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Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.

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Biochar has been proved as a promising and efficient filler in bioretention facilities for enhancing the stormwater pollutants removal. However, the migration behaviors of stormwater pollutants in biochar filled bioretention facilities is unclear. In this study, as one of the most typical stormwater pollutants, naphthalene was selected as an example and a HYDRUS-1D model was first used to understand the migration behavior of naphthalene in a bioretention facility. In comparison with the conventional bioretention soil media (sandy loam), the amended biochar filled bioretention cell showed that the naphthalene removal rate was enhanced by up to 10.1%. Meanwhile, the experimental data was well-fitted by the "two-site sorption model" in HYDRUS-1D model. Another, the effect of rainfall intensity on the naphthalene migration in both bioretention columns was further investigated. The HYDRUS-1D model fitting indicated that the increase in rainfall intensity promoted naphthalene migration by increasing hydraulic conductivity and water flux. In addition, static batch experiments revealed that the biochar filled fillers achieved about 50% higher adsorption capacity than sandy loam. The sensitivity analysis from the HYDRUS-1D model data verified adsorption coefficient Kd and longitudinal dispersivity λ are the main factors affecting naphthalene migration. Finally, the model simulation displays that the proportion of naphthalene retained by the fillers was highest during high rainfall intensities, indicating that the fillers remain the most important fate for naphthalene. This study presents research on the behavior and mechanisms of stormwater pollutant transport through improved bioretention facilities.

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The triptans class of pharmaceuticals, which was created to treat acute migraine, is made up of indole-containing drugs that bind to a subset (1B/1D) of 5-hydroxytryptamine receptors and are agonists of serotonin receptors. At the moment, naratriptan, eletriptan, zolmitriptan, rizatriptan, almotriptan, and frovatriptan are the seven types of triptans available on the market. Among these are the FDA-approved triptans, Zolmitriptan and Sumatriptan, which are selective serotonin (5-hydroxytryptamine) agonists. Zolmitriptan, a synthetic tryptamine derivative and a well-known member of the triptan family, is available as an orally disintegrating tablet, nasal spray, and tablet. There are melt formulations of rizatriptan and zolmitriptan available on the market that are easier to use and absorb, comparable to regular pills. Recently, the FDA approved zolmitriptan, a medication with tolerability comparable to sumatriptan. Whereas zolmitriptan is only available as an oral melt or tablet, sumatriptan is available as a nasal spray, oral preparation, or self-injectable kit. The only known antimigraine drugs that were widely utilized before the triptan period were ergotamine and dihydroergotamine. However, zolmitriptan binds to plasma proteins only 25% of the time because of significant first-pass degradation. Researchers have looked into fresh ideas for solving this issue and innovations to overcome its pharmacokinetic difficulties. This article emphasizes the role of zolmitriptan in the treatment of migraines, highlighting its pharmacological properties, production, metabolism, and structural features.

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The finished precision rolling bearings after processing are required to pass the life test before they can be put into the market. The life testing takes a lot of time and expense. Aiming to solve the problem of time and expense, the 1D-CNN and 1D-CNN-LSTM hybrid neural networks are used for deep learning based on the existing rolling bearing life big data results (a total of 791152 date). Taking the wear of bearing as the target, the life prediction of bearing is carried out by using Python. The results show that: (1) 1D-CNN-LSTM algorithm and "all parameters" are selected as the best prediction options. (2) "XYZ direction displacement" and "all parameters" have the best fitting effect on the predicted wear value, and the MAPE is 4.18877, 1.2102, 2.68903 and 1.19981, respectively. The 1D-CNN-LSTM algorithm is slightly better than the 1D-CNN algorithm. (3) Using 1D-CNN-LSTM algorithm and "all parameters" to predict the bearing wear life will obtain good results. Compared with the highest 1D-CNN and "Four Bearing Temperatures" parameters, it is reduced by 14.7 times. (4) The prediction process and results provide a wear prediction method for relevant bearing enterprises in the experimental running-in stage. It can also provide reliable research ideas for subsequent related enterprises and scholars.

