Analysis Of Strategies Research Articles

Mechanism Study of Xiaoyao San against Nonalcoholic Steatohepatitis-Related Liver Fibrosis Based on a Combined Strategy of Transcriptome Analysis and Network Pharmacology.

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of liver disease worldwide. Nonalcoholic steatohepatitis (NASH) is an advanced form of NAFLD. The livers of patients with NASH are more likely to develop fibrosis. Xiaoyao San (XYS) is a classic traditional Chinese medicine (TCM) formula that has been widely used in treating liver diseases. In this study, we elucidated the effects and mechanism of XYS in treating NASH-related liver fibrosis by combining high-throughput sequencing-based high-throughput screening with network pharmacology analysis. Our work revealed that XYS may play a role in preventing NASH-related liver fibrosis by regulating biological functions related to the extracellular matrix (ECM), inflammation, and metabolism. Additionally, Bupleuri Radix, Poria, Zingiberis Rhizoma Recens, and Paeoniae Radix Alba are the key herbs of XYS that could partially represent the functions of XYS. These regulatory effects are mediated by targeting signal transducer and activator of transcription 3 (STAT3), nuclear factor kappa B (NFκB), and peroxisome proliferator-activated receptor gamma (PPARγ) signaling. Narcissin, casuarictin, and γ-sitosterol were identified as representative active compounds in XYS targeting STAT3, NFκB, and PPARγ, respectively. Taken together, our findings provide a novel strategy for investigating the pharmacological effects and biological mechanisms of a TCM formula.

Characterizing features affecting local ancestry inference performance in admixed populations.

In recent years, significant efforts have been made to improve methods for genomic studies of admixed populations using Local Ancestry Inference (LAI). Accurate LAI is crucial to ensure downstream analyses reflect the genetic ancestry of research participants accurately. Here, we test analytic strategies for LAI to provide guidelines for optimal accuracy, focusing on admixed populations reflective of Latin America's primary continental ancestries - African (AFR), Amerindigenous (AMR), and European (EUR). Simulating LD-informed admixed haplotypes under a variety of 2 and 3-way admixture models, we implemented a standard LAI pipeline, testing three reference panel compositions to quantify their overall and ancestry-specific accuracy. We examined LAI miscall frequencies and true positive rates (TPR) across simulation models and continental ancestries. AMR tracts have notably reduced LAI accuracy as compared to EUR and AFR tracts in all comparisons, with TPR means for AMR ranging from 88-94%, EUR from 96-99% and AFR 98-99%. When LAI miscalls occurred, they most frequently erroneously called European ancestry in true Amerindigenous sites. Using a reference panel well-matched to the target population, even with a lower sample size, LAI produced true-positive estimates that were not statistically different from a high sample size but mismatched reference, while being more computationally efficient. While directly responsive to admixed Latin American cohort compositions, these trends are broadly useful for informing best practices for LAI across other admixed populations. Our findings reinforce the need for inclusion of more underrepresented populations in sequencing efforts to improve reference panels.

Monte Carlo inference for semiparametric Bayesian regression

Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This paper introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, positive, and compactly-supported data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes. The R package SeBR is available on CRAN.

Green innovation for a sustainable environment: Modeling the new green knowledge production function for OECD countries

AbstractThe world community faces serious challenges such as climate change, biological loss, and environmental degradation. Developing ecological technologies is vital for coping with these unreversible crises. This study proposed a modified green knowledge production function to achieve sustainable development goals and generate policies for promoting environmental technologies. It examined the impact of market demands, environmental research and development expenditures, human capital, and environmental protection policies on the production of green environment technologies. Panel data included 16 OECD countries from 1990 to 2019. Data analysis strategies included the Gengenbach, Urbain, and Westerlund panel co‐integration test, fully modified least squares, mean group dynamic least squares, and estimators such as feasible generalized least squares and panel‐corrected standard errors for robust checks. A novel panel causality test via a half‐panel jackknife estimator was also applied for causality analyses. The findings showed that human capital, market size, and environmental policy stringency played significant roles in producing environmental technologies and innovations. Although environmental expenditures made by the government positively affected the supply of green innovation, no statistically significant results were observed. The study highlights the importance of enhancing environmental innovation factors by promoting policies, including human capital development, environmental sanctions, and market demands for a sustainable environment.

