Causal association between 731 immunocyte phenotypes and liver cirrhosis: A bidirectional two-sample mendelian randomization analysis.

BACKGROUND: Liver cirrhosis is a progressive hepatic disease whose immunological basis has attracted increasing attention. However, it remains unclear whether a concrete causal association exists between immunocyte phenotypes and liver cirrhosis. AIM: To explore the concrete causal relationships between immunocyte phenotypes and liver cirrhosis through a mendelian randomization (MR) study. METHODS: Data on 731 immunocyte phenotypes were obtained from genome-wide association studies. Liver cirrhosis data were derived from the Finn Gen dataset, which included 214403 individuals of European ancestry. We used inverse variable weighting as the primary analysis method to assess the causal relationship. Sensitivity analyses were conducted to evaluate heterogeneity and horizontal pleiotropy. RESULTS: The MR analysis demonstrated that 11 immune cell phenotypes have a positive association with liver cirrhosis [P < 0.05, odds ratio (OR) > 1] and that 9 immunocyte phenotypes were negatively correlated with liver cirrhosis (P < 0.05, OR < 1). Liver cirrhosis was positively linked to 9 immune cell phenotypes (P < 0.05, OR > 1) and negatively linked to 10 immune cell phenotypes (P < 0.05; OR < 1). None of these associations showed heterogeneity or horizontally pleiotropy (P > 0.05). CONCLUSION: This bidirectional two-sample MR study demonstrated a concrete causal association between immunocyte phenotypes and liver cirrhosis. These findings offer new directions for the treatment of liver cirrhosis.

Identification of novel pQTL-SNPs associated with lung adenocarcinoma risk: A multi-stage study.

BACKGROUND AND OBJECTIVE: To explore the association between protein quantitative trait loci (pQTL-SNPs) and the risk of LUAD. METHODS: "Blood +" high depth blood proteomics analysis was performed on plasma from female LUAD patients and female healthy controls, and combined with proteomics data from tumors and adjacent non-tumor tissues of female LUAD patients to screen proteins uniformly expressed in plasma and tissues. pQTL-SNPs were then screened through multiple databases and subjected to multilevel screening. The associations between selected pQTL-SNPs and LUAD risk were evaluated by Female Lung Cancer Consortium in Asia GWAS (FLCCA GWAS). Enzyme linked immunosorbent assay (ELISA) is used to determine the levels of candidate protein. RESULTS: A total of 7 pQTL-SNPs were significantly associated with altered LUAD risk (p < 0.05). Meanwhile, the expression of their corresponding target proteins were all decreased in both plasma and tumor tissues of LUAD cases, which may play a role of tumor suppressor proteins. After mutation of 3 pQTL-SNPs (rs7683000, rs73224660, and rs2776937), the expression of corresponding target proteins BST1 and NRP1 decreased, and as potential tumor suppressor proteins, which may promote tumorigenesis and further increasing the risk of developing LUAD (OR >1, p < 0.05); while after mutation the other pQTL-SNP rs62069916, the corresponding target protein APOH expression was increased, while as a potential tumor suppressor protein, which may inhibit tumorigenesis and further reduced the risk of developing LUAD (OR <1, p < 0.05). In addition, the expression of NRP1 and APOH were significant decreased in LUAD cell lines and validated in plasma of LUAD patients. CONCLUSION: A total of 4 pQTL-SNPs (rs7683000, rs73224660, rs2776937, and rs62069916) may associate with altered LUAD risk by regulating the expression of target proteins (BST1, NRP1, and APOH) after mutation.

Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank.

This study investigates the relationship between modifiable risk factors and dementia subtypes using data from 460,799 participants in the UK Biobank. Utilizing univariate Cox proportional hazards regression models, we examined the associations between 83 modifiable risk factors and the risks of all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD). Composite scores for different domains were generated by aggregating risk factors associated with ACD, AD, and VD, respectively, and their joint associations were assessed in multivariable Cox models. Additionally, population attributable fractions (PAF) were utilized to estimate the potential impact of eliminating adverse characteristics of the risk domains. Our findings revealed that an unfavorable medical history significantly increased the risk of ACD, AD, and VD (hazard ratios (HR) = 1.88, 95% confidence interval (95% CI): 1.74-2.03, p < 0.001; HR = 1.80, 95% CI: 1.54-2.10, p < 0.001; HR = 2.39, 95% CI: 2.10-2.71, p < 0.001, respectively). Blood markers (PAF = 12.1%; 17.4%) emerged as the most important risk domain for preventing ACD and VD, while psychiatric factors (PAF = 18.3%) were the most important for preventing AD. This study underscores the potential for preventing dementia and its subtypes through targeted interventions for modifiable risk factors. The distinct insights provided by HR and PAF emphasize the importance of considering both the strength of the associations and the population-level impact of dementia prevention strategies. Our research provides valuable guidance for developing effective public health interventions aimed at reducing the burden of dementia, representing a significant advancement in the field.

