Standard Data Research Articles

Cancer survival statistics in China 2019–2021: a multicenter, population-based study

BackgroundA milestone goal of the Healthy China Program (2019–2030) is to achieve 5-year cancer survival at 43.3% for all cancers combined by 2022. To assess the progress towards this target, we analyzed the updated survival for all cancers combined and 25 specific cancer types in China from 2019 to 2021. MethodsWe conducted standardized data collection and quality control for cancer registries across 32 provincial-level regions in China, and included 6,410,940 newly diagnosed cancer patients from 281 cancer registries during 2008–2019, with follow-up data on vital status available until December 2021. We estimated the age-standardized 5-year relative survival overall and by site, age group, and period of diagnosis using the International Cancer Survival Standard Weights, and quantified the survival changes to assess the progress in cancer control. ResultsIn 2019–2021, the age-standardized 5-year relative survival for all cancers combined was 43.7% (95% confidence interval [CI], 43.6–43.7). The 5-year relative survival varied by cancer type, ranging from 8.5% (95% CI, 8.2–8.7) for pancreatic cancer to 92.9% (95% CI, 92.4–93.3%) for thyroid cancer. Eight cancers had 5-year survival of over 60%, including cancers of the thyroid, breast, testis, bladder, prostate, kidney, uterus, and cervix. The 5-year relative survival was generally lower in males than in females. From 2008 to 2021, we observed significant survival improvements for cancers of the lung, prostate, bone, uterus, breast, cervix, nasopharynx, larynx, and bladder. The most significant improvement was in lung cancer. ConclusionsProgress in cancer control was evident in China. This highlights the importance of a comprehensive approach to control and prevent cancer.

High-throughput functional mapping of variants in an arrhythmia gene, KCNE1, reveals novel biology

BackgroundKCNE1 encodes a 129-residue cardiac potassium channel (IKs) subunit. KCNE1 variants are associated with long QT syndrome and atrial fibrillation. However, most variants have insufficient evidence of clinical consequences and thus limited clinical utility.MethodsIn this study, we leveraged the power of variant effect mapping, which couples saturation mutagenesis with high-throughput sequencing, to ascertain the function of thousands of protein-coding KCNE1 variants.ResultsWe comprehensively assayed KCNE1 variant cell surface expression (2554/2709 possible single-amino-acid variants) and function (2534 variants). Our study identified 470 loss- or partial loss-of-surface expression and 574 loss- or partial loss-of-function variants. Of the 574 loss- or partial loss-of-function variants, 152 (26.5%) had reduced cell surface expression, indicating that most functionally deleterious variants affect channel gating. Nonsense variants at residues 56–104 generally had WT-like trafficking scores but decreased functional scores, indicating that the latter half of the protein is dispensable for protein trafficking but essential for channel function. 22 of the 30 KCNE1 residues (73%) highly intolerant of variation (with > 70% loss-of-function variants) were in predicted close contact with binding partners KCNQ1 or calmodulin. Our functional assay data were consistent with gold standard electrophysiological data (ρ = − 0.64), population and patient cohorts (32/38 presumed benign or pathogenic variants with consistent scores), and computational predictors (ρ = − 0.62). Our data provide moderate-strength evidence for the American College of Medical Genetics/Association of Molecular Pathology functional criteria for benign and pathogenic variants.ConclusionsComprehensive variant effect maps of KCNE1 can both provide insight into IKs channel biology and help reclassify variants of uncertain significance.

Advancements and characteristics of scholar profiling research in China

This study analyzes the development and characteristics of scholar profiling research in China from 2016 to 2022, utilizing content analysis of 38 core journal articles. Scholar profiling, adapted from user profiling in big data, involves extracting and analyzing academic data to build models that describe scholars' characteristics and behaviors. Our findings highlight the use of diverse data sources, including commercial databases and social platforms, and emphasize the importance of data preprocessing and modeling techniques. While applied research dominates, the field faces challenges such as the lack of standardized data collection and depth in model construction. The study underscores the need for robust profiling tools and broader application scenarios to enhance the effectiveness of scholar profiling systems.