Optimizing Asset Management: Comprehensive Analysis and Innovative Strategy Design to Increase the Effectiveness of Life Cycle Delivery at PT Pembangkitan Jawa-Bali Services

Asset management is the art and science of making the right decisions and optimizing the delivery of value from organizational assets. ( Institute of Asset Management , 2010). PT PLN Pembangkitan Jawa Bali Services, which is often abbreviated as PT PJB Services, is a subsidiary of PT Pembangkitan Jawa-Bali (PT PJB) . This company operates in the field of power plant operation and maintenance services, as well as supporting services related to power plants. The company has a strong focus on asset management, given its role in managing and maintaining critical power generation assets. This research examines the implementation of multidimensional strategies in optimizing asset management at PT Pembangkitan Jawa-Bali Services (PJB Services). The main focus of the analysis is to test the variables that influence the success of asset performance at PT Pembangkitan Jawa Bali Services based on group 3 ( Life Cycle Delivery ) consist of 11 key dimensions of asset management: Technical Standards & Legislation , Asset Creation & Acquisition , Systems Engineering , Configuration Management , Maintenance Delivery. Reliability Engineering, Asset Operation, Resource Management, Shutdown & Outage Management, Fault & Incident Response, Asset Decommissioning & Disposal. The main aim of the research is to design an integrated approach to improve the effectiveness of corporate asset management. The method used in this research is the Structural Equation Model with the Partial Least Square approach. The research results revealed that the accepted hypothesis is H1, H2, H3, H4, H9, H10, H12, H16, H17, H18, and H19 are acceptable because they have t-statistic values > 1.96 and p-values < 0.05. Meanwhile, the hypotheses H5, H6, H7, H8, H11, H13, H14, and H15 were rejected because they had t-statistic values > 1.96 and p-values > 0.05.

NIR and MIR spectral feature information fusion strategy for multivariate quantitative analysis of tobacco components

The determination of total nicotine, total sugar, reducing sugar and total nitrogen contents in tobacco is of great significance to tobacco quality evaluation and formulation design. To quickly detect the content of 4 components of tobacco, using near-infrared (NIR) and mid-infrared (MIR) spectral data from 129 solid samples of tobacco powder provided by Shanghai Tobacco Group Co., Ltd., Two NIR-MIR spectral fusion techniques are studied, that is, fusion technology 1 is to establish a model by fusing feature variables after variable selection of each spectrum. The fusion technology 2 is to first fuse the NIR-MIR spectral data and then select the variables to establish the model. Both fusion technologies use successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), backward interval PLS (biPLS), forward interval PLS (fiPLS), synergy interval PLS (siPLS), and interval interaction moving window partial least squares (iMWPLS) algorithms to filter wavelength variables. The results showed that for total nicotine and total sugar, the PLSR model established by fusion technology method 2 combined with iMWPLS algorithm is the best, and its RMSEP decreases from 0.2314 to 1.3225 to 0.0821 and 0.8079 respectively compared with the full spectrum fusion method, which is superior to the single NIR and MIR models and NIR-MIR fusion technology 1. For reducing sugars, the simple full-spectrum fusion model has the best analytical ability and the lowest RMSEP, which is superior to the single NIR-MIR models and all models established by two spectral fusion techniques combined with six wavelength selection algorithms. For total nitrogen, the prediction effect of fusion technology 1 combined with iMWPLS algorithm model was significantly improved compared with single NIR and MIR models and NIR-MIR fusion technology 2, and its RMSEP was 0.0634. The results showed that the two NIR-MIR spectral fusion techniques made full use of the complementary information provided by NIR and MIR spectroscopy, and successfully applied them to the rapid detection of total nicotine, total sugar, reducing sugar and total nitrogen content in tobacco, which provided a new method and idea for the rapid detection of tobacco components.

