Managing Type 2 Diabetes During the COVID-19 Pandemic: Scoping Review and Qualitative Study Using Systematic Literature Review and Reddit.

BACKGROUND: The COVID-19 pandemic impacted how people accessed health services and likely how they managed chronic conditions such as type 2 diabetes (T2D). Social media forums present a source of qualitative data to understand how adaptation might have occurred from the perspective of the patient. OBJECTIVE: Our objective is to understand how the care-seeking behaviors and attitudes of people living with T2D were impacted during the early part of the pandemic by conducting a scoping literature review. A secondary objective is to compare the findings of the scoping review to those presented on a popular social media platform Reddit. METHODS: A scoping review was conducted in 2021. Inclusion criteria were population with T2D, studies are patient-centered, and study objectives are centered around health behaviors, disease management, or mental health outcomes during the COVID-19 pandemic. Exclusion criteria were populations with other noncommunicable diseases, examining COVID-19 as a comorbidity to T2D, clinical treatments for COVID-19 among people living with T2D, genetic expressions of COVID-19 among people living with T2D, gray literature, or studies not published in English. Bias was mitigated by reviewing uncertainties with other authors. Data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective. Data from the Reddit forums related to T2D from March 2020 to early March 2021 were downloaded, and support vector machines were used to classify if a post was published in the context of the pandemic. Latent Dirichlet allocation topic modeling was performed to gather topics of discussion specific to the COVID-19 pandemic. RESULTS: A total of 26 studies conducted between February and September 2020, consisting of 13,673 participants, were included in this scoping literature review. The studies were qualitative and relied mostly on qualitative data from surveys or questionnaires. Themes found from the literature review were "poorer glycemic control," "increased consumption of unhealthy foods," "decreased physical activity," "inability to access medical appointments," and "increased stress and anxiety." Findings from latent Dirichlet allocation topic modeling of Reddit forums were "Coping With Poor Mental Health," "Accessing Doctor & Medications and Controlling Blood Glucose," "Changing Food Habits During Pandemic," "Impact of Stress on Blood Glucose Levels," "Changing Status of Employment & Insurance," and "Risk of COVID Complications." CONCLUSIONS: Topics of discussion gauged from the Reddit forums provide a holistic perspective of the impact of the pandemic on people living with T2D, which were found to be comparable to the findings of the literature review. The study was limited by only having 1 reviewer for the literature review, but biases were mitigated by consulting authors when there were uncertainties. Qualitative analysis of Reddit forms can supplement traditional qualitative studies of the behaviors of people living with T2D.

Artificial intelligence in plant breeding.

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.

Can digital industrialization promote energy conservation development in China? Empirical evidence based on national big data comprehensive pilot zone policy.

Digital industrialization represented by big data provides substantial support for the high-quality development of the digital economy, but its impact on urban energy conservation development requires further research. To this end, based on the panel data of Chinese cities from 2010 to 2019 and taking the establishment of the national big data comprehensive pilot zone (NBDCPZ) as a quasi-natural experiment, this paper explores the impact, mechanism, and spatial spillover effect of digital industrialization represented by big data on urban energy conservation development using the Difference-in-Differences (DID) method. The results show that digital industrialization can help achieve urban energy conservation development, which still holds after a series of robustness tests. Mechanism analysis reveals that digital industrialization impacts urban energy conservation development by driving industrial sector output growth, promoting industrial upgrading, stimulating green technology innovation, and alleviating resource misallocation. Heterogeneity analysis indicates that the energy conservation effect of digital industrialization is more significant in the central region, intra-regional demonstration comprehensive pilot zones, large cities, non-resource-based cities, and high-level digital infrastructure cities. Additionally, digital industrialization can promote energy conservation development in neighboring areas through spatial spillover effect. This paper enriches the theoretical framework concerning the relationship between digital industrialization and energy conservation development. The findings have significant implications for achieving the coordinated development of digitalization and conservation.

Youth mental health care use during the COVID-19 pandemic in Alberta, Canada: an interrupted time series, population-based study.