Sensory stimulation interventions in ICU: A comprehensive systematic review on enhancing consciousness in unconscious patients

Background: Unconsciousness is a common and critical condition among patients admitted to the Intensive Care Unit (ICU). The potential impact of sensory stimulation as a therapeutic intervention for improving consciousness in these patients has gained increasing attention. This systematic review aims to evaluate the effectiveness of sensory stimulation programs in enhancing consciousness levels among unconscious patients in the ICU. Objectives: The primary objective of this review is to assess the effectiveness of sensory stimulation programs in promoting consciousness among unconscious patients in the ICU. Secondary objectives include identifying specific types of sensory stimulation techniques employed, examining eligibility criteria for patient inclusion in relevant studies, and exploring the reported results and conclusions relating to the review questions and objectives. Inclusion and Exclusion Criteria: Studies were included if they focused on sensory stimulation interventions for unconscious patients in the ICU. There were no restrictions on publication date or language. Studies that did not assess consciousness as an outcome or lacked sufficient data were excluded. Sources of Evidence: PubMed, EMBASE, Cochrane Library, and Web of Science will be searched to identify relevant articles. The last search was conducted on 2022. Assessment of Risk of Bias: The included studies were assessed for risk of bias using standardized tools, such as the PEDRO tool for randomized controlled trials. Discrepancies in the assessment were resolved through consensus or consultation with a third reviewer. Methods for Presenting and Synthesizing Results: Study characteristics, patient demographics, intervention details, outcomes, and results were extracted and summarized. Number of Included Studies and Participants: A total of 11 studies involving 500 participants were included. The characteristics of the included studies, such as study design, sample size, sensory stimulation techniques used, and outcome measures, were summarized. Charting Methods: Two independent reviewers will extract data and assess the eligibility of studies based on predefined inclusion and exclusion criteria. Any discrepancies will be resolved through discussion or consultation with a third reviewer. A standardized data extraction form will be used to record relevant information, including study characteristics, patient demographics, intervention details, outcomes, and results. Results: Upon completion of the data extraction process, study characteristics, sensory stimulation techniques, and reported results will be summarized. If feasible, a meta-analysis will be conducted to provide a quantitative synthesis of the findings. Any limitations and potential biases identified among the included studies will be discussed.

European Homicide Monitor: Research, New Developments, and Future

Homicide as the most serious form of lethal violence has always represented an important research focus in criminology. Much of the existing research, however, is based on aggregated homicide data and is limited to macrolevel analyses. The European Homicide Monitor (EHM) is an initiative promoting standardized international data collection, with the aim of collecting disaggregated and detailed data on homicide. Originally developed by researchers from the Netherlands, Sweden, and Finland, the EHM coding scheme is currently applied in several European countries. In this article, we take stock of the use of the EHM approach by describing its key principles and aims. We also review research conducted within the EHM framework and identify the most significant developments in its use, such as expanding to the Global South, to the historical past, and to more accurate measurement of drug-related incidents. We conclude this investigation by discussing the challenges facing future research in this domain.

Investigating the impact of inhaled paraquat: A comprehensive evaluation protocol

This paper provides a complete protocol for studying the effects of inhaled paraquat (PQ), a toxic herbicide that has negative effects systemically and on the lungs. The protocol aims to evaluate the effects of aerosolized PQ exposure on lung and systemic injury in an animal model, which will provide significant information for therapeutic interventions for PQ-induced pulmonary and systemic damage.The protocol involves the following key components: 1. Study groups: By including control, non-treated aerosolized PQ-exposed, and treated PQ-exposed animals with various agent groups in the experiment, lung and systemic injury in each group could be evaluated, and different measured parameters could be compared among groups. 2. PQ exposure: Animals in the PQ-exposed groups are subjected to PQ aerosol inhalation, simulating occupational or accidental exposure in farmers working with this herbicide. 3. Assessment measures: To determine the degree of lung and systemic injury and its physiological effects, several assessments, such as biochemical markers, histopathological analysis, and functional tests, are used.The protocol offers reliable and accurate results by using standardized methods and data collection. The effect of PQ exposure on lung and systemic injury could be evaluated by statistical analysis of the collected data, which also makes it easier to identify possible protective agents or interventions.This comprehensive evaluation protocol provides an essential basis for studying the mechanisms behind PQ-induced lung and systemic injury and assessing the effectiveness of preventative or therapeutic strategies in minimizing its adverse effects.