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In this paper, a novel numerical model capable of high-resolution, accurate simulation of the accumulation, wash-off, and migration of nonpoint source (NPS) pollutants on roads is proposed, effectively addressing the challenge of limited pipe network data for high-density urban building communities. This approach is based on a 1D-2D hydrodynamic and water quality dynamic bidirectional coupling model: GAST-SWMM. The calculation accuracy of the GAST two-dimensional road NPS wash-off model is validated via comparison with experimental data. The obtained Nash-Sutcliffe efficiency (NSE) is greater than 0.8. Moreover, the model was used to simulate the NPSs in a densely populated urban region of Xi'an, China, lacking building community pipeline data. The NPS pollutant transport and fate under the influence of both road runoff and the building community hydrodynamic water quality during rainfall events with a specific return period were examined. The proposed model can effectively and accurately replicate the accumulation and removal of NPS pollutants on a two-dimensional road and their dynamic interaction with the drainage network. With increasing rainfall return period, the peak time of the surface contaminant total load is postponed. The maximum surface pollutant load durations during rainfall events with 2-, 10-, and 50-year return periods are 60, 75, and 80 minutes, respectively. During the peak surface pollutant load time, the overflow pollutant fraction can exceed 85% for a 50-year rainfall return period. The simulation method presented in this paper accurately captures the spatial and temporal variations in NPS pollutants in densely populated urban areas, even when pipe network data for building communities are lacking. This method offers valuable technical assistance for urban environmental management and water quality protection.

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Prostate cancer (PCa) is one of the most common and prevalent cancers in men worldwide. The majority of PCa-related deaths result from metastasis rather than primary tumors. Several studies have focused on the relationship between male-specific genes encoded on the Y chromosome and PCa metastasis; however, the relationship between the male specific protein encoded on the Y chromosome and tumor suppression has not been fully clarified. Here, we report a male specific protein of this type, the histone H3 lysine 4 (H3K4) demethylase JARID1D, which has the ability to inhibit the gene expression program related to cell invasion, and can thus form a phenotype that inhibits the invasion of PCa cells. However, JARID1D exhibits low expression level in advanced PCa, and which is related to rapid invasion and metastasis in patients with PCa. Curcumin, as a multi-target drug, can enhance the expression and demethylation activity of JARID1D, affect the androgen receptor (AR) and epithelial-mesenchymal transition (EMT) signaling cascade, and inhibit the metastatic potential of castration resistant cancer (CRPC). These findings suggest that using curcumin to increase the expression and demethylation activity of JARID1D may be a feasible strategy to inhibit PCa metastasis by regulating EMT and AR.

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Researchers have recently shown a great deal of interest in molybdenum diselenide (MoSe2)-based solar cells due to their outstanding semiconducting characteristics. However, discrepancies in the band arrangement at the MoSe2/ETL (electron transport layer) and hole transport layer (HTL)/MoSe2 interfaces impede performances. In this research, a device combination with Ag/FTO/ETL/MoSe2/HTL/Ni is employed, where 7 HTLs and 3 different ETLs have been utilized to explore which device arrangement is superior. To achieve the most effective device arrangement, the effects of various device variables, such as thickness, donor density, acceptor density, defect density, temperature, series, and shunt resistance, are optimized. The computational evaluation under AM 1.5 light spectrums (100 mW/cm2) is performed using the SCAPS-1D simulator. When the several device parameters were optimized, the device that was correlated with Ag/FTO/SnS2/MoSe2/V2O5/Ni revealed the highest overall performances among the three different ETL (In2S3, SnS2, ZnSe)-based devices, with measuring a PCE of 34.07 %, a VOC of 0.918 V, a JSC of 42.565 mAcm-2, and an FF of 87.19 %. This recommended MoSe2-based solar cell exhibits outstanding efficiency in terms of maintenance and comparison to numerical thin film solar cells, highlighting MoSe2 as an attractive option for solar energy systems while eliminating toxicity challenges.

