Realizing brain therapy with "smart medicine": mechanism and case report of molecular hydrogen inhalation for Parkinson's disease.

The Michael J. Fox Foundation has been funding research on Parkinson's disease for 35 years, but has yet to find a cure. This is due to a problem with the philosophy behind the development of modern medical treatments. In this paper, we will introduce "smart medicine" with a substance that can solve all the problems of central nervous system drugs. The substance is the smallest diatomic molecule, the hydrogen molecule. Due to their size, hydrogen molecules can easily penetrate the cell membrane and enter the brain. In the midbrain of Parkinson's disease patients, hydroxyl radicals generated by the Fenton reaction cause a chain reaction of oxidation of dopamine, but hydrogen entering the midbrain can convert the hydroxyl radicals into water molecules and inhibit the oxidation of dopamine. In this paper, we focus on the etiology of neurological diseases, especially Parkinson's disease, and present a case in which hydrogen inhalation improves the symptoms of Parkinson's disease, such as body bending and hand tremor. And we confidently state that if Michael J. Fox encountered "smart medicine" that could be realized with molecular hydrogen, he would not be a "lucky man" but a "super-lucky man."

Molecular Hydrogen Protects against Various Tissue Injuries from Side Effects of Anticancer Drugs by Reducing Oxidative Stress and Inflammation.

While drug therapy plays a crucial role in cancer treatment, many anticancer drugs, particularly cytotoxic and molecular-targeted drugs, cause severe side effects, which often limit the dosage of these drugs. Efforts have been made to alleviate these side effects by developing derivatives, analogues, and liposome formulations of existing anticancer drugs and by combining anticancer drugs with substances that reduce side effects. However, these approaches have not been sufficiently effective in reducing side effects. Molecular hydrogen (H2) has shown promise in this regard. It directly reduces reactive oxygen species, which have very strong oxidative capacity, and indirectly exerts antioxidant, anti-inflammatory, and anti-apoptotic effects by regulating gene expression. Its clinical application in various diseases has been expanded worldwide. Although H2 has been reported to reduce the side effects of anticancer drugs in animal studies and clinical trials, the underlying molecular mechanisms remain unclear. Our comprehensive literature review revealed that H2 protects against tissue injuries induced by cisplatin, oxaliplatin, doxorubicin, bleomycin, and gefitinib. The underlying mechanisms involve reductions in oxidative stress and inflammation. H2 itself exhibits anticancer activity. Therefore, the combination of H2 and anticancer drugs has the potential to reduce the side effects of anticancer drugs and enhance their anticancer activities. This is an exciting prospect for future cancer treatments.

Generative AI for diabetologists: a concise tutorial on dataset analysis.

Objectives: This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience. Methods: The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the 'Group' variable, cross-validation analysis, and NT-proBNP regression. Results: The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data. Conclusions: The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.

Contradictory Resuscitation Procedure for Cardiopulmonary Arrest.

Drawing from a comprehensive Japan-based literature review and the author's personal experience, this article presents findings that highlight potential improvements in clinical outcomes, such as reduced mortality rates, by optimizing the current resuscitation procedure for cardiopulmonary arrest. Many countries have adopted similar procedures for cardiopulmonary arrest. This article presents a prioritized resuscitation method based on scientific evidence, aiming to improve survival rates. The study, which was conducted in Japan, revealed inconsistencies in the current resuscitation procedure for cardiopulmonary arrest. The study did not involve direct participants but relied on literature review for data collection. A literature review was conducted to analyze the survival rates of various resuscitation methods. The interventions reviewed in the literature included cardiopulmonary resuscitation, automated external defibrillator, and automatic mechanical chest compressions. The survival rate of cardiopulmonary arrest in Japan was found to be low. The results of the literature review suggest that cardiopulmonary resuscitation or automatic mechanical chest compressions should be applied before using an automated external defibrillator. The study emphasizes the need to prioritize resuscitation methods with higher survival rates. This article presents a prioritized resuscitation method based on scientific evidence, aiming to improve survival rates. It is hoped that this new approach will lead to a significant improvement in the survival rates of cardiopulmonary arrest patients.

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.

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.

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.