Integration Of Datasets Research Articles

Emergency department visits in Parkinson's disease: The impact of comorbid conditions

IntroductionOlder adults have complex, often overlapping, medical conditions requiring careful management that may lead to increased emergency department usage compared to younger adults. Parkinson's disease (PD), a progressive neurodegenerative disorder characterized by distinct motor and nonmotor features, frequently occurs with additional comorbid disease. Classifying comorbid conditions into clinical subgroups allows for further understanding of the heterogeneity in outcomes in patients with PD. The current study examines the reasons for emergency department (ED) visits in a cohort of patients with PD and identifies comorbidities that are potential risk factors for specific ED presenting conditions. MethodsUsing data from Optum's de-identified Integrated Claims-Clinical dataset years 2010–2018, patients with PD were identified based on ICD-9/10 diagnosis codes. We identified all ED visits occurring after the first observed diagnosis code for PD. Comorbid conditions were classified using the AHRQ Clinical Classification Software (CCS). We classified patients using Latent Class Analysis (LCA) and conducted multiple logistic regression models with the outcome of reason-for-visit to examine the associations with comorbidity-profile class, patient demographics, and socio-economic characteristics. ResultsThe most common reasons for ED admission were injuries such as fractures and contusions, diseases of the circulatory system, and general signs and symptoms, including abdominal pain, malaise, and fatigue. Comorbid medical conditions often observed in this patient population include depression, diabetes mellitus, and chronic pulmonary disease. Patients in the “Poorest Health” classification of the LCA had greater odds for ED admission for diseases related to the gastrointestinal system, musculoskeletal system, and injury/poisoning categories and reduced odds for admission for diseases of the circulatory system. DiscussionPatients with PD who present to the emergency department with injuries are more likely to be in poor health overall with a high comorbidity burden. Clarifying the complex medical needs of patients with PD is the first step to further individualize care, which may reduce ED visits in this population, improve quality of life, and lessen the footprint on the healthcare system.

The incremental cost of non-alcoholic steatohepatitis and type 2 diabetes in the United States using real-world data

Background Non-alcoholic steatohepatitis (NASH) and type 2 diabetes (T2D) are both linked to substantial healthcare costs and are often co-occurring. We aim to quantify the incremental cost of NASH and T2D using real-world data. Methods Adults (≥18 years old) with ≥2 diagnosis codes for NASH and/or ≥2 diagnosis codes for T2D between 1/1/2016 and 12/31/2021 and ≥24 months of continuous claims enrollment (study period) were identified in electronic health records or claims in the Veradigm Integrated Dataset. Patients were stratified into 3 cohorts: NASH-only, T2D-only, and NASH + T2D. We calculated annualized costs for the 24-month study period and fit a generalized linear model (excluding the most expensive 1%) that controlled for disease cohort, age, sex, and modified Charlson comorbidity index to estimate the per year all-cause healthcare costs and incremental cost of adding T2D to a NASH diagnosis (or vice versa). Results We identified 23,111 patients diagnosed with NASH-only, 3,548,786 patients with T2D-only, and 30,339 patients with NASH + T2D. The model-predicted mean costs per year were $7,668 for patients with NASH-only, $11,226 for patients with T2D-only, and $16,812 for patients with NASH + T2D. The incremental increase in costs per year of adding T2D to NASH was 63% (+$4,846), and the incremental increase in costs per year of adding NASH to T2D was 42% (+$4,692). Conclusions Both NASH and T2D contribute to the high healthcare costs among patients with a dual diagnosis. Results from our analysis indicate that NASH comprises a high portion of total healthcare costs among patients with NASH and T2D.

Estimating the contribution of CD4 T cell subset proliferation and differentiation to HIV persistence