Cyber Risk Assessment Framework for the Construction Industry Using Machine Learning Techniques

Construction 4.0 integrates digital technologies that increase vulnerability to cyber threats. A dedicated cyber risk assessment framework is essential for proactive risk mitigation. However, existing studies on this subject within the construction sector are scarce, with most discussions still in the preliminary stages. This study introduces a cyber risk assessment framework that integrates machine learning techniques, pioneering a data-driven approach to quantitatively assess cyber risks while considering industry-specific vulnerabilities. The framework builds on over 20 literature reviews related to construction cybersecurity and semi-structured interviews with two industry experts, ensuring both rigor and alignment with practical industrial needs. This study also addresses the challenges of data collection and proposes potential solutions, such as a standardized data collection format with preset fields that computers can automatically populate using data from construction companies. Additionally, the framework proposes dynamic machine learning models that adjust based on new data, facilitating continuous risk monitoring tailored to industry needs. Furthermore, this study explores the potential of advanced language models in cybersecurity management, positioning them as intelligent cybersecurity consultants that provide answers to security inquiries. Overall, this study develops a conceptual machine learning framework aimed at creating a robust, off-the-shelf cyber risk management system for industry practitioners.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Prevalence and factors associated with medication administration errors in the neonatal intensive care unit: A multicentre, nationwide direct observational study.

To determine the prevalence of medication administration errors and identify factors associated with medication administration errors among neonates in the neonatal intensive care units. Prospective direct observational study. The study was conducted in the neonatal intensive care units of five public hospitals in Malaysia from April 2022 to March 2023. The preparation and administration of medications were observed using a standardized data collection form followed by chart review. After data collection, error identification was independently performed by two clinical pharmacists. Multivariable logistic regression was used to identify factors associated with medication administration errors. A total of 743 out of 1093 observed doses had at least one error, affecting 92.4% (157/170) neonates. The rate of medication administration errors was 68.0%. The top three most frequently occurring types of medication administration errors were wrong rate of administration (21.2%), wrong drug preparation (17.9%) and wrong dose (17.0%). Factors significantly associated with medication administration errors were medications administered intravenously, unavailability of a protocol, the number of prescribed medications, nursing experience, non-ventilated neonates and gestational age in weeks. Medication administration errors among neonates in the neonatal intensive care units are still common. The intravenous route of administration, absence of a protocol, younger gestational age, non-ventilated neonates, higher number of medications prescribed and increased years of nursing experience were significantly associated with medication administration errors. The findings of this study will enable the implementation of effective and sustainable interventions to target the factors identified in reducing medication administration errors among neonates in the neonatal intensive care unit. We adhered to the STROBE checklist. An expert panel consisting of healthcare professionals was involved in the identification of independent variables.

Screening for undiagnosed pancreatic exocrine insufficiency (PEI) in a cohort of diabetic patients using faecal elastase testing and PEI scoring system.

Type 1 and type 2 diabetes mellitus (DM) are often accompanied by mild forms of pancreatic exocrine insufficiency (PEI). The prevalence rates of PEI in diabetic patients are unclear and variable depending on the testing modality and the studies published. The clinical consequences of PEI in diabetics are also not well defined. We aimed to determine the prevalence of PEI in a diabetic cohort using the faecal elastase-1 (FE-1) assay as a screening test and to validate a patient-reported symptom-based scoring system, the (PEI-S) for diagnosing PEI within this patient population. Two hundred and three diabetic patients attending diabetic and gastroenterology outpatients of a university hospital without previously known PEI were recruited for the study. Demographic parameters, PEI score (PEI-S), and glycated hemoglobin (HBA1c) were documented in standardized data sheets, and a stool sample was obtained. A FE-1 value < 200μg/g and or a PEIS of > 0.6 was used as the screening cut-off for PEI. One hundred sixty-six patients returned faecal samples. The prevalence of PEI, as measured by low FE-1, was 12%. Smoking was associated with an increased risk of developing PEI in this diabetic population. No other independent risk factors were identified. The PEI-S system did not differentiate between people with diabetes having a normal and low FE1. 12% of this mixed, real-life cohort of type 1 and 2 DM patients had undiagnosed PEI, as defined by an FE-1 score of less than 200μg/g. While this may appear low, given the rising prevalence of type 2 DM worldwide, there is likely an unrecognized burden of PEI, which has long-term health consequences for those affected. The PEI-S, a symptom-scoring system for patients with PEI, did not perform well in this patient group.

