Mediated PHB2 Dephosphorylation Contributes to Diabetic Cardiomyopathy by Disrupting Mitochondrial Quality Surveillance".

[This corrects the article DOI: 10.34133/research.0001.].

Modulator-assisted solvent-free synthesis of amorphous zirconium terephthalate catalyst for efficient oxidative desulfurization.

Oxidative desulfurization (ODS) emerges as a critical player in enhancing efficient fuel desulfurization and promoting sustainable clean energy. Metal-organic frameworks (MOFs) show great potential as ODS catalysts because of their exceptional porosity and versatility. This study explores the use of amorphous metal-organic frameworks (aMOFs), which combine MOFs' structural advantages with unique properties of amorphous materials, to enhance catalytic efficiency in ODS. Traditional methods for synthesizing MOFs rely on solvent-thermal or solvent-free methods, each with limitations in environmental impact or scalability. To address this, we introduce a novel strategy utilizing a small quantity of benzoic acid (BA) modifier to facilitate the solvent-free, one-pot, mechanical synthesis of amorphous zirconium terephthalate (GU-2BA-3h). The resulting GU-2BA-3h demonstrates exceptional ODS performance, efficiently removing 1000 ppm of dibenzothiophene (DBT) in just 6 min at 60 °C. Amorphous GU-2BA-3h features an expanded external surface area, increased acidic sites, and exceptional stability, resulting in a high turnover frequency (19.6 h-1) and outstanding catalytic activity (53.2 mmol g-1 h-1), establishing it as a highly efficient ODS catalyst. This remarkable performance arises from the formation of dangling carboxyl groups and active metal sites due to the competitive coordination of benzoic acid with the linker. Experimental evidence confirms that these carboxyl groups and exposed Zr-OH sites interact with oxidants, generating hydroxyl radicals that effectively eliminate sulfur-containing compounds. Furthermore, the methodology exhibits universality in constructing amorphous Zr-based MOFs, and provides an eco-friendly, cost-effective route for efficient ODS catalyst production.

Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial.

The latest X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging for tissue characterization and material decomposition. However, both radiation dose and imaging speed need improvement for contrast-enhanced and other studies. Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed in a New Zealand clinical trial. Particularly, we present a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and real-world data. The simulation and phantom experiments demonstrate consistently improved results under different acquisition conditions on both in- and off-domain structures using a fixed network. The image quality of 8 patients from the clinical trial are evaluated by three radiologists in comparison with the standard image reconstruction with a full-view dataset. It is shown that our proposed approach is essentially identical to or better than the clinical benchmark in terms of diagnostic image quality scores. Our approach has a great potential to improve the safety and efficiency of PCCT without compromising image quality.

Sustainable high-strength and dimensionally stable composites through in situ regulation and reconstitution of bamboo-derived lignin and hemicellulose contents.

The development of modern construction and transportation industries demands increasingly high requirements for thin, lightweight, high-strength, and highly tough composite materials, such as metal carbides and concrete. Bamboo is a green, low-carbon, fast-growing, renewable, and biodegradable material with high strength and toughness. However, the density of its inner layer is low due to the functional gradient (the volume fraction of vascular bundles decreases from the outer layer to the inner layer), resulting in low performance, high compressibility, and significant amounts of bamboo waste. We utilized chemical and mechanical treatments of bamboo's low-density, low-strength inner layers to create lightweight, ultra-thin, high-strength, and high-toughness composites. The treatment included the partial removal of lignin and hemicellulose to alter the chemical components, followed by mechanical drying and hot pressing. The treated bamboo had 100.8 % higher tensile strength (150.35 MPa), 47.7 % higher flexural strength (97.67 MPa), and 132.0 % higher water resistance and was approximately 68.9 % thinner than the natural bamboo. The excellent physical and mechanical properties of the treated bamboo are attributed to the contraction of parenchyma cells during delignification, the interlocking due to the collapse of parenchyma cells during mechanical drying, and an increase in the density of hydrogen bonds between cellulose molecular chains during hot pressing. Our research provides a new strategy for obtaining sustainable, ultra-thin, lightweight, high-strength, and high-toughness composite materials from bamboo for construction and transportation applications.

Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography.

