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In order to reduce energy consumption, improve driving mileage, and make vehicles adopt driver styles, research on automatic optimization of control strategy based on driver style is conducted in this paper. According to the structure of the powertrain, the vehicle control strategy is designed and a driver-style recognition model based on fuzzy recognition is added to the rule-based control strategy to improve the driver adaptation of the vehicle. In order to further improve the energy-saving effect of the strategy, the control strategy based on driver style is automatically optimized by the Isight optimization platform to make the strategy reach optimum. The test results show that the strategy based on driver style is able to adapt to different styles of drivers and the economy of the vehicle is improved by 2.06% compared with pre-optimization, which validates the effectiveness of the strategy.

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human–machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

In order to improve the shifting quality of pure electric commercial vehicles, a torque control strategy based on the driving intention during the shifting process is presented in this paper. Firstly, dynamic analysis is conducted on the lifting and twisting stage in the two-speed Automated Mechanical Transmission (AMT) shift process without a synchronizer. Secondly, fuzzy identification is performed on the driver’s expected acceleration, incorporating the driver’s acceleration intention into the lifting and twisting process, and, further, the output time correction factor k is deblurred. Finally, the control time of the lifting and reducing torque is corrected to achieve dynamic adjustment of the control parameters during the shift process. The actual vehicle test results indicate that the proposed control strategy can enhance the shifting quality and adapt the performance of a vehicle to the driver’s expectations and requirements.

Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive.

In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.

ABSTRACT Chilling is an important limiting factor for seed germination of soybean (Glycine max [L.] Merr.). To reveal the regulatory mechanism of chilling tolerance during the soybean germination stage, based on previous studies, the chilling tolerance line R48 and chilling sensitive line R89 in chromosome segment substitution lines were selected for physiological index determination and transcriptome sequencing. It was found that reactive oxygen species (ROS) scavenging system related enzymes, ROS, and osmotic regulators were significantly different between the two lines. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment were performed on the differentially expressed genes obtained by transcriptome sequencing. It was found that terms or pathways related to flavonoids, unsaturated fatty acids, and abscisic acid were highly enriched. In addition, weighted gene coexpression network analysis (WGCNA) method was used to analyze the physiological index data and transcriptome sequencing data. Four main coexpression modules significantly related to physiological indicators were obtained, and the hub genes in each module were screened according to eigengene‐based connectivity value. Haplotype analysis of important candidate genes using soybean germplasm resources showed that there were significant differences in germination indexes between different major haplotypes of Glyma.17G163200. Based on the results of enrichment analysis and WGCNA, the regulation model of low temperature tolerance during soybean germination was preliminarily drawn. This study will provide theoretical guidance for analyzing the molecular regulation mechanism of cold tolerance in soybean germination stage.

Climate change is one of the most critical challenges facing Australia and the global community today. Data from the Australian Bureau of Meteorology (BoM) indicates that Australia has been experiencing rising temperatures, particularly since the late 20th century. The frequency, duration, and intensity of heatwaves are projected to continue increasing . Since national records began in 1910, Australia has warmed by an average of 1.47°C (±0.24°C), with the highest official temperature recorded at 50.7 degrees Celsius in Onslow, Western Australia (WA), on January 13, 2022. Furthermore, a recent unprecedented high temperature of +41.6°C was recorded during winter on August 26, 2024, in Yampi Sound, WA. Among all natural disasters in Australia, heatwave (HW) represents a leading silent killer and pose a significant public health threat. However, innovative methods for assessing vulnerability for HW-related health services remain limited.

