慢性鼻窦炎（CRS）的病因及发病机制尚未完全明确。目前多组学和大数据以其数据的多样性、易处理性等优势，已被用于探索多种疾病的发病机制和生物标志物等，多种高级机器学习方法也有助于完善CRS的精准诊疗服务，并在CRS的研究中取得了一定进展。然而，多组学数据网络的建设以及其研究成果的临床转化等，仍是制约该技术进一步发展的主要问题。本文对多组学和大数据驱动的CRS研究现状及其面临的机遇与挑战进行了论述。.

BACKGROUND: Left ventricular (LV) thrombus is not common but poses significant risks of embolic stroke or systemic embolism. However, the distinction in embolic risk between nonischemic cardiomyopathy (NICM) and ischemic cardiomyopathy (ICM) remains unclear.
RESULTS: In total, 2738 LV thrombus patients from the JROAD-DPC (Japanese Registry of All Cardiac and Vascular Diseases Diagnosis Procedure Combination) database were included. Among these patients, 1037 patients were analyzed, with 826 (79.7%) having ICM and 211 with NICM (20.3%). Within the NICM group, the distribution was as follows: dilated cardiomyopathy (DCM; 41.2%), takotsubo cardiomyopathy (27.0%), hypertrophic cardiomyopathy (18.0%), and other causes (13.8%). The primary outcome was a composite of embolic stroke or systemic embolism (SSE) during hospitalization. The ICM and NICM groups showed no significant difference in the primary outcome (5.8% vs. 7.6%, p = 0.34). Among NICM, SSE occurred in 12.6% of patients with DCM, 7.0% with takotsubo cardiomyopathy, and 2.6% with hypertrophic cardiomyopathy. Multivariate logistic regression analysis for SSE revealed an odds ratio of 1.4 (95% confidence interval [CI], 0.7-2.7, p = 0.37) for NICM compared to ICM. However, DCM exhibited a higher adjusted odds ratio for SSE compared to ICM (2.6, 95% CI 1.2-6.0, p = 0.022).
CONCLUSIONS: This nationwide shows comparable rates of embolic events between ICM and NICM in LV thrombus patients, with DCM posing a greater risk of SSE than ICM. The findings emphasize the importance of assessing the specific cause of heart disease in NICM, within LV thrombus management strategies.

OBJECTIVE: The objective of the study was to assess the clinical predictive value of the dynamics of absolute lymphocyte count (ALC) for 90-day all-cause mortality in sepsis patients in intensive care unit (ICU).
METHODS: Retrospective cohort study using big data.
METHODS: This study was conducted using the Medical Information Mart for Intensive Care IV database V.2.0 database.
METHODS: The primary outcome was 90-day all-cause mortality.
METHODS: Patients were included if they were diagnosed with sepsis on the first day of ICU admission. Exclusion criteria were ICU stay under 24 hours; the absence of lymphocyte count on the first day; extremely high lymphocyte count (>10×109/L); history of haematolymphatic tumours, bone marrow or solid organ transplants; survival time under 72 hours and previous ICU admissions. The analysis ultimately included 17 329 sepsis patients.
RESULTS: The ALC in the non-survivors group was lower on days 1, 3, 5 and 7 after admission (p<0.001). The ALC on day 7 had the highest area under the curve (AUC) value for predicting 90-day mortality. The cut-off value of ALC on day 7 was 1.0×109/L. In the restricted cubic spline plot, after multivariate adjustments, patients with higher lymphocyte counts had a better prognosis. After correction, in the subgroups with Sequential Organ Failure Assessment score ≥6 or age ≥60 years, ALC on day 7 had the lowest HR value (0.79 and 0.81, respectively). On the training and testing set, adding the ALC on day 7 improved all prediction models\' AUC and average precision values.
CONCLUSIONS: Dynamic changes of ALC are closely associated with 90-day all-cause mortality in sepsis patients. Furthermore, the ALC on day 7 after admission is a better independent predictor of 90-day mortality in sepsis patients, especially in severely ill or young sepsis patients.

