武藤 佳恭

Instructors are always interested in methods to activate learner incentives and motivation to increase learning effectiveness. This paper introduces the Python Package Index (PyPI) as a powerful tool to maximize learner incentives on software and presents an example of its application in agriculture and science. The more useful the PyPI application is, the more it will be downloaded worldwide, providing an external review for the learner, and strengthening their incentive. However, many existing tutorials on PyPI, including the official site, are not updated on the twine library for uploading files to the PyPI site. This paper presents an updated tutorial on using PyPI for counting disaggregated objects such as bugs and pests, and for software reproducibility validation via Code Ocean. Additionally, generative AI is introduced as an indispensable assistant for tasks such as understanding technical terms and providing solutions for encountered problems.

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.

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."

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.

With the proliferation of the Internet, the number of cyber-attacks has increased worldwide. To prevent cyber-attacks, network administrators need to use analytical tools to disclose six new critical statistics such as Internet Protocol (IP) authenticity and classification, IP ownership, frequency of attacks, time of day, day of the week, and country of attack. There are four SSH attack analysis tools available, but existing tools lack visualization and cannot provide the necessary attack statistics. This paper proposes SSHAA, the first open source PyPI analysis tool that meets the requirement. Text mining technology is used for extracting data from SSH log files, classifying IPs with identified country names and calculating a variety of statistics. PyPI allows SSHAA to run on Windows, MacOS, and Linux operating systems. This paper highlights how to debut a PyPI application using SSHAA which will be the world’s first tutorial in the security journals for maximum software dissemination. According to PePy, SSHAA has been downloaded 7668 times worldwide. The substantial number of downloads signifies the efficacy of the proposed tool. The proposed system offers ten functional statistics for SSH attack analysis. The six new functional statistics to information security are significant.

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.

This paper investigates the trends in erectile dysfunction research by integrating individual keywords such as COVID-19, mental health, psychiatry, and drug use. The study spans a six-year period from September 21, 2017, to September 21, 2023, and utilizes resources from the National Library of Medicine. For instance, the quantity of relevant documents is determined using the Google search engine. A custom date range can be set on the browser via tools, and the search is limited to the nih.gov site domain. The phrase-site search command used is executed with “erectile dysfunction” COVID-19 site:nih.gov. The result revealed that from Sept 2021 to 2022, research on COVID-19 and erectile dysfunction spiked. However, the previous year saw a rise in studies linking drugs or mental health with erectile dysfunction. Meanwhile, psychiatry-focused publications have consistently grown. The time-series trends of erectile dysfunction linked to COVID-19 are substantiated by a comprehensive literature review.

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.

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.

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.

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.

Context: Programming, particularly with the widely-used scripting language Python, is a powerful tool for expressing ideas in science and technology. PyPI, or the Python Package Index, is a management tool for Python packages. Despite the availability of many tutorials on PyPI, including on its official website, many users have encountered difficulties due to outdated information and issues with the twine upload library. Objectives: This paper demonstrates the use of a gas chromatography program to prototype the PyPI application, with the goal of maximizing its dissemination and validating its reproducibility in science and engineering worldwide. Methods: The peer-reviewed Python program on gas chromatography is converted to the PyPI application. Third-party evaluations motivate learners to boost programming skills. The more the downloads from third-party evaluations, the greater the incentive for learners. Results: The gas chromatography program is successfully converted to agci PyPI application. Learners can be excited to check their progress on external downloads to motivate them to learn and program. This tutorial shows how to verify software reproducibility of the PyPI application via Code Ocean. Conclusion: This paper demonstrated the real gas chromatography program to the PyPI application and showed how to verify software reproducibility for maximizing dissemination skills and boosting the motivation of learners for advancing chemometrics research.

There is no tool for calculating a case fatality rate (CFR) and a lag time of COVID-19. This paper proposes a new policymaker tool, covidlag for policymakers to calculate the CFR and the lag time with associated relationship from infection to death. The more the infections, the more the COVID-19 deaths. The less the infections, the less the deaths. We took advantage of this correlation between infection and death. In other words, the number of daily cases and that of daily deaths are used for calculating CFRs and lag times in the US. Scoring policies are based on dividing the number of daily cumulative deaths by the population in millions. The proposed covidlag algorithm with a strong correlation from infection to death with maxima and minima can generate lag times and CFRs in ongoing time series. covidlag allows users to provide the appropriate degree of curve-fitting polynomials of infection and death to generate maxima and minima for calculating time lag and CFR. The introduced hiscovid discovered that New Zealand made a single mistake in March 2022 while the US made multiple major mistakes during the pandemic. The hiscovid tool found that the COVID-19 surge in New Zealand is due to lifting of travel border restrictions. The CFR and lag time calculations play a key role for policymakers in analyzing the COVID-19 pandemic.

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.