Persistence of HIV in people living with HIV (PWH) on suppressive antiretroviral therapy (ART) has been linked to physiological mechanisms of CD4+ T cells. Here, in the same 37 male PWH on ART we measure longitudinal kinetics of HIV DNA and cell turnover rates in five CD4 cell subsets: naïve (TN), stem-cell- (TSCM), central- (TCM), transitional- (TTM), and effector-memory (TEM). HIV decreases in TTM and TEM but not in less-differentiated subsets. Cell turnover is ~10 times faster than HIV clearance in memory subsets, implying that cellular proliferation consistently creates HIV DNA. The optimal mathematical model for these integrated data sets posits HIV DNA also passages between CD4 cell subsets via cellular differentiation. Estimates are heterogeneous, but in an average participant’s year ~10 (in TN and TSCM) and ~104 (in TCM, TTM, TEM) proviruses are generated by proliferation while ~103 proviruses passage via cell differentiation (per million CD4). In simulations, therapies blocking proliferation and/or enhancing differentiation could reduce HIV DNA by 1-2 logs over 3 years. In summary, HIV exploits cellular proliferation and differentiation to persist during ART but clears faster in more proliferative/differentiated CD4 cell subsets and the same physiological mechanisms sustaining HIV might be temporarily modified to reduce it.

Bilastine 0.6% Preservative-Free Eye Drops as an Effective Once-Daily Treatment for the Signs and Symptoms of Allergic Conjunctivitis: A Pooled Analysis of 2 Randomized Clinical Trials.

Allergic conjunctivitis is the most common type of ocular allergy. The objective of this study was to evaluate the efficacy of a new once-daily, preservative-free, bilastine 0.6% eye drop formulation for the treatment of allergic conjunctivitis. Two double-masked, vehicle-controlled, clinical studies (a phase 2 dose-ranging study and a phase 3 efficacy study) were conducted to assess the efficacy of bilastine ophthalmic solution for treatment of the signs and symptoms of allergic conjunctivitis. Both studies used the Ora-CAC® Conjunctival Allergen Challenge (CAC) model to enable observation of allergic responses under controlled conditions. Each study was analyzed separately and then combined to create an integrated data set. Efficacy was achieved for the primary efficacy endpoint of ocular itching with 3 bilastine concentrations (0.2%, 0.4%, and 0.6%) at 15 minutes and 8 hours after instillation. Bilastine 0.6% ophthalmic solution was also efficacious at 16 hours after instillation. Bilastine 0.6% ophthalmic solution demonstrated noninferiority to ketotifen 0.025% at the onset of action. According to the integrated data set, differences between vehicle and bilastine 0.6% after instillation were significant at all time points both at onset (15 minutes) and after a prolonged duration (16 hours). This multitrial assessment suggests that bilastine 0.6% ophthalmic solution is efficacious for the treatment of the signs and symptoms of allergic conjunctivitis, with a rapid onset and prolonged duration of action, and has a favorable safety profile. The added convenience of once-daily dosing may contribute to adherence and improve quality of life.

DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information.

The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for one class of therapeutic peptides. It is worth noting that currently no predictive method considers sequence length information as a distinct feature of therapeutic peptides. In this article, a novel deep learning approach with matrix factorization for predicting therapeutic peptides (DeepTPpred) by integrating length information are proposed. The matrix factorization layer can learn the potential features of the encoded sequence through the mechanism of first compression and then restoration. And the length features of the sequence of therapeutic peptides are embedded with encoded amino acid sequences. To automatically learn therapeutic peptide predictions, these latent features are input into the neural networks with self-attention mechanism. On eight therapeutic peptide datasets, DeepTPpred achieved excellent prediction results. Based on these datasets, we first integrated eight datasets to obtain a full therapeutic peptide integration dataset. Then, we obtained two functional integration datasets based on the functional similarity of the peptides. Finally, we also conduct experiments on the latest versions of the ACP and CPP datasets. Overall, the experimental results show that our work is effective for the identification of therapeutic peptides.

Research on point cloud simplification algorithm for ring forgings based on joint entropy evaluation

There are numerous redundant points in the point cloud model of ring forgings obtained by 3D laser scanner. How to remove the redundant points while keeping the model characteristics unchanged is a critical issue. This paper proposes a point cloud simplification algorithm based on the joint entropy evaluation theory. Firstly, the K-D tree is used to search for the K-neighbors of the sampled points. Secondly, a surface is fitted to the spatial neighborhood of the sampled points using the least squares method. The curvature operator of the sampled points is derived on the fitted surface using Riemannian geometry theory. After that, an energy operator is defined by using the normal vectors and distances of the sampled points and their neighborhood points. The joint entropy values of all points in the model are determined based on the probability distributions of these two operators in the local neighborhood. Finally, the data points are sorted by entropy value. Data points with high entropy values are put into the data set U1. Data points with low entropy values are clustered through the K-Means algorithm of swarm optimization. The redundant points outside the cluster centers are removed, and the cluster centers are put into the data set U2. The final simplification results are obtained by integrating data sets U1 and U2. The experimental results show that the point cloud simplification algorithm proposed in this paper is effective and feasible.