Background and ObjectiveUntil now, there has been an unmet need for treatments promoting chronic-phase post-stroke functional recovery. We previously found that morroniside promoted endogenous neurogenesis in ischemic stroke, but its therapeutic window was limited to the first 48 h. Here, we aimed to explore whether deep brain stimulation (DBS) combined with morroniside could enhance neurogenesis in rats subjected to focal ischemic stroke and contributes to functional recovery.MethodsBeginning 2 weeks after the endothelin-1-induced stroke, rats were administered DBS of lateral cerebellar nucleus consecutively for 14 days, followed by morroniside for 7 consecutive days post-stimulation. Behavioral tests were used for assessing motor function. Local field potentials were recorded to evaluate neuronal excitability. Nissl staining was used to assess infarct volume. Immunofluorescence staining and Western blotting were carried out to uncover the stroke recovery mechanisms of DBS combined with morroniside treatment.ResultsThe results showed that this combined treatment improved behavioral outcomes, enhanced cortical local field potentials, and diminished infarct volumes at 35 days post-stroke. Moreover, it notably amplified neurogenic responses post-stroke, evidenced by the proliferation of BrdU/SOX2 and BrdU/DCX in the subventricular zone, and their subsequent differentiation into BrdU/NeuN and BrdU/VgulT1 in the ischemic penumbra. Moreover, the combined treatment also elevated the amount of BrdU/Olig2 and the level of axonal sprouting-related proteins in the perilesional cortex.ConclusionOur results demonstrated that the combined treatment extended the neurorestorative efficacy of morroniside, reduced infarct size, enhanced neuronal excitability and accelerated sensorimotor function recovery. This therapeutic approach may emerge as a potential clinical intervention for chronic ischemic stroke.

The current study analyzed the chromosomal karyotype of Quasipaa spinosa David, 1875 from Hunan Province, China. The karyotype, C-banding, BrdU-banding pattern were characterized using direct preparation of bone-marrow cells and hemocyte cultures. The findings indicated that Q. spinosa was a diploid species (2n = 26) that lacked heteromorphic chromosomes and secondary constrictions. C-banding analysis revealed an abundance of positive signals in the centromere regions, while the BrdU-banding pattern showed three phases in both male and female, occurring consistently and in chronological sequence during S-phase. Notably, there was no asynchronous replication in the late phase. This study enhanced our understanding of the karyotypic structure of Q. spinosa by conventional cytogenetic techniques, thus providing essential scientific insights into the cytogenetics of Q. spinosa.

The skin microbiota plays an essential role in helping the host adapt to different environments and maintain health. By examining the characteristics of amphibian skin flora alongside ontogenetic traits, we can gain insights into the adaptation mechanisms of amphibian skin flora to environmental changes during development. In this study, we analyzed the skin microbiota of Quasipaa spinosa during metamorphosis using Illumina sequencing. Venn diagrams and UpSet analysis revealed that the LTS (hindlimb tadpoles’ skin, aquatic habitat) and FTS (forelimb tadpoles’ skin, shift from aquatic to amphibious habitats) groups exhibited a higher number of unique amplicon sequence variants (ASVs), while the TS (tadpoles’ skin, aquatic habitat) and LFS (land frogs’ skin, amphibious habitats) groups displayed a lower abundance of ASVs. Diversity analysis indicated similarities in the microorganisms between the LTS and the FTS groups, with higher microbial diversity compared to the TS and the LFS groups. Additionally, microbial co-occurrence network analysis indicated a more stable microecology in the LTS group and FTS group. Proteobacteria, Firmicutes, and Bacteroidota were identified as the dominant phyla, although their relative abundances varied widely among groups. LEfSe (Linear discriminant analysis effect size) showed significant enrichment of beneficial bacteria at various developmental stages, including Bacteroides, Bacillus, and Lactobacillus. Functional prediction analysis shows significant differences in skin microorganism functions across various developmental stages, with a primary focus on metabolic functions. This study provides valuable insights into the compositional dynamics of skin microbiota in Q. spinosa at various developmental stages.

Tilianin, a flavonoid compound derived from Dracocephalum moldavica L., is recognized for its diverse biological functionalities, in particular alleviating myocardial ischemia-reperfusion injury (MIRI). There is ample evidence suggesting that the NLRP3 inflammasome has a significant impact on the development of MIRI. In this study, rats undergoing the ligation and subsequent release of the left anterior descending (LAD) coronary artery and H9c2 cardiomyocytes subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) were used to investigate the effects of tilianin on NLRP3 inflammasome and its anti-MIRI mechanisms. Upon reperfusion, the rats were intraperitoneally injected with tilianin at doses of 3, 10, 30 mg/kg. H9c2 cells were treated with tilianin at concentrations of 10, 30, and 50 μg/mL. Echocardiography, TTC staining and TUNEL staining demonstrated that tilianin remarkably improved cardiac function and mitigated myocardial damage in MIRI rats. Additionally, notable inflammatory response reduction by tilianin was evidenced by subsequent hematatoxylin-eosin (HE) staining, inflammatory cytokines assay, and quantitative proteomics. Further western blotting analysis and immunofluorescence staining showed tilianin decreased the levels of TLR4, p-NF-κB, NLRP3, and ASC in MIRI rats and H9c2 cells exposed to OGD/R, alongside a significant reduction in cleaved gasdermin D, mature IL-1β and IL-18. Molecular docking, cellular thermal shift assay (CETSA) and co-immunoprecipitation (co-IP) assay revealed that tilianin impeded the interaction between NLRP3 and NEK7. Taken together, tilianin protects cardiomyocytes from MIRI by suppressing NLRP3 inflammasome through the inhibition of the TLR4/NF-κB signaling pathway and the disruption of the NEK7/NLRP3 interface. These findings underscore the potential of tilianin as a promising therapeutic candidate for MIRI.