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Background: Zinc (Zn) content is of great importance in healthy human diet, crop productivity and stress tolerance in soils with zinc deficiency. The genes used to increase yield per unit area such as semi-dwarf 1 (sdw1) is commonly considered to reduce mineral content of grain. Methods: In the present study, influence of sdw1.d, a widely used allele for short plant height in barley breeding, on zinc accumulation and tolerance to zinc deficiency were investigated. A near isogenic line of sdw1.d allele, its recurrent parent Tokak 157/37 and donor parent Triumph were grown in zinc-deficient and-sufficient hydroponic cultures. Two experiments were conducted until heading stage and physiological maturity. Results: In zinc-deficient conditions, sdw1.d allele increased shoot dry weight by 112.4 mg plant-1, shoot Zn concentration by 0.9 ppm, but decreased root Zn concentration by 6.6 ppm. It did not affect grain characteristics, but increased grain Zn content. In zinc-sufficient conditions, sdw1.d allele increased shoot Zn content, and decreased root Zn content. sdw1.d did not affect grain weight but increased grain Zn concentration by about 30% under zinc-sufficient conditions. The results showed that sdw1.d allele has no negative effect on tolerance to zinc deficiency, and even promotes tolerance to zinc deficiency by more Zn translocation. It was revealed that sdw1.d allele improves Zn accumulation under both zinc-deficient and zinc-sufficient condition. The sdw1.d allele could contribute to solving the problems in plant growth and development caused by zinc-deficiency via improving tolerance to zinc-deficiency. It could also provide a better Zn biofortification.

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Background: Islet transplantation is a promising treatment for type 1 diabetes that aims to restore insulin production and improve glucose control, but long-term graft survival remains a challenge due to immune rejection. Methods: ScRNA-seq data from syngeneic and allogeneic islet transplantation grafts were obtained from GSE198865. Seurat was used for filtering and clustering, and UMAP was used for dimension reduction. Differentially expressed genes were analyzed between syngeneic and allogeneic islet transplantation grafts. Gene set variation analysis (GSVA) was performed on the HALLMARK gene sets from MSigDB. Monocle 2 was used to reconstruct differentiation trajectories, and cytokine signature enrichment analysis was used to compare cytokine responses between syngeneic and allogeneic grafts. Results: Three distinct macrophage clusters (Mø-C1, Mø-C2, and Mø-C3) were identified, revealing complex interactions and regulatory mechanisms within macrophage populations. The significant activation of macrophages in allogeneic transplants was marked by the upregulation of allograft rejection-related genes and pathways involved in inflammatory and interferon responses. GSVA revealed eight pathways significantly upregulated in the Mø-C2 cluster. Trajectory analysis revealed that Mø-C3 serves as a common progenitor, branching into Mø-C1 and Mø-C2. Cytokine signature enrichment analysis revealed significant differences in cytokine responses, highlighting the distinct immunological environments created by syngeneic and allogeneic grafts. Conclusion: This study significantly advances the understanding of macrophage roles within the context of islet transplantation by revealing the interactions between immune pathways and cellular fate processes. The findings highlight potential therapeutic targets for enhancing graft survival and function, emphasizing the importance of understanding the immunological aspects of transplant acceptance and longevity.

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INTRODUCTION: Dual-expressing lymphocytes (DEs) are unique immune cells that express both B cell receptors (BCRs, surface antibody) and T cell receptors (TCRs). In type 1 diabetes, DE antibodies are predominated by one antibody (x-mAb), an IgM monoclonal antibody with a germline-encoded CDR3 that recognizes self-reactive TCRs. We explored if x-mAb and its interacting TCRs have distinct structural features. METHODS: Using bioinformatics, we compared x-mAb and its most common interacting TCRαß to billions of antigen receptor sequences to determine if they were unique or randomly generated. RESULTS: X-mAb represents a unique class of human antibodies with a conserved CDR3 sequence (CARx1-4DTAMVYYFYDW), consisting of a fixed DJH motif (DTAMVYYFDYW) paired with various VH genes. A public TCRß clonotype (CASSPGTEAFF) associated with x-mAb on DEs features two invariant segments, VßD (CASSPGT) and DJß (PGTEAFF), key to two large families of public TCRß clonotypes-CASSPGT-Jßx and CASSPGT-Jßx-formed by recombining the VßD motif with Jß genes and the DJß motif with Vß genes. B cells also use CASSPGT as a VHD motif for public IGH clonotypes (CASSPGT-Jßx). DISCUSSION: DEs, unlike conventional T and B cells, use invariant motifs to create public antibodies and TCRs, a trait previously seen only in cartilaginous fish.