BAYES ESTIMATION OF EXPONENTIALLY DISTRIBUTED SURVIVAL DATA UNDER SYMMETRIC AND ASYMMETRIC LOSS FUNCTIONS

In a research study, population data are often not available, so the population parameter is unknown. Meanwhile, knowledge about the population parameter is needed to know the characteristics of the studied population. Therefore, it is needed to estimate the parameter of the population which can be estimated by sample data. There are several methods of parameter estimation which are generally classified into classical and Bayesian method. This research studied the Bayesian parameter estimation method to determine the parameters of the exponentially distributed survival data associated with the reliability measure of the estimates under symmetric and asymmetric loss functions for complete sample data in a closed form. The symmetric loss functions used in this research are Squared Error Loss Function (SELF) and Minimum Expected Loss Function (MELF). The asymmetric loss functions used are the General Entropy Loss Function (GELF) and Linex Loss Function (LLF). Performance of some loss functions used in this research are then compared through numerical simulation to select the best loss function in determining the parameter estimation of the exponentially distributed survival data. We also studied which loss function is best for underestimation and overestimation modeling. Based on simulation results, the Bayes estimates using MELF is the best method to estimate population parameters of the exponentially distributed survival data for the overestimation modeling, while LLF is the best for the underestimation modeling. We provided direct application in a case study of fluorescence lamp survival data. The results show that the best method to estimate the parameter of the standard fluorescence life data is using LLF for underestimation with and MELF for overestimation with .

Enhancing OpenEP: atrial conduction velocity and conduction velocity heterogeneity quantification through EP Workbench

Abstract Background Alterations in atrial conduction velocity have been implicated in the pathogenesis of atrial arrhythmias, with conduction velocity thought to be slower and more heterogeneous in areas promoting atrial fibrillation. However, clinical studies of conduction velocity are challenging since there are no available platforms that provide researchers and clinicians with the ability to quantify conduction velocity and analyse conduction heterogeneity from electroanatomic mapping data. Purpose In this paper we sought to address these limitations by enhancing the OpenEP Python library by (1) automating calculations using previously-published conduction velocity estimation algorithms; (2) providing an interactive graphical representing of conduction velocity and conduction heterogeneity and (3) implementing a histogram analysis tool to quantify conduction velocity and enable the identification of slow conduction regions. Method Two well-known and previously published methods for calculating cardiac conduction velocity, Triangulation and polynomial surface fitting, were implemented. Conduction velocity estimation was improved further by excluding regions with wave collisions and focal discharges. These regions were identified using the divergence of conduction velocity vector fields, with positive divergence representing focal sources and negative divergence representing areas of collision. Finally, calculated conduction velocities using electrogram data were interpolated over the surface mesh via Radial Basis Function (RBF) interpolation method. All algorithms were implemented in the OpenEP Python library (openep-py), with visualisation tools provided in the complementary graphical interface, EP Workbench. Results An extensible "Analysis" feature was created in openep-py, to provide conduction velocity and divergence calculations which are fully customisable through the graphical interface based on user preferences. The EP Workbench desktop application displays resulting cardiac surface maps to depict calculated conduction velocity and divergence fields, together with conventional activation and voltage maps (Figure 1). The histogram analysis tool can be used to identify regions of slow conduction velocity from electroanatomic mapping data. Using a threshold of &amp;lt;0.3m/s, two slow conducting channels are visualised on the posterior wall of a test case (Figure 2). The tool is not specific to conduction velocity and may also be used to quantify other data types in EP Workbench. Finally, computed conduction velocity and divergence values and surface maps are stored in the standard OpenEP data structure, allowing further analysis following data export. Conclusion We have extended OpenEP to provide a comprehensive set of tools for conduction velocity calculation and analysis which will facilitate investigation of the structural and functional basis of conduction velocity alterations in disease states.