Cardiac computed tomography is widely used for diagnosis of cardiovascular disease, the leading cause of morbidity and mortality in the world. Diagnostic performance depends strongly on the temporal resolution of the CT images. To image the beating heart, one can reduce the scanning time by acquiring limited-angle projections. However, this leads to increased image noise and limited-angle-related artifacts. The ability to reconstruct high quality images from limited-angle projections is highly desirable and remains a major challenge. With the development of deep learning networks, such as U-Net and transformer networks, progresses have been reached on image reconstruction and processing. Here we propose a hybrid model based on the U-Net and Swin-transformer (U-Swin) networks. The U-Net has the potential to restore structural information due to missing projection data and related artifacts, then the Swin-transformer can gather a detailed global feature distribution. Using synthetic XCAT and clinical cardiac COCA datasets, we demonstrate that our proposed method outperforms the state-of-the-art deep learning-based methods.

Modulation of T Cell Differentiation in Mice with COPD Combined with Lung Cancer Through Key Targets of PD-1 by Tao Hong Si Wu Tang.

The objective is to assess the anti-inflammatory effect of Tao Hong Si Wu Tang combined with anti-PD-1 in a mouse model of COPD combined with lung cancer, elucidating its mechanism through modulation of PD-1/PD-L binding, regulation of Th1/Th2 and Th17/Treg balance, inhibition of IL-4 and IL-17, and promotion of IFN-γ and TGF-ß levels in peripheral blood. One hundred male C57/BL6 mice were randomly allocated to five groups: A (blank control), B (model control), C (THSW), D (anti-PD-1), and E (THSW + anti-PD-1), with 20 mice in each group. The COPD model was induced using fumigation and LPS intra-airway drip, followed by the establishment of lung cancer by Lewis cell inoculation. Treatment groups received Tao Hong Si Wu Tang or/and PD-1 monoclonal antibody. Various indicators were assessed, including macroscopic observation, HE staining of lung tissue, ELISA for cytokines, flow cytometry for cell proportions, and immunohistochemistry/western blotting for protein expression. Lung tissue analysis revealed significant differences between groups, with marked tumor formation observed in groups B-E. Serum levels of IL-4, IFN-γ, IL-17, and TGF-ß were significantly altered, along with changes in CD4 + T/CD8 + T ratio and cytokine-producing cell populations. Expression levels of key proteins were also significantly affected across treatment groups. Tao Hong Si Wu Tang demonstrated anti-inflammatory effects comparable to anti-PD-1, potentially through modulation of PD-1/PD-L binding, correction of Th1/Th2 and Th17/Treg imbalance, and modulation of cytokine levels. These findings suggest a role for Tao Hong Si Wu Tang in ameliorating inflammation and immune dysregulation in COPD combined with lung cancer.

Directed differentiation of human embryonic stem cells into parathyroid cells and establishment of parathyroid organoids.

Differentiation of human embryonic stem cells (hESCs) into human embryonic stem cells-derived parathyroid-like cells (hESC-PT) has clinical significance in providing new therapies for congenital and acquired parathyroid insufficiency conditions. However, a highly reproducible, well-documented method for parathyroid differentiation remains unavailable. By imitating the natural process of parathyroid embryonic development, we proposed a new hypothesis about the in vitro differentiation of parathyroid-like cells. Transcriptome, differentiation marker protein detection and parathyroid hormone (PTH) secretion assays were performed after the completion of differentiation. To optimize the differentiation protocol and further improve the differentiation rate, we designed glial cells missing transcription factor 2 (GCM2) overexpression lentivirus transfection assays and constructed hESCs-derived parathyroid organoids. The new protocol enabled hESCs to differentiate into hESC-PT. HESC-PT cells expressed PTH, GCM2 and CaSR proteins, low extracellular calcium culture could stimulate hESC-PT cells to secrete PTH. hESC-PT cells overexpressing GCM2 protein secreted PTH earlier than their counterpart hESC-PT cells. Compared with the two-dimensional cell culture environment, hESCs-derived parathyroid organoids secreted more PTH. Both GCM2 lentiviral transfection and three-dimensional cultures could make hESC-PT cells functionally close to human parathyroid cells. Our study demonstrated that hESCs could differentiate into hESC-PT in vitro, which paves the road for applying the technology to treat hypoparathyroidism and introduces new approaches in the field of regenerative medicine.

Systematic Review on Learning-based Spectral CT.

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Anisotropic Rotunda-Shaped Carboxymethylcellulose/Carbon Nanotube Aerogels Supported Phase Change Materials for Efficient Solar-Thermal Energy Conversion.

