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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.

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The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average - 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

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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.

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We report on the supercapacitive swing adsorption (SSA) of carbon dioxide at different voltage windows in the presence of oxygen using activated carbon electrodes, and deliquescent, aqueous electrolytes. The presence of O2 in the CO2/N2 gas mixture results in an up to 11 times higher CO2 adsorption capacity with 3 M MgBr2 (at 0.6 V) and up to 4-5 times higher adsorption capacity with 3 M MgCl2 (at 1 V). A tradeoff between high CO2 adsorption capacities and lower coulombic efficiencies was observed at voltages above 0.6 V. The energetic and adsorptive performance of the electrodes in the presence of oxygen below 0.5 V was similar to the performance with a CO2/N2 mixture without oxygen at 1 V. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) of the electrodes demonstrate that the specific capacitance increases while the diffusion resistance decreases in the presence of oxygen. Oxygen concentrations ranging between 5-20 % give similar energetic and adsorptive performance. The electrodes exhibit stable performance for up to 100 cycles of operation.

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PURPOSE: Early diagnosis of benign and malignant vertebral compression fractures by analyzing imaging data is crucial to guide treatment and assess prognosis, and the development of radiomics made it an alternative option to biopsy examination. This systematic review and meta-analysis was conducted with the purpose of quantifying the diagnostic efficacy of radiomics models in distinguishing between benign and malignant vertebral compression fractures. METHODS: Searching on PubMed, Embase, Web of Science and Cochrane Library was conducted to identify eligible studies published before September 23, 2023. After evaluating for methodological quality and risk of bias using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), we selected studies providing confusion matrix results to be included in random-effects meta-analysis. RESULTS: A total of sixteen articles, involving 1,519 vertebrae with pathological-diagnosed tumor infiltration, were included in our meta-analysis. The combined sensitivity and specificity of the top-performing models were 0.92 (95 % CI: 0.87-0.96) and 0.93 (95 % CI: 0.88-0.96), respectively. Their AUC was 0.97 (95 % CI: 0.96-0.99). By contrast, radiologists' combined sensitivity was 0.90 (95 %CI: 0.75-0.97) and specificity was 0.92 (95 %CI: 0.67-0.98). The AUC was 0.96 (95 %CI: 0.94-0.97). Subsequent subgroup analysis and sensitivity test suggested that part of the heterogeneity might be explained by differences in imaging modality, segmentation, deep learning and cross-validation. CONCLUSION: We found remarkable diagnosis potential in correctly distinguishing vertebral compression fractures in complex clinical contexts. However, the published radiomics models still have a great heterogeneity, and more large-scale clinical trials are essential to validate their generalizability.

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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.

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BACKGROUND: To develop and evaluate a predictive nomogram for polyuria during general anesthesia in thoracic surgery. METHODS: A retrospective study was designed and performed. The whole dataset was used to develop the predictive nomogram and used a stepwise algorithm to screen variables. The stepwise algorithm was based on Akaike's information criterion (AIC). Multivariable logistic regression analysis was used to develop the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the model's discrimination ability. The Hosmer-Lemeshow (HL) test was performed to check if the model was well calibrated. Decision curve analysis (DCA) was performed to measure the nomogram's clinical usefulness and net benefits. P < 0.05 was considered to indicate statistical significance. RESULTS: The sample included 529 subjects who had undergone thoracic surgery. Fentanyl use, gender, the difference between mean arterial pressure at admission and before the operation, operation type, total amount of fluids and blood products transfused, blood loss, vasopressor, and cisatracurium use were identified as predictors and incorporated into the nomogram. The nomogram showed good discrimination ability on the receiver operating characteristic curve (0.6937) and is well calibrated using the Hosmer-Lemeshow test. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: Individualized and precise prediction of intraoperative polyuria allows for better anesthesia management and early prevention optimization.

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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.

