The Cellular Thermal Shift Assay (CETSA) enables the study of protein-ligand interactions in a cellular context. It provides valuable information on the binding affinity and specificity of both small and large molecule ligands in a relevant physiological context, hence forming a unique tool in drug discovery. Though high-throughput lab protocols exist for scaling up CETSA, subsequent data analysis and quality control remain laborious and limit experimental throughput. Here, we introduce a scalable and robust data analysis workflow which allows integration of CETSA into routine high throughput screening (HT-CETSA). This new workflow automates data analysis and incorporates quality control (QC), including outlier detection, sample and plate QC, and result triage. We describe the workflow and show its robustness against typical experimental artifacts, show scaling effects, and discuss the impact of data analysis automation by eliminating manual data processing steps.

Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.

This article presents the data obtained from a Systematic Literature Review (SLR) on the use of metaverse and extended technologies for immersive journalism [1]. Boolean operators, both in English and Spanish, were used to retrieve scientific literature using Publish or Perish 8 software on Scopus, Web of Science and Google Scholar between 2017 and 2022. After finding all the scientific literature, a methodological process was carried out using selection criteria and following the PRISMA model to obtain a total sample of 61 scientific articles. The DESLOCIS framework was used for the evaluation and quantitative and qualitative analysis of the retrieved data. The first dataset [2] contains the metadata of the retrieved publications according to the phases of the PRISMA statement. The second dataset [3] contains the characteristics of these publications according to the DESLOCIS framework. The data offer the possibility to develop new longitudinal studies and meta-analyzes in the field of immersive journalism.

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software\'s capabilities to achieve the most accurate and credible results.

Esketamine nasal spray (ESK-NS) is a new drug for treatment-resistant depression, and we aimed to detect and characterize the adverse events (AEs) of ESK-NS using the Food and Drug Administration (FDA) adverse event reporting system (FAERS) database between 2019 Q1 and 2023 Q4. Reporting odds ratio (ROR), proportional reporting ratio (PRR), and multi-item gamma Poisson shrinker (MGPS) were performed to detect risk signals from the FAERS data to identify potential ESK-NS-AEs associations. A total of 14,606 reports on AEs with ESK-NS as the primary suspected drug were analyzed. A total of 518 preferred terms signals and 25 system organ classes mainly concentrated in psychiatric disorders (33.20%), nervous system disorders (16.67%), general disorders and administration site conditions (14.21%), and others were obtained. Notably, dissociation (n = 1,093, ROR 2,257.80, PRR 899.64, EBGM 876.86) exhibited highest occurrence rates and signal intensity. Moreover, uncommon but significantly strong AEs signals, such as hand-eye coordination impaired, feeling guilty, and feelings of worthlessness, were observed. Additionally, dissociative disorder (n = 57, ROR 510.92, PRR 506.70, EBGM 386.60) and sedation (n = 688, ROR 172.68, PRR 155.53, and EBGM 142.05) both presented strong AE signals, and the former is not recorded in the Summary of Product Characteristics (SmPC). In clinical applications, close attention should be paid to the psychiatric disorders and nervous system disorders, especially dissociation. Meanwhile, clinical professionals should be alert for the occurrence of AEs signals not mentioned in the SmPC and take preventive measures to ensure the safety of clinical use.

BACKGROUND: The extensive and continuous reuse of sensitive health data could enhance the role of population health research on public decisions. This paper describes the design principles and the different building blocks that have supported the implementation and deployment of Population Health Information Research Infrastructure (PHIRI), the strengths and challenges of the approach and some future developments.
METHODS: The design and implementation of PHIRI have been developed upon: (i) the data visiting principle-data does not move but code moves; (ii) the orchestration of the research question throughout a workflow that ensured legal, organizational, semantic and technological interoperability and (iii) a \'master-worker\' federated computational architecture that supported the development of four uses cases.
RESULTS: Nine participants nodes and 28 Euro-Peristat members completed the deployment of the infrastructure according to the expected outputs. As a consequence, each use case produced and published their own common data model, the analytical pipeline and the corresponding research outputs. All the digital objects were developed and published according to Open Science and FAIR principles.
CONCLUSIONS: PHIRI has successfully supported the development of four use cases in a federated manner, overcoming limitations for the reuse of sensitive health data and providing a methodology to achieve interoperability in multiple research nodes.

BACKGROUND: Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.
OBJECTIVE: This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.
METHODS: A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.
RESULTS: Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.
CONCLUSIONS: Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.

BACKGROUND: Clinical studies are often limited by resources available, which results in constraints on sample size. We use simulated data to illustrate study implications when the sample size is too small.
RESULTS: Using 2 theoretical populations each with N = 1000, we randomly sample 10 from each population and conduct a statistical comparison, to help make a conclusion about whether the 2 populations are different. This exercise is repeated for a total of 4 studies: 2 concluded that the 2 populations are statistically significantly different, while 2 showed no statistically significant difference.
CONCLUSIONS: Our simulated examples demonstrate that sample sizes play important roles in clinical research. The results and conclusions, in terms of estimates of means, medians, Pearson correlations, chi-square test, and P values, are unreliable with small samples.

Background and Objectives: Although statins are recommended for secondary prevention of acute ischemic stroke, some population-based studies and clinical evidence suggest that they might be used with an increased risk of intracranial hemorrhage. In this nested case-control study, we used Taiwan\'s nationwide universal health insurance database to investigate the possible association between statin therapy prescribed to acute ischemic stroke patients and their risk of subsequent intracerebral hemorrhage and all-cause mortality in Taiwan. Materials and Methods: All data were retrospectively obtained from Taiwan\'s National Health Insurance Research Database. Acute ischemic stroke patients were divided into a cohort receiving statin pharmacotherapy and a control cohort not receiving statin pharmacotherapy. A 1:1 matching for age, gender, and index day, and propensity score matching was conducted, producing 39,366 cases and 39,366 controls. The primary outcomes were long-term subsequent intracerebral hemorrhage and all-cause mortality. The competing risk between subsequent intracerebral hemorrhage and all-cause mortality was estimated using the Fine and Gray regression hazards model. Results: Patients receiving statin pharmacotherapy after an acute ischemic stroke had a significantly lower risk of subsequent intracerebral hemorrhage (p < 0.0001) and lower all-cause mortality rates (p < 0.0001). Low, moderate, and high dosages of statin were associated with significantly decreased risks for subsequent intracerebral hemorrhage (adjusted sHRs 0.82, 0.74, 0.53) and all-cause mortality (adjusted sHRs 0.75, 0.74, 0.74), respectively. Conclusions: Statin pharmacotherapy was found to safely and effectively reduce the risk of subsequent intracerebral hemorrhage and all-cause mortality in acute ischemic stroke patients in Taiwan.

Against the backdrop of continuous socio-economic development, there is a growing concern among people about food quality and safety. Individuals are increasingly realizing the critical importance of healthy eating for bodily health; hence the continuous rise in demand for detecting food pollution. Simultaneously, the rapid expansion of global food trade has made people\'s pursuit of high-quality food more urgent. However, traditional methods of food analysis have certain limitations, mainly manifested in the high degree of reliance on personal subjective judgment for assessing food quality. In this context, the emergence of artificial intelligence and biosensors has provided new possibilities for the evaluation of food quality. This paper proposes a comprehensive approach that involves aggregating data relevant to food quality indices and developing corresponding evaluation models to highlight the effectiveness and comprehensiveness of artificial intelligence and biosensors in food quality evaluation. The potential prospects and challenges of this method in the field of food safety are comprehensively discussed, aiming to provide valuable references for future research and practice.