BACKGROUND: Direct oral anticoagulants (DOACs) have had significant impact on the management of venous thromboembolism (VTE) in adults, but these agents were not approved for use in pediatric patients until 2021. Our objective was to analyze the characteristics of pediatric patients treated with DOACs prior to and following U.S. Food and Drug Administration (FDA) approval for children and evaluate their impact on hospital outcomes.
METHODS: We utilized the Epic Cosmos dataset (Cosmos), a de-identified dataset of over 220 million patients, to identify patients aged 1-18 years admitted with a first-occurrence diagnosis of VTE between January 1, 2017 and June 30, 2023. Patients were grouped by anticoagulation received (unfractionated heparin, low molecular weight heparin, and/or DOACs).
RESULTS: Among 5138 eligible patients, 18.1% received DOACs as all or part of their anticoagulation treatment, while 81.9% received heparin therapies alone. Patients treated with DOACs were older than patients treated with heparin monotherapy at 17.4 and 13.0 years, respectively. Non-DOAC patients were more likely to have chronic conditions and were less likely to have pulmonary embolism. Patients treated with DOACs demonstrated shorter overall length of stay and duration of intensive care unit (ICU) admission.
CONCLUSIONS: DOACs remain infrequently utilized in pediatric patients, especially in those under 13 years old. Initiation on heparin therapy and transition to DOACs remains common, with 80.6% of DOAC patients receiving heparin during their hospitalization. While DOAC monotherapy is not currently endorsed as first-line therapy for DVT or PE in children, it is being used clinically. Further research is needed to clarify the impact of DOAC use on patient adherence, VTE recurrence, and healthcare cost.

Dictyostelium represents a stripped-down model for understanding how cells make decisions during development. The complete life cycle takes around a day and the fully differentiated structure is composed of only two major cell types. With this apparent reduction in \"complexity,\" single cell transcriptomics has proven to be a valuable tool in defining the features of developmental transitions and cell fate separation events, even providing causal information on how mechanisms of gene expression can feed into cell decision-making. These scientific outputs have been strongly facilitated by the ease of non-disruptive single cell isolation-allowing access to more physiological measures of transcript levels. In addition, the limited number of cell states during development allows the use of more straightforward analysis tools for handling the ensuing large datasets, which provides enhanced confidence in inferences made from the data. In this chapter, we will outline the approaches we have used for handling Dictyostelium single cell transcriptomic data, illustrating how these approaches have contributed to our understanding of cell decision-making during development.

UNASSIGNED: Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM).
UNASSIGNED: A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions).
UNASSIGNED: In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles.
UNASSIGNED: This survey highlighted KSLM members\' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.

UNASSIGNED: To assess the impact of the COVID-19 pandemic on the health condition of people ≥75 years of age and on their family caregivers in Spain.
UNASSIGNED: Multicentric, mixed method concurrent study.
UNASSIGNED: This work, which will be conducted within the primary care setting in 11 administrative regions of Spain, will include three coordinated studies with different methodologies. The first is a population-based cohort study that will use real-life data to analyze the rates and evolution of health needs, care provision, and services utilization before, during, and after the pandemic. The second is a prospective cohort study with 18 months of follow-up that will evaluate the impact of COVID-19 disease on mortality, frailty, functional and cognitive capacity, and quality of life of the participants. Finally, the third will be a qualitative study with a critical social approach to understand and interpret the social, political, and economic dimensions associated with the use of health services during the pandemic. We have followed the SPIRIT Checklist to address trial protocol and related documents. This research is being funded by the Instituto de Salud Carlos III since 2021 and was approved by its ethics committee (June 2022).
UNASSIGNED: The study findings will reveal the long-term impact of the COVID-19 pandemic on the older adults and their caregivers. This information will serve policymakers to adapt health policies to the needs of this population in situations of maximum stress, such as that produced by the COVID-19 pandemic.
UNASSIGNED: Identifier: NCT05249868 [ClinicalTrials.gov].