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The segmentation of airway from computed tomography (CT) images plays a vital role in pulmonary disease diagnosis, evaluation, surgical planning, and treatment. Nevertheless, it is still challenging for current methods to handle distal thin and low-contrast airways, leading to mis-segmentation issues. This paper proposes a detail-sensitive 3D-UNet (DS-3D-UNet) that incorporates two new modules into 3D-UNet to segment airways accurately from CT images. The feature recalibration module is designed to give more attention to the foreground airway features through a new attention mechanism. The detail extractor module aims to restore multi-scale detailed features by fusion of features at different levels. Extensive experiments were conducted on the ATM'22 challenge dataset composed of 300 CT scans with airway annotations to evaluate its performance. Quantitative comparisons prove that the proposed model achieves the best performance in terms of Dice similarity coefficient (92.6%) and Intersection over Union (86.3%), outperforming other state-of-the-art methods. Qualitative comparisons further exhibit the superior performance of our method in segmenting thin and confused distal bronchi. The proposed model could provide important references for the diagnosis and treatment of pulmonary diseases, holding promising prospects in the field of digital medicine. Codes are available at https://github.com/nighlevil/DS-3D-UNet/tree/master .

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Twisted partially coherent light, characterized by its unique twist factor, offers novel control over the statistical properties of random light. However, the recognition of the twist factor remains a challenge due to the low coherence and the stochastic nature of the optical beam. This paper introduces a method for the recognition of twisted partially coherent beams by utilizing a circular aperture at the source plane. This aperture produces a characteristic hollow intensity structure due to the twist phase. A deep learning model is then trained to identify the twist factor of these beams based on this signature. The model, while simple in structure, effectively eliminates the need for complex optimization layers, streamlining the recognition process. This approach offers a promising solution for enhancing the detection of twisted light and paves the way for future research in this field.

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Alpha-amylases are crucial hydrolase enzymes which have been widely used in food, feed, fermentation, and pharmaceutical industries. Methods for low-cost production of α-amylases are highly desirable. Soybean seed, functioning as a bioreactor, offers an excellent platform for the mass production of recombinant proteins for its ability to synthesize substantial quantities of proteins. In this study, we generated and characterized transgenic soybeans expressing the α-amylase AmyS from Bacillus stearothermophilus. The α-amylase expression cassettes were constructed for seed specific expression by utilizing the promoters of three different soybean storage peptides and transformed into soybean via Agrobacterium-mediated transformation. The event with the highest amylase activity reached 601 U/mg of seed flour (one unit is defined as the amount of enzyme that generates 1 micromole reducing ends per min from starch at 65 °C in pH 5.5 sodium acetate buffer). The optimum pH, optimum temperature, and the enzymatic kinetics of the soybean expressed enzyme are similar to that of the E. coli expressed enzyme. However, the soybean expressed α-amylase is glycosylated, exhibiting enhanced thermostability and storage stability. Soybean AmyS retains over 80% activity after 100 min at 75 °C, and the transgenic seeds exhibit no significant activity loss after one year of storage at room temperature. The accumulated AmyS in the transgenic seeds represents approximately 15% of the total seed protein, or about 4% of the dry seed weight. The specific activity of the transgenic soybean seed flour is comparable to many commercial α-amylase enzyme products in current markets, suggesting that the soybean flour may be directly used for various applications without the need for extraction and purification.

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Carbon-fixing bacterial communities are essential drivers of carbon fixation in estuarine ecosystems that critically affect the global carbon cycle. This study compared the abundances of the Calvin cycle functional genes cbbL and cbbM and Reductive tricarboxylic acid cycle gene aclB, as well as compared carbon-fixing bacterial community features in the two estuaries, predicted potential ecological functions of carbon-fixation bacteria, and analyzed their symbiosis strategies in two estuaries having different geographical distributions. Gammaproteobacteria was the dominant carbon-fixing bacterial community in the two estuaries. However, a higher number of Alphaproteobacteria were noted in the Liaohe Estuary, and a higher number of Betaproteobacteria were found in the Yalujiang Estuary. The carbon-fixing functional gene levels exhibited the order of aclB > cbbL > cbbM, and significant effects of Cu, Pb, and petroleum were observed (p < 0.05). Nitrogen-associated nutrient levels are major environmental factors that affect carbon-fixing bacterial community distribution patterns. Spatial factors significantly affected cbbL carbon-fixing functional bacterial community structure more than environmental factors. With the increase in offshore distance, the microbial-led processes of methylotrophy and nitrogen fixation gradually weakened, but a gradual strengthening of methanotrophy and nitrification was observed. Symbiotic network analysis of the microorganisms mediating these ecological processes revealed that the carbon-fixing bacterial community in these two estuaries had a non-random symbiotic pattern, and microbial communities from the same module were strongly linked among the carbon, nitrogen, and sulfur cycle. These findings could advance the understanding of carbon fixation in estuarine ecosystems.

