Downregulation of Chloride voltage-gated channel 7 contributes to hyperalgesia following spared nerve injury.

Alterations in anion balance potential, along with the involvement of cation-chloride cotransporters, play pivotal roles in the development of hyperalgesia after peripheral nerve injury (PNI). Chloride voltage-gated channel 7 (CLCN7) is the predominant member of the CLC protein family. Investigations on CLCN7 have focused primarily on its involvement in osteosclerosis and lysosomal storage disorders; nevertheless, its contribution to neuropathic pain (NP) has not been determined. In this investigation, we noted high expression of CLCN7 in neurons situated within the spinal dorsal horns (SDHs) and dorsal root ganglions (DRGs). Immunofluorescence analysis revealed that CLCN7 was predominantly distributed among IB4-positive and CGRP-positive neurons. Furthermore, the expression of CLCN7 was observed to be mainly reduced in neurons within the SDHs and in small and medium-sized neurons located in the DRGs of spared nerve injury (SNI) mice. Knockdown of CLCN7 via siRNA in the DRGs resulted in increased mechanical and thermal hyperalgesia in naïve mice. Furthermore, the excitability of cultured DRG neurons in vitro was augmented upon treatment with CLCN7 siRNA. These findings suggested that CLCN7 downregulation following SNI was crucial for the manifestation of mechanical and thermal hyperalgesia, highlighting potential targeting strategies for treating NP.

Pangenome graph layout by Path-Guided Stochastic Gradient Descent.

MOTIVATION: The increasing availability of complete genomes demands for models to study genomic variability within entire populations. Pangenome graphs capture the full genomic similarity and diversity between multiple genomes. In order to understand them, we need to see them. For visualization, we need a human-readable graph layout: a graph embedding in low (e.g. two) dimensional depictions. Due to a pangenome graph's potential excessive size, this is a significant challenge. RESULTS: In response, we introduce a novel graph layout algorithm: the Path-Guided Stochastic Gradient Descent (PG-SGD). PG-SGD uses the genomes, represented in the pangenome graph as paths, as an embedded positional system to sample genomic distances between pairs of nodes. This avoids the quadratic cost seen in previous versions of graph drawing by SGD. We show that our implementation efficiently computes the low-dimensional layouts of gigabase-scale pangenome graphs, unveiling their biological features. AVAILABILITY AND IMPLEMENTATION: We integrated PG-SGD in ODGI which is released as free software under the MIT open source license. Source code is available at https://github.com/pangenome/odgi.

Genetic variation underlying differential ammonium and nitrate responses in Arabidopsis thaliana.

Nitrogen is an essential element required for plant growth and productivity. Understanding the mechanisms and natural genetic variation underlying nitrogen use in plants will facilitate the engineering of plant nitrogen use to maximize crop productivity while minimizing environmental costs. To understand the scope of natural variation that may influence nitrogen use, we grew 1,135 Arabidopsis thaliana natural genotypes on two nitrogen sources, nitrate and ammonium, and measured both developmental and defense metabolite traits. By using different environments and focusing on multiple traits, we identified a wide array of different nitrogen responses. These responses are associated with numerous genes, most of which were not previously associated with nitrogen responses. Only a small portion of these genes appear to be shared between environments or traits, while most are predominantly specific to a developmental or defense trait under a specific nitrogen source. Finally, by using a large population, we were able to identify unique nitrogen responses, such as preferring ammonium or nitrate, which appear to be generated by combinations of loci rather than a few large-effect loci. This suggests that it may be possible to obtain novel phenotypes in complex nitrogen responses by manipulating sets of genes with small effects rather than solely focusing on large-effect single gene manipulations.

TLR7 enhancing follicular helper T (Tfh) cells response in C57BL/6 mice infected with Plasmodium yoelii NSM TLR7 mediated Tfh cells in P. yoelii infected mice.

Toll-like receptors (TLRs) play an important role in inducing innate and acquired immune responses against infection. However, the effect of Toll-like receptor 7 (TLR7) on follicular helper T (Tfh) cells in mice infected with Plasmodium is still not clear. The results showed that the splenic CD4+ CXCR5+ PD-1+ Tfh cells were accumulated after Plasmodium yoelii NSM infection, the content of splenic Tfh cells was correlated to parasitemia and/or the red blood cells (RBCs) counts in the blood. Moreover, the expression of TLR7 was found higher than TLR2, TLR3 and TLR4 in splenic Tfh cells of the WT mice. TLR7 agonist R848 and the lysate of red blood cells of infected mice (iRBCs) could induce the activation and differentiation of splenic Tfh cells. Knockout of TLR7 leads to a decrease in the proportion of Tfh cells, down-regulated expression of functional molecules CD40L, IFN-γ, IL-21 and IL-10 in Tfh cells; decreased the proportion of plasma cells and antibody production and reduces the expression of STAT3 and Ikzf2 in Tfh cells. Administration of R848 could inhibit parasitemia, enhance splenic Tfh cell activation and increase STAT3 and Ikzf2 expression in Tfh cells. In summary, this study shows that TLR7 could regulate the function of Tfh cells, affecting the immune response in the spleen of Plasmodium yoelii NSM-infected mice.