Abstract The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

To investigate the structure–antioxidant activity relationship, Pleurotus ferulae polysaccharides were extracted using ultrasonic (U-PFPS) and microwave/ultrasonic-assisted methods (MU-PFPS). Compared to U-PFPS with a molecular weight of 1.566 × 103 kDa, MU-PFPS exhibited a lower molecular weight of 89.26 kDa. In addition, unlike U-PFPS, which is primarily composed of glucose (Glu:Man:Gal = 91.1:3.5:5.4), MU-PFPS has a more balanced composition of Glu:Man:Gal in the ratio of 39.4:27.8:32.8 and contains more branched chains. Furthermore, antioxidant analysis revealed that high concentration (at concentrations above 600 μg/mL) MU-PFPS demonstrated stronger protective effects against oxidative damage in RAW264.7 cells than U-PFPS did. Collectively, these data suggest that lower molecular weight and higher branching degree of polysaccharides at appropriate concentrations may correlate with enhanced antioxidant enzyme activities. Our work provides a method for isolating polysaccharides with higher antioxidant activity and offers insights into the structure–activity relationship of polysaccharides, laying the foundation for future applications in polysaccharide modification and structural characterization.

Background: Electronic and electrical waste (e-waste) production has emerged to be of global environmental public health concern. E-waste workers, who are frequently exposed to hazardous chemicals through occupational activities, face considerable health risks. Methods: To investigate the metabolic and exposomic changes in these workers, we analyzed whole blood samples from 100 male e-waste workers and 49 controls from the GEOHealth II project (2017–2018 in Accra, Ghana) using LC-MS/MS. A specialized computational workflow was established for exposomics data analysis, incorporating two curated reference libraries for metabolome and exposome profiling. Two feature detection algorithms, asari and centWave, were applied. Results: In comparison to centWave, asari showed better sensitivity in detecting MS features, particularly at trace levels. Principal component analysis demonstrated distinct metabolic profiles between e-waste workers and controls, revealing significant disruptions in key metabolic pathways, including steroid hormone biosynthesis, drug metabolism, bile acid biosynthesis, vitamin metabolism, and prostaglandin biosynthesis. Correlation analyses linked metal exposures to alterations in hundreds to thousands of metabolic features. Functional enrichment analysis highlighted significant perturbations in pathways related to liver function, vitamin metabolism, linoleate metabolism, and dynorphin signaling, with the latter being observed for the first time in e-waste workers. Conclusions: This study provides new insights into the biological impact of prolonged metal exposure in e-waste workers.

Soybean quality and production are determined by seed viability. A seed’s capacity to sustain germination via dry storage is known as its seed life. Thus, one of the main objectives for breeders is to preserve genetic variety and gather germplasm resources. However, seed quality and germplasm preservation have become significant obstacles. In this study, four artificially simulated aging treatment groups were set for 0, 24, 72, and 120 hours. Following an aging stress treatment, the transcriptome and metabolome data were compared in two soybean lines with notable differences in seed vigor—R31 (aging sensitive) and R80 (aging tolerant). The results showed that 83 (38 upregulated and 45 downregulated), 30 (19 upregulated and 11 downregulated), 90 (52 upregulated and 38 downregulated), and 54 (25 upregulated and 29 downregulated) DEGs were differentially expressed, respectively. A total of 62 (29 upregulated and 33 downregulated), 94 (49 upregulated and 45 downregulated), 91 (53 upregulated and 38 downregulated), and 135 (111 upregulated and 24 downregulated) differential metabolites accumulated. Combining the results of transcriptome and metabolome investigations demonstrated that the difference between R31 and R80 responses to aging stress was caused by genes related to phenylpropanoid metabolism pathway, which is linked to the seed metabolite caffeic acid. According to this study’s preliminary findings, the aging-resistant line accumulated more caffeic acid than the aging-sensitive line, which improved its capacity to block lipoxygenase (LOX) activity. An enzyme activity inhibition test was used to demonstrate the effect of caffeic acid. After soaking seeds in 1 mM caffeic acid (a LOX inhibitor) for 6 hours and artificially aging them for 24 hours, the germination rates of the R31 and R80 seeds were enhanced. In conclusion, caffeic acid has been shown to partially mitigate the negative effects of soybean seed aging stress and to improve seed vitality. This finding should serve as a theoretical foundation for future research on the aging mechanism of soybean seeds.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