Backside Analysis Strategy to Identify a Package-Related Failure Mode at an Automotive Magnetic Sensor Device

This paper presents a root cause analysis case study of defective Hall effect sensor devices. The study identified a complex failure mode caused by chip–package interaction, which has a similar signature to discharging defects such as ESDFOS (electrostatic discharge from outside to surface). However, the study revealed that the defect was induced by local mechanical force applied to IC structures due to the presence of large irregular shaped filler particles within the mold compound. Extensive failure analysis work was conducted to identify the failure mode, including the development of a new backside analysis strategy to preserve the mold compound during IC defect localization and screening. A combination of different failure analysis techniques was used, including CMP delayering, PFIB trenching, SEM PVC imaging and large area FIB cross-sectioning. The study found that the mold compound of the package caused thermo-mechanical strain onto the silica filler particle due to epoxy shrinkage during the molding process. Additionally, extra-large, irregularly shaped filler particles (called twin particles), located on top of the chip surface, can cause locally high compression stresses onto the IC layers, initiating cracks in the isolation layers under certain conditions forming a leakage path over the time. Thermo-mechanical finite element analysis was applied to verify the mechanical load condition for these large irregular shaped filler particles. As a result, an additional polyimide layer was introduced onto the IC to mitigate the mechanical stress of mold compound particles to avoid this failure mode.

Short-term air pollution prediction using graph convolutional neural networks

Pollution is a major concern in the present day, causing multiple illnesses and deaths, specifically in developing countries in Asia and Africa. While it has drawn worldwide attention as governments try to issue laws to meet certain criteria for air pollution levels, pollution concentration forecasting has become a major challenge. Particularly, short term forecasting will help to gain information regarding concentrations of harmful pollutants for the upcoming hours and enable better decision-making with regards to controlling air pollution. In this paper, we investigate spatio-temporal graph-based models to determine the best methods for spatial and temporal analysis of data. The models have the additional capacity to perform multi-variate predictions of correlated data, i.e., predicting multiple pollutant concentrations simultaneously, thus requiring lower computational efforts. A real-world pollution dataset measured over Delhi, India, is used to comparing the proposed models with baselines, which shows the Spatio-Temporal Graph Convolution Neural Network (STGCN) models to be performing better than others. For a better understanding of model architectures with the most effective strategies for spatial and temporal data analysis, three models, namely STGCN-A, STGCN-B, STGCN-C have been developed. The models have been compared with 6 other baselines over multiple forecasting horizons of 1 h, 24 h, and 48 h timesteps using various metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE). On the PM2.5 dataset of Delhi, STGCN-B achieves a performance of 10.53 MAE, 6.92 RMSE and 25.25 MAPE for a 1 h forecast, while STGCN-C achieves 20.18 MAE, 14.73 RMSE and 55.45 MAPE for a 24 h forecast. In general, both structures achieve similar results, with STGCN-C being better in many cases. They are further analysed through observation-prediction graphs and Taylor diagrams, which give an insight into our findings. The models are additionally validated on a benchmark real-world dataset from California, USA for better understanding of the spatio-temporal relations and model performances on a more stable dataset, where STGCN-C performs best for PM2.5 with 4.30 RMSE, 1.98 MAE, 25.96 MAPE for 1 h predictions for univariate data and 3.63 RMSE, 1.88 MAE and 25.91 MAPE in multivariate forecasting. The developed spatio-temporal graph-based models hold promising applications in urban air quality management, aiding policymakers in implementing targeted interventions to mitigate pollution-related health risks. Furthermore, these models can support public health agencies by providing timely and accurate forecasts of pollutant concentrations, enabling proactive measures to safeguard community well-being. Our study showcases the efficacy of spatio-temporal graph-based models in accurately forecasting air pollutant concentrations, with particular emphasis on short-term predictions. By leveraging multi-variate capabilities, our proposed models demonstrate superior performance compared to baseline approaches across various forecasting horizons.

A Framework to Support the Standardized Management of Real-World Data.

Relying on our experience on the development of data registration and management systems for clinical and biological data coming from patients with hematological malignancies, as well as on the design of strategies for data collection and analysis to support multi-center, clinical association studies, we designed a framework for the standardized collection and transformation of clinically relevant real-world data into evidence, to meet the challenges of gathering biomedical data collected during daily clinical practice in order to promote basic and clinical research.

Translation, Cross-Cultural Adaptation, and Validation of the Croatian Version of the Athlete Psychological Strain Questionnaire (APSQ).