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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.

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Diabetes mellitus (DM) is one of the most prevalent diseases worldwide, greatly impacting patients' quality of life. This article reviews the progress in Salvia miltiorrhiza, an ancient Chinese plant, for the treatment of DM and its associated complications. Extensive studies have been conducted on the chemical composition and pharmacological effects of S miltiorrhiza, including its anti-inflammatory and antioxidant activities. It has demonstrated potential in preventing and treating diabetes and its consequences by improving peripheral nerve function and increasing retinal thickness in diabetic individuals. Moreover, S miltiorrhiza has shown effectiveness when used in conjunction with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers (ARBs), and statins. The safety and tolerability of S miltiorrhiza have also been thoroughly investigated. Despite the established benefits of managing DM and its complications, further research is needed to determine appropriate usage, dosage, long-term health benefits, and safety.

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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.

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Numerous endeavors have been dedicated to the development of composite polymer electrolyte (CPE) membranes for all-solid-state batteries (SSBs). However, insufficient ionic conductivity and mechanical properties still pose great challenges in practical applications. In this study, a flexible composite electrolyte membrane (FCPE) with fast ion transport channels was prepared using a phase conversion process combined with in situ polymerization. The polyvinylidene fluoride-hexafluoro propylene (PVDF-HFP) polymer matrix incorporated with lithium lanthanum zirconate (LLZTO) formed a 3D net-like structure, and the in situ polymerized polyvinyl ethylene carbonate (PVEC) enhanced the interface connection. This 3D network, with multiple rapid pathways for Li+ that effectively control Li+ flux, led to uniform lithium deposition. Moreover, the symmetrical lithium cells that used FCPE exhibited high stability after 1200 h of cycling at 0.1 mA cm-2. Specifically, all-solid-state lithium batteries coupled with LiFePO4 cathodes can stably cycle for over 100 cycles at room temperature with high Coulombic efficiencies. Furthermore, after 100 cycles, the infrared spectrum shows that the structure of FCPE remains stable. This work demonstrates a novel insight for designing a flexible composite electrolyte for highly safe SSBs.

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Monodisperse mesoporous carbon spheres (MCS) were synthesized and their potential applications in ethylene propylene diene monomer (EPDM) foam were evaluated. The obtained MCS exhibited a high specific surface area ranging from 621-to 735 m2/g along with large pore sizes. It was observed that the incorporation of MCS into EPDM foam rubber significantly enhances its mechanical properties. The prepared MCS-40 rubber composites exhibit the highest tear strength of 210 N/m and tensile strength of 132.72 kPa, surpassing those of other samples. The enhancement mechanism was further investigated by employing computer simulation technology. The pores within the MCS allowed for the infiltration of EPDM molecular chains, thereby strengthening the interaction forces between the filler and matrix. Moreover, a higher specific surface area resulted in greater adsorption of molecular chains onto the surface of these carbon spheres. This research offers novel insights for understanding the enhancement mechanism of monodisperse mesoporous particles/polymer composites (MCS/EPDM) and highlights their potential application in high-performance rubber composites.

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Low-carbon cementitious materials based on blast furnace slag (BFS) and municipal solid waste incineration (MSWI) fly ash play a pivotal role in the construction industry by substituting cement clinker. This innovation significantly reduces CO2 emissions and enables the extensive utilization of both industrial solid waste and hazardous urban waste on a large scale. However, the application of MSWI fly ash as a precursor for alkali-activated cementitious materials presents a significant leaching risk of heavy metal during the extended reaction process, posing a critical barrier to the efficient and widespread utilization of these solid waste. Three static leaching methods [horizontal vibration (HV), sulphuric acid & nitric acid (SN), and acetic acid buffer solution (AAB)], along with acid neutralization capacity (ANC) leaching tests, were applied in BFS-fly ash-based cementitious materials (BFCM) to assess the leaching behavior of high-risk elements-Cr, Sb, and Pb-within MSWI fly ash. The A4 matrix (BFS: MSWI fly ash:FGDG = 70:20:10) exhibits a compressive strength of 72.51 MPa at 180 day, with the leaching concentrations of target elements remaining below the standard limit under chemical attack (H+ and OH-). The critical pH determined is 9.2 from the ANC leaching test results. Visual MINTEQ simulation illustrates the occurrence states of Cr, Sb, and Pb as (CrO4)2-, [Sb(OH)6]-, and Pb(OH)3- within the BFCM system, respectively. The "double salt effect", intended to enhance the dissociation degree of BFS, acts as the driving force behind the long-term hydration reaction. It also serves as an assurance in controlling the long-term leaching risk of object elements. The dissociation degree of BFS within A4 matrix increased by 38.71 %, with the relative content of the typical low-solubility double salt "Ettringite" reaching 29 % at 180 d. This study provides novel theoretical and data-driven evidence to investigate the leaching behavior associated with MSWI fly ash and the accomplishment of replacing cement clinker with low-carbon BFCM.