SOD1-ALS-Browser: a web-utility for investigating the clinical phenotype in SOD1 amyotrophic lateral sclerosis

Objective Variants in the superoxide dismutase (SOD1) gene are among the most common genetic causes of amyotrophic lateral sclerosis. Reflecting the wide spectrum of putatively deleterious variants that have been reported to date, it has become clear that SOD1-linked ALS presents a highly variable age at symptom onset and disease duration. Methods Here we describe an open access web tool for comparative phenotype analysis in ALS: https://sod1-als-browser.rosalind.kcl.ac.uk/. The tool contains a built-in dataset of clinical information from 1383 people with ALS harboring a SOD1 variant resulting in one of 162 unique amino acid sequence alterations and from a non-SOD1 comparator ALS cohort of 13,469 individuals. We present two examples of analyses possible with this tool, testing how the ALS phenotype relates to SOD1 variants that alter amino acid residue hydrophobicity and to distinct variants at the 94th residue of SOD1, where six are sampled. Results and conclusions The tool provides immediate access to the datasets and enables bespoke analysis of phenotypic trends associated with different protein variants, including the option for users to upload their own datasets for integration with the server data. The tool can be used to study SOD1-ALS and provides an analytical framework to study the differences between other user-uploaded ALS groups and our large reference database of SOD1 and non-SOD1 ALS. The tool is designed to be useful for clinicians and researchers, including those without programming expertise, and is highly flexible in the analyses that can be conducted.

Effects of 66 years of water management and hydroclimatic change on the urban hydrology and water quality of the Panke catchment, Berlin, Germany

Long-term records of combined stream flow and water chemistry can be an invaluable source of information on changes in the quantity and quality of water resources. To understand the effect of hydroclimate and water management on the heavily urbanized Panke catchment in Berlin, Germany, an extensive search, collation and digitization of historic data from various sources was undertaken. This integrated a unique 66-year spatially distributed record of stream water quality, a 21-year record of groundwater quality and a 31-year stream flow record. These data were analysed in the context of hydroclimatic variability, as well as the history and technological evolution of water resource management in the catchment. To contextualize the effect of droughts, “average” and wet years the Standard Precipitation Index (SPI) was applied. As upstream sites have been less regulated by human impacts, the flow regime is most sensitive to changes in hydroclimatic conditions, while downstream sites are more influenced by wastewater effluents, urban storm drains and inter-basin transfers for flood alleviation. However, at all sites, a general increase in maximum event discharge was observed until a recent drought, starting in 2018. In general, water quality in the catchment has gradually improved as a result of management change and increasingly effective wastewater treatment, though in some places legacy and/or contemporary urban and rural groundwater contamination may be affecting the stream. Hydroclimatic changes, particularly drought years can affect water quality classes, and alter the chemostatic/dynamic behaviour of catchment export patterns. These insights from the Panke catchment underline the importance of strategic adaptation and improvement of water treatment and water resource management in order to enhance the quality of urban water courses. It also demonstrates the importance of long-term integrated data sets.

Stricturing Crohn’s Disease Single-Cell RNA Sequencing Reveals Fibroblast Heterogeneity and Intercellular Interactions