The aim of this study is to translate, cross-culturally adapt, and validate the Croatian Athlete Psychological Strain Questionnaire (APSQ-Cro) as part of the Sport Mental Health Assessment Tool 1 (SMHAT-1) validation. We assessed the reliability and applicability of the APSQ-Cro among Croatian athletes. The international sports community is increasingly focused on mental health issues in athletes, highlighting the need for early detection tools like the Athlete Psychological Strain Questionnaire (APSQ) and SMHAT-1. We included 869 Croatian competing athletes across 54 sports who received a link to access the WEB-based questionnaire. The Croatian Olympic Board helped in distributing the questionnaires, aiming to reach as many and as diverse a group of registered competing athletes in Croatia as possible. Results showed a Cronbach's alpha of 0.75 for the entire questionnaire, indicating acceptable reliability. An exploratory strategy of factor analysis was used to determine the underlying structure of the APSQ-Cro. For this purpose, the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test for sphericity were performed to ensure the suitability of the data. The KMO test ensured sampling adequacy, with a measure of 0.77 indicating suitability for factor analysis, while Bartlett's test confirmed significant correlations among variables (χ2 = 2779.155, df = 45, p < 0.001), validating the dataset's appropriateness for data reduction techniques. The factor analysis, together with the Cattell scree test and varimax rotation, resulted in a two-factor structure for the APSQ-Cro. Factor 1 included items related to internal psychological struggles, while Factor 2 included items related to external pressures from the athletic environment. These two factors explained 53% of the variability, with Cronbach's alphas of 0.75 and 0.88 for the respective factors. The APSQ-Cro is a valid and reliable tool for assessing distress in Croatian athletes. Croatian athletes' sporting experience will be improved with the broad adoption of the APSQ-Cro, which can help detect early signs of psychological distress and subsequently improve mental health outcomes.

The Relationship between Maternal Smoking and Infant Birth Weight: Improving Accuracy through Urine Cotinine Analysis and Effective Medical Record Strategies.

We conducted a study to determine if antenatally collected maternal urine cotinine (a metabolite of nicotine) measurements can be used to assess the neonatal impact of nicotine exposure during pregnancy. This was a prospective longitudinal cohort of mother-infant dyads. Only term singleton pregnancies were included. The primary outcome measure was the correlation between maternal urine cotinine and infant birth weight. We analysed data from 238 mother-neonate dyads. Smoking habits were recorded during routine prenatal check-ups and urine samples were collected to measure cotinine and creatinine levels. Urine cotinine was detected in 50.4% (120/238) of women from the whole cohort, but only 16% (38/238) self-reported as smokers (chi-square 39.7, p < 0.0001), and these women had significantly smaller babies (p = 0.010). There was a significant negative correlation between maternal urine cotinine and birth weight (Spearman's coefficient = -0.0226, p = 0.013). Female babies born to women with nicotine in their urine were significantly smaller (p = 0.001). Infant birth weight significantly reduced in mothers with exposure to nicotine during pregnancy. The number of women exposed to nicotine during late pregnancy (measured in urine) was markedly higher than self-reported and national smoking percentages, suggesting an urgent need for an improvement in medical record reporting on smoking habits to better assess neonatal outcomes.

In situ structural determination of cyanobacterial phycobilisome–PSII supercomplex by STAgSPA strategy

Photosynthesis converting solar energy to chemical energy is one of the most important chemical reactions on earth. In cyanobacteria, light energy is captured by antenna system phycobilisomes (PBSs) and transferred to photosynthetic reaction centers of photosystem II (PSII) and photosystem I (PSI). While most of the protein complexes involved in photosynthesis have been characterized by in vitro structural analyses, how these protein complexes function together in vivo is not well understood. Here we implemented STAgSPA, an in situ structural analysis strategy, to solve the native structure of PBS–PSII supercomplex from the cyanobacteria Arthrospira sp. FACHB439 at resolution of ~3.5 Å. The structure reveals coupling details among adjacent PBSs and PSII dimers, and the collaborative energy transfer mechanism mediated by multiple super-PBS in cyanobacteria. Our results provide insights into the diversity of photosynthesis-related systems between prokaryotic cyanobacteria and eukaryotic red algae but are also a methodological demonstration for high-resolution structural analysis in cellular or tissue samples.