For the drawbacks of phase change materials such as poor shape stability and weak solar-thermal conversion ability, a rotunda-shaped carboxymethylcellulose/carbon nanotube aerogel (CA) with three-dimensional network was constructed by freeze casting with a special mold, and then impregnated with polyethylene glycol (PEG) in this work. The PEG/CA had an enthalpy of 183.21 J/g, and a thermal conductivity of 0.324 W m-1 K-1, which was 57 % higher than the pure PEG. The ability of PEG/CA to convert solar energy to thermal energy was a positive correlation between the inclusion of CNTs and the composite material's thermal conductivity. Under simulated sunlight, its solar-thermal conversion efficiency reaches 94.41 %, and after 10 min of irradiation, the surface temperature can reach 65 °C and the internal temperature can reach 44.67 °C. This rotunda-shaped PEG/CA is promising for the efficient use of renewable solar energy due to its strong solar-thermal conversion and thermal storage capabilities.

Deep learning-based harmonization of trabecular bone microstructures between high- and low-resolution CT imaging.

BACKGROUND: Osteoporosis is a bone disease related to increased bone loss and fracture-risk. The variability in bone strength is partially explained by bone mineral density (BMD), and the remainder is contributed by bone microstructure. Recently, clinical CT has emerged as a viable option for in vivo bone microstructural imaging. Wide variations in spatial-resolution and other imaging features among different CT scanners add inconsistency to derived bone microstructural metrics, urging the need for harmonization of image data from different scanners. PURPOSE: This paper presents a new deep learning (DL) method for the harmonization of bone microstructural images derived from low- and high-resolution CT scanners and evaluates the method's performance at the levels of image data as well as derived microstructural metrics. METHODS: We generalized a three-dimensional (3D) version of GAN-CIRCLE that applies two generative adversarial networks (GANs) constrained by the identical, residual, and cycle learning ensemble (CIRCLE). Two GAN modules simultaneously learn to map low-resolution CT (LRCT) to high-resolution CT (HRCT) and vice versa. Twenty volunteers were recruited. LRCT and HRCT scans of the distal tibia of their left legs were acquired. Five-hundred pairs of LRCT and HRCT image blocks of 64 × 64 × 64 $64 \times 64 \times 64 $ voxels were sampled for each of the twelve volunteers and used for training in supervised as well as unsupervised setups. LRCT and HRCT images of the remaining eight volunteers were used for evaluation. LRCT blocks were sampled at 32 voxel intervals in each coordinate direction and predicted HRCT blocks were stitched to generate a predicted HRCT image. RESULTS: Mean ± standard deviation of structural similarity (SSIM) values between predicted and true HRCT using both 3DGAN-CIRCLE-based supervised (0.84 ± 0.03) and unsupervised (0.83 ± 0.04) methods were significantly (p < 0.001) higher than the mean SSIM value between LRCT and true HRCT (0.75 ± 0.03). All Tb measures derived from predicted HRCT by the supervised 3DGAN-CIRCLE showed higher agreement (CCC  ∈ $ \in $ [0.956 0.991]) with the reference values from true HRCT as compared to LRCT-derived values (CCC  ∈ $ \in $ [0.732 0.989]). For all Tb measures, except Tb plate-width (CCC = 0.866), the unsupervised 3DGAN-CIRCLE showed high agreement (CCC  ∈ $ \in $ [0.920 0.964]) with the true HRCT-derived reference measures. Moreover, Bland-Altman plots showed that supervised 3DGAN-CIRCLE predicted HRCT reduces bias and variability in residual values of different Tb measures as compared to LRCT and unsupervised 3DGAN-CIRCLE predicted HRCT. The supervised 3DGAN-CIRCLE method produced significantly improved performance (p < 0.001) for all Tb measures as compared to the two DL-based supervised methods available in the literature. CONCLUSIONS: 3DGAN-CIRCLE, trained in either unsupervised or supervised fashion, generates HRCT images with high structural similarity to the reference true HRCT images. The supervised 3DGAN-CIRCLE improves agreements of computed Tb microstructural measures with their reference values and outperforms the unsupervised 3DGAN-CIRCLE. 3DGAN-CIRCLE offers a viable DL solution to retrospectively improve image resolution, which may aid in data harmonization in multi-site longitudinal studies where scanner mismatch is unavoidable.

Quasi-supervised learning for super-resolution PET.

Low resolution of positron emission tomography (PET) limits its diagnostic performance. Deep learning has been successfully applied to achieve super-resolution PET. However, commonly used supervised learning methods in this context require many pairs of low- and high-resolution (LR and HR) PET images. Although unsupervised learning utilizes unpaired images, the results are not as good as that obtained with supervised deep learning. In this paper, we propose a quasi-supervised learning method, which is a new type of weakly-supervised learning methods, to recover HR PET images from LR counterparts by leveraging similarity between unpaired LR and HR image patches. Specifically, LR image patches are taken from a patient as inputs, while the most similar HR patches from other patients are found as labels. The similarity between the matched HR and LR patches serves as a prior for network construction. Our proposed method can be implemented by designing a new network or modifying an existing network. As an example in this study, we have modified the cycle-consistent generative adversarial network (CycleGAN) for super-resolution PET. Our numerical and experimental results qualitatively and quantitatively show the merits of our method relative to the state-of-the-art methods. The code is publicly available at https://github.com/PigYang-ops/CycleGAN-QSDL.