Community-based participatory research (CBPR) using barbershop interventions is an emerging approach to address health disparities and promote health equity. Barbershops serve as trusted community settings for health education, screening services, and referrals. This narrative mini-review provides an overview of the current state of knowledge regarding CBPR employing barbershop interventions and explores the potential for big data involvement to enhance the impact and reach of this approach in combating chronic disease. CBPR using barbershop interventions has shown promising results in reducing blood pressure among Black men and improving diabetes awareness and self-management. By increasing testing rates and promoting preventive behaviors, barbershop interventions have been successful in addressing infectious diseases, including HIV and COVID-19. Barbershops have also played roles in promoting cancer screening and increasing awareness of cancer risks, namely prostate cancer and colorectal cancer. Further, leveraging the trusted relationships between barbers and their clients, mental health promotion and prevention efforts have been successful in barbershops. The potential for big data involvement in barbershop interventions for chronic disease management offers new opportunities for targeted programs, real-time monitoring, and personalized approaches. However, ethical considerations regarding privacy, confidentiality, and data ownership need to be carefully addressed. To maximize the impact of barbershop interventions, challenges such as training and resource provision for barbers, cultural appropriateness of interventions, sustainability, and scalability must be addressed. Further research is needed to evaluate long-term impact, cost-effectiveness, and best practices for implementation. Overall, barbershops have the potential to serve as key partners in addressing chronic health disparities and promoting health equity.

OBJECTIVE: Guideline adherence in radiotherapy is crucial for maintaining treatment quality and consistency, particularly in non-trial patient settings where most treatments occur. The study aimed to assess the impact of guideline changes on treatment planning practices and compare manual registry data accuracy with treatment planning data.
METHODS: This study utilised the DBCG RT Nation cohort, a collection of breast cancer radiotherapy data in Denmark, to evaluate adherence to guidelines from 2008 to 2016. The cohort included 7448 high-risk breast cancer patients. National guideline changes included, fractionation, introduction of respiratory gating, irradiation of the internal mammary lymph nodes, use of the simultaneous integrated boost technique and inclusion of the Left Anterior Descending coronary artery in delineation practice. Methods for structure name mapping, laterality detection, detection of temporal changes in population mean lung volume, and dose evaluation were presented and applied. Manually registered treatment characteristic data was obtained from the Danish Breast Cancer Database for comparison.
RESULTS: The study found immediate and consistent adherence to guideline changes across Danish radiotherapy centres. Treatment practices before guideline implementation were documented and showed a variation among centres. Discrepancies between manual registry data and actual treatment planning data were as high as 10% for some measures.
CONCLUSIONS: National guideline changes could be detected in the routine treatment data, with a high degree of compliance and short implementation time. Data extracted from treatment planning data files provides a more accurate and detailed characterisation of treatments and guideline adherence than medical register data.

An ever-increasing amount of data on a person\'s daily functioning is being collected, which holds information to revolutionize person-centered healthcare. However, the full potential of data on daily functioning cannot yet be exploited as it is mostly stored in an unstructured and inaccessible manner. The integration of these data, and thereby expedited knowledge discovery, is possible by the introduction of functionomics as a complementary \'omics\' initiative, embracing the advances in data science. Functionomics is the study of high-throughput data on a person\'s daily functioning, that can be operationalized with the International Classification of Functioning, Disability and Health (ICF).A prerequisite for making functionomics operational are the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This paper illustrates a step by step application of the FAIR principles for making functionomics data machine readable and accessible, under strictly certified conditions, in a practical example. Establishing more FAIR functionomics data repositories, analyzed using a federated data infrastructure, enables new knowledge generation to improve health and person-centered healthcare. Together, as one allied health and healthcare research community, we need to consider to take up the here proposed methods.