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As a mature non-destructive testing technology, near-infrared (NIR) spectroscopy can effectively identify and distinguish the structural characteristics of wood. The Wood Defect One-Dimensional Visual Geometry Group 19-Finite Element Analysis (WD-1D-VGG19-FEA) algorithm is used in this study. 1D-VGG19 classifies the near-infrared spectroscopy data to determine the knot area, fiber deviation area, transition area, and net wood area of the solid wood board surface and generates a two-dimensional image of the board surface through inversion. Then, the nonlinear three-dimensional model of wood with defects was established by using the inverse image, and the finite element analysis was carried out to predict the elastic modulus of wood. In the experiment, 270 points were selected from each of the four regions of the wood, totaling 1080 sets of near-infrared data, and the 1D-VGG19 model was used for classification. The results showed that the identification accuracy of the knot area was 95.1%, the fiber deviation area was 92.7%, the transition area was 90.2%, the net wood area was 100%, and the average accuracy was 94.5%. The error range of the elastic modulus prediction of the three-dimensional model established by the VGG19 classification model in the finite element analysis is between 2% and 10%, the root mean square error (RMSE) is about 598. 2, and the coefficient of determination (R2) is 0. 91. This study shows that the combination of the VGG19 algorithm and finite element analysis can accurately describe the nonlinear defect morphology of wood, thus establishing a more accurate prediction model of wood mechanical properties to maximize the use of wood mechanical properties.

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Gamma/delta (γδ) T cells are unconventional lymphocytes that recognize diverse ligands via somatically recombined T cell antigen receptors (γδ TCRs). The molecular mechanism by which ligand recognition initiates γδ TCR signaling, a process known as TCR triggering, remains elusive. Unlike αß TCRs, γδ TCRs are not mechanosensitive and do not require co-receptors or typical binding-induced conformational changes for triggering. Here, we show that γδ TCR triggering by nonclassical MHC class Ib antigens, a major class of ligands recognized by γδ T cells, requires steric segregation of the large cell-surface phosphatases CD45 and CD148 from engaged TCRs at synaptic close-contact zones. Increasing access of these inhibitory phosphatases to sites of TCR engagement, by elongating MHC class Ib ligands or truncating CD45/148 ectodomains, abrogates TCR triggering and T cell activation. Our results identify a critical step in γδ TCR triggering and provide insight into the core triggering mechanism of endogenous and synthetic tyrosine-phosphorylated immunoreceptors.

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Type 2 diabetes mellitus (T2DM) is a complex chronic disease characterized by decreased insulin secretion and the development of insulin resistance. Previous genome-wide association studies demonstrated that single-nucleotide polymorphisms (SNPs) present in genes coding for ion channels involved in insulin secretion increase the risk of developing this disease. We determined the association of 16 SNPs found in CACNA1D, KCNQ1, KCNJ11, and CACNA1E genes and the increased probability of developing T2DM. In this work, we performed a case-control study in 301 Mexican adults, including 201 cases with diabetes and 100 controls without diabetes. Our findings indicate a moderate association between T2DM and the C allele, and the C/C genotype of rs312480 within CACNA1D. The CAG haplotype surprisingly showed a protective effect, whereas the CAC and CGG haplotypes have a strong association with T2DM. The C allele and C/C genotype of rs5219 were significantly associated with diabetes. Also, an association was observed between diabetes and the A allele and the A/A genotype of rs3753737 and rs175338 in CACNA1E. The TGG and CGA haplotypes were also found to be significantly associated. The findings of this study indicate that the SNPs examined could serve as a potential diagnostic tool and contribute to the susceptibility of the Mexican population to this disease.