Fibroblasts play a key role in stricture formation in Crohn's disease (CD) but understanding its pathogenesis requires a systems-level investigation to uncover new treatment targets. We studied full-thickness CD tissues to characterize fibroblast heterogeneity and function by generating the first single-cell RNA sequencing (scRNAseq) atlas of strictured bowel and providing proof of principle for therapeutic target validation. We performed scRNAseq of 13 fresh full-thickness CD resections containing noninvolved, inflamed nonstrictured, and strictured segments as well as 7 normal non-CD bowel segments. Each segment was separated into mucosa/submucosa or muscularis propria and analyzed separately for a total of 99 tissue samples and 409,001 cells. We validated cadherin-11 (CDH11) as a potential therapeutic target by using whole tissues, isolated intestinal cells, NanoString nCounter, next-generation sequencing, proteomics, and animal models. Our integrated dataset revealed fibroblast heterogeneity in strictured CD with the majority of stricture-selective changes detected in the mucosa/submucosa, but not the muscle layer. Cell-cell interaction modeling revealed CXCL14+ as well as MMP/WNT5A+ fibroblasts displaying a central signaling role in CD strictures. CDH11, a fibroblast cell-cell adhesion molecule, was broadly expressed and up-regulated, and its profibrotic function was validated using NanoString nCounter, RNA sequencing, tissue target expression, invitro gain- and loss-of-function experiments, proteomics, and knock-out and antibody-mediated CDH11 blockade in experimental colitis. A full-thickness bowel scRNAseq atlas revealed previously unrecognized fibroblast heterogeneity and interactions in CD strictures and CDH11 was validated as a potential therapeutic target. These results provide a new resource for a better understanding of CD stricture formation and open potential therapeutic developments. This work has been posted as a preprint on Biorxiv under doi: 10.1101/2023.04.03.534781.

Comparative studies of fermented coffee fruits post-treatments on chemical and sensory properties of roasted beans in Yunnan, China

In this study, medium roasted coffee with four different fermented coffee fruits post-treatments (dry, wet, semi-dry and hot air dry) was used as the material. Chemical profile and sensorial analysis were used to comprehensively analyze the effects of post-treatments on coffee flavor characteristics from multiple dimensions. A total of 31 water-soluble chemical components and 39 volatile compounds were identified in roasted coffee, and distinct post-treatments based on chemical orientation make coffee highly differentiated. In addition, the principal component analysis (PCA) of the chemical composition integrated data set showed that the first two principal components could explain 54.9% of the sample variability. All four post-treatments can be classified as “specialty coffees” according to the Specialty Coffee Association (SCA) protocol, with various organoleptic characteristics and flavor attributes. As a result, the fermented coffee fruits post-treatment method further determines the quality characteristics of coffee, thus meeting the needs of different niche markets.

Creation of a Memory Disorders Clinic Biorepository to Characterize a Clinic Population and Accurately Refer to Research Studies (P2-6.006)

<h3>Objective:</h3> 1) To build a biorepository of blood samples from a memory disorders clinic population, 2) To make samples available to Alzheimer’s Disease and Related Dementias (ADRD) researchers, 3) To further characterize the samples with plasma biomarkers <h3>Background:</h3> The Duke University Memory Disorders Clinic receives requests from researchers for participants from our clinic population and enrolls patients in clinical trials. Results of demographic and clinical information, genetic testing and plasma biomarkers will characterize patients for referral to research studies with increased accuracy. <h3>Design/Methods:</h3> We approach Duke Memory Disorders Clinic patients to contribute a blood sample for ADRC research. Staff processes and stores the sample. A portion is banked at the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD). NCRAD performs APOE testing free of charge and returns results to Duke. Staff enters patient demographics and clinical information from the EHR into a REDCap database. Information from REDCap including APOE status is gathered in a publicly viewed secure web-based platform developed at Duke called the Alzheimer’s disease RElational and iNtegrated dAtaset (ARENA). Researchers at Duke and outside institutions can request samples or participants. <h3>Results:</h3> We collected samples from 115 patients. Patients continue to be enrolled. Demographics include 85% white participants, 11% black participants, 4% other race/unknown; 56% female; 55% with family history of Alzheimer’s disease or dementia; average age 76. Diagnoses include 40% mild cognitive impairment; 27% clinical Alzheimer’s disease; 24% dementia; 14% normal cognition; 3% subjective cognitive impairment. Average MoCA score is 20. The rate of APOE 4 status is overall 53% with 5% APOE 2/4, 38% APOE 3/4, 10% APOE 4/4. <h3>Conclusions:</h3> The rate of APOE4 in the biorepository is higher than in the general population. The biorepository provides a rich source of APOE4 samples and participants for research. In the future, we will evaluate plasma-based biomarkers (NfL, Aβ40/42, p-tau). <b>Disclosure:</b> Dr. Johnson has nothing to disclose. Mr. Perry has nothing to disclose. The institution of Dr. Parker has received research support from NIH. Dr. Liu has received personal compensation in the range of $0-$499 for serving as a Consultant for Darwin Research Group. Ms. Beam has received personal compensation in the range of $500-$4,999 for serving as a Faculty for Cognitive Assessment Toolkit with AAPA/PA Foundation. Ms. Sanfilippo has nothing to disclose. The institution of Dr. Lutz has received research support from NIH. Dr. O’Brien has nothing to disclose.

