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<b>Background:</b><b> </b>Menthol-flavored tobacco products are disproportionately used in low-income African American communities, a result of decades of targeted marketing and systemic inequities. Menthol use has been associated with lower quit rates, often compounded by factors such as lower trust in healthcare systems, reduced access to cessation programs, and other structural barriers. Despite this, few studies have systematically examined the explanatory mechanisms that might clarify why menthol-flavored tobacco is linked to poorer cessation outcomes among participants in tobacco cessation programs. <b>Aims:</b><b> </b>This study aimed to investigate the potential mechanisms by which menthol tobacco use is associated with lower quit rates across three types of smoking cessation interventions. <b>Methods:</b><b> </b>Participants were randomized into one of three smoking cessation interventions: in-person (CEASE), self-help, or online/hybrid programs. Smoking abstinence was assessed three months post-intervention as the primary outcome. Secondary analyses explored whether demographic, socioeconomic, or behavioral factors mediated the association between menthol use and quit rates across the intervention arms. <b>Results:</b><b> </b>Menthol tobacco use was significantly associated with lower quit rates (p < 0.01). This association was not explained by demographic, socioeconomic, health, or addiction-related factors. While menthol use was associated with lower education and employment levels, demographic characteristics, physical or mental health, or addiction did not explain the effect of menthol on tobacco cessation. These findings suggest that the lower quit rates observed among menthol users cannot be attributed to any third factors assessed in this study. <b>Conclusions:</b><b> </b>Menthol tobacco use independently predicts lower quit rates, and the mechanisms behind this disparity remain unclear. The consistent findings across different intervention types highlight the need for further research to uncover the underlying pathways and to design targeted strategies to improve cessation outcomes for menthol users.

<b>Background: </b>Tobacco use remains disproportionately high among low-income and racial-ethnic minority populations. The CEASE program, with its self-help, hybrid/online, and in-person modalities, has demonstrated efficacy in promoting tobacco cessation. However, predictors of successful cessation among participants in these groups remain unclear. <b>Objective: </b>To identify baseline predictors of successful tobacco cessation among low-income and racial-ethnic minority participants in the CEASE program, with a focus on demographic, socioeconomic, behavioral, and psychosocial factors. <b>Methods: </b>Participants were allocated into three intervention arms: self-help, CEASE hybrid/online, and CEASE in-person. Baseline characteristics, including demographics (e.g., age, gender), socioeconomic status (e.g., education, employment), substance use profiles (e.g., cigarette packs per week, use of other tobacco products, menthol tobacco use), physical health (e.g., general health, number of cardiometabolic risk conditions), mental health (e.g., depressive symptoms, perceived stress), perceived social support, and nicotine dependence, were analyzed as potential predictors of cessation success. Multivariable logistic regression models were used to identify factors associated with successful quitting, controlling for the study arm. <b>Results:</b> In addition to the study arm, gender, baseline depression, cardiometabolic conditions, tobacco flavor, and the use of other tobacco products were significant predictors of quit success. Individuals receiving in-person interventions had significantly higher odds of quitting (AOR = 3.79, <i>p</i> < 0.05). Women were significantly less likely to quit compared to men (AOR = 0.24, <i>p</i> < 0.01). Participants with a greater number of cardiometabolic risk conditions were more likely to quit (AOR = 1.93, <i>p</i> < 0.05), while those with higher levels of depression had lower odds of quitting (AOR = 0.61, <i>p</i> < 0.05). Menthol tobacco users were also less likely to quit (AOR = 0.10, <i>p</i> < 0.05). Interestingly, individuals who used other forms of tobacco in addition to cigarettes had increased odds of quitting (AOR = 2.86, <i>p</i> < 0.05). No other factors, including demographic variables (e.g., age), socioeconomic status (e.g., education, marital status), substance use profiles (e.g., cigarette packs per week, NRT use), or nicotine dependence, were significant predictors of cessation success. <b>Conclusion:</b> Baseline self-reported anxiety/depression and depressive symptoms play a critical role in reducing the likelihood of successful tobacco cessation among low-income and racial-ethnic minority participants in the CEASE program. These findings underscore the importance of addressing mental health challenges as part of tobacco cessation interventions to enhance their efficacy. Future research should explore targeted strategies for integrating mental health support into cessation programs to improve outcomes for underserved populations.

<b>Background: </b>Recently specific interactions and crosslinks between the gut microbiota and the lungs have been recognized, particularly with regard to respiratory immune and anti-microbial reactions. This is often known as the “gut-lung axis” or “a common mucosal immunological system”.<b> Objective: </b>The aim of the current systematic review was to evaluate evidence, from published clinical trials and cohort studies, if probiotics may have an effect in improving and managing COVID-19 symptoms.<b> Materials and methods: </b>The available studies were searched through a comprehensive search of electronic databases that included PubMed, Science Direct, Scirus, ISI Web of Knowledge, Google Scholar and CENTRAL (Cochrane Central Register of Controlled Trials), using a combination of the following keywords: “COVID-19" OR<i> </i>"SARS-CoV-2" AND "Microbiota" OR "Probiotics” OR “Gut Lung Axis”. The literature was reviewed until August 31, 2022<b>. Results: </b>Only 3 studies were included. One of them evaluated the efficacy of probiotics in COVID-19 patients to obtain complete remission of all signs and symptoms. The clinical trial proves that probiotics have a significant effect on complete remission of all signs and symptoms of COVID-19 patients with statistical significant difference. Only one clinical trial out of the 3 included studies had evaluated the need for O2 therapy during the study between the probiotics and control groups, but without statistical significant difference<b>. </b>No statistical significant difference between the probiotics group and placebo group was observed regarding fatal prognosis during the only clinical trial that measured death as an outcome<b>. Conclusion: </b>We couldn’t judge on these results as they are insufficient data for pooling and meta-analysis. However, what we can say is “Most probably Probiotics have no role in treatment of COVID-19 infection”.

This essay explores how AI can enhance clinical research and, particularly, its pivotal role in the development of medical devices. A data-driven approach to medical device development that can streamline regulatory compliance and quality assurance is discussed. Methods that generate insights from pre-stage data and utilize it during development are detailed. The effectiveness of this approach in compliance audits, 510(k) submissions, and quality system audits - reducing time, effort, and risks is analyzed. The findings are illustrated with practical examples and takeaway recommendations. When reading a scientific article, how many times have you judged the quality of the research by looking at the methodology section? Artificial intelligence algorithms can be developed with the most robust and innovative technology, but if they are not properly validated, they will be worthless in the eyes of regulatory authorities. Conversely, outdated and simplistic models can still gain regulatory clearance if robustness is effectively demonstrated. For better or worse, ethics, economics, and robustness are often sacrificed in the constant government struggle to keep up with the technological edge of AI development. The slow crawl of lawmakers is constant in every field. Automating small tasks can save time and reduce risks when playing catch-up with a changing regulatory framework so the rest of the AI development can continue uninhibitedly. This dives into using FDA open data to collaborate with a food and drug law company and develop several bottom-up initiatives that supply knowledge needed for regulatory compliance and quality systems development. Methods that input pre-stage data and output actionable insights as models are provided. By sharing these resources and advice as academic researchers, efficiency in streamlining processes is maximized, thereby letting more time and resources be allocated to the actual development [1].

Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.

In Central Africa, mushrooms are critically important non-timber forest products (NTFPs), both nutritionally and economically. A strain of edible and medicinal lignicolous fungus, <i>Pleurotus</i> tuber-regium (Rumph. ex Fr.) Singer 1951 (strain 190212), isolated from tissue (sclerotia), on PDA medium, was tested on corn grain and sawdust seedling substrates and on palm oil male inflorescence (<i>Elaeis guineensis</i> Jacq.), ground corn (<i>Zea mays</i> L) stalks and grass (<i>Paspalum notatum</i> L) soaked for 24 hrs then drained for 24 hours, and unsoaked ground corn (<i>Zea mays</i> L) stalks. The highest mycelial growth rate recorded was about 0.9 cm on the PDA medium; 5.97 cm on the corn-based seedling medium and 11.95 cm on the sawdust-based seedling medium. Total mycelial invasion on the PDA medium was observed on day 10, day 14 on the corn-based seedling medium, and day 24 on the sawdust-based seedling medium. The onset of mycelial invasion was noticeable on day 3 of seeding for all treatments T<SUB>0</SUB> (control), T<SUB>1</SUB> (Final substrate based on soaked ground corn stalks), T<SUB>2</SUB> (Final substrate based on unsoaked ground corn stalks), and T<SUB>3</SUB> (Final substrate based on turf). Total invasion of mycelium was obtained at day 15 of incubation for treatments T<SUB>1</SUB> and T<SUB>2</SUB>, at day 18 for treatment T3 and at day 24 for treatment T0. The results obtained on treatments T<SUB>1</SUB> and T<SUB>2</SUB> respectively (14.95±3.12% and 15.65±1.06%) of the maize stalk substrate, lead us to believe that the strain 190212 of <i>Pleurotus tuber-regium</i> species used has adapted and requires an improvement of the medium with nitrogen-rich additives such as soybean meal. This could achieve the theoretical yield of 20% or more, according to which a substrate can be considered better in producing sporophores.