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OBJECTIVE: Increased angiogenesis is a pathological feature of psoriasis, but the pathomechanisms of angiogenesis in psoriasis are not clear. Interleukin-17A (IL-17A) is the major effect factor in the pathogenesis of psoriasis. Our results showed that IL-17A can promote angiogenesis and cause endothelial cell inflammation. Autophagy plays an important role not only in regulating inflammation, but also in regulating angiogenesis. Whether angiogenesis in psoriasis is related to autophagy remains unclear. In this study, we treated human umbilical vein endothelial cells (HUVECs) with IL-17A to simulate increased angiogenesis to study whether increased angiogenesis in psoriasis is related to autophagy. METHODS AND RESULTS: Our results showed that treatment of HUVECs with IL-17A significantly increased angiogenesis and expression levels of mRNA for multiple proinflammatory cytokines (CCL20, IL-8, CCL2, IL-6, and IL-1ß) and, while decreasing intracellular levels of nitric oxide (NO) and NO synthase (NOS) activity. Moreover, IL-17A inhibited autophagy as shown that IL-17A significantly increased expression levels of LC3II and p62 proteins. Induction of autophagy ameliorated IL-17A-mediated inflammatory response and inhibited angiogenesis, accompanied by increased p-AMPKα(Thr172) and p-ULK1(Ser555) expression, and decreased p-mTOR(Ser2448) and p-ULK1(Ser757) expression. Furthermore, inhibition of either AMPK or lysosomal acidification completely overrode autophagy-induced changes in angiogenesis and NOS activity. Finally, induction of autophagy decreased apoptosis and caspase-3 activity in IL-17A-treated HUVECs. CONCLUSIONS: These results showed that IL-17A is involved in angiogenesis and inflammatory response by inhibiting autophagy through AMPK signaling pathway, suggesting that autophagy may be a new therapeutic target for psoriasis.

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Previous studies have revealed links between metal(loid)s and health problems; however, the link between metal(loid)s and obesity remains controversial. We evaluated the cross-sectional association between metal(loid) exposure in whole blood and obesity among the general population. Vanadium (V), chromium (Cr), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), molybdenum (Mo), cadmium (Cd), antimony (Sb), thallium (T1), and lead (Pb) were measured in 3029 subjects in Guangdong Province (China) using ICP-MS. The prevalence of overweight and obesity (OWO) and abdominal obesity (AOB) was calculated according to body mass index (BMI) and waist circumference (WC), respectively. Multivariate analysis showed that elevated blood Cu, Cd, and Pb levels were inversely associated with the risk of OWO, and these associations were confirmed by a linear dose-response relationship. Elevated blood Co concentration was associated with a decreased risk of AOB. A quantile g-computation approach showed a significantly negative mixture-effect of 13 metal(loid)s on OWO (OR: 0.96; 95% CI: 0.92, 0.99). Two metals-Ni and Mo-were inversely associated with the risk of OWO but positively associated with AOB. We cross-grouped the two obesity measurement types and found that the extremes of metal content were present in people with AOB only. In conclusion, blood Cu, Mo, Ni, Cd, and Pb were inversely associated with the risk of OWO. The presence of blood Co may be protective, while Ni and Mo exposure might increase the risk of AOB. The association between metal(loid) exposure and obesity warrants further investigation in longitudinal cohort studies.

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