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Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggested research provides evidence of the reliability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based deep-learning model for investigating SZ. This study suggests Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) for the various EEG-rhythm-based (gamma, beta, alpha, theta, and delta) diagnoses of SZ. The model includes multiple 1-D-Convolution (Con-1-D) folds with steps greater than 1, which enables the model to programmatically and effectively learn how to reduce the incoming signal. The Con-1-D layers and numerous Gated Recurrent Unit (GRU) layers comprise the Exponential-Linear-Unit activation function. This powerful activation function facilitates in-deep-network training and improves classification performance. The Densely-Coupled Convolutional Gated Recurrent Unit (DCGRU) layers enable RDCGRU to address the training accuracy loss brought on by vanishing or exploding gradients, and this might make it possible to develop intense, deep versions of RDCGRU for more complex problems. The sigmoid activation function is implemented in the digital (binary) classifier's output nodes. The RDCGRU deep learning model attained the most excellent accuracy, 88.88 %, with alpha-EEG rhythm. The research achievements: The RDCGRU deep learning model's GRU cells responded superiorly to the alpha-EEG rhythm in EEG-based verification of SZ.

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Tyrosine Kinase Inhibitors (TKI), such as Imatinib, are known for their effectiveness in achieving complete remission from Chronic Myeloid Leukemia (CML), a malignancy caused by a reciprocal translocation between the terminal fragments of the long arms of chromosomes 9 and 22 that leads to the famous chimeric BCR::ABL1 gene. Mutations in this fusion gene may induce resistance to TKI treatment, which requires prescribing a second-, or third-generation TKI medication. We report here a case of a Moroccan CML patient with secondary resistance to the frontline TKI treatment (Imatinib), in which, BCR::ABL1 cDNA sequencing reveals the novel mutation p.K375M at the ABL1 Kinase Domain. In-silico prediction tools confirm the pathogenicity of the p.K375M substitution. Homology analysis indicated that the residue is highly conserved and located in a stable region. This potentially pathogenic mutation is likely to disrupt the BCR::ABL1-Imatinib binding, leading to the observed resistance. To overcome the treatment resistance, Imatinib should be substituted with a second-generation TKI medication, such as Dasatinib, Bosutinib, or Nilotinib. The present study further widens the spectrum of TKI resistance mutations and emphasizes particularly the crucial role of molecular investigation in personalizing treatment for CML patients, ensuring efficient follow-up and appropriate healthcare.

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BACKGROUND: Our previous genome­wide association studies (GWAS) have suggested rs912304 in 14q12 as a suggestive risk variant for type 1 diabetes (T1D). However, the association between this risk region and T1D subgroups and related clinical risk features, the underlying causal functional variant(s), putative candidate gene(s), and related mechanisms are yet to be elucidated. METHODS: We assessed the association between variant rs912304 and T1D, as well as islet autoimmunity and islet function, stratified by the diagnosed age of 12. We used epigenome bioinformatics analyses, dual luciferase reporter assays, and expression quantitative trait loci (eQTL) analyses to prioritize the most likely functional variant and potential causal gene. We also performed functional experiments to evaluate the role of the causal gene on islet function and its related mechanisms. RESULTS: We identified rs912304 as a risk variant for T1D subgroups with diagnosed age ≥ 12 but not < 12. This variant is associated with residual islet function but not islet-specific autoantibody positivity in T1D individuals. Bioinformatics analysis indicated that rs912304 is a functional variant exhibiting spatial overlaps with enhancer active histone marks (H3K27ac and H3K4me1) and open chromatin status (ATAC-seq) in the human pancreas and islet tissues. Luciferase reporter gene assays and eQTL analyses demonstrated that the biallelic sites of rs912304 had differential allele-specific enhancer activity in beta cell lines and regulated STXBP6 expression, which was defined as the most putative causal gene based on Open Targets Genetics, GTEx v8 and Tiger database. Moreover, Stxbp6 was upregulated by T1D-related proinflammatory cytokines but not high glucose/fat. Notably, Stxbp6 over-expressed INS-1E cells exhibited decreasing insulin secretion and increasing cell apoptosis through Glut1 and Gadd45ß, respectively. CONCLUSIONS: This study expanded the genomic landscape regarding late-onset T1D risk and supported islet function mechanistically connected to T1D pathogenesis.