Dietary Aflatoxin G1 exposure causes an imbalance between pulmonary tissue-resident alveolar macrophages and monocyte-derived macrophages in both mother and offspring mice.

Aflatoxin G1 (AFG1) is a mycotoxin commonly found in agricultural products, including dried fruits, meat, and milk products. Oral AFG1 administration induced tumor necrosis factor (TNF)-α-dependent chronic pulmonary inflammation, promoting AFG1-induced damage in alveolar epithelial cell, which is associated with lung adenocarcinoma. Pulmonary macrophages may be divided into tissue-resident alveolar macrophages (TRAMs) and monocyte-derived macrophages (MoMs), which involve in chronic lung inflammation. However, whether these macrophages contribute to AFG1-induced chronic pulmonary inflammation remains unknown. In this study, we found oral AFG1 administration disrupted the balance between TRAMs and MoMs, increasing MoMs infiltration and decreasing the number of TRAMs. AFG1 upregulated TNF-α expression in MoMs, but downregulated sialic acid binding Ig-like lectin F (Siglec-F) expression in TRAMs. Inhibition of TNF-α-dependent inflammation rescued the imbalance between TRAMs and MoMs in AFG1-treated lung tissues. Additionally, AFG1 stimulated MoMs differentiation to the proinflammatory M1 phenotype in vitro. Using a specific in vitro TRAM model, AFG1 downregulated Siglec-F and the M2 phenotypic markers arginase 1 and YM1, and upregulated the M1 phenotypic markers IL-6, iNOS and TNF-α, altering the TRAMs phenotype to the pro-inflammatory M1 phenotype in vitro. Additionally, mouse maternal dietary exposure to AFG1 caused an imbalance in pulmonary macrophages, decreasing TRAMs and increasing MoMs population in offspring, which was associated with proliferative lesions in the alveolar septa. Thus, dietary AFG1 exposure triggered an imbalance in pulmonary macrophages in both mother and offspring mice, and induced pro-inflammatory phenotypic alterations, which contributed to AFG1-induced chronic lung inflammation. These results provide clues to how AFG1-induced immunotoxicity and genotoxicity in humans might be prevented.

Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images.

Detecting and segmenting unstained living adherent cells in differential interference contrast (DIC) images is crucial in biomedical research, such as cell microinjection, cell tracking, cell activity characterization, and revealing cell phenotypic transition dynamics. We present a robust approach, starting with dataset transformation. We curated 520 pairs of DIC images, containing 12,198 HepG2 cells, with ground truth annotations. The original dataset was randomly split into training, validation, and test sets. Rotations were applied to images in the training set, creating an interim "α set." Similar transformations formed "ß" and "γ sets" for validation and test data. The α set trained a Mask R-CNN, while the ß set produced predictions, subsequently filtered and categorized. A residual network (ResNet) classifier determined mask retention. The γ set underwent iterative processing, yielding final segmentation. Our method achieved a weighted average of 0.567 in average precision (AP)0.75bbox and 0.673 in AP0.75segm, both outperforming major algorithms for cell detection and segmentation. Visualization also revealed that our method excels in practicality, accurately capturing nearly every cell, a marked improvement over alternatives.

Transformer-based approaches for neuroimaging: an in-depth review of their role in classification and regression tasks.

In the ever-evolving landscape of deep learning (DL), the transformer model emerges as a formidable neural network architecture, gaining significant traction in neuroimaging-based classification and regression tasks. This paper presents an extensive examination of transformer's application in neuroimaging, surveying recent literature to elucidate its current status and research advancement. Commencing with an exposition on the fundamental principles and structures of the transformer model and its variants, this review navigates through the methodologies and experimental findings pertaining to their utilization in neuroimage classification and regression tasks. We highlight the transformer model's prowess in neuroimaging, showcasing its exceptional performance in classification endeavors while also showcasing its burgeoning potential in regression tasks. Concluding with an assessment of prevailing challenges and future trajectories, this paper proffers insights into prospective research directions. By elucidating the current landscape and envisaging future trends, this review enhances comprehension of transformer's role in neuroimaging tasks, furnishing valuable guidance for further inquiry.

Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.

The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.

Poly I: C Alleviated Duck Intestinal Injury Infected with Duck Viral Enteritis by Inhibiting Apoptosis.

Duck enteritis virus (DEV) may lead to vascular injury, gastrointestinal mucosal erosion, lymphoid organ injury, and Polyinosinic-polycytidylic acid (Poly I:C) has an antiviral effect by inducing low levels of interferon. The purpose of this study was to explore the pathogenesis of DEV-induced intestinal injury in ducks and to verify the therapeutic effects of different concentrations of Poly I:C. In this study, duck enteritis model was established by infecting healthy Pekin ducks with DEV. Duck intestinal tissues were extracted from normal control group, model group, and treatment group with different doses of Poly I:C. In vivo, HE and TUNEL staining were used to observe the morphological changes and apoptosis. In vitro, the proliferation and apoptosis of duck intestinal epithelial cells were evaluated by MTT assay, TUNEL staining, and flow cytometry. The results showed that Poly I:C protected ducks from DEV toxicity by improving intestinal morphology and inhibiting apoptosis. In addition, the antiviral effect of Poly I:C on DEV was found in a dose-dependent manner, with a more relatively obvious effect at a high dose of Poly I:C. All in all, these results demonstrated that Poly I:C played a vital role in the apoptosis induced by DEV in ducks and modest dose of Poly I:C treatment worked well and may provide important reference for the development of new antiviral drugs in the future.

Ultrasonication-Boosted Resorcinol-Formaldehyde Resin Nanoparticle Bromine Fixation and Corresponding Upgraded Aquatic Applications.

Bromine (Br2) and related species removal from water systems are rather complicated due to the complicated chemistry instability, and materials with high Br2 removal rate and efficiency, along with stimuli/apparatus suitable for highly corrosive environments, are necessary. Ultrasonication as a non-destructive process is especially suitable in scenarios where conventional stir apparatus is not applicable, such as highly corrosive environments. Considering the validity nature of Br2 and combining the advantages of ultrasonic with a highly stable Br2 fixation method through aromatic polymer nanoparticles, we demonstrate highly efficient acoustic-aided Br2 removal in aqueous solutions with two times capacity compared to the non-treated sample. Related aquatic applications are also proposed for the materials to be cost-effective, including silver (Ag) recovery, recyclable MnO2-mediated Br2 deep removal, and aqueous zinc anode modification. The coupled novel-material-based processes motivate the strategic design of water purification with high-safety and sustainable industrial procedures and post-value-added utilizations.

Contribution of gut-derived T cells to extraintestinal autoimmune diseases.

The mammalian intestine harbors abundant T cells with high motility, where these cells can affect both intestinal and extraintestinal disorders. Growing evidence shows that gut-derived T cells migrate to extraintestinal organs, contributing to the pathogenesis of certain autoimmune diseases, including type 1 diabetes (T1D) and multiple sclerosis (MS). However, three key questions require further elucidation. First, how do intestinal T cells egress from the intestine? Second, how do gut-derived T cells enter organs outside the gut? Third, what is the pathogenicity of gut-derived T cells and their correlation with the gut microenvironment? In this Opinion, we propose answers to these questions. Understanding the migration and functional regulation of gut-derived T cells might inform precise targeting for achieving safe and effective approaches to treat certain extraintestinal autoimmune diseases.

Crosstalk among Alternative Polyadenylation, Genetic Variants and Ubiquitin Modification Contribute to Lung Adenocarcinoma Risk.