BACKGROUND: During the COVID-19 pandemic, youth had rising mental health needs and changes in service accessibility. Our study investigated changes in use of mental health care services for Canadian youth in Alberta before and during the COVID-19 pandemic. We also investigated how youth utilization patterns differed for subgroups based on social factors (i.e., age, gender, socioeconomic status, and geography) previously associated with health care access. METHODS: We used cross-sectional population-based data from Alberta, Canada to understand youth (15-24 year) mental health care use from 2018/19 to 2021/22. We performed interrupted time series design, segmented regression modeling on type of mental health care use (i.e., general physician, psychiatrist, emergency room, and hospitalization) and diagnosis-related use. We also investigated the characteristics of youth who utilized mental health care services and stratified diagnosis-related use patterns by youth subgroups. RESULTS: The proportion of youth using mental health care significantly increased from 15.6% in 2018/19 to 18.8% in 2021/22. Mental health care use showed an immediate drop in April 2020 when the COVID-19 pandemic was declared and public health protections were instituted, followed by a steady rise during the next 2 years. An increase was significant for general physician and psychiatrist visits. Most individual diagnoses included in this study showed significant increasing trends during the pandemic (i.e., anxiety, adjustment, ADHD, schizophrenia, and self-harm), with substance use showing an overall decrease. Mortality rates greatly increased for youth being seen for mental health reasons from 71 per 100,000 youth in 2018/19 to 163 per 100,000 in 2021/22. In addition, there were clear shifts over time in the characteristics of youth using mental health care services. Specifically, there was increased utilization for women/girls compared to men/boys and for youth from wealthier neighborhoods. Increases over time in the utilization of services for self-harm were limited to younger youth (15-16 year). CONCLUSIONS: The study provides evidence of shifts in mental health care use during the COVID-19 pandemic. Findings can be used to plan for ongoing mental health needs of youth, future pandemic responses, and other public health emergencies.

Association between a selective 5-HT4 receptor agonist and incidence of major depressive disorder: emulated target trial.

BACKGROUND: The serotonin 4 receptor (5-HT4R) is a promising target for the treatment of depression. Highly selective 5-HT4R agonists, such as prucalopride, have antidepressant-like and procognitive effects in preclinical models, but their clinical effects are not yet established. AIMS: To determine whether prucalopride (a 5-HT4R agonist and licensed treatment for constipation) is associated with reduced incidence of depression in individuals with no past history of mental illness, compared with anti-constipation agents with no effect on the central nervous system. METHOD: Using anonymised routinely collected data from a large-scale USA electronic health records network, we conducted an emulated target trial comparing depression incidence over 1 year in individuals without prior diagnoses of major mental illness, who initiated treatment with prucalopride versus two alternative anti-constipation agents that act by different mechanisms (linaclotide and lubiprostone). Cohorts were matched for 121 covariates capturing sociodemographic factors, and historical and/or concurrent comorbidities and medications. The primary outcome was a first diagnosis of major depressive disorder (ICD-10 code F32) within 1 year of the index date. Robustness of the results to changes in model and population specification was tested. Secondary outcomes included a first diagnosis of six other neuropsychiatric disorders. RESULTS: Treatment with prucalopride was associated with significantly lower incidence of depression in the following year compared with linaclotide (hazard ratio 0.87, 95% CI 0.76-0.99; P = 0.038; n = 8572 in each matched cohort) and lubiprostone (hazard ratio 0.79, 95% CI 0.69-0.91; P < 0.001; n = 8281). Significantly lower risks of all mood disorders and psychosis were also observed. Results were similar across robustness analyses. CONCLUSIONS: These findings support preclinical data and suggest a role for 5-HT4R agonists as novel agents in the prevention of major depression. These findings should stimulate randomised controlled trials to confirm if these agents can serve as a novel class of antidepressant within a clinical setting.

Characteristics of sialolithiasis in Israel, a big-data retrospective study of 5100 cases.