The performance of AlphaMissense to identify genes influencing disease

A novel algorithm, AlphaMissense, has been shown to have an improved ability to predict the pathogenicity of rare missense genetic variants. However, it is not known whether AlphaMissense improves the ability of gene-based testing to identify disease-influencing genes. Using whole-exome sequencing data from the UK Biobank, we compared gene-based association analysis strategies including sets of deleterious variants: predicted loss-of-function (pLoF) variants only, pLoF plus AlphaMissense pathogenic variants, pLoF with missense variants predicted to be deleterious by any of five commonly utilized annotation methods (Missense (1/5)) or only variants predicted to be deleterious by all five methods (Missense (5/5)). We measured performance to identify 519 previously identified positive control genes, which can lead to Mendelian diseases, or are the targets of successfully developed medicines. These strategies identified 850k pLoF variants and 5 million deleterious missense variants, including 22k likely pathogenic missense variants identified exclusively by AlphaMissense. The gene-based association tests found 608 significant gene associations (at P<1.25x10-7) across 24 common traits and diseases. Compared to pLoFs plus Missense (5/5), tests using pLoFs and AlphaMissense variants found slightly more significant gene-disease and gene-trait associations, albeit with a marginally lower proportion of positive control genes. Nevertheless, their overall performance was similar. Merging AlphaMissense with Missense (5/5), whether through their intersection or union, did not yield any further enhancement in performance. In summary, employing AlphaMissense to select deleterious variants for gene-based testing did not improve the ability to identify genes that are known to influence disease.

Field-Assisted Efficient Capturing and Analysis of Airborne Nanoparticulate Matter Using a Multifunctional Nanoporous Membrane.

As the potential adverse health and environmental effects of nanoscale pollutants have garnered significant attention, the demand for monitoring and capturing ultrafine particulate matter has been growing. With the rise in ultrafine dust emissions, this issue has become increasingly important. However, submicron particles require advanced strategies to be captured because of their limited inertial effect. For example, electrostatic air filters have been investigated for their improved performance in the fine particle regime. On the other hand, Raman spectroscopy was proposed as a promising analytical strategy for aerosol particles because it can be used to conveniently detect analytes in a label-free manner. Thus, the synergistic integration of these strategies can open new applications for addressing environment-related challenges. This study presents a multifunctional approach for achieving both air filtration and surface-enhanced Raman scattering (SERS) for analyte identification. We propose a nanoporous membrane composed of a thin gold layer, copper, and copper oxide to provide the desired functions. The structures are produced by performing scalable electrodeposition and subsequent electron-beam evaporation, attaining an excellent filtration efficiency of 95.9% with an applied voltage of 5 kV for 300 nm KCl particles and a pressure drop of 121 Pa. Raman intensity measurements confirm that the nanodendritic surface of the membrane intensifies the Raman signals and allows for the detection of 10 μL of nanoplastic particle dispersion with a concentration of 50 μg/mL. Rhodamine 6G aerosol stream with an approximate particle deposition rate of 0.040 × 106 mm-2·min-1 is also identified in a minimum detectable time of 50 s. The membrane is shown to be recyclable owing to its structural robustness in organic solvents. In addition, the fatigue resistance of the structure is evaluated through 22,000 iterative loading cycles at a pressure of 177 kPa. No performance degradation is observed after the fatigue loading.

Nontarget analysis and characterization of p-phenylenediamine-quinones and -phenols in tire rubbers by LC-HRMS and chemical species-specific algorithm

BackgroundN,N′-disubstituted p-phenylenediamine-quinones (PPDQs) are oxidization derivatives of p-phenylenediamines (PPDs) and have raised extensive concerns recently, due to their toxicities and prevalence in the environment, particularly in water environment. PPDQs are derived from tire rubbers, in which other PPD oxidization products besides reported PPDQs may also exist, e.g., unknown PPDQs and PPD-phenols (PPDPs). ResultsThis study implemented nontarget analysis and profiling for PPDQ/Ps in aged tire rubbers using liquid chromatography-high-resolution mass spectrometry and a species-specific algorithm. The algorithm took into account the ionization behaviors of PPDQ/Ps in both positive and negative electrospray ionization, and their specific carbon isotopologue distributions. A total of 47 formulas of PPDQ/Ps were found and elucidated with tentative or accurate structures, including 25 PPDQs, 18 PPDPs and 4 PPD-hydroxy-quinones (PPDHQs). The semiquantified total concentrations of PPDQ/Ps were 14.08–30.62 μg/g, and the concentrations followed the order as: PPDPs (6.48–17.39) > PPDQs (5.86–12.14) > PPDHQs (0.16–1.35 μg/g). SignificanceThe high concentrations and potential toxicities indicate that these PPDQ/Ps could seriously threaten the eco-environment, as they may finally enter the environment, accordingly requiring further investigation. The analysis strategy and data-processing algorithm can be extended to nontarget analysis for other zwitterionic pollutants, and the analysis results provide new understandings on the environmental occurrence of PPDQ/Ps from source and overall perspectives.