Novel mRNA Signature for Anti-TNF-α Therapy Primary Response in Patients With Ulcerative Colitis.

Ulcerative colitis (UC), an idiopathic, chronic inflammatory disorder of the colonic mucosa, is commonly treated with antitumor necrosis factor α (anti-TNF-α) agents. However, only approximately two-thirds have an initial response to these therapies. We integrated gene expression profiling from 3 independent data sets of 79 UC patients before they began anti-TNF-α therapy and calculated the differentially expressed genes between patient response and nonresponse to anti-TNF-α therapy and developed a de novo response-associated transcription signature score (logOR_Score) to demonstrate the predictive capability of anti-TNF-α therapy for therapeutic efficacy. Furthermore, we performed association analysis of the logOR_Score and clinical features, such as disease activity and immune microenvironment. A total of 2522 responsive and 1824 nonresponsive genes were identified from the integrated data set. Responsive genes were significantly enriched in metabolism-related pathways, whereas nonresponsive ones were associated with immune response-related pathways. The logOR_Score enabled the accurate prediction of the therapeutic efficacy of anti-TNF-α in 4 independent patient cohorts and outperformed the predictions made based on 6 transcriptome-based signatures. In terms of clinical features, the logOR_Score correlated highly with the activity of UC. From an immune microenvironment perspective, logOR_Scores of CD8+IL-17+ T cells, follicular B cells, and innate lymphoid cells significantly decreased in inflamed UC tissue. The de novo response-associated transcription signature may provide novel insights into the personalized treatment of patients with UC. Comprehensive analyses of the response-related subtypes and the association between logOR_Score and clinical features and immune microenvironment may provide insights into the underlying UC pathogenesis.

Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework.

With the emergence of novel methods for improving machine learning (ML) transparency, traditional decision-support-focused information systems seem to need an upgrade in their approach toward providing more actionable insights for practitioners. Particularly, given the complex decision-making process of humans, using insights obtained from group-level interpretation of ML models for designing individual interventions may lead to mixed results. The present study proposes a hybrid ML framework by integrating established predictive and explainable ML approaches for decision support systems involving the prediction of human decisions and designing individualized interventions accordingly. The proposed framework is aimed at providing actionable insights for designing individualized interventions. It was showcased in the context of college students' attrition problem using a large and feature-rich integrated data set of freshman college students containing information about their demographics, educational, financial, and socioeconomic factors. A comparison of feature importance scores at the group- vs. individual-level revealed that while group-level insights might be useful for adjusting long-term strategies, using them as a one-size-fits-all strategy to design and implement individual interventions is subject to suboptimal outcomes.

The Municipal Drinking Water Database

Drinking water services in the U.S. are critical for public health and economic development but face technical, political, and administrative challenges. Understanding the root cause of these challenges and how to overcome them is hindered by the lack of integrative, comprehensive data about drinking water systems and the communities they serve. The Municipal Drinking Water Database (MDWD) combines financial, institutional, demographic, and environmental conditions of U.S. municipalities and their community water systems (CWS). Municipally owned and operated CWS are ubiquitous and play a critical role in ensuring safe, affordable drinking water services for most Americans; they also offer important opportunities for understanding municipal government behavior and decision making. The MDWD is publicly available and will enable researchers and practitioners interested in viewing or tracking drinking water spending, the financial condition of municipal governments, or myriad demographic, political, institutional, and physical characteristics of municipal drinking water systems to access the data quickly and easily. This paper describes the database and its creation, details examples of how the data can be used and discusses illustrative analyses of trends and insights that can be gleaned from the database. Building and sharing more integrated datasets provides new opportunities for asking novel questions about the drivers and consequences of local decision making about drinking water.

