Exploring the connection between frailty and cardiovascular diseases.

This study explores the significant correlation between frailty and an elevated risk of mortality in COVID-19 patients, suggesting that increased frailty screening could enhance disease management and optimize resource distribution. An analysis of peer-reviewed papers on frailty and cardiovascular diseases (CVD) over a ten-year period reveals a peak of 4480 articles from September 2021 to September 2022. The literature review conducted on frailty and CVD highlights the high prevalence of frailty in older adults with CVD and its role as a predictor of cardiovascular death. The study suggests that frailty can inform treatment decisions, offering more personalized care. However, standardizing frailty assessment in clinical practice and trials is needed. The impact of frailty on coronary artery disease, peripheral artery disease, and atrial fibrillation requires further research. The study also discusses the increasing global burden of CVD among older adults due to aging populations and improved care. It highlights the challenges posed by older age, multiple comorbidities, polypharmacy, frailty, and adverse noncardiovascular outcomes. The review focuses on geriatric conditions that significantly impacted health status, quality of life, and overall prognosis. The study concludes that frailty significantly increases the risk of CVD events and major adverse cardiovascular events in older adults without prior CVD. Screening for frailty could help identify those at higher risk and facilitate targeted preventive measures.

Impact of COVID-19 on arthritis with generative AI.

OBJECTIVE: The study aims to examine the effects of the COVID-19 pandemic on the prevalence of arthritis in the US using a specific generative AI tool. METHODS: The AI tool with Bing.com/copilot, designed to generate Python code, uses data from the Centers for Disease Control and Prevention (CDC) to visualize trends and uncover insights in four key areas: (1) The prevalence of arthritis in adults aged 18 years and older who have diabetes, (2) The prevalence of fair or poor health in adults aged 18 years and older who have arthritis, (3) The prevalence of activity limitations due to arthritis in adults aged 18 years and older with doctor-diagnosed arthritis, (4) The prevalence of arthritis in adults aged 18 years and older who are obese. This research did not require approval from an institutional review board or an ethics committee. RESULTS: The findings reveal a significant decline in the prevalence of arthritis among adults with conditions such as diabetes and obesity during the COVID-19 pandemic. There was also an observed improvement in activity limitations among patients with doctor-diagnosed arthritis. CONCLUSION: The study highlights the potential impact of the pandemic on chronic disease management, particularly arthritis. It underscores the importance of continued monitoring and care for patients with arthritis, especially during a global health crisis like the COVID-19 pandemic. The use of AI tools in generating insights from health data proves to be valuable in this context.

Vaccine effects on COVID-19 infection with bivalent boosting by age group.

This paper examines time-series vaccine effectiveness on COVID-19 infection with/without a bivalent booster dose by 6 age groups such as 18-29, 30-49, 50-64, 65-79, 80+, and all_ages respectively. CDC's COVID data on rates of COVID-19 cases and deaths by updated (bivalent) booster status was used in this study. This result concludes that there is no difference between vaccines with or without a bivalent booster dose for preventing COVID-19 infection in 6 age groups 18-29, 30-49, 50-64, 65-79, 80+, and all_ages. Vaccination is effective in two age groups of 65-79 and 80+ for preventing COVID-19 infection. However, vaccine effectiveness against COVID-19 infection has not been confirmed in the 18-29 and 30-49 age groups.

Artificial intelligence universal biomarker prediction tool.

Through experiencing cardiopulmonary arrest, an artificial intelligence universal biomarker prediction tool was developed to help patients understand improvement in the trends of their disease. PyPI tool handles two biomarkers, hbA1c for diabetes and NP-proBNP for heart failure, to predict the next hospital visit. Predicting improvement in disease is a great hope for patients.

Generative AI for analysis and identification of Medicare improper payments by provider type and HCPC code.