Ubiquitin modification and alternative polyadenylation play crucial roles in the onset and progression of cancer. Hence, this study aims to comprehensively and deeply understand gene regulation and associated biological processes in lung adenocarcinoma (LUAD) by integrating both mechanisms. Alternative polyadenylation (APA)-related E3 ubiquitin ligases in LUAD were identified through multiple databases, and the association between selected genetic loci influencing gene expression (apaQTL-SNPs) and LUAD risk were evaluated through the GWAS database of the Female Lung Cancer Consortium in Asia (FLCCA). Subsequently, the interaction between RNF213 and ZBTB20, as well as their functional mechanisms in LUAD, were investigated using bioinformatics analysis, Western blot, co-immunoprecipitation, and colony formation experiments. A total of five apaQTL-SNPs (rs41301932, rs4494603, rs9890400, rs56066320, and rs41301932), located on RNF213, were significantly associated with LUAD risk (p < 0.05), and they inhibit tumor growth through ubiquitin-mediated degradation of ZBTB20.

Identification of diagnostic biomarkers of rheumatoid arthritis based on machine learning-assisted comprehensive bioinformatics and its correlation with immune cells.

Background: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammatory cell infiltration, which can lead to chronic disability, joint destruction and loss of function. At present, the pathogenesis of RA is still unclear. The purpose of this study is to explore the potential biomarkers and immune molecular mechanisms of rheumatoid arthritis through machine learning-assisted bioinformatics analysis, in order to provide reference for the early diagnosis and treatment of RA disease. Methods: RA gene chips were screened from the public gene GEO database, and batch correction of different groups of RA gene chips was performed using Strawberry Perl. DEGs were obtained using the limma package of R software, and functional enrichment analysis such as gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), and gene set (GSEA) were performed. Three machine learning methods, least absolute shrinkage and selection operator regression (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest tree (Random Forest), were used to identify potential biomarkers of RA. The validation group data set was used to verify and further confirm its expression and diagnostic value. In addition, CIBERSORT algorithm was used to evaluate the infiltration of immune cells in RA and control samples, and the correlation between confirmed RA diagnostic biomarkers and immune cells was analyzed. Results: Through feature screening, 79 key DEGs were obtained, mainly involving virus response, Parkinson's pathway, dermatitis and cell junction components. A total of 29 hub genes were screened by LASSO regression, 34 hub genes were screened by SVM-RFE, and 39 hub genes were screened by Random Forest. Combined with the three algorithms, a total of 12 hub genes were obtained. Through the expression and diagnostic value verification in the validation group data set, 7 genes that can be used as diagnostic biomarkers for RA were preliminarily confirmed. At the same time, the correlation analysis of immune cells found that γδT cells, CD4+ memory activated T cells, activated dendritic cells and other immune cells were positively correlated with multiple RA diagnostic biomarkers, CD4+ naive T cells, regulatory T cells and other immune cells were negatively correlated with multiple RA diagnostic biomarkers. Conclusions: The results of novel characteristic gene analysis of RA showed that KYNU, EVI2A, CD52, C1QB, BATF, AIM2 and NDC80 had good diagnostic and clinical value for the diagnosis of RA, and were closely related to immune cells. Therefore, these seven DEGs may become new diagnostic markers and immunotherapy markers for RA.

The impact of psoriasis on idiopathic pulmonary fibrosis: a two-sample Mendelian randomization study.

BACKGROUND: The association between psoriasis and pulmonary fibrosis has been reported in observational studies. However, the association is vulnerable to bias from using immunosuppressants such as methotrexate, which can cause fibrosis in multiple organs. The present study is to investigate whether psoriasis could independently increase the risk of idiopathic pulmonary fibrosis (IPF). METHODS: We conducted a two-sample Mendelian randomization (MR) study using summary statistics from genome-wide association studies. The random-effects inverse variance weighted analysis was used as the primary method. Some secondary analyses were further performed, including a sensitivity analysis using a genetic instrument that excluded extended single nucleotide polymorphisms (SNPs) in the major histocompatibility complex (MHC) gene region. RESULTS: Our study included 9267 cases and 364,071 controls for psoriasis, 2018 cases, and 373,064 controls for IPF of European ancestry, respectively. Genetically predicted psoriasis was associated with a higher risk of IPF (odds ratio (OR), 1.14; 95% confidence interval (CI), 1.08-1.22; P < 0.001). Sensitivity analyses did not uncover any statistically significant evidence of bias resulting from pleiotropy or the genetic instruments, including SNPs in the MHC gene region. CONCLUSIONS: These MR analyses support that genetically predicted psoriasis was associated with the risk of IPF, implying that pulmonary fibrosis in patients with psoriasis should not be neglected, even if they are not receiving immunosuppressant therapy.