Assessing childhood community experiences

AimsCommunity-level childhood experiences make up important aspects of early life that are associated with long-term health outcomes. However, to date, there have been no validated measures that assess community-level experiences during childhood. Thus, we aimed to develop and validate a measure that would assess community-level experiences associated with both risk and resilience. MethodsData were collected from 577 undergraduate participants (68.7% female). Exploratory and confirmatory factor analytic strategies were used to examine the factor structure of the Childhood Community Experiences Scale (CCES) comprised of the Community Risk Scale and the Community Protective Scale. Convergent validity and predictive utility of the CCES with existing measures of individual risk and resilience and adult psychosocial functioning were also examined. ResultsResults indicated a 5-factor solution for the Community Risk Scale and a 6-factor solution for the Community Protective Scale to fit the data best. Further, results suggest the convergent validity and predictive utility of the CCES with measures of adult psychosocial functioning.This measure allows for a brief, self-report assessment that can be used to assess childhood community-level risk and protective factors.

Meat consumption and incident type 2 diabetes: an individual-participant federated meta-analysis of 1·97 million adults with 100 000 incident cases from 31 cohorts in 20 countries

Meat consumption could increase the risk of type 2 diabetes. However, evidence is largely based on studies of European and North American populations, with heterogeneous analysis strategies and a greater focus on red meat than on poultry. We aimed to investigate the associations of unprocessed red meat, processed meat, and poultry consumption with type 2 diabetes using data from worldwide cohorts and harmonised analytical approaches. This individual-participant federated meta-analysis involved data from 31 cohorts participating in the InterConnect project. Cohorts were from the region of the Americas (n=12) and the Eastern Mediterranean (n=2), European (n=9), South-East Asia (n=1), and Western Pacific (n=7) regions. Access to individual-participant data was provided by each cohort; participants were eligible for inclusion if they were aged 18 years or older and had available data on dietary consumption and incident type 2 diabetes and were excluded if they had a diagnosis of any type of diabetes at baseline or missing data. Cohort-specific hazard ratios (HRs) and 95% CIs were estimated for each meat type, adjusted for potential confounders (including BMI), and pooled using a random-effects meta-analysis, with meta-regression to investigate potential sources of heterogeneity. Among 1 966 444 adults eligible for participation, 107 271 incident cases of type 2 diabetes were identified during a median follow-up of 10 (IQR 7-15) years. Median meat consumption across cohorts was 0-110 g/day for unprocessed red meat, 0-49 g/day for processed meat, and 0-72 g/day for poultry. Greater consumption of each of the three types of meat was associated with increased incidence of type 2 diabetes, with HRs of 1·10 (95% CI 1·06-1·15) per 100 g/day of unprocessed red meat (I2=61%), 1·15 (1·11-1·20) per 50 g/day of processed meat (I2=59%), and 1·08 (1·02-1·14) per 100 g/day of poultry (I2=68%). Positive associations between meat consumption and type 2 diabetes were observed in North America and in the European and Western Pacific regions; the CIs were wide in other regions. We found no evidence that the heterogeneity was explained by age, sex, or BMI. The findings for poultry consumption were weaker under alternative modelling assumptions. Replacing processed meat with unprocessed red meat or poultry was associated with a lower incidence of type 2 diabetes. The consumption of meat, particularly processed meat and unprocessed red meat, is a risk factor for developing type 2 diabetes across populations. These findings highlight the importance of reducing meat consumption for public health and should inform dietary guidelines. The EU, the Medical Research Council, and the National Institute of Health Research Cambridge Biomedical Research Centre.

Realist analysis of qualitative data in health professions education research.

Qualitative realist analysis is gaining in popularity in health professions education research (HPER) as part of theory-driven program evaluation. Although realist approaches such as syntheses and evaluations typically advocate mixed methods, qualitative data dominate currently. Various forms of qualitative analysis have been articulated in HPER, yet realist analysis has not. Although realist analysis is interpretive, it moves beyond description to explain generative causation employing retroductive theorising. Ultimately, it attempts to build and/or 'test' (confirm, refute or refine) theory about how, why, for whom, when and to what extent programs work using the context-mechanism-outcome configuration (CMOC) heuristic. This paper aims to help readers better critique, conduct and report qualitative realist analysis. We describe four fundamentals of qualitative realist analysis: (1) simultaneous data collection/analysis; (2) retroductive theorising; (3) configurational analysis (involving iterative phases of identifying CMOCs, synthesising CMOCs into demi-regularities and translating demi-regularities into program theory); and (4) realist analysis quality (relevance, rigour, richness). Next, we provide a critical analysis of realist analyses employed in 15 HPER outputs-three evaluations and 12 syntheses. Finally, drawing on our understandings of realist literature and our experiences of conducting qualitative realist analysis (both evaluations and syntheses), we articulate three common analysis challenges (coding, consolidation and mapping) and strategies to mitigate these challenges (teamwork, reflexivity and consultation, use of data analysis software and graphical representations of program theory). Based on our critical analysis of the literature and realist analysis experiences, we encourage researchers, peer reviewers and readers to better understand qualitative realist analysis fundamentals. Realist analysts should draw on relevant realist reporting standards and literature on realist analysis to improve the quality and reporting of realist analysis. Through better understanding the common challenges and mitigation strategies for realist analysis, we can collectively improve the quality of realist analysis in HPER.

Optimasi Human Error pada Proses Produksi Air Minum Dalam Kemasan di PT. Telaga Tanjung Pomosda Nganjuk

PT. Telaga Tanjung Pomosda, a bottled mineral water manufacturer in Nganjuk, East Java, faces challenges in maintaining high production standards and addressing operational issues such as product rejection and production process errors, impacting overall company efficiency and reputation. This study identifies operational error factors and evaluates the effectiveness of improvement methods. Research procedures utilize HTA, SHERPA, and Poka-Yoke to identify and reduce human errors in bottled mineral water production at PT. Telaga Tanjung Pomosda Nganjuk, focusing on error analysis, probability assessment, and Poka-Yoke strategy implementation. The bottled water packaging process involves critical stages such as cup packaging, pressing, and packing. Errors in each stage can result in delays, decreased water volume, imperfectly pressed lids, and packing mistakes, hindering the entire production process. Research at PT. Telaga Tanjung Pomosda identifies operator issues in machine settings, water measurement, lid positioning, task interruptions, packaging, and cup positioning, with solutions including routine checks and training to enhance product quality

A novel approach to quantitate biodistribution and transduction of adeno-associated virus gene therapy using radiolabeled AAV vectors in mice

An understanding of recombinant adeno-associated viruses (AAVs) biodistribution profiles is an important element of a preclinical development program. Here, we have developed a radiolabeling strategy utilizing the co-delivery of 125I (non-residualizing) and 111In (residualizing) radionuclides conjugated AAVs to provide a detailed distribution quantification at tissue level delineating between the cellular internalized AAV (degraded, 111In-125I) and AAV remaining in the extracellular matrix (intact, 125I). This labeling method has been successfully applied to AAV9 and AAV-PHP.eB as tool molecules without altering the physical properties and biological activities of the AAVs. Upon labeling with either of the radioactive probes, these molecules were systemically injected into C57BL/6 mice. The biodistribution results indicate AAVs, with a fast distribution profile, were mainly located in the extracellular matrix of highly perfused organs such as liver and spleen at early time points, leading to a difference between capsid quantification and vector genome quantification. The results suggest that the 125I-AAV/111In-AAV co-delivery approach offers a robust and efficient analytical strategy to investigate the detailed tissue distribution of AAV vectors, including both vector genome and protein capsids. This novel method has the potential to be applied to capsid optimization, selection, and lead candidate development.