OBJECTIVE: The aim of this study was to identify risk factors for sialolithiasis patients using a large community and hospital-based cohort. METHODS: A retrospective case-control study was conducted on 20,396 individuals, including 5100 sialolithiasis patients and 15,296 matched controls. Demographics and laboratory data were obtained from electronic medical records. Statistical analyses were performed to identify significant differences between the two groups. A p-value of <0.05 was considered significant. RESULTS: Sialolithiasis was more prevalent in women, with a mean age at diagnosis of 55.75 years. Several geographic location variables emerged as risk factors for sialolithiasis including Israeli birth, higher socioeconomic communities, and specific areas of residency. Tobacco smoking (odds ratio = 1.46) was a significant risk factor. Low high-density lipoprotein levels, elevated triglycerides, and elevated amylase levels were associated with sialolithiasis. CONCLUSIONS: This study provides valuable insights into the demographic and laboratory characteristics of sialolithiasis patients, indicating that area of residency and lifestyle factors contribute to the risk of developing sialolithiasis. The findings may contribute to a better understanding of the disease and the development of preventative measures or early diagnostics tools.

Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.

Digital epidemiology is the process of investigating the dynamics of disease-related patterns, both social and clinical, as well as the causes of these trends in epidemiology. Digital epidemiology, utilising big data from a variety of digital sources, has emerged as a viable method for early detection and monitoring of viral outbreaks. The present review gives an overview of digital epidemiology, emphasising its importance in the timely detection of infectious disease outbreaks. Researchers may discover and track outbreaks in real time using digital data sources such as search engine queries, social media trends, and digital health records. However, data quality, concerns about privacy, and data interoperability must be addressed to maximise the effectiveness of digital epidemiology. As the global landscape of infectious diseases evolves, integrating digital epidemiology becomes critical to improving pandemic preparedness and response efforts. Integrating digital epidemiology into routine monitoring systems has the potential to improve global health outcomes and save lives in the event of viral outbreaks.

Construction framework of smart tourism big data mining model driven by blockchain technology.

In the era of sharing economy, the tourism market is increasingly characterized by personalized demand, mobile consumption and product segmentation. This paper aims to apply big data mining technology in the field of smart tourism. Firstly, it focuses on image summary selection and collaborative filtering technology based on big data mining. It then demonstrates the integration of blockchain in smart tourism, emphasizing the use of decentralized structures and smart contracts to achieve data security and transparency, and describes the testing process of smart tourism platforms, including data preparation and platform operational efficiency testing. Finally, the research results of this paper are summarized, and the development potential and practical application value of smart tourism are demonstrated. The results show that in the smart tourism big data mining model, the minimum support for the data set is 10 % and 20 %, respectively. Moreover, with the increase of the number of nodes in the same data set, the running time decreases gradually. It can be seen that smart tourism big data mining has strong scalability.

Microfluidics in smart food safety.

The evolution of food safety practices is crucial in addressing the challenges posed by a growing global population and increasingly complex food supply chains. Traditional methods are often labor-intensive, time-consuming, and susceptible to human error. This chapter explores the transformative potential of integrating microfluidics into smart food safety protocols. Microfluidics, involving the manipulation of small fluid volumes within microscale channels, offers a sophisticated platform for developing miniaturized devices capable of complex tasks. Combined with sensors, actuators, big data analytics, artificial intelligence, and the Internet of Things, smart microfluidic systems enable real-time data acquisition, analysis, and decision-making. These systems enhance control, automation, and adaptability, making them ideal for detecting contaminants, pathogens, and chemical residues in food products. The chapter covers the fundamentals of microfluidics, its integration with smart technologies, and its applications in food safety, addressing the challenges and future directions in this field.

From bed to bench and back again: Challenges facing deployment of intracranial pressure data analysis in clinical environments.