The model organism Caenorhabditis elegans and its relationship with the gut microbiome are gaining traction, especially for the study of neurodegenerative diseases such as Parkinson’s Disease (PD). Gut microbes are known to be able to alter kynurenine metabolites in the host, directly influencing innate immunity in C. elegans. While the mitochondrial unfolded protein response (UPRmt) was first characterized in C. elegans in 2007, its relevance in host–microbiome interactions has only become apparent in recent years. In this review, we provide novel insights into the current understanding of the microbiome–gut–brain axis with a focus on tripartite interactions between the UPRmt, kynurenine pathway, and microbiome in C. elegans, and explore their relationships for PD remediations.

Land-use and land-cover changes (LUCCs) significantly impact carbon sequestration by modifying the structure and function of terrestrial ecosystems. This study utilized GIS and remote sensing techniques to forecast future LUCC patterns and their influence on regional carbon budgets, which is essential for sustainable development. We devised a coupled system dynamics (SD) model integrated with a patch-generating land-use simulation (PLUS) model to simulate LUCCs under diverse future scenarios using multisource environmental data. Additionally, the InVEST model was employed to quantify carbon storage in terrestrial ecosystems. By establishing three scenarios—ecological priority (EP), highly urbanized (HU), and coordinated development (CD)—this study’s aim was to predict the LUCC patterns and carbon storage distribution of the Tuojiang River Basin (TRB), China, up to 2035. The results showed that (1) from 2000 to 2020, significant LUCCs occurred in the TRB, primarily involving the conversion of cultivated land into construction areas and forestland; (2) LUCCs had a substantial impact on carbon storage in the TRB, with the EP scenario demonstrating the highest carbon storage by 2035 due to extensive forest expansion, while the HU scenario indicated a decline in carbon storage associated with rapid urbanization; and (3) the mountainous regions of the TRB, dominated by forestland, consistently exhibited higher carbon storage, whereas the Chengdu Plain region in the upper basin displayed the lowest. In conclusion, we recommend prioritizing the CD scenario in future development strategies to balance economic growth with ecological protection while simultaneously enhancing carbon storage. Our findings offer valuable insights to shape future LUCC policies in the Tuojiang River Basin, underscoring the adaptability of the coupled model approach to a wide range of geographic scales and contexts.

PurposeTo develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).Material and methodsThe study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the “One-vs-Rest” strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.ResultsFollowing pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P

Abstract Background Spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies. This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation. Methods There were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40 keV-equivalent image (VMI40keV), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λHU) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups. Results There were significant differences in VMI40keV, IC, Eff-Z, and λHU in AP and VP among the three groups (all p 0.05). The VMI40keV, IC, Eff-Z, and λHU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p

Abstract Gut-microbiota derived metabolites are important regulators of host biology and metabolism. To understand the impacts of the microbial metabolite 4-cresol sulfate (4-CS) on four chronic diseases [type 2 diabetes mellitus, metabolic syndrome (MetS), non-alcoholic fatty liver disease, and chronic kidney disease (CKD)], we conducted association analyses of plasma 4-CS quantified by liquid chromatography coupled to mass spectrometry (LC–MS) in 3641 participants of the Nagahama study. Our results validated the elevation of 4-CS in CKD and identified a reducing trend in MetS. To delineate the holistic effects of 4-CS, we performed a phenome-wide association analysis (PheWAS) with 937 intermediate biological and behavioral traits. We detected associations between 4-CS and 39 phenotypes related to blood pressure regulation, hepatic and renal functions, hematology, sleep quality, intraocular pressure, ion regulation, ketone and fatty acid metabolisms, disease history and dietary habits. Among them, 19 PheWAS significant traits, including fatty acids and 14 blood pressure indices, were correlated with MetS, suggesting that 4-CS is a potential biomarker for MetS. Consistent associations of this gut microbial-derived metabolite on multiple endophenotypes underlying distinct etiopathogenesis support its role in the overall host health, with prospects of probiotic-based therapeutic solutions in chronic diseases.