Trained immunity from the perspective of Plasmodium infection.

The immune system of vertebrates includes innate immunity and adaptive immunity, and the network between them enables the host to fight against invasions of various pathogens. Recently, studies discovered that immune memory is one of the features of innate immunity, breaking the previous opinion that immune memory exists only in adaptive immunity. Immune memory supports innate immune cells to respond efficiently upon reinfection or restimulation. During the Plasmodium infection, the innate immune system is the first to be triggered, and innate immune cells are activated by components from Plasmodium or Plasmodium-infected red blood cells. Innate immune cells could be induced to develop memory after the activation and may play an important role in the subsequent infection of Plasmodium or other pathogens and stimulation. This review will discuss the recent findings relevant to trained immunity and Plasmodium infection, facilitating the understanding of the role of trained immunity in malaria and other diseases and the development of therapeutic strategies based on trained immunity.

Scaling Supercapacitive Swing Adsorption of CO2 Using Bipolar Electrode Stacks.

Supercapacitive swing adsorption (SSA) modules with bipolar stacks having 2, 4, 8, and 12 electrode pairs made from BPL 4 × 6 activated carbon are constructed and tested for carbon dioxide capture applications. Tests are performed with simulated flue gas (15%CO2 /85%N2) at 2, 4, 8, and 12 V, respectively. Reversible adsorption with sorption capacities (≈58 mmol kg-1) and adsorption rates (≈38 µmol kg-1 s-1) are measured for all stacks. The productivity scales with the number of cells in the module, and increases from 70 to 390 mmol h-1 m-2. The energy efficiency and energy consumption improve with increasing number of bipolar electrodes from 67% to 84%, and 142 to 60 kJ mol-1, respectively. Overall, the results show that SSA modules with bipolar electrodes can be scaled without reducing the adsorptive performance, and with improvement of energetic performance.

Isolation, identification, and pathogenicity of two novel goose astrovirus from goslings with severe gout in China.

Goose astroviruses (GAstVs) are important pathogens which can cause gout in goslings leading to huge economic losses for the goose farming industry in China. In 2023, an infectious disease characterized by visceral gout broke out in commercial goose farms in Guangxi and Guangdong provinces of China. In this study, two GAstV strains of GXNN and GDCS were successfully isolated from these two disease-ridden goose farms. The complete genomic lengths of these two strains were 7166 bp, and phylogenetic analysis showed that they were both GAstV-2 subtypes. The 3-dimensional structures of the capsid protein were predicted and six characteristic mutation sites at amino acid positions 60, 61, 228, 229, 456 and 523 were found within the strong antigenic regions. A recombination event occurred at 6833-7070 nt between the GAstV TZ03 and Turkey astrovirus CA/00 and this was detected in both the GXNN and GDCS strains. Another recombinant event occurred at 63-2747 nt between the GAstV XT1 and GAstV SDPY and this was detected in the GDCS strain. When 1-day-old goslings were infected with the novel GXNN and GDCS strains, they showed severe visceral gout. This was accompanied by enlarged spleens, liver hemorrhages and urate deposits in the kidneys and ureters and their blood urea nitrogen levels were significantly elevated. The mortality rates of the GXNN- and GDCS-infected groups were pathogenically high at 80 % and 60 %, respectively. These results will promote our understanding of the evolution and epidemic potential of GAstVs in China.

Multi-feature, Chinese-Western medicine-integrated prediction model for diabetic peripheral neuropathy based on machine learning and SHAP.

BACKGROUND: Clinical studies have shown that diabetic peripheral neuropathy (DPN) has been on the rise, with most patients presenting with severe and progressive symptoms. Currently, most of the available prediction models for DPN are derived from general clinical information and laboratory indicators. Several Traditional Chinese medicine (TCM) indicators have been utilised to construct prediction models. In this study, we established a novel machine learning-based multi-featured Chinese-Western medicine-integrated prediction model for DPN using clinical features of TCM. MATERIALS AND METHODS: The clinical data of 1581 patients with Type 2 diabetes mellitus (T2DM) treated at the Department of Endocrinology of the First Affiliated Hospital of Anhui University of Chinese Medicine were collected. The data (including general information, laboratory parameters and TCM features) of 1142 patients with T2DM were selected after data cleaning. After baseline description analysis of the variables, the data were divided into training and validation sets. Four prediction models were established and their performance was evaluated using validation sets. Meanwhile, the accuracy, precision, recall, F1 score and area under the curve (AUC) of ROC were calculated using ten-fold cross-validation to further assess the performance of the models. An explanatory analysis of the results of the DPN prediction model was carried out using the SHAP framework based on machine learning-based prediction models. RESULTS: Of the 1142 patients with T2DM, 681 had a comorbidity of DPN, while 461 did not. There was a significant difference between the two groups in terms of age, cause of disease, systolic pressure, HbA1c, ALT, RBC, Cr, BUN, red blood cells in the urine, glucose in the urine, and protein in the urine (p < 0.05). T2DM patients with a comorbidity of DPN exhibited diverse TCM symptoms, including limb numbness, limb pain, hypodynamia, thirst with desire for drinks, dry mouth and throat, blurred vision, gloomy complexion, and unsmooth pulse, with statistically significant differences (p < 0.05). Our results showed that the proposed multi-featured Chinese-Western medicine-integrated prediction model was superior to conventional models without characteristic TCM indicators. The model showed the best performance (accuracy = 0.8109, precision = 0.8029, recall = 0.9060, F1 score = 0.8511, and AUC = 0.9002). SHAP analysis revealed that the dominant risk factors that caused DPN were TCM symptoms (limb numbness, thirst with desire for drinks, blurred vision), age, cause of disease, and glycosylated haemoglobin. These risk factors were exerted positive effects on the DPN prediction models. CONCLUSIONS: A multi-feature, Chinese-Western medicine-integrated prediction model for DPN was established and validated. The model improves early-stage identification of high-risk groups for DPN in the diagnosis and treatment of T2DM, while also providing informative support for the intelligent management of chronic conditions such as diabetes.