The DNA-dependent protein kinase catalytic subunit exacerbates endotoxemia-induced myocardial microvascular injury by disrupting the MOTS-c/JNK pathway and inducing profilin-mediated lamellipodia degradation.

Rationale: The DNA-dependent protein kinase catalytic subunit (DNA-PKcs) promotes pathological mitochondrial fission during septic acute kidney injury. The mitochondrial open reading frame of the 12S rRNA type-c (MOTS-c) is a mitochondria-derived peptide that exhibits anti-inflammatory properties during cardiovascular illnesses. We explored whether endotoxemia-induced myocardial microvascular injury involved DNA-PKcs and MOTS-c dysregulation. Methods: To induce endotoxemia in vivo, endothelial cell-specific DNA-PKcs-knockout mice were injected intraperitoneally with a single dose of lipopolysaccharide (10 mg/kg) and evaluated after 72 h. Results: Lipopolysaccharide exposure increased DNA-PKcs activity in cardiac microvascular endothelial cells, while pharmacological inhibition or endothelial cell-specific genetic ablation of DNA-PKcs reduced lipopolysaccharide-induced myocardial microvascular dysfunction. Proteomic analyses showed that endothelial DNA-PKcs ablation primarily altered mitochondrial protein expression. Verification assays confirmed that DNA-PKcs drastically repressed MOTS-c transcription by inducing mtDNA breaks via pathological mitochondrial fission. Inhibiting MOTS-c neutralized the endothelial protective effects of DNA-PKcs ablation, whereas MOTS-c supplementation enhanced endothelial barrier function and myocardial microvascular homeostasis under lipopolysaccharide stress. In molecular studies, MOTS-c downregulation disinhibited c-Jun N-terminal kinase (JNK), allowing JNK to phosphorylate profilin-S173. Inhibiting JNK or transfecting cells with a profilin phosphorylation-defective mutant improved endothelial barrier function by preventing F-actin depolymerization and lamellipodial degradation following lipopolysaccharide treatment. Conclusions: DNA-PKcs inactivation during endotoxemia could be a worthwhile therapeutic strategy to restore MOTS-c expression, prevent JNK-induced profilin phosphorylation, improve F-actin polymerization, and enhance lamellipodial integrity, ultimately ameliorating endothelial barrier function and reducing myocardial microvascular injury.

Flipover outperforms dropout in deep learning.

Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons and reverts their outputs using a negative multiplier during training. This approach offers stronger regularization than conventional dropout, refining model performance by (1) mitigating overfitting, matching or even exceeding the efficacy of dropout; (2) amplifying robustness to noise; and (3) enhancing resilience against adversarial attacks. Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.

TH17 cell: a double-edged sword in the development of inflammatory bowel disease.

Inflammatory bowel disease (IBD) is a chronic nonspecific inflammatory disease of the gastrointestinal tract, and its pathogenesis has not been fully understood. Extensive dysregulation of the intestinal mucosal immune system is critical in the development and progression of IBD. T helper (Th) 17 cells have the characteristics of plasticity. They can transdifferentiate into subpopulations with different functions in response to different factors in the surrounding environment, thus taking on different roles in regulating the intestinal immune responses. In this review, we will focus on the plasticity of Th17 cells as well as the function of Th17 cells and their related cytokines in IBD. We will summarize their pathogenic and protective roles in IBD under different conditions, respectively, hoping to further deepen the understanding of the pathological mechanisms underlying IBD and provide insights for future treatment.

Unprecedented 100% conversion from pyridinic to pyrrolic nitrogen configuration for electrochemically active nitrogen-doped carbon materials.