Integrated ecohydrological hydrometric and stable water isotope data of a drought-sensitive mixed land use lowland catchment

Abstract. Data from long-term experimental catchments are the foundation of hydrological sciences and are crucial for benchmarking process understanding, observing trends and natural cycles, and being prerequisites for testing predictive models. Integrated data sets which capture all compartments of our landscapes are particularly important in times of land use and climate change. Here, we present ecohydrological data measured at multiple spatial scales which allow differentiation of “blue” water fluxes (which maintain streamflow generation and groundwater recharge) and “green” water fluxes (which sustain vegetation growth). There are two particular unique aspects to this data set: (a) we measured water stable isotopes in the different landscape compartments (i.e. in precipitation, surface water, soil, groundwater, and plant water), and (b) we conducted this monitoring during the extreme drought of 2018 in central Europe. Stable water isotopes are so useful in hydrology as they provide “fingerprints” of the pathways water took when moving through a catchment. Thus, isotopes allow one to evaluate the dynamic relationships between water storage changes and fluxes, which is fundamental to understanding how catchments respond to hydroclimate perturbations or abrupt land use conversion. Second, as we provide the data until 2020, one can also investigate recovery of water stores and fluxes after extreme droughts. Last but not least, lowland headwaters are often understudied systems despite them providing important ecosystem services such as groundwater and drinking water provision and management for forestry and agriculture. The data are available at https://doi.org/10.18728/igb-fred-826.3 (Dämpfling, 2023).

Dissecting the molecular features of bovine-arrested eight-cell embryos using single-cell multi-omics sequencing†.

The regulation of mammalian early-embryonic development is a complex, coordinated process that involves widespread transcriptomic and epigenetic remodeling. The main cause of developmental failure in preimplantation embryos after in vitro fertilization is the irreversible arrested-at-cleavage stage. To deepen our understanding of this embryonic block, we profiled a single-cell multi-omics map of copy number variations (CNVs), the transcriptome, the DNA methylome, and the chromatin state of bovine eight-cell embryos with a two-cell fate that either arrested or developed into blastocysts. To do this, we sequenced a biopsied blastomere and tracked the developmental potential of the remaining cells. Aneuploid embryos inferred by CNVs from DNA- and RNA-library data tended to lose their developmental potency. Analysis of distinct genomic regions of DNA methylation and chromatin accessibility revealed that enrichment of gene function and signaling pathways, such as the MAPK signaling pathway, was altered in arrested euploid eight-cell embryos compared with blastocyst-developed euploid eight-cell embryos. Moreover, the RNA expression and chromatin accessibility of embryonic genome activation-associated genes were lower in arrested euploid embryos than in blastocyst-developed embryos. Taken together, our results indicate that the developmental block of eight-cell embryos can be caused by multiple molecular layers, including CNVs, abnormality of DNA methylation and chromatin accessibility, and insufficient expression of embryonic genome activation-associated genes. Our integrated and comprehensive data set provides a valuable resource to further dissect the exact mechanisms underlying the arrest of bovine eight-cell embryos in vitro.

Association of serum bicarbonate with the development of kidney stones in patients with chronic kidney disease: a retrospective cohort study.

Epidemiological studies demonstrate an association between kidney stones and risk of chronic kidney disease (CKD) and CKD progression. Metabolic acidosis, as a consequence of CKD, results in a reduced urine pH which promotes the formation of some types of kidney stones and inhibits the formation of others. While metabolic acidosis is a risk factor for CKD progression, the association of serum bicarbonate with risk of incident kidney stones is not well understood. We used an Integrated Claims-Clinical dataset of US patients to generate a cohort of patients with non-dialysis-dependent CKD with two serum bicarbonate values of 12 to <22mmol/L (metabolic acidosis) or 22 to <30mmol/L (normal serum bicarbonate). Primary exposure variables were baseline serum bicarbonate and change in serum bicarbonate over time. Cox proportional hazards models evaluated time to first occurrence of kidney stones during a median 3.2-year follow-up. A total of 142 884 patients qualified for the study cohort. Patients with metabolic acidosis experienced post-index date kidney stones at greater frequency than patients with normal serum bicarbonate at the index date (12.0%vs 9.5%, P<.0001). Both lower baseline serum bicarbonate [hazard ratio (HR) 1.047; 95% confidence interval (CI) 1.036-1.057] and decreasing serum bicarbonate over time (HR 1.034; 95% CI 1.026-1.043) were associated with increased risk of kidney stone development. Metabolic acidosis was associated with a higher incidence of kidney stones and shorter time to incident stone formation in patients with CKD. Future studies may investigate the role of correcting metabolic acidosis to prevent stone formation.