Culters are a popular and economically important carnivorous freshwater fish, widely distributed in rivers, lakes, and reservoirs in China. An investigation of Myxozoa was conducted to enhance the understanding of Myxozoan diversity in Culters in China, as only 15 Myxosporean species have been previously reported in 6 Culters species. A new species with typical Myxobolus characteristics was discovered exclusively in the gills of Chanodichthys dabryi, Bleeker, 1871, and no other species were found in other Culters fish or organs. The new species elicited whitish plasmodia in the serosa layer of the gill arch, with no distinct inflammatory reaction observed. This species is morphologically different from all reported Myxobolus spp. from Culters, differing in plasmodium and spore size, as well as the coils of polar filaments. Molecular analysis further supports that it does not match any sequences available in GenBank. Therefore, we identified it as a new species and named it Myxobolus dabryi n. sp.

Chemotherapy-induced peripheral neuropathy (CIPN) remains a clinical challenge for up to 80% of breast cancer survivors. In an open-label study, participants underwent three interventions: standard care (duloxetine) for 1 month (Phase 1), oral cannabidiol (CBD) for 2 months (Phase 2), and CBD plus multi-modal exercise (MME) for another 2 months (Phase 3). Clinical outcomes and gut microbiota composition were assessed at baseline and after each phase. We present the case of a 52-year-old female with a history of triple-negative breast cancer in remission for over five years presenting with CIPN. She showed decreased monocyte counts, c-reactive protein, and systemic inflammatory index after each phase. Duloxetine provided moderate benefits and intolerable side effects (hyperhidrosis). She experienced the best improvement and least side effects with the combined (CBD plus MME) phase. Noteworthy were clinically meaningful improvements in CIPN symptoms, quality of life (QoL), and perceived physical function, as well as improvements in pain, mobility, hand/finger dexterity, and upper and lower body strength. CBD and MME altered gut microbiota, showing enrichment of genera that produce short-chain fatty acids. CBD and MME may improve CIPN symptoms, QoL, and physical function through anti-inflammatory and neuroprotective effects in cancer survivors suffering from long-standing CIPN.

Abstract The increasing application of RNA sequencing to study non-model species demands easy-to-use and efficient bioinformatics tools to help researchers quickly uncover biological and functional insights. We developed ExpressAnalyst ( www.expressanalyst.ca ), a web-based platform for processing, analyzing, and interpreting RNA-sequencing data from any eukaryotic species. ExpressAnalyst contains a series of modules that cover from processing and annotation of FASTQ files to statistical and functional analysis of count tables or gene lists. All modules are integrated with EcoOmicsDB, an ortholog database that enables comprehensive analysis for species without a reference transcriptome. By coupling ultra-fast read mapping algorithms with high-resolution ortholog databases through a user-friendly web interface, ExpressAnalyst allows researchers to obtain global expression profiles and gene-level insights from raw RNA-sequencing reads within 24 h. Here, we present ExpressAnalyst and demonstrate its utility with a case study of RNA-sequencing data from multiple non-model salamander species, including two that do not have a reference transcriptome.

Abstract. The neutrophil-to-lymphocyte ratio (NLR) is an important predictive prognostic tool. However, its accuracy for predicting sepsis remains debatable. This study aimed to evaluate the factors influencing the ability of NLR to predict sepsis. Adult patients with sepsis or septic shock from the eICU database were enrolled in this study. Results showed that there was a significant difference in NLR between the survival and nonsurvival groups (median [lower quartile–upper quartile], 15.27 [7.92–26.28] vs. 17.97 [8.94–31.85], P = 0.008). Area under the curve (AUC) of NLR on the sixth day was the highest. AUC of NLR was significantly higher in the Simplified Acute Physiological Score (SAPS) 3 ≤52 group than that in the SAPS3 > 52 group (0.78 ± 0.05 vs. 0.69 ± 0.03, P = 0.007). AUC of NLR in the nonshock group was significantly higher than that in the shock group (0.72 ± 0.04 vs. 0.65 ± 0.05, P < 0.01). The ability of NLR as a prognosis predictor of sepsis was influenced by admission time, SAPS3, and shock.