Evaluation of ST6Gal1 expression and clinicopathological significance in human glioma.

Glioma is the most common brain tumor, accounting for a large majority of cancer-related deaths. ß-galactoside α2, 6 sialyltranferase 1 (ST6Gal1), the primary enzyme responsible for the conjugation of α2, 6 sialic acids to protein and lipid targets, is strongly associated with the occurrence and development of several brain tumor types. Still, the expression, targets, and functions of ST6Gal1 in glioma patients remain undetermined. As sialylation of the Ig-like cell adhesion family molecules have prominent roles in the latter's regulation in other biological contexts, we screened for members that have potential to be regulated by ST6Gal1 in silico and examined co-expressed protein modules using data derived from the Cancer Genome Atlas (TCGA) database, and we identified neural cell adhesion molecule (NCAM1) as a major ST6Gal1-interacting target. Bioinformatic binding analysis confirmed the interaction of ST6Gal1 and NCAM1. Immunohistochemistry was then used to evaluate post-operative samples from 156 patients with gliomas. ST6Gal1 and NCAM1 were co-expressed in gliomas, and their expression correlated significantly (p = 0.002) by univariate analysis. Our study also found that the expression levels of both ST6Gal1 and NCAM1 corresponded negatively with glioma grade, isocitrate dehydrogenase (IDH) mutation, and proliferation index (Ki67). Consistently, Kaplan-Meier survival curves showed that lower ST6Gal1 and NCAM1 protein levels are linked to unfavorable outcomes in glioma patients (p = 0.018 and p < 0.001, respectively). Our data indicate that ST6Gal1 may participate in the inhibition of oncogenesis and malignant progression via interacting with and targeting NCAM1 in glioma, thus presenting a novel strategy for intervention.

Unexpected changes in source apportioned results derived from different ambient VOC metrics.

Although most source apportionments of VOCs use mixing ratios, about 23 % of published studies use mass concentrations. Thus, systematically exploring the changes in VOC source apportioned results caused by metric differences is important to assess the differences in key precursor apportionment results given the observed increases in O3 pollution situation. Different monitoring instruments measured hourly VOC volumetric concentrations in three typical Chinese cities (i.e., Qingdao, Shijiazhuang, and Zhumadian). Converting volumetric to mass concentrations under standard and/or actual temperature-pressure conditions, VOC values with different metrics were obtained. The impacts of different metrics on the source apportionments were then investigated. Compared to the positive matrix factorization of the volumetric data (VC-PMF), the VOC species concentrations with low relative molecular mass (RMM) in the factor profiles substantially decreased in mass data analyses (MC-PMF). However, those species with high RMM substantially increased. There were no substantial differences in the apportioned source contributions based on standard and actual condition mass concentrations. However, the high-low rankings of percent contributions apportioned using the volumetric and mass data produced substantial differences. Compared with the VC-PMF results, the percent contributions of sources dominated by species with low RMM (e.g., natural gas usage and mixed sources containing natural gas usage) apportioned by MC-PMF decreased, while those of sources that emitted high RMM species (e.g., solvent usage and mixed sources containing solvent usage) increased. Source apportionments based on the volumetric concentration data had more practical significance compared to the mass concentration data results for control strategy development since the mass data analyses created issues.

[Simultaneous determination of maltol and seventeen saponins in red and black ginseng by UPLC-PDA wavelength switching method].

This study developed a UPLC-PDA wavelength switching method to simultaneously determine the content of maltol and seventeen saponins in red and black ginseng and compared the quality differences of two different processed products of red and black ginseng. A Waters HSS T3 column(2. 1 mm×100 mm, 1. 8 µm) at 30 ℃ was adopted, with the mobile phase of acetonitrile(A) and water containing 0. 1% phosphoric acid(B) under gradient elution, the flow rate of 0. 3 m L·min~(-1), and the injection volume of 2 µL.The wavelength switching was set at 273 nm within 0-11 min and 203 nm within 11-60 min. The content results of multiple batches of red and black ginseng samples were analyzed by the hierarchical cluster analysis(HCA) and principal component analysis(PCA) to evaluate the quality difference. The results showed that the 18 constituents exhibited good linear relationships within certain concentration ranges, with the correlation coefficients(r) greater than 0. 999 1. The relative standard deviations(RSDs) of precision,repeatability, and stability were all less than 5. 0%. The average recoveries ranged from 95. 93% to 104. 2%, with an RSD of 1. 8%-4. 2%. The content determination results showed that the quality of red and black ginseng samples was different, and the two types of processed products were intuitively distinguished by HCA and PCA. The method is accurate, reliable, and reproducible. It can be used to determine the content of maltol and seventeen saponins in red and black ginseng and provide basic information for the quality evaluation and comprehensive utilization of red and black ginseng.

Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data.

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.

Integrating apaQTL and eQTL analysis identifies a potential causal variant associated with lung adenocarcinoma risk in the Chinese population.

Alternative polyadenylation (APA) plays a crucial role in cancer biology. Here, we used data from the 3'aQTL-atlas, GTEx, and the China Nanjing Lung Cancer GWAS database to explore the association between apaQTL/eQTL-SNPs and the risk of lung adenocarcinoma (LUAD). The variant T allele of rs277646 in NIT2 is associated with an increased risk of LUAD (OR = 1.12, P = 0.015), lower PDUI values, and higher NIT2 expression. The 3'RACE experiment showed multiple poly (A) sites in NIT2, with the rs277646-T allele causing preferential use of the proximal poly (A) site, resulting in a shorter 3'UTR transcript. This leads to the loss of the hsa-miR-650 binding site, thereby affecting LUAD malignant phenotypes by regulating the expression level of NIT2. Our findings may provide new insights into understanding and exploring APA events in LUAD carcinogenesis.

Case report: Two novel compound heterozygous variant of SLC12A3 gene in a gitelman syndrome family and literature review.

A 36-year-old unmarried male chef was incidentally diagnosed with hypokalemia during an evaluation for an acute perianal abscess. Despite potassium supplementation, he developed progressive weakness in his lower limbs, culminating in an inability to stand. Investigations confirmed severe hypokalemia, metabolic alkalosis, hypomagnesemia, secondary hyperaldosteronism, and low urinary calcium excretion, with normotension. The patient's long-standing stunted growth and lean physique since childhood were noted. Biochemical assays further identified type 2 diabetes mellitus and metabolic syndrome. Genetic analysis revealed three heterozygous SLC12A3 mutations (M1: c.421G>A: p.G141R, M2: c.509T>A:p.L170Q, and M3: c.704C>A: p.T235K), compound heterozygo us and derived from both parents, with M1 and M3 reported here for the first time. Treatment with spironolactone and oral potassium chloride stabilized his potassium levels. Following the administration of SGLT2 inhibitors in patients receiving hypoglycemic therapy, we observed a mild decrease in serum sodium levels. This case highlights the criticality of vigilant metabolic surveillance in Gitelman syndrome and advises prudence with SGLT2 inhibitors in those with concurrent type 2 diabetes, given the risk of potentially aggravate sodium loss.

Analyzing pain patterns in the emergency department: Leveraging clinical text deep learning models for real-world insights.

OBJECTIVE: To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text deep learning algorithm. MATERIALS AND METHODS: A fine-tuned, domain-specific, transformer-based clinical text deep learning model was used to interpret free-text nursing assessments in the electronic medical records of 235,789 adult presentations to the ED over a three-year period. The model classified presentations according to whether the patient had pain on arrival at the ED. Interrupted time series analysis was used to determine the incidence of pain in patients on arrival over time. We described the changes in the population characteristics and incidence of patients with pain on arrival occurring with the start of the Covid-19 pandemic. RESULTS: 55.16% (95%CI 54.95%-55.36%) of all patients presenting to this ED had pain on arrival. There were differences in demographics and arrival and departure patterns between patients with and without pain. The Covid-19 pandemic initially precipitated a decrease followed by a sharp, sustained rise in pain on arrival, with concurrent changes to the population arriving in pain and their treatment. DISCUSSION: Applying a clinical text deep learning model has successfully identified the incidence of pain on arrival. It represents an automated, reproducible mechanism to identify pain from routinely collected medical records. The description of this population and their treatment forms the basis of intervention to improve care for patients with pain. The combination of the clinical text deep learning models and interrupted time series analysis has reported on the effects of the Covid-19 pandemic on pain care in the ED, outlining a methodology to assess the impact of significant events or interventions on pain care in the ED. CONCLUSION: Applying a novel deep learning approach to identifying pain guides methodological approaches to evaluating pain care interventions in the ED, giving previously unavailable population-level insights.