Economic growth and carbon emissions in Pakistan: the effects of China's Logistics Industry.

The logistics business is a crucial contributor to economic development, yet it is also the leading source of carbon emissions. Economic growth at the expense of environmental deterioration is a challenging issue; this phenomenon offered a new avenue for scholars and policymakers to investigate and address these issues. The recent study is one of the attempts to explore this intricate subject. The goal of this research is to determine whether or not the Chinese logistics sector has an impact on Pakistan's GDP and carbon emissions as a result of CPEC. The research utilized data from 2007Q1 to 2021Q4 using the ARDL approach for an empirical estimate. Due to the mixed order of variable integration and finite data set, the ARDL technique is well deserved, which helps reach sound policy inferences. The study's key results indicated that China's logistic business enhances Pakistan's economic development and carbon emissions in the short and long term. Similarly, China's energy usage, technology, and transportation contribute to Pakistan's economic progress at the price of environmental damage. The empirical study may be a model for other developing nations, given Pakistan's viewpoint. With the support of the empirical results, policymakers in Pakistan and other associated countries would be able to plan for sustainable growth in conjunction with CPEC.

Machine learning-based prediction models for atopic dermatitis diagnosis and evaluation

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease seriously affecting the quality of life of patients. Reliable and accurate evaluation methods are necessary for early diagnosis and effective AD treatment. Thus, this study used machine learning (ML) to explore a novel diagnostic and therapeutic effect evaluation model for AD. Firstly, candidate model genes were screened from an integrated data set of four AD-related microarray datasets: GSE133477, GSE32924, GSE58558, and GSE107361, using Robust Rank Aggregation (RRA), and protein-protein interaction network (PPI). Next, three recognized models (REC) and three AD-associated gene models (AAG) established with LASSO, Logistic linear regression (LR), and random forest (RF) were developed and tested separately using GSE130588 and GSE99802 datasets. The results revealed that REC model of LASSO (model genes including IL7R, KRT16, CCL2, CD53, CCL18 and CCL22), REC model of LR(including IL7R, KRT16, CCL18) and AAG model of LR (including MX1, CCNB1, SERPINB13, ADAM19, CEP55, VMP1, TTC39A, and FCHSD1) accurately classified AD lesions and non-lesions based on the good AUCs (LASSO (REC):0.8761, and LR (REC and AAG):0.8302 in GSE130588; LASSO (REC): 0.7761, and LR (REC and AAG):0.8783 in GSE99802). In Dupilumab, Crisaborole, and fezakinumab-treated samples, the LASSO (REC) and LR(AAG) models were positively correlated with SCORD (Pearson correlation coefficients of 0.55 and 0.69, respectively) and negatively correlated with the treatment length. In addition, the two models also accurately predicted the infiltration of immune cells in the skin lesions and non-lesions. Therefore, the ML-based predictive model provides a new approach to predicting AD diagnosis and the therapeutic effect of AD treatment options.

Identification of clade-wide putative cis-regulatory elements from conserved non-coding sequences in Cucurbitaceae genomes.

Cis-regulatory elements regulate gene expression and play an essential role in the development and physiology of organisms. Many conserved non-coding sequences (CNSs) function as cis-regulatory elements. They control the development of various lineages. However, predicting clade-wide cis-regulatory elements across several closely related species remains challenging. Based on the relationship between CNSs and cis-regulatory elements, we present a computational approach that predicts the clade-wide putative cis-regulatory elements in 12 Cucurbitaceae genomes. Using 12-way whole-genome alignment, we first obtained 632 112 CNSs in Cucurbitaceae. Next, we identified 16 552 Cucurbitaceae-wide cis-regulatory elements based on collinearity among all 12 Cucurbitaceae plants. Furthermore, we predicted 3271 potential regulatory pairs in the cucumber genome, of which 98 were verified using integrative RNA sequencing and ChIP sequencing datasets from samples collected during various fruit development stages. The CNSs, Cucurbitaceae-wide cis-regulatory elements, and their target genes are accessible at http://cmb.bnu.edu.cn/cisRCNEs_cucurbit/. These elements are valuable resources for functionally annotating CNSs and their regulatory roles in Cucurbitaceae genomes.