#675 Application of ANCA-associated vasculitis Histopathological Classification and Renal Risk Score for prediction of end-stage kidney disease

Abstract Background and Aims Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease predominantly characterized by inflammation of small-sized blood vessels along with the presence of circulating ANCA. The kidney is frequently involved and, even though immunosuppressive therapy has dramatically improved the outcome of AAV patients, a significant proportion of patients progress to end-stage kidney disease (ESKD). Therefore, establishing and validating tools to predict renal outcomes in patients with AAV is crucial. Here we analyzed the application of two tools in predicting kidney survival in a Portuguese single-center cohort with AAV. Method We conducted a retrospective cohort study at a tertiary-care hospital in Portugal (Hospital de São João, Porto), from December 2010 to June 2023. Patients with a kidney biopsy-proven AAV were included. Serological ANCA-negative patients and patients with overlap syndromes, such as AAV in combination with anti-glomerular basement membrane disease, were excluded. Based on electronic records, information on demographics and clinical variables were collected. Kidney biopsies were reviewed to calculate: Berden histopathological classification and the Brix Renal Risk Score (RRS). Kidney survival was defined as the time from diagnosis to the need for kidney replacement therapy. A Kaplan-Meier analysis was performed to compare kidney survival based on the mentioned classifications. Results Fifty-one patients were enrolled, 78% (n = 40) of whom were positive for myeloperoxidase (MPO). The majority were men (61%, n = 31) with a mean age of 65 (± 12) years and a mean serum creatinine of 4.59 mg/dL (± 2.74) at diagnosis. During a median follow-up of 39 (IQR, 7-84) months, 20 patients (39%) progressed to end-stage kidney disease (ESKD), 10 patients (20%) died with preserved renal function and 21 patients (41%) did not need hemodialysis. Concerning Berden classification, 13 patients with a crescentic class, 4 patients with a mixed class and 3 patients with a sclerotic class developed ESKD. After 48 months of follow-up, renal survival was 100%, 90%, 61% and 26.7% in the focal, mixed, crescentic and sclerotic class groups, respectively. Berden classification did not correlate with kidney survival (p = 0.211). Concerning RRS, 10 patients in the high-risk group and 5 patients in the medium-risk group developed ESKD. After 48 months of follow-up, renal survival was 100% in the low-risk group, 85.7% in the medium-risk group, and 41.6% in the high-risk group. A correlation was established between kidney survival and RRS score (p = 0.003). Conclusion In our cohort RRS was predictive of ESKD, contrary to Berden histopathological classification. The information derived from these risk models strongly add to standard biochemical and clinical data. Patients with higher risk of ESKD need close follow-up and a tailored treatment plan regarding preparation for renal replacement therapy.

#3105 Tocilizumab for treatment of antibody mediated rejection in kidney transplant patients

Abstract Background and Aims Chronic active antibody-mediated rejection (caABMR) causes 40% of long-term graft failure after kidney transplantation (KT). Early treatment is of great importance, however, hard clinical evidence is still lacking. Currently the combination of plasmapheresis (PF), rituximab and intravenous immunoglobulin is used as standard treatment. Tocilizumab (TCZ) is a monoclonal antibody, IL-6 receptor antagonist, and it inhibits the production of donor-specific antibodies (DSA). We hypothesized that tocilizumab administration has an additional effect on graft survival and non-inferior to standard therapy in KT patients with ABMR. Method Retrospective single-center noninferiority case-control study was performed by involving 73 KT patients (43.5 ± 14.3 years, females 31, eGFR 19.5 ± 13.4) with biopsy proven ABMR diagnosed between June 2017 and May 2022. 19 patients received 8 mg/kg intravenous TCZ therapy for 6 months (12 aABMR, 7 caABMR, 38.6 ± 12.8 years, females 6). In the control group (C, n=54, 10 aABMR, 44 caABMR, 45.2 ± 14.5 years, females 25), 20 patients received plasmapheresis treatment, the rest stayed on optimized maintenance therapy (tacrolimus, mycophenolic acid, prednisolone). Baseline demographic data, standard laboratory data, proteinuria and DSA level were also monitored and analyzed. Results Graft loss occurred in 13 cases (6 aABMR, 7 caABMR) in KT on TCZ therapy and 43 (6 aABMR, 37 caABMR) in C group, meaning significantly lower risk of graft loss in the TCZ group (RR 0.69, p=0.036) during the median follow-up of 11.3 (IQR 2.5-23.1) months. In TCZ group, all caABMR patients lost their graft during the observation. In TCZ group, 1 year after the TCZ treatment, DSA (pooled) was lower (7028 (575-17782) versus 1605 (551-16903), p&amp;lt;0.05). Conclusion Early administration of TCZ seems to be noninferior to standard therapy and effective to prevent early graft loss as part of standard therapy in aABMR. Further clinical studies are needed to clarify clinical indications of TCZ and its long-term effects on the course of chronic active ABMR.