Temperature-responsive hydrogel-grafted vessel-on-a-chip: Exploring cold-induced endothelial injury.

Cold-induced vasoconstriction is a significant contributor that leads to chilblains and hypothermia in humans. However, current animal models have limitations in replicating cold-induced acral injury due to their low sensitivity to cold. Moreover, existing in vitro vascular chips composed of endothelial cells and perfusion systems lack temperature responsiveness, failing to simulate the vasoconstriction observed under cold stress. This study presents a novel approach where a microfluidic bioreactor of vessel-on-a-chip was developed by grafting the inner microchannel surface of polydimethylsiloxane with a thermosensitive hydrogel skin composed of N-isopropyl acrylamide and gelatin methacrylamide. With a lower critical solution temperature set at 30°C, the gel layer exhibited swelling at low temperatures, reducing the flow rate inside the channel by 10% when the temperature dropped from 37°C to 4°C. This well mimicked the blood stasis observed in capillary vessels in vivo. The vessel-on-a-chip was further constructed by culturing endothelial cells on the surface of the thermosensitive hydrogel layer, and a perfused medium was introduced to the cells to provide a physiological shear stress. Notably, cold stimulation of the vessel-on-a-chip led to cell necrosis, mitochondrial membrane potential (ΔΨm) collapse, cytoskeleton disaggregation, and increased levels of reactive oxygen species. In contrast, the static culture of endothelial cells showed limited response to cold exposure. By faithfully replicating cold-induced endothelial injury, this groundbreaking thermosensitive vessel-on-a-chip technology offers promising advancements in the study of cold-induced cardiovascular diseases, including pathogenesis and therapeutic drug screening.

Establishment and external validation of an early warning model of diabetic peripheral neuropathy based on random forest and logistic regression.

OBJECTIVE: The primary objective of this study was to investigate the risk factors for diabetic peripheral neuropathy (DPN) and to establish an early diagnostic prediction model for its onset, based on clinical data and biochemical indices. METHODS: Retrospective data were collected from 1,446 diabetic patients at the First Affiliated Hospital of Anhui University of Chinese Medicine and were split into training and internal validation sets in a 7:3 ratio. Additionally, 360 diabetic patients from the Second Affiliated Hospital were used as an external validation cohort. Feature selection was conducted within the training set, where univariate logistic regression identified variables with a p-value < 0.05, followed by backward elimination to construct the logistic regression model. Concurrently, the random forest algorithm was applied to the training set to identify the top 10 most important features, with hyperparameter optimization performed via grid search combined with cross-validation. Model performance was evaluated using ROC curves, decision curve analysis, and calibration curves. Model fit was assessed using the Hosmer-Lemeshow test, followed by Brier Score evaluation for the random forest model. Ten-fold cross-validation was employed for further validation, and SHAP analysis was conducted to enhance model interpretability. RESULTS: A nomogram model was developed using logistic regression with key features: limb numbness, limb pain, diabetic retinopathy, diabetic kidney disease, urinary protein, diastolic blood pressure, white blood cell count, HbA1c, and high-density lipoprotein cholesterol. The model achieved AUCs of 0.91, 0.88, and 0.88 for the training, validation, and test sets, respectively, with a mean AUC of 0.902 across 10-fold cross-validation. Hosmer-Lemeshow test results showed p-values of 0.595, 0.418, and 0.126 for the training, validation, and test sets, respectively. The random forest model demonstrated AUCs of 0.95, 0.88, and 0.88 for the training, validation, and test sets, respectively, with a mean AUC of 0.886 across 10-fold cross-validation. The Brier score indicates a good calibration level, with values of 0.104, 0.143, and 0.142 for the training, validation, and test sets, respectively. CONCLUSION: The developed nomogram exhibits promise as an effective tool for the diagnosis of diabetic peripheral neuropathy in clinical settings.