BACKGROUND: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting.
OBJECTIVE: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program.
METHODS: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency.
RESULTS: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies.
CONCLUSIONS: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation.
BACKGROUND: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.

Coronavirus disease (COVID-19) is an infectious respiratory disease that was first found in Wuhan, China, on December 31, 2019. It is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of November 10, 2020, more than fifty million cases have been confirmed, and more than one million deaths have been reported globally. This situation has created a serious challenge for all countries to institute a variety of control measures to track and slow down the spread of the virus and prevent the increasing number of deaths. In recent years, there has been an ongoing interest in using Artificial Intelligence (A.I.) in healthcare to create new treatments and detecting diseases. The objective of this study is to analyze the application and the impact of A.I. on the breakout of COVID-19 and discuss the contribution of A.I. to the fight against the pandemic based on the most recent applications used in the United Arab Emirates, including Dubai Police Movement Restriction Monitoring System, Taxis Preventive Measures Compliance System, Mobile App \"Wai-Eye,\" Smart Helmets, Virtual Doctor, and The Department of Health - Abu Dhabi (DoH) Remote Healthcare App. The method used in this study is based on a meta-analysis of recent COVID-19 studies from various databases such as ScienceDirect, Sage Journals, SpringerLink, ResearchGate, Emerald Open Research, and IEEE Xplore. The COVID-19 data was based on Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE). Results showed that A.I. applications provided the necessary prevention of the spread of COVID-19, assisted in monitoring restrictions and preventive measures violations, and provided remote healthcare, which directly impacted the number of hospital visits amidst the lockdown. The study concluded that A.I. has proven to be effective in supporting governments in fighting the pandemic.

We describe a performance budget planning model developed for a research university, comprised of a set of 88 key variables and 38 non-linear structural equations that describe interactions among them. These equations, based on the knowledge of research university\'s financial working and theoretical considerations, relate expenditures and revenues to teaching and research operations. We demonstrate the value of this model for developing insight into the financial structure of the university. In particular, we show how the model aids in (1) comparing the effect of various policy alternatives on the performance of the university, (2) performing comparative statics analysis of any subset of variables of interest, (3) choice of policy variables and policy alternatives, and (4) gaining insight into the structure of the interactions for a given policy alternative in terms of the causal chain between policy variables and outcome variables. We also describe a computer implementation of the model and discuss a class of mathematical tools for policy planning analysis that facilitate the use and manipulation of models based on sets of nonlinear constraints.

Mobile health (mHealth) apps have received increasing attention, due to their abilities to support patients who suffer from various conditions. mHealth apps may be especially helpful for patients with chronic diseases, by providing pertinent information, tracking symptoms, and inspiring adherence to medication regimens. To achieve these objectives, researchers need to prototype mHealth apps with dedicated software architectures. In this paper, a cloud-based mHealth application development concept is presented for chronic patient supportive care apps. The concept integrates existing software platforms and services for simplified app development that can be reused for other target applications. This developmental method also facilitates app portability, through the use of common components found across multiple mobile platforms, and scalability, through the loose coupling of services. The results are demonstrated by the development of native Android and cross-platform web apps, in a case study that presents an mHealth solution for endocrine hormone therapy (EHT). A performance analysis methodology, an app usability evaluation, based on focus group responses, and alpha and pre-beta testing results are provided.

BACKGROUND: Probiotics are the live microbes that exert beneficial effects on the health of the host cell, creating novel nanoformulations via probiotic bacteria, thus gaining tremendous momentum recently. The probiotic bacteria are being employed in synthesizing or more specifically biosynthesizing several nanoparticles like metallic as well as non-metallic nanoparticles. Biosynthesis of metallic nanoparticles is currently the focus of research nowadays due to several applications. Research inputs have led to the exploration of probiotic bacteria in biosynthesizing novel metallic nanoparticles.
OBJECTIVE: The present review explores various research and patent reports on metallic nanoparticles biosynthesized using probiotic bacteria.
METHODS: Through the sites, www.freepatentsonline.com and www.uspto.gov/patft, patents have been retrieved including US patents, EP and WIPO patents.
RESULTS: Various reports and patents have revealed that probiotic bacteria can effectively produce metallic nanoparticles. These nanoparticles have found applications in cosmetics, pharmaceutics, medicine and biotechnology. Areas of future research can include the exploration of formulation aspects of metallic nanoparticles of iron, zinc, tellurium and synthesis of these particles using yeast, fungi, plant extracts and several biomaterials.
CONCLUSIONS: Use of probiotic bacteria in synthesizing metallic nanoparticles is an effective biosynthetic approach. However, the technique needs wider exploration for newer metallic/nonmetallic/metalloid NPs for therapeutic applications.

In the current cadaveric study an unusual sizeable accessory phrenic nerve (APN) was encountered emerging from the trunk of the supraclavicular nerves and forming a triangular loop that was anastomosing with the phrenic nerve. That neural loop surrounded the superficial cervical artery which displayed a spiral course. The form of a triangular loop of APN involving the aforementioned artery and originating from the supraclavicular nerve to the best of our knowledge has not been documented previously in the literature. The variable morphological features of the APN along with its clinical applications are briefly discussed.

To determine whether the effect observed in a study is related to the nanoparticle only or to their synergic effect with the \"free\" metal ions, the real concentration of silver (104±8 and 100±2 mg L(-1)) after AgNP synthesis is obtained through ICP-MS and ICP OES in the solution after the AgNP synthesis and in different fractions after centrifugation (at 8100 g for 40 min). From the resuspension of the AgNP contained in the solution (AgNP-total) after synthesis (AgNP-res), concentrations of 49±3 and 51±3 mg L(-1) are found and concentrations of 50±7 and 47±2 mg L(-1) in the supernatant (Ag-sup) are found using ICP-MS and ICP OES respectively. The characterization of AgNP-total, AgNP-res and Ag-sup is performed by HRTEM and UV-vis, corroborating the results in terms of Ag determination, and indicates that half of the total silver concentration is in the AgNP form and that the other half is in the \"free\" silver form. The results of the stability test of the NPs indicate a 7% decrease in Ag as NP three months after its synthesis.