#2564 Terlipressin given by continuous infusion versus boluses in the treatment of hepatorenal syndrome: a systematic review and meta-analysis

Abstract Background and Aims Hepatorenal syndrome (HRS) is one of the complications among patients with cirrhosis. Available guidelines recommended terlipressin with albumin as part of HRS management. To date there is no consensus on whether terlipressin should be given via bolus or continuous intravenous infusion. This study aims to compare the effectiveness and safety of continuous intravenous infusion versus intravenous boluses of terlipressin in the treatment of patients with HRS and cirrhosis. Method A comprehensive search for databases of randomized controlled trials (RCTs) comparing continuous intravenous infusion versus intravenous boluses of terlipressin among adult patients with hepatorenal syndrome was done. PubMed, EMBASE, the Cochrane Library, and ClinicalTrials.gov were searched using relevant terms including hepatorenal syndrome, acute kidney injury, cirrhosis, terlipressin, bolus, and continuous infusion until December 2023. Response to therapy defined as decrease in serum creatinine or regression of acute kidney injury was the primary outcome of interest, while safety in terms of drug-related events, mortality, and transplant-free survival were the secondary outcomes. Data extraction was performed using a standardized data form. The reviewers independently screened the studies and assessed the methodological quality using the Cochrane Risk of Bias tool and any discrepancies were resolved by consensus among the authors. Random-effects meta-analysis was done using Review Manager 5.4. Results A total of three studies, one high quality and two moderate quality RCTs, were included in the review, involving a total of 240 patients. Pooled analysis showed that continuous infusion was as effective or better than bolus infusion in achieving the primary outcome (RR 1.12, 95% CI 0.95-1.32, I2 = 0%). In one study, 28- and 90-day mortality were not significantly different, while 90-day transplant-free survival was not significantly different between continuous and bolus groups from another trial (53% vs 69% p = 0.26). Higher incidence of adverse events were also reported in the bolus group, RR 0.37, 95% CI 0.13-1.04 (p = 0.06). Conclusion There is limited evidence to suggest that continuous infusion of terlipressin may demonstrate resolution of acute kidney injury more frequently than bolus infusion, and that continuous infusion is associated with decreased adverse events. Larger randomized controlled trials with higher quality are needed to ascertain the effects on mortality, need for renal replacement therapy, and transplant-free survival.

Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities.

The causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables. However, particularly in the presence of hidden variables, many causal structures are compatible with the same set of independencies inferred from the marginal distributions of observed variables. Additional constraints allow further testing for the compatibility of data with specific causal structures. An existing family of causally informative inequalities compares the information about a set of target variables contained in a collection of variables, with a sum of the information contained in different groups defined as subsets of that collection. While procedures to identify the form of these groups-decomposition inequalities have been previously derived, we substantially enlarge the applicability of the framework. We derive groups-decomposition inequalities subject to weaker independence conditions, with weaker requirements in the configuration of the groups, and additionally allowing for conditioning sets. Furthermore, we show how constraints with higher inferential power may be derived with collections that include hidden variables, and then converted into testable constraints using data processing inequalities. For this purpose, we apply the standard data processing inequality of conditional mutual information and derive an analogous property for a measure of conditional unique information recently introduced to separate redundant, synergistic, and unique contributions to the information that a set of variables has about a target.