Introduction: Numerous complex physiological models derived from intracranial pressure (ICP) monitoring data have been developed. More recently, techniques such as machine learning are being used to develop increasingly sophisticated models to aid in clinical decision-making tasks such as diagnosis and prediction. Whilst their potential clinical impact may be significant, few models based on ICP data are routinely available at a patient's bedside. Further, the ability to refine models using ongoing patient data collection is rare. In this paper we identify and discuss the challenges faced when converting insight from ICP data analysis into deployable tools at the patient bedside. Research question: To provide an overview of challenges facing implementation of sophisticated ICP models and analyses at the patient bedside. Material and methods: A narrative review of the barriers facing implementation of sophisticated ICP models and analyses at the patient bedside in a neurocritical care unit combined with a descriptive case study (the CHART-ADAPT project) on the topic. Results: Key barriers found were technical, analytical, and integrity related. Examples included: lack of interoperability of medical devices for data collection and/or model deployment; inadequate infrastructure, hindering analysis of large volumes of high frequency patient data; a lack of clinical confidence in a model; and ethical, trust, security and patient confidentiality considerations governing the secondary use of patient data. Discussion and conclusion: To realise the benefits of ICP data analysis, the results need to be promptly delivered and meaningfully communicated. Multiple barriers to implementation remain and solutions which address real-world challenges are required.

Evaluation of the value of Internet platform Enterprises in the digital economy by the big data cooperation asset valuation model.

With the innovation of information technology, the national industry has been adjusted and upgraded, and the development of the Internet industry has had a huge impact on economic development. The investment and financing of network enterprises and the merger and acquisition of network companies need to evaluate the value of network companies. In this regard, this paper evaluated the value of Internet platform enterprises under the digital economy based on the Big Data (BD) cooperation asset valuation model. This paper analyzed the factors affecting the value evaluation of Internet enterprises and discussed the advantages of BD cooperative asset valuation model in the value evaluation of Internet enterprises in the digital economy. The BD cooperation asset valuation model was constructed, and the value evaluation experiment of Internet platform enterprises under the digital economy was carried out. The experimental results of this paper showed that in the evaluation of the profitability value of Internet enterprises, the difference between the net sales interest rate was 0.14%-0.51 %. The difference between the net interest rate of equity was 0.09%-0.67 %, and the difference between the net interest rate of total assets was 0.19%-0.92 %; in terms of the evaluation of the operating capacity of Internet enterprises, the difference between the current asset turnover rate was 0.05-0.16. The difference of non-current asset turnover rate was 0.02-0.15, and the difference of total asset turnover rate was 0.01-0.16. The evaluation value based on the BD cooperation asset valuation model was not different from the actual enterprise value, which showed that the BD cooperation asset valuation model had good advantages in the evaluation of the value of Internet enterprises.

Data Analytics for Predicting Situational Developments in Smart Cities: Assessing User Perceptions.

The analysis of large volumes of data collected from heterogeneous sources is increasingly important for the development of megacities, the advancement of smart city technologies, and ensuring a high quality of life for citizens. This study aimed to develop algorithms for analyzing and interpreting social media data to assess citizens' opinions in real time and for verifying and examining data to analyze social tension and predict the development of situations during the implementation of urban projects. The developed algorithms were tested using an urban project in the field of transportation system development. The study's material included data from social networks, messenger channels and chats, video hosting platforms, blogs, microblogs, forums, and review sites. An interdisciplinary approach was utilized to analyze the data, employing tools such as Brand Analytics, TextAnalyst 2.32, GPT-3.5, GPT-4, GPT-4o, and Tableau. The results of the data analysis showed identical outcomes, indicating a neutral perception among users and the absence of social tension surrounding the project's implementation, allowing for the prediction of a calm development of the situation. Additionally, recommendations were developed to avert potential conflicts and eliminate sources of social tension for decision-making purposes.

Understanding the Use of Mobility Data in Disasters: Exploratory Qualitative Study of COVID-19 User Feedback.