Nitrogen-doped carbons with promising electrochemical performance exhibit a strong dependence on nitrogen configuration. Therefore, accurate control of nitrogen configurations is crucial to clarify their influence. Unfortunately, there is still no well-defined conversion route to finely control nitrogen configuration. Herein, we proposed the concept of 100% conversion from pyridinic to pyrrolic nitrogen in carbon materials through low-temperature pyrolysis and alkali activation of hydroxypyridine-3-halophenol-formaldehyde resins. Their dehalogenation pyrolysis promotes formation of carbon intermediates and conversion of tautomeric pyridone and hydroxypyridine into pyrrolic and pyridinic nitrogen through eliminating carbonyl and hydroxyl functionalities, respectively. Continuous thermal alkali activation introduces hydroxyl groups into carbon materials, converting pyridinic species to intermediate hydroxypyridine and pyridone; subsequently, these configurations transform to pyridinic and pyrrolic nitrogen, respectively, and finally, an excessive alkali ensures 100% conversion from pyridinic to pyrrolic nitrogen. NaOH activation for pyrrolic and pyridinic nitrogen co-doped carbon and KOH activation for model nitrogen-containing compounds including acridine, phenanthridine, and acridone further confirm that alkali activation plays an indispensable role in 100% conversion from pyridinic to pyrrolic units through the tautomeric hydroxypyridine and pyridone intermediates. Low-temperature alkali-induced controllable conversion of nitrogen configuration in carbon materials is suitable modulating nitrogen configurations for almost all nitrogen-doped carbon materials in electrochemical applications.

Deep-silicon photon-counting x-ray projection denoising through reinforcement learning.

BACKGROUND: In recent years, deep reinforcement learning (RL) has been applied to various medical tasks and produced encouraging results. OBJECTIVE: In this paper, we demonstrate the feasibility of deep RL for denoising simulated deep-silicon photon-counting CT (PCCT) data in both full and interior scan modes. PCCT offers higher spatial and spectral resolution than conventional CT, requiring advanced denoising methods to suppress noise increase. METHODS: In this work, we apply a dueling double deep Q network (DDDQN) to denoise PCCT data for maximum contrast-to-noise ratio (CNR) and a multi-agent approach to handle data non-stationarity. RESULTS: Using our method, we obtained significant image quality improvement for single-channel scans and consistent improvement for all three channels of multichannel scans. For the single-channel interior scans, the PSNR (dB) and SSIM increased from 33.4078 and 0.9165 to 37.4167 and 0.9790 respectively. For the multichannel interior scans, the channel-wise PSNR (dB) increased from 31.2348, 30.7114, and 30.4667 to 31.6182, 30.9783, and 30.8427 respectively. Similarly, the SSIM improved from 0.9415, 0.9445, and 0.9336 to 0.9504, 0.9493, and 0.0326 respectively. CONCLUSIONS: Our results show that the RL approach improves image quality effectively, efficiently, and consistently across multiple spectral channels and has great potential in clinical applications.

Dimeric ent-kauranoids isolated from Isodon japonica var. Glaucocalyx and their anti-inflammatory activities.

The phytochemical investigation of the aerial parts of Isodon japonica var. glaucocalyx afforded four undescribed (glaucocalyxin O-R, 1-4) and six known ent-kauranoids (5-10). Their structures were established using NMR and MS measurements. Compounds 1 and 2 are dimeric ent-kaurane-type diterpenoids. Moreover, the plausible biogenetic pathways for compounds 1 and 2 were proposed as Michael addition between two monomers. Eight compounds were assayed for their anti-inflammatory activity by evaluating NO production in LPS-induced RAW 267.4 cells, and compounds 7, 8 and 9 exhibited relatively remarkable anti-inflammatory activities at 10 µM.

Insecticide resistance status of Aedes aegypti in border areas of Yunnan Province.

BACKGROUND: Aedes aegypti is a main vector of arboviral diseases, principally dengue, chikungunya, and Zika. Insecticides remain the most effective vector control method. Pyrethroid is the main insecticide currently used, and the long-term use of insecticides can cause mosquitoes to develop knockdown resistance. Studying the mutation sites and genotypes of Ae. aegypti can reveal the mutation characteristics and regional distribution of the kdr gene in an Ae. aegypti population. Testing for a correlation between the mutation rate in various populations and pyrethrin resistance can clarify the resistance mechanism. RESULTS: The bioassay results showed that all 15 populations are resistant. In the study of the kdr gene, three non-synonymous mutations were identified in the DNA of first generation females from the wild Ae. aegypti population: S989P (TCC-CCC), V1016G (GTA-GGA), and F1534C (TTC-TGC). The mortality rate of the various populations was correlated with the mutation rate at the V1016G + F1534C locus, but not the S989P + V1016G locus. CONCLUSION: Aedes aegypti populations in border regions of Yunnan Province are resistant to permethrin and beta-cyfluthrin. The insecticidal effect of beta-cyfluthrin is stronger than that of permethrin. The mutation rate at sites V1016G + F1534C is negatively correlated with the mortality of Ae. aegypti based on bioassays. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.