Feasibility and acceptability of a cohort study baseline data collection of device-measured physical behaviors and cardiometabolic health in Saudi Arabia: expanding the Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) in the Middle East.

Physical behaviors such physical activity, sedentary behavior, and sleep are associated with mortality, but there is a lack of epidemiological data and knowledge using device-measured physical behaviors. To assess the feasibility of baseline data collection using the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) protocols in the specific context of Saudi Arabia. ProPASS is a recently developed global platform for collaborative research that aims to harmonize retrospective and prospective data on device-measured behaviors and health. Using ProPASS methods for collecting data to perform such studies in Saudi Arabia will provide standardized data from underrepresented countries. This study explored the feasibility of baseline data collection in Saudi Arabia between November and December 2022 with a target recruitment of 50 participants aged ≥ 30 years. Established ProPASS methods were used to measure anthropometrics, measure blood pressure, collect blood samples, carry out physical function test, and measure health status and context of physical behaviors using questionnaires. The ActivPal™ device was used to assess physical behaviors and the participants were asked to attend two sessions at (LHRC). The feasibility of the current study was assessed by evaluating recruitment capability, acceptability, suitability of study procedures, and resources and abilities to manage and implement the study. Exit interviews were conducted with all participants. A total of 75 participants expressed an interest in the study, out of whom 54 initially agreed to participate. Ultimately, 48 participants were recruited in the study (recruitment rate: 64%). The study completion rate was 87.5% of the recruited participants; 95% participants were satisfied with their participation in the study and 90% reported no negative feelings related to participating in the study. One participant reported experiencing moderate skin irritation related to placement of the accelerometer. Additionally, 96% of participants expressed their willingness to participate in the study again. Based on successful methodology, data collection results, and participants' acceptability, the ProPASS protocols are feasible to administer in Saudi Arabia. These findings are promising for establishing a prospective cohort in Saudi Arabia.

Mathematical Modeling of The Estimate of Methane Gas Generation, of The Organic Fraction Of Msw From The Agreste Alagoano Sanitary Landfill, From Standard Data And Experimental Data

Objective: The objective of this study is to carry out mathematical modeling in the generation of methane gas, through two mathematical models, using standard data and data obtained in the laboratory. Theoretical framework: The emission of methane gas, generated by the decomposition of solid waste in landfills, is one of the serious problems of air pollution and one way to minimize the impacts of methane on the environment is through its use to generate energy. To this end, there are ways to estimate the generation of CH4 during the useful life of the landfill. Method: For modeling, the computer programs of LandGEM and the Intergovernmental Panel on Climate Change (IPCC) were used to carry out the theoretical estimate of CH4 production during the useful life of the Agreste Alagoano landfill in two scenarios: one with standard program values for the local climate region and another with information obtained from experimental data from research by Santana (2022). Results and Discussion: When standard input data was adopted, the LandGEM model presented a 38% higher value in CH4 production compared to the IPCC model. When considering input data obtained experimentally, the IPCC showed CH4 generation 32.66% higher than the LandGEM, this was possible due to the COD value that was high as a result of the L0 of the organic fraction, exceeding the theoretical value of 0.15 to the experimental value of 0.27 in the new scenario. Implications of the research: Determining parameters for modeling methane gas generation will help in managing landfill waste and its energy recovery potential, in addition to the application of mathematical models for estimating biogas production potential as tools essential for both energy and environmental studies. Originality/value: This research presents as originality the determination of experimental parameters applied to the mathematical modeling of methane gas generation in landfills in the rural region, since studies on modeling in landfills with the climatic conditions of the rural region of Brazil are not common in the literature.

Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis.

