Analysis Of Strategies Research Articles

Application of spatial-omics to the classification of kidney biopsy samples in transplantation.

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

Text analysis and optimization strategy of an app’s privacy policy from the perspective of dual perception: taking Chinese shopping apps as an example

Purpose This paper aims to conduct an in-depth analysis of the shortcomings of apps’ privacy policies and to propose improvement and optimization strategies, which are of great significance for establishing a transparent and responsible privacy protection framework that ensures compliant collection and use of users’ information and effective protection of their privacy. Design/methodology/approach This paper obtained privacy policy texts for 100 shopping apps through Web crawlers and manual downloads. Based on the perspective of perceived usefulness, thematic analysis is conducted through the latent Dirichlet allocation topic model and comparison with existing policies. Based on the perspective of perceived ease of use, readability analysis is conducted through content analysis and formula calculation. Findings The apps privacy policies can be divided into seven themes. The authors benchmark these seven topics with the Personal Information Protection Law of the People’s Republic of China, the E-Commerce Law of the People’s Republic of China and the General Data Protection Regulation. It is found that there are omissions in the information collection and use and juvenile protection of the existing apps. Through the indicators’ readability analysis and calculation, it is found that the existing apps privacy policies have good performance in the readability indicators such as naming method, frame directory and so on. However, text personalization and text readability need to be improved and optimized. Originality/value At the theoretical level, this paper constructs a model from the dual perception perspectives of perceived usefulness and perceived ease of use and analyses the apps’ privacy policy texts at a fine-grained level. At the practical level, based on large-scale apps’ privacy policy text data, this paper conducts multi-dimensional research from theme analysis, authoritative law benchmarking analysis, content analysis and text readability calculation and analysis. At the same time, this paper identifies the current problems of apps’ privacy policies and puts forward countermeasure suggestions for their content improvement and optimization.

6.H. Round table: Harnessing Patient-Reported Data for Health Innovation: Insights from the OECD PaRIS Initiative

Abstract Summary This round table will disseminate knowledge and foster discussions on data reported by patients with a chronic disease in primary care settings in 20 countries for enhancing health system responsiveness and effectiveness. By delving into the OECD’s Patient-Reported Indicator Surveys (PaRIS) survey’s development, adaptation, and analytical strategies, participants will leave with a richer understanding of how patient-reported data can inform and transform health policy and practice in Europe and beyond. Rationale The round table aligns with the Conference theme by focusing on the transformative potential of patient-reported data in health systems. OECD PaRIS provide a unique lens through which to examine patient-centered care, showcasing innovative methods to capture and utilize data across diverse health system contexts. This round table will explore the development, adaptation, and analysis of this flagship survey, revealing its critical role in shaping health policies and practices that are attuned to patient needs and experiences. Objectives 1. Understanding Development and Testing Processes: To gain insights into the systematic approaches used in developing patient-reported instruments that are both robust and sensitive across multiple healthcare settings; 2. Exploring Co-development and Adaptation: To discuss the challenges and strategies involved in adapting health surveys to various national contexts while ensuring stakeholder engagement and data relevancy; 3. Analyzing Data for Policy Making: To comprehend the analytical frameworks used in processing patient-reported data for crafting effective health policies and identifying factors that influence patient outcomes. Key messages • Patient reported data on experiences and outcomes provide essential information for health systems performance. • Unique insights into the systematic collection of such information can be gathered through the proposed discussion on the experience of designing and implementing the PaRIS Survey.

Phase III Evaluation of Treatment Combinations in Three-Arm Trials

Phase III trials that randomly assign patients to a control treatment (C), an experimental treatment (A), or a combination treatment (AB) should be designed with the goal to recommend the best treatment: AB (if it is better than A and C), A (if it is better than C, and AB is not better than A), or C (if neither AB nor A is better than C). However, this goal can be challenging to achieve with statistical confidence. We performed a survey of cancer trials published in five journals from January 2018 to May 2024 to assess the trial designs being used in this setting and found that three quarters of them did not have a provision for a formal comparison of the AB treatment arm with the A treatment arm, a possible shortcoming. A limited simulation evaluates two analysis strategies that incorporate an AB versus A comparison and is used to formulate some recommendations for designing these types of trials.