The 2022 Medicare Fee-For-Service Improper Payments Report reveals an estimated $80.57 billion in improper payments, with a payment error rate of 15.62%. This paper uses generative AI to analyze and identify which provider types and HCPC codes are most strongly associated with these errors. The paper employs generative AI to produce two Python codes: one generates a time-series trend graph of Medicare improper payments from 2010 to 2022, and the other calculates the number of payment errors by provider type and HCPC code. These codes are designed for novice and non-programmers. Three datasets are used, such as Medicare Fee-for-Service Comprehensive Error Rate Testing dataset released on March 8, 2023, merged codes such as HCPC codes and PCT codes. The result suggests what systems should be improved to reduce Medicare improper payments. Generative AI is being introduced to help novice and non-programmers analyze Medicare improper payments with datasets, aiding researchers in conducting similar tasks in the future.

Clinical Use and Treatment Mechanism of Molecular Hydrogen in the Treatment of Various Kidney Diseases including Diabetic Kidney Disease.

As diabetes rates surge globally, there is a corresponding rise in the number of patients suffering from diabetic kidney disease (DKD), a common complication of diabetes. DKD is a significant contributor to chronic kidney disease, often leading to end-stage renal failure. However, the effectiveness of current medical treatments for DKD leaves much to be desired. Molecular hydrogen (H2) is an antioxidant that selectively reduces hydroxyl radicals, a reactive oxygen species with a very potent oxidative capacity. Recent studies have demonstrated that H2 not only possesses antioxidant properties but also exhibits anti-inflammatory effects, regulates cell lethality, and modulates signal transduction. Consequently, it is now being utilized in clinical applications. Many factors contribute to the onset and progression of DKD, with mitochondrial dysfunction, oxidative stress, and inflammation being strongly implicated. Recent preclinical and clinical trials reported that substances with antioxidant properties may slow the progression of DKD. Hence, we undertook a comprehensive review of the literature focusing on animal models and human clinical trials where H2 demonstrated effectiveness against a variety of renal diseases. The collective evidence from this literature review, along with our previous findings, suggests that H2 may have therapeutic benefits for patients with DKD by enhancing mitochondrial function. To substantiate these findings, future large-scale clinical studies are needed.

Impact of COVID-19 on mental health in the US with generative AI.

This paper investigates the impact of COVID-19 on mental health in the US using a large CDC dataset and a new method with generative AI for automatically generating Python code. The generated code was used to investigate and visualize the time-series impact of COVID-19 on mental health by eight categories over time. The paper aims to activate research on mental health during COVID-19 and demonstrates the use of generative AI in psychiatry research for novice or non-programmer researchers.

A time-series COVID-19 policy outcome analysis tool to measure human behavior from a herd instinct perspective.

Purpose: There are 47 municipalities and prefectures in Japan that operate similar COVID-19 policies in a unified manner. There are significant differences regarding their policy outcomes. In order to investigate when the outcomes are different, we made a COVID-19 policy outcome analysis tool, jpcovid for evaluating time-series scores of individual prefectures, not a policy analysis tool. Methods: Scoring policies is based on a single population mortality metric: the number of COVID-19 deaths divided by the population in millions from a demographic perspective. Results: Although uniformed policies have been adopted by the 47 prefectures in Japan, there are significant differences in the calculated scores among the 47 prefectures. This difference can be caused by differences in the herding instincts of the community with COVID-19 variants. The herd instinct is an inherent tendency to associate with others and follow the group's behavior or a behavior wherein people tend to react to the actions of others without considering the reason. The snapshot scoring tool, jpscore showed that Niigata has the best score of 67.9 while Osaka has the worst score of 727.9. jpcovid allows users to identify when herd instincts made changes in time-series scores. Conclusions: This is the world's first large-scale measurement on the herd instinct of prefectures in Japan. The proposed method can be applied to other countries in general. Supplementary Information: The online version contains supplementary material available at 10.1007/s12553-023-00759-x.

Why the power of diversity does not always produce better groups and societies.

Diversity is supposed to create better groups and societies but sometimes fails. It is explained why the power of diversity may not create better groups in the current diversity prediction theory. Diversity may hurt civic life and introduce distrust. This is because the current diversity prediction theory is based on real numbers that ignore individual abilities. Its diversity prediction theory maximizes performance with infinite population size. Contrary to this, collective intelligence or swarm intelligence is not maximized by infinite population size, but by population size. The extended diversity prediction theory using the complex number allows us to express individual abilities or qualities. The diversity of complex numbers always produces better groups and societies. The wisdom of crowds, collective intelligence, swarm intelligence or nature-inspired intelligence is implemented in the current machine learning or artificial intelligence, called Random Forest. The problem of the current diversity prediction theory is detailed in this paper.

Review of hikikomori: A global health issue, identification and treatment.

In the early 2000s, Japanese society became aware of the social phenomenon of hikikomori or social withdrawal among Japanese youth. However, the hikikomori phenomenon was not a domestic Japanese social problem, but a global social and health issue or a global silent epidemic. A literature review was conducted on the global silent epidemic issue and identifying hikikomori and effective treatment. This paper will reveal how to identify hikikomori with biomarkers or determinants and treatments for hikikomori. Impact of COVID-19 on hikikomori was briefly investigated.

Effectiveness of vaccination with symptoms by age groups.

This paper examines effectiveness of vaccination with symptoms by age groups in the US and Hamamatsu city in Japan. The efficacy of vaccination has been reported in Singapore, but both datasets such as the US CDC dataset and the Hamamatsu dataset contradict the Singapore results. Both local government and government datasets are publicly available for peer review and reader validation.

hiscovid for visualizing and identifying when policymakers made mistakes against COVID-19.

There are two types of policy outcome analysis tools: snapshot tool and time-series tool. hiscovid is a time-series policy outcome scoring tool of COVID-19 policies by country where the daily cumulative population mortality is used for scoring the outcomes of COVID-19 country policies to visualize and identify when policymakers made mistakes. hiscovid allows policymakers to observe the progress and transition of scores over time to learn lessons from the past decision-making mistakes for correcting the current policies to reduce unnecessary deaths. The lower the score, the better the policy. hiscovid attracted 1480 users worldwide.

How to build disaster-resilient cities and societies for making people happy.

The current disaster approaches and strategies have failed. Our society is also vulnerable to COVID-19 pandemics and natural disasters. This paper surveys the conventional disaster governance regimes and examines whether a sustainable and resilient society against natural and human-induced disasters can be built. This paper aims to shed light on resilient city and country building from the perspective of disaster prevention. Current disaster strategies must be updated in order to mitigate the cost of disaster events and to make people happy. This paper quantifies the scores of the COVID-19 policies for strengthening disaster prevention technology.

The overlooked benefits of hydrogen-producing bacteria.

Intestinal bacteria can be classified into "beneficial bacteria" and "harmful bacteria." However, it is difficult to explain the mechanisms that make "beneficial bacteria" truly beneficial to human health. This issue can be addressed by focusing on hydrogen-producing bacteria in the intestines. Although it is widely known that molecular hydrogen can react with hydroxyl radicals, generated in the mitochondria, to protect cells from oxidative stress, the beneficial effects of hydrogen are not fully pervasive because it is not generally thought to be metabolized in vivo. In recent years, it has become clear that there is a close relationship between the amount of hydrogen produced by intestinal bacteria and various diseases, and this report discusses this relationship.

Policy analysis and data mining tools for controlling COVID-19 policies.

Much research has been done on the efficacy of vaccines against the COVID-19 pandemic, but the claims have not yet been realized in the real world. This paper proposes three COVID-19 policy outcome analysis tools such as jpscore for scoring and revealing the best prefecture policy in Japan, scorecovid for scoring and revealing the best country policy in the world, and finally hiscovid for visualizing and identifying when policymakers made mistakes in time-series scores. Poorly scored countries or prefectures can learn good strategies from the best country or prefecture with excellent scores. Three tools are based on a single metric dividing the number of COVID-19 deaths by the population in millions. Three tools suggest us that the sustainable mandatory test-isolation strategy should be adopted in the world for mitigating the pandemic. This paper also addresses what is lacking in Japan for scientific evidence-based research for mitigating the pandemic. Visualization tools and sorted and time-series scores of policy outcomes help policymakers make the right decisions.