To elucidate potential molecular mechanisms differentiating osteoarthritis (OA) and rheumatoid arthritis (RA) through a bioinformatics analysis of differentially expressed genes (DEGs) in patient synovial cells, aiming to provide new insights for clinical treatment strategies. Gene expression datasets GSE1919, GSE82107, and GSE77298 were downloaded from the Gene Expression Omnibus (GEO) database to serve as the training groups, with GSE55235 being used as the validation dataset. The OA and RA data from the GSE1919 dataset were merged with the standardized data from GSE82107 and GSE77298, followed by batch effect removal to obtain the merged datasets of differential expressed genes (DEGs) for OA and RA. Intersection analysis was conducted on the DEGs between the two conditions to identify commonly upregulated and downregulated DEGs. Enrichment analysis was then performed on these common co-expressed DEGs, and a protein-protein interaction (PPI) network was constructed to identify hub genes. These hub genes were further analyzed using the GENEMANIA online platform and subjected to enrichment analysis. Subsequent validation analysis was conducted using the GSE55235 dataset. The analysis of differentially expressed genes in the synovial cells from patients with Osteoarthritis (OA) and Rheumatoid Arthritis (RA), compared to a control group (individuals without OA or RA), revealed significant changes in gene expression patterns. Specifically, the genes APOD, FASN, and SCD were observed to have lower expression levels in the synovial cells of both OA and RA patients, indicating downregulation within the pathological context of these diseases. In contrast, the SDC1 gene was found to be upregulated, displaying higher expression levels in the synovial cells of OA and RA patients compared to normal controls.Additionally, a noteworthy observation was the downregulation of the transcription factor PPARG in the synovial cells of patients with OA and RA. The decrease in expression levels of PPARG further validates the alteration in lipid metabolism and inflammatory processes associated with the pathogenesis of OA and RA. These findings underscore the significance of these genes and the transcription factor not only as biomarkers for differential diagnosis between OA and RA but also as potential targets for therapeutic interventions aimed at modulating their expression to counteract disease progression. The outcomes of this investigation reveal the existence of potentially shared molecular mechanisms within Osteoarthritis (OA) and Rheumatoid Arthritis (RA). The identification of APOD, FASN, SDC1, TNFSF11 as key target genes, along with their downstream transcription factor PPARG, highlights common potential factors implicated in both diseases. A deeper examination and exploration of these findings could pave the way for new candidate targets and directions in therapeutic research aimed at treating both OA and RA. This study underscores the significance of leveraging bioinformatics approaches to unravel complex disease mechanisms, offering a promising avenue for the development of more effective and targeted treatments.

Mobilizing stakeholders for implant removals in Burkina Faso using landscape assessment data

BackgroundSuccessful efforts to encourage uptake of subdermal contraceptive implants, with a lifespan of three to five years, necessitate planning to ensure that quality removal services are available when desired. In Burkina Faso, implant use has tripled over the past 8 years and now comprises almost half of the contraceptive method mix. Population Monitoring for Action (PMA) surveys identified barriers to obtaining quality removal when desired, particularly when the implant is not palpable, or providers lack needed skills or supplies. The Expanding Family Planning Choices (EFPC) project supported ministries of health in four countries with evaluation and strengthening of implant removal services.MethodsAn implant removal landscape assessment was conducted at 24 health facilities in three regions of Burkina Faso with high implant use that included provider observations of implant removal, interviews with providers and health facility managers, and facility readiness surveys. The project used landscape data to mobilize stakeholders through a series of participatory workshops to develop a collaborative roadmap and commit to actions supporting quality implant removals.ResultsLandscape findings revealed key gaps in provision of quality removal services, including high levels of provider confidence for implant insertion and removal (82% and 71%, respectively), low competence performing simple and difficult removals (19.2% and 11.1%, respectively), inadequate supplies and equipment (no facilities had all necessary materials for removal), lack of difficult removal management systems, and a lack of standard data collection tools for removal. Exposure to the data convinced stakeholders to focus on removals rather than expanding insertion services. While not all roadmap commitments were achieved, the process led to critical investments in quality implant removals.ConclusionLandscape data revealed that facilities lack needed supplies and equipment, and providers lack skills needed to perform quality implant removals, limiting client reproductive choice. Disseminating this data enabled stakeholders to identify and commit to evidence-based priority actions. Stakeholders have since capitalized on program learnings and the roadmap, including following MOH guidance for implant removal supplies and health provider training. Our experience in Burkina Faso offers a replicable model of how data can direct collective action to improve quality of contraceptive implant removals.