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This study aimed to investigate the prevalence of insulin autoantibody (IAA), glutamic acid decarboxylase antibody (GADA), and insulinoma-associated antigen-2 antibody (IA-2A) in type 1 diabetes (T1D) children based on the presence of predisposing HLA-II alleles. Additionally, to assess the sequence homology between autoantigens of islet cells and selected proteins derived from gut bacteria in terms of their binding capacities to HLA risk alleles, HLA-DRB1/DQB1 alleles were determined by PCR-SSOP in 111 T1D children (probands) along with 222 parents and 133 siblings. Autoantibodies were measured by ELISA, and in silico analysis was run as follows: protein extraction, homology and epitope prediction, peptide alignment, and HLA-peptide docking. Higher significant frequencies of DRB1*03:01, DQB1*02:01, and DQB1*03:02 alleles and DRB1*03:01 ~ DQB1*02:01 haplotype and lower frequencies of DRB1*11:01, DRB1*14:01, and DQB1*03:01 alleles were found in probands compared to parents and siblings. DRB1*11:01 ~ DQB1*03:01, DRB1*14:01 ~ DQB1*05:03, and DRB1*15:01-DQB1*06:02 haplotypes were significantly less frequent in the probands compared to parents. Out of 111 probands, 21 were seronegative, 90 tested positive for one autoantibody, and 15 showed the concurrent presence of three autoantibodies. Logistic regression analysis revealed that DRB1*04 ~ DQB1*03:02 haplotype was associated with the induction of GADA and IA-2A, while DRB1*11:01 ~ DQB1*03:01 was associated with seronegativity. Epitopes derived from GAD and gut bacteria showed strong binding capacities to HLA risk alleles. Due to the sequence similarities between gut bacteria-derived proteins and islet cell autoantigens and their potential for binding to HLA risk alleles, dysbiosis of gut microbiota can be considered another risk factor for the development of T1D, especially in genetically susceptible individuals.

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The role of circRNAs in sepsis-induced lung injury is not clear. This study investigated the role and molecular mechanism of a novel circRNA in sepsis-induced lung injury and explored its prognostic value in sepsis patients. In this study, aberrant circRNA expression profiling in lung tissues from mice with sepsis-induced lung injury was analyzed using high-throughput sequencing. CircRNA-Cacna1d was verified by quantitative real-time polymerase chain reaction, and its biological function in sepsis-induced lung injury was validated in vitro and in vivo. The interactions among circRNA-Cacna1d, miRNAs, and their downstream genes were verified. Furthermore, the clinical value of circRNA-Cacna1d in peripheral blood from sepsis patients was also evaluated. We found that circRNA-Cacna1d expression was significantly increased in lung tissues of sepsis mice and microvascular endothelial cells after lipopolysaccharide (LPS) challenge. CircRNA-Cacna1d knockdown alleviated inflammatory response and ameliorated the permeability of vascular endothelium, thereby mitigating sepsis-induced lung injury and significantly improving the survival rate of sepsis mice. Mechanistically, circRNA-Cacna1d directly interacted with miRNA-185-5p and functioned as a miRNA sponge to regulate the RhoA/ROCK1 signaling pathway. The expression level of circRNA-Cacna1d in patients with early sepsis was significantly higher than that in the healthy controls. Higher levels of circRNA-Cacna1d in sepsis patients were associated with increased disease severity and poorer outcomes. In conclusions, circRNA-Cacna1d may play a role in sepsis-induced lung injury by regulating the RhoA/ROCK1 axis by acting as miRNA-185-5p sponge. CircRNA-Cacna1d is a potential therapeutic target for sepsis-induced lung injury and a prognostic biomarker in sepsis.