BACKGROUND: Human mobility data have been used as a potential novel data source to guide policies and response planning during the COVID-19 global pandemic. The COVID-19 Mobility Data Network (CMDN) facilitated the use of human mobility data around the world. Both researchers and policy makers assumed that mobility data would provide insights to help policy makers and response planners. However, evidence that human mobility data were operationally useful and provided added value for public health response planners remains largely unknown. OBJECTIVE: This exploratory study focuses on advancing the understanding of the use of human mobility data during the early phase of the COVID-19 pandemic. The study explored how researchers and practitioners around the world used these data in response planning and policy making, focusing on processing data and human factors enabling or hindering use of the data. METHODS: Our project was based on phenomenology and used an inductive approach to thematic analysis. Transcripts were open-coded to create the codebook that was then applied by 2 team members who blind-coded all transcripts. Consensus coding was used for coding discrepancies. RESULTS: Interviews were conducted with 45 individuals during the early period of the COVID-19 pandemic. Although some teams used mobility data for response planning, few were able to describe their uses in policy making, and there were no standardized ways that teams used mobility data. Mobility data played a larger role in providing situational awareness for government partners, helping to understand where people were moving in relation to the spread of COVID-19 variants and reactions to stay-at-home orders. Interviewees who felt they were more successful using mobility data often cited an individual who was able to answer general questions about mobility data; provide interactive feedback on results; and enable a 2-way communication exchange about data, meaning, value, and potential use. CONCLUSIONS: Human mobility data were used as a novel data source in the COVID-19 pandemic by a network of academic researchers and practitioners using privacy-preserving and anonymized mobility data. This study reflects the processes in analyzing and communicating human mobility data, as well as how these data were used in response planning and how the data were intended for use in policy making. The study reveals several valuable use cases. Ultimately, the role of a data translator was crucial in understanding the complexities of this novel data source. With this role, teams were able to adapt workflows, visualizations, and reports to align with end users and decision makers while communicating this information meaningfully to address the goals of responders and policy makers.

Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on hidden Markov model.

To address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. The traditional sensitivity analysis model was optimized using fuzzy random optimization, and an analytical calculation model of the mean and standard deviation of the fuzzy random reliability sensitivity was established. A big data hidden Markov model and expectation-maximization algorithm were used to improve the digital characteristics of fuzzy random variables. The fuzzy random sensitivity optimization model was used to confirm the effect of concrete compressive strength, thick-diameter ratio, reinforcement ratio, uncertainty coefficient of calculation model, and soil depth on the overall structural reliability of a reinforced concrete double-layer wellbore in deep alluvial soil. Through numerical calculations, these characteristics were observed to be the main influencing factors. Furthermore, while the soil depth was negatively correlated, the other influencing factors were all positively correlated with the overall reliability. This study provides an effective reference for the safe construction of deep underground structures in the future.

Comparison of embolic risk in left ventricular thrombus between nonischemic and ischemic cardiomyopathy: A nationwide database analysis.

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. METHODS AND 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.

Improving Likert scale big data analysis in psychometric health economics: reliability of the new compositional data approach.

Bipolar psychometric scales data are widely used in psychologic healthcare. Adequate psychological profiling benefits patients and saves time and costs. Grant funding depends on the quality of psychotherapeutic measures. Bipolar Likert scales yield compositional data because any order of magnitude of agreement towards an item assertion implies a complementary order of magnitude of disagreement. Using an isometric log-ratio (ilr) transformation the bivariate information can be transformed towards the real valued interval scale yielding unbiased statistical results increasing the statistical power of the Pearson correlation significance test if the Central Limit Theorem (CLT) of statistics is satisfied. In practice, however, the applicability of the CLT depends on the number of summands (i.e., the number of items) and the variance of the data generating process (DGP) of the ilr transformed data. Via simulation we provide evidence that the ilr approach also works satisfactory if the CLT is violated. That is, the ilr approach is robust towards extremely large or infinite variances of the underlying DGP increasing the statistical power of the correlation test. The study generalizes former results pointing out the universality and reliability of the ilr approach in psychometric big data analysis affecting psychometric health economics, patient welfare, grant funding, economic decision making and profits.

BART, the new robotic assistant: big data, artificial intelligence, robotics, and telemedicine integration for an ICU 4.0.

We are in the era of Health 4.0 when novel technologies are providing tools capable of improving the quality and safety of the services provided. Our project involves the integration of different technologies (AI, big data, robotics, and telemedicine) to create a unique system for patients admitted to intensive care units suffering from infectious diseases capable of both increasing the personalization of care and ensuring a safer environment for caregivers.

Combating Chronic Disease with Barbershop Health Interventions: A Review of Current Knowledge and Potential for Big Data.

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.