The clinical characteristics and prognosis of patients with acute non-ST-segment elevation myocardial infarction complicated with impaired activity daily living

Abstract Background Impaired activities of daily living (ADL) have been identified as an independent risk factor for cardiovascular diseases. This study aims to describe the clinical characteristics, treatment modalities, and prognosis of acute non-ST-segment elevation myocardial infarction (NSTEMI) patients with impaired ADL. Methods The data for this study were derived from a Health and Medical Big Data Superplatform, comprising a retrospective cohort of patients admitted to and discharged from 72 secondary and tertiary hospitals from 2010 to 2023. The inclusion criteria encompassed patients with a discharge diagnosis of NSTEMI.ADL prior to NSTEMI was evaluated using the Barthel index(BI),a widely used assessment tool for evaluating ADL.The patients' BI were obtained from patients’ resident health records, and patients were categorized into two groups: ADL normal and ADL impaired group. A comparative analysis was conducted between the two groups regarding clinical characteristics, treatment regimens, and prognosis. Results The study included 28,227 NSTEMI patients with normal ADL and 1,576 NSTEMI patients with impaired ADL. Baseline data revealed that the ADL impaired group had a higher proportion of females (49.4% vs. 43.2%, p < 0.001), older age (77.0 [69.0, 83.0] vs. 71.0 [66.0, 78.0], p < 0.001), and a higher proportion of patients with KILLIP III and above (25.86% vs. 17.44%, p < 0.001). Additionally, the ADL impaired group had a greater number of pre-existing comorbidities. Regarding treatment, the proportion of patients undergoing percutaneous coronary intervention (PCI) was significantly lower in the ADL impaired group (16.5% vs. 65.3%, p < 0.001). Moreover, the ADL impaired group showed lower utilization rates of antiplatelet agents, ACEI/ARBs, beta-blockers, ARNI, statins, and nitrates, while the utilization rates of diuretics and cardiac glycosides were higher. In terms of prognosis, the ADL impaired group exhibited a higher in-hospital mortality rate (6.03% vs. 3.35%, p < 0.001). During a median follow-up period of 876 days, the ADL impaired group had significantly higher rates of all-cause mortality (61.2% vs. 35.9%, p < 0.001) and cardiovascular mortality (41.8% vs. 24.8%, p < 0.001), while the rate of revascularization (6.69% vs. 9.20%, p = 0.008) was lower. There were no significant differences in the risks of recurrent non-fatal myocardial infarction (10.7% vs. 10.8%, p = 0.935), recurrent stroke (10.90% vs. 9.48%, p = 0.08), and hemorrhagic stroke (0.82% vs. 0.74%, p = 0.806). Following adjustment for age, gender, KILLIP classification, medical history, and treatment strategies in a multivariable Cox regression analysis, impaired ADL remained an independent risk factor for increased all-cause mortality (aHR 1.167, 95%CI: 1.091-1.249, p < 0.001). Conclusions The decline in ADL is significantly associated with adverse clinical outcomes among NSTEMI patients.Baseline characteristics of patientsMedications of patients

Epidemiology of Dementia in the Campania Region (Italy): A 2015-2020 population-based study

Abstract As the global population ages, dementia poses a significant health challenge, ranking seventh among leading causes of death worldwide. Considering the high societal and financial burden of dementia, our study aims to provide estimates of prevalence and incidence for any dementia and Alzheimer’s dementia (AD) in the Campania Region (South Italy). It is Population-based study, using routinely collected healthcare data of individuals living in the Campania Region (South Italy) from 2015 to 2020. We included individuals aged ≥65 years who had at least one administrative record for dementia and/or AD. Prevalence rates adjusted for age and sex used 2020 European population as reference. To estimate incidence, we tested three possible algorithms, which differed for the duration of the time interval between study baseline (Jan 1, 2015) and index date (first record for dementia and/or AD in administrative databases). We employed a clinical database for the validation of our algorithms towards neuropsychological test results. Among individuals, 80,392 had dementia, of which 35,748 had AD. The age- and sex-standardized prevalence rates per 1,000 individuals for any dementia and AD were 77.64 (95%CI=77.57; 77.68) and 34.05 (95%CI=34.01; 34.09), respectively. There were 82.10 incident cases of any dementia per 100,000 per year (0.79 sensitivity and 0.62 specificity), and 59.89 incident cases of AD per 100,000 per year (0.80 sensitivity and 0.59 specificity). The capture-recapture method showed low number of undetected cases (1.7% for any dementia and 3.0% for AD). Our algorithms showed acceptable performance with AUC ranging from 0.59 to 0.72, and double likelihood ratio of correctly identifying individuals above and below MMSE standard cut-offs (24 and 26). Our algorithm, integrating administrative and clinical data, holds potential for assessing dementia’s epidemiological burden, identifying risk factors, planning healthcare access, and developing prevention strategies. Key messages • Providing comprehensive estimates of dementia burden aids public health planning and resource allocation. • Integrated administrative and clinical data provide robust tools for epidemiological analysis and preventive strategies.