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BACKGROUND: Type 1 diabetes mellitus (T1D) is an autoimmune disease caused by destruction of pancreatic islet beta-cells. There is significant residual beta-cell function, measured through circulating C-peptide, present at the time of T1D diagnosis but this subsequently decreases with time. Higher residual beta-cell function at diagnosis associates with better glycaemic control and less glucose variability, and later in the disease course with less hypoglycaemia, lower glucose variability and fewer microvascular complications. There is therefore value in preserving residual beta cell function in new onset T1D Immunotherapeutic agents can protect residual beta-cell function in type 1 diabetes. However, clinical trials of such agents, whilst demonstrating C-peptide preservation in short term studies, have yet to be taken forward into routine clinical care due to concerns around safety and long-term efficacy. Here we report the case of a gentleman with newly diagnosed T1D whose glycaemic control and insulin requirement improved whilst on a five year infusion programme of infliximab, a monoclonal antibody against TNF-alpha, for colitis. CASE PRESENTATION: A 52-year-old White Caucasian man was diagnosed with T1D in August 2018. Glucose was 25.6 mmol/L, HbA1c was 98mmol/mol and GAD antibodies were strongly positive. HbA1c marginally improved to 91mmol/mol following initiation of insulin detemir 5 units at night and 1:10 g of insulin aspart (November 2018). In June 2019, he developed rectal bleeding and abdominal pain. Following colonoscopy, he was diagnosed with "indeterminate colitis" and commenced on 6-weekly infusions of 400-450 mg infliximab. Thus far, he has received 32 doses and achieved colitis remission. Following infliximab initiation there was increased frequency of mild-moderate hypoglycaemia and he was gradually weaned off and discontinued detemir in June 2020. Since then, HbA1c improved from 57mmol/mol in August 2019 to 52mmol/mol in April 2022, remaining stable at 51mmol/mol. His most recent HbA1c is 54mmol/mol in February 2024. His c-peptide was 550pmol/L in October 2022 and 442pmol/L in February 2024, suggesting well-preserved beta-cell function almost 6 years post-diagnosis. CONCLUSIONS: Our patient's improvement in glycaemic control can be explained by immunomodulation and C peptide preservation from infliximab. With the growing focus on type 1 diabetes disease modulation and working towards an 'insulin free T1D', our findings strengthen the evidence base for the repurposing of and long-term treatment with anti-TNF-α agents to preserve beta-cell function in new onset T1D.

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Transition metal sulfides (TMSs) show the potential to be competitive candidates as next-generation anode materials for Li-ion batteries (LIBs) due to their high theoretical specific capacity. However, sluggish ionic/electronic transportation and huge volume change upon lithiation/delithiation remain major challenges in developing practical TMS anodes. We rationally combine structural design and interface engineering to fabricate a tubular-like nanocomposite with embedded crystalline Cu9S5 nanoparticles and amorphous MoSx in a carbon matrix (C/Cu9S5-MoSx NTs). On the one hand, the hybrid integrated the advantages of 1D hollow nanostructures and carbonaceous materials, whose high surface-to-volume ratios, inner void, flexibility, and high electronic conductivity not only enhance ion/electron transfer kinetics but also effectively buffer the volume changes of metal sulfides during charge/discharge. On the other hand, the formation of crystalline-amorphous heterostructures between Cu9S5 and MoSx could further boost charge transfer due to an induced built-in electric field at the interface and the presence of a long-range disorder phase. In addition, amorphous MoSx offers an extra elastic buffer layer to release the fracture risk of Cu9S5 crystalline nanoparticles during repetitive electrochemical reactions. Benefiting from the above synergistic effect, the C/Cu9S5-MoSx electrode as an LIB anode in an ether-based electrolyte achieves a high-rate capability (445 mAh g-1 at 6 A g-1) and superior ultralong-term cycling stability, which delivers an initial discharge capacity of 561 mAh g-1 at 2 A g-1 and its retention capacity after 3600 cycles (376 mAh g-1) remains higher than that of commercial graphite (372 mAh g-1).