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The quest for tolerant plants with excellent phytoremediation potential is a stark reality. Additionally, the use of dumpsite soil for growing vegetables and ornamental plants is relatively a common practice by farmers in urban cities across Nigeria. Hinged on these concerns, this study was poised to evaluate and compare the bioaccumulation factor (BAF) and translocation factor (TF) of Zn, Cu, Cr, Pb and Ni in two common vegetable species (Spinacea oleraceae and Celosia argentea) grown on the experimented Olusosun dumpsite soil and the undisturbed sandy loam top soils of the University of Lagos biological garden. The latter represents the control group. This study observed a considerable increase in metallic concentrations in the vegetable species grown on Olusosun dumpsite soil in comparison to the control. The level of Zn, Cu and Ni (except for Pb and Cr) were within the FAO/WHO permissible limit. Both vegetable species experimented on Olusosun dumpsite soil have BAFs Pb>Ni>Cu>Cr for Celosia argentia and Zn>Cu>Cr>Pb>Ni for Spinacea oleraceae. The use of dumpsite soil for growing vegetables have increased the levels of Pb, Cu, Zn, Ni, Cd, and Cr in their different parts and may further pose a serious threat to human health in the future if such practice continues.

Abstract Background The Ghana School Feeding Programme (GSFP) provides public primary school pupils with a free daily meal. Each meal is expected to follow set menus, providing 30% of children’s’ (6-12 years) energy requirements. This study assessed the nutritional quality and diversity of planned and provided GSFP meals, engaging school caterers to identify how meal quality in the Greater Accra Region could be enhanced. Methods A cross-sectional mixed methods study design was used. Multistage sampling was used to select 129 schools implementing the GSFP in six districts of the Greater Accra Region. GSFP district menus were collected as well as a one-week school caterer recall of provided school meals. The meal served on the day of data collection was recorded and photographed. Nutritional quality was evaluated based on nutrient profiling methods: energy density (low225kcal/100g) and nutrient density (low10%). Meal diversity was assessed by a simple count composed of 5 food groups: cereals, pulses/nuts/seeds, animal-source, vegetables and fruits. Caterers’ views on programme facilitators and barriers were also explored. Results Planned menus included 14-20 weekly options, composed of eight minimally processed traditional dishes. All meals, except white rice, had a high nutrient density/100g. Energy density was varied (low, n=2; medium, n=2; high, n=4). Meals included only 2/5 or 3/5 food groups, mainly starchy staples, pulses/nuts/seeds, and sometimes vegetables. Fruit was never reported. About half of caterers (51.1%) reported deviating from the planned menus: 11.7% served alternative meals, with some including animal-sourced food (17.0%), and 39.4% repeated meals provided during the week, often based on starchy staples, influencing overall nutritional quality. Most caterers reported food item cost and lack of food purchase guidelines as barriers to providing school meals, while food safety training and guidelines for food preparation were facilitators. Conclusions While school meals