Analytical Tools for HCP Detection, Identification, and Quantitation.

Identifying and quantifying host cell proteins (HCPs) are crucial in developing and manufacturing biopharmaceutical products. ELISA has been used widely for identifying and quantifying HCPs in biopharmaceutical products. Liquid chromatography-mass spectrometry (LC-MS) has recently emerged as an orthogonal tool for HCP analysis, providing comprehensive and quantitative information on individual HCPs. This chapter explores the advancements in LC-MS/MS methodologies for HCP analysis, including sample preparation, data acquisition modes, and data analysis strategies. It also discusses the challenges of HCP identification and quantitation, and recent innovations to overcome these limitations. The application of LC-MS/MS methods in HCP profiling holds significant promise for improving process control and ensuring product safety in biopharmaceutical manufacturing.

Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review

The Internet of Things (IoT) has radically changed the industrial world, enabling the integration of numerous systems and devices into the industrial ecosystem. There are many areas of the manufacturing industry in which IoT has contributed, including plants’ remote monitoring and control, energy efficiency, more efficient resources management, and cost reduction, paving the way for smart manufacturing in the framework of Industry 4.0. This review article provides an up-to-date overview of IoT systems and machine learning (ML) algorithms applied to smart manufacturing (SM), analyzing four main application fields: security, predictive maintenance, process control, and additive manufacturing. In addition, the paper presents a descriptive and comparative overview of ML algorithms mainly used in smart manufacturing. Furthermore, for each discussed topic, a deep comparative analysis of the recent IoT solutions reported in the scientific literature is introduced, dwelling on the architectural aspects, sensing solutions, implemented data analysis strategies, communication tools, performance, and other characteristic parameters. This comparison highlights the strengths and weaknesses of each discussed solution. Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications.

New Quadratic Discriminant Analysis Algorithms for Correlated Audiometric Data.

Paired organs like eyes, ears, and lungs in humans exhibit similarities, and data from these organs often display remarkable correlations. Accounting for these correlations could enhance classification models used in predicting disease phenotypes. To our knowledge, there is limited, if any, literature addressing this topic, and existing methods do not exploit such correlations. For example, the conventional approach treats each ear as an independent observation when predicting audiometric phenotypes and is agnostic about the correlation of data from the two ears of the same person. This approach may lead to information loss and reduce the model performance. In response to this gap, particularly in the context of audiometric phenotype prediction, this paper proposes new quadratic discriminant analysis (QDA) algorithms that appropriately deal with the dependence between ears. We propose two-stage analysis strategies: (1) conducting data transformations to reduce data dimensionality before applying QDA; and (2) developing new QDA algorithms to partially utilize the dependence between phenotypes of two ears. We conducted simulation studies to compare different transformation methods and to assess the performance of different QDA algorithms. The empirical results suggested that the transformation may only be beneficial when the sample size is relatively small. Moreover, our proposed new QDA algorithms performed better than the conventional approach in both person-level and ear-level accuracy. As an illustration, we applied them to audiometric data from the Medical University of South Carolina Longitudinal Cohort Study of Age-related Hearing Loss. In addition, we developed an R package, PairQDA, to implement the proposed algorithms.

Effectiveness of gait analysis in management of osteoarthritis knee at primary care settings in Asia: a systematic review and meta-analysis protocol

Background: This systematic review will focus on the effectiveness of gait analysis on osteoarthritis knee management. The objectives of this systematic review and meta-analysis are to synthesize the clinical effectiveness and to compare the effectiveness of gait analysis in the osteoarthritis knee management in Asian population. Methods: This review will be conducted and reported according to PRISMA guidelines. To discover relevant papers, the search technique will use MeSH terms such as osteoarthritis knee, gait analysis, osteoarthritis knee management, primary healthcare, effectiveness, and Asia. Other synonymous words will be searched using the Boolean operator "and"/"or" on PubMed, Embase, Scopus, and Cochrane. Randomized controlled trials assessing the efficacy of gait analysis in the therapy of osteoarthritis knee patients in primary care settings in Asia will be considered. A random effects meta-analysis will be carried out using the REVMAN software version 5.3.5. The STATA software version SE16 will be used for both cumulative and comparative meta-analysis. Conclusions: This review and meta-analysis will be among the first to give an extensive purview on the effectiveness of gait analysis in osteoarthritis knee management at primary care settings in Asian population. This study will also acknowledge the effects of different gait analysis strategies with an attempt to compare the same. Trial Registration: The protocol has been registered at the International Prospective Register of Systematic Reviews (PROSPERO registration number: CRD42024540444).

Potential and challenges of clinical high-dimensional flow cytometry: A call to action.

Clinical biomarker strategies increasingly integrate translational research to gain new insights into disease mechanisms or to define better biomarkers in clinical trials. High-dimensional flow cytometry (HDFCM) holds the promise to enhance the exploratory potential beyond traditional, targeted biomarker strategies. However, the increased complexity of HDFCM poses several challenges, which need to be addressed in order to fully leverage its potential and to align with current regulatory requirements in clinical flow cytometry. These challenges include among others extended timelines for assay development and validation, the necessity for extensive knowledge in HDFCM, and sophisticated data analysis strategies. However, no guidelines exist on how to manage such challenges in adopting clinical HDFCM. Our CYTO 2024 workshop "Potential and challenges of clinical high-dimensional flow cytometry" aimed to find consensus across the pharmaceutical industry and broader scientific community on the overall benefits and most urgent challenges of HDFCM in clinical trials. Here, we summarize the insights we gained from our workshop. While this report does not provide a blueprint, it is a first step in defining and summarizing the most pressing challenges in implementing HDFCM in clinical trials. Furthermore, we compile current efforts with the goal to overcome some of these challenges. As such we bring the scientific community and health authorities together to build solutions, which will accelerate and simplify the full adoption of HDFCM in clinical trials.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

A systematic UHPLC-Q-TOF-MS/MS-based strategy for analysis of chemical constituents and related in vivo metabolites of Buyang Huanwu decoction

Ethnopharmacological relevanceBuyang Huanwu Decoction (BYHWD), a traditional Chinese medicine, is one of the classic prescriptions for the treatment of ischemic stroke in clinical practice. It has the effects of tonifying qi, activating blood circulation, and promoting meridian circulation. However, its chemical analysis has not been clarified, which greatly hinders its further clinical application. Therefore, it is necessary to clarify the chemical constituents and metabolites profile of BYHWD in vivo. Aim of the studyCharacterizing the chemical basis of BYHWD in vitro, and combing studies of related metabolism in vivo to screen out the potential active components of BYHWD with pharmacological effects in vivo. Materials and methodsTwelve male rats weighed 200 ± 20 each were selected for the experiments. According to the fragmentation of different structural types of components and diagnostic ions, UHPLC-Q-TOF-MS/MS was used to classify and clarify the unknown components of BYHWD and identify the material basis of BYHWD in vitro. Then, rat plasma, tissues, feces, and urine were collected for analysis. Based on the similarity of MS responses (accurate molecular weight and secondary fragmentation) and chromatographic behavior (retention time), the in vivo prototype and metabolites were analyzed. Through the phase I and phase II metabolism law, a metabolite library was established to analyze the prototype-matched metabolites. ResultsA total of 121 in vitro compounds and 55 in vivo prototypes of BYHWD were identified, corresponding to 123 matched prototypes. It was mainly composed of flavonoids, triterpene saponins, nucleosides and lactones both in vitro and in vivo. Quercetin, ligustilide, astragaloside IV, calycosin, paeoniflorin and ferulic acid were the main prototypes and metabolites in plasma and urine. ConclusionQuercetin, ligustilide, astragaloside IV, calycosin, paeoniflorin and ferulic acid were the main active ingredients of BYHWD.

A qualitative study revisiting the theoretical lens of brand image and developing the consumer brand image schema

Purpose Brand image remains a “nebulous” construct with very few frameworks dedicated to understanding the process of uncovering brand image. This is supported by explicit calls for greater clarity on how to examine brand image, particularly in different settings. Political branding is one setting that has received some attention, however, research has focused on “party” political brands and neglected “non-party” brands (independent candidates). Therefore, this study aims to examine how young citizens interpret independent non-party brands through the theoretical lens of brand image in the context of Guernsey and develop a systematic brand image framework that provides greater clarity to this topic area. Design/methodology/approach A qualitative interpretivist approach using focus group discussions was adopted to investigate the phenomenon from the perspective of young voters 18–24 years of age. Focus group discussions ceased upon reaching theoretical saturation and a six-staged thematic analytical strategy was adopted to analyse the findings. Findings This study revealed deep insight into the political brand image of non-party brands from the perspective of young voters in an under-explored context. More specifically, this study uncovered that Guernsey’s political brands were seen as “accessible”; however, there was little differentiation, identification and connection between young voters and politicians. Further, this study uncovered a series of opportunities for strategists such as the desire for a younger generational view and younger representation in the Guernsey Parliament and greater clarity, distinction and authenticity related to political brand image. Originality/value This study addresses explicit calls for further research on brand image with a distinct focus on non-party political brands. Further, this study concludes by presenting the “consumer brand image schema”; a systematic framework which can be used to uncover brand image within and beyond the setting of politics. Further, the framework operationalises the complex concept of brand image and provides a three-stage process to examine and develop brand image. This will enable strategists to develop targeted strategies and adopt appropriate tactics to manage brand image to ensure brands are differentiated and interpreted as authentic, relatable, engaging, accessible and identifiable.

Electrochemical Immunosensors on Laser-Induced Graphene Platforms for Monitoring of Anti-RBD Antibodies After SARS-CoV-2 Infection

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has posed a major challenge to global health. The development of fast, accurate, and accessible diagnostic methods is essential in controlling the disease and mitigating its impacts. In this context, electrochemical biosensors present themselves as promising tools for the efficient monitoring of SARS-CoV-2 infection. We have developed a highly specific biosensor for the detection of anti-SARS-CoV-2 antibodies in patient sera. The use of the RBD-S region as an antigen, although purified to minimize cross-linking, poses a specific challenge. The structural similarity between SARS-CoV-2 and other respiratory viruses, as well as the complexity of the serum matrix, hinders robust analytical strategies to ensure diagnostic accuracy. This work presents a novel immunosensor for COVID-19 diagnosis using laser-induced graphene (LIG) electrodes subjected to electrochemical reduction with graphene (named rGraphene-LIG). In the present study, we chose an initial approach focused on demonstrating the concept and evaluating the feasibility of the rGraphene-LIG sensor for SARS-CoV-2 detection. The rGraphene-LIG electrodes presented a notable current increase for the redox probe in the aqueous solution of a mixture of 5 mmol L−1 potassium ferricyanide/ferrocyanide ([Fe(CN)6]3−/[Fe(CN)6]4−) in 0.1 mol L−1 KCl (pH set at 7.4). As a proof of concept, the rGraphene-LIG electrode was applied for antibody determination in real samples using cyclic voltammetry, and a limit of detection (LOD) of 0.032 μg L−1 was achieved. When determining antigens in commercial samples, we obtained an LOD of 560 ηg mL−1 and a limit of quantification of 1677 ηg mL−1. The results of the electrochemical experiments were in accordance with the surface roughness obtained from atomic force microscopy images. Based on these results, the rGraphene-LIG electrode is shown to be an excellent platform for immunoglobulin detection when present in individuals after antigenic exposure caused by SARS-CoV-2.

Progress and challenges in structural, in situ and operando characterization of single-atom catalysts by X-ray based synchrotron radiation techniques.

Single-atom catalysts (SACs) represent the ultimate size limit of nanoscale catalysts, combining the advantages of homogeneous and heterogeneous catalysts. SACs have isolated single-atom active sites that exhibit high atomic utilization efficiency, unique catalytic activity, and selectivity. Over the past few decades, synchrotron radiation techniques have played a crucial role in studying single-atom catalysis by identifying catalyst structures and enabling the understanding of reaction mechanisms. The profound comprehension of spectroscopic techniques and characteristics pertaining to SACs is important for exploring their catalytic activity origins and devising high-performance and stable SACs for industrial applications. In this review, we provide a comprehensive overview of the recent advances in X-ray based synchrotron radiation techniques for structural characterization and in situ/operando observation of SACs under reaction conditions. We emphasize the correlation between spectral fine features and structural characteristics of SACs, along with their analytical limitations. The development of IMST with spatial and temporal resolution is also discussed along with their significance in revealing the structural characteristics and reaction mechanisms of SACs. Additionally, this review explores the study of active center states using spectral fine characteristics combined with theoretical simulations, as well as spectroscopic analysis strategies utilizing machine learning methods to address challenges posed by atomic distribution inhomogeneity in SACs while envisaging potential applications integrating artificial intelligence seamlessly with experiments for real-time monitoring of single-atom catalytic processes.

Chemical Fingerprinting of PM2.5 via Sequential Speciation Analysis Using Electrochemical Mass Spectrometry.

Chemical fingerprinting to characterize the occurrence state and abundance of organic and inorganic constituents within fine particulate matter (PM2.5) is useful in evaluating the associated health risks and tracing pollution sources. Herein, an analytical strategy for the rapid analysis of metal and organic constituents in PM2.5 was developed employing a combination of sequential chemical extraction coupled with mass spectrometry detection. H2O, CH3OH, EDTA-2Na, electrochemical oxidation, and electrochemical reduction were sequentially utilized to extract the chemical constituents in PM2.5 samples on a homemade device employing simultaneous online detection using two linear trap quadrupole mass spectrometers (LTQ-MS) with electrospray ionization (ESI) in positive and negative modes. After a single analytical procedure, dozens of metals (e.g., Pb, Cr, and Cu), organic compounds (e.g., amines, polycyclic aromatic hydrocarbons, and aliphatic acids), and negative ions (e.g., NO3-, NO2-, and Cl-) were comprehensively detected in the water-soluble, liposoluble, insoluble, oxidizable, and reducible fractions of PM2.5 samples, and their physical and chemical relationships were established.

Diagnostic reasoning and cognitive error in emergency medicine: Implications for teaching and learning.

Accurate diagnosis in emergency medicine (EM) is high stakes and challenging. Research into physicians' clinical reasoning has been ongoing since the late 1970s. The dual-process theory has established itself as a valid model, including in EM. It is based on the distinction between two information-processing systems. System 1 rapidly generates one or more diagnostic hypotheses almost instantaneously, driven by experiential knowledge, while System 2 proceeds more slowly and analytically, applying formal rules to arrive at a final diagnosis. We reviewed the literature on dual-process theory in the fields of cognitive science, medical education and emergency medicine. The literature reflects two prominent interpretations regarding the relationship between the fast and slow phases and these interpretations carry very different implications for the training of clinical learners. One interpretation, prominent in the EM community, presents it as a "check-and-balance" framework in which most diagnostic error is caused by cognitive biases originating within System 1. As a result, EM residents are frequently advised to deploy analytical (System 2) strategies to correct such biases. However, such teaching approaches are not supported by research into the nature of diagnostic reasoning. An alternative interpretation assumes a harmonious relationship between Systems 1 and 2 in which both fast and slow processes are driven by underlying knowledge that conditions performance and the occurrence of errors. Educational strategies corresponding to this alternative have not been explored in the EM literature. In this paper, we offer proposals for improving the teaching and learning of diagnostic reasoning by EM residents.

Long-Range Three-Dimensional Tracking of Nanoparticles Using Interferometric Scattering Microscopy.

Tracking nanoparticle movement is highly desirable in many scientific areas, and various imaging methods have been employed to achieve this goal. Interferometric scattering (iSCAT) microscopy has been particularly successful in combining very high spatial and temporal resolution for tracking small nanoparticles in all three dimensions. However, previous works have been limited to an axial range of only a few hundred nanometers. Here, we present a robust and efficient measurement and analysis strategy for three-dimensional tracking of nanoparticles at high speed and with nanometer precision. After discussing the principle of our approach using synthetic data, we showcase the performance of the method by tracking gold nanoparticles with diameters ranging from 10 to 80 nm in water, demonstrating an axial tracking range from 4 μm for the smallest particles up to over 30 μm for the larger ones. We point out the limitations and robustness of our system across various noise levels and discuss its promise for applications in cell biology and material science, where the three-dimensional motion of nanoparticles in complex media is of interest.

Investigating the Impact of PUBG on Academic Performance and Mental Health among Pakistani Teenagers

The PUBG game holds widespread popularity among Pakistani teenagers; however, concerns have surfaced regarding its potential impact on academic achievement and mental health. Prolonged play may result in migraines, eye strain, and back pain, while violent games could potentially influence harsh speech patterns in young individuals. The decision to ban PUBG has attracted attention due to its perceived violent nature. This research aims to ascertain PUBG's detrimental effects on students' academic performance in Pakistan and seeks to find interventions for addressing PUBG addiction. A quantitative study in Pakistan, with a sample size of 100 responders, aims to assess the game's effects on students' emotions and behavior. Thematic analysis, utilizing an interview guide, will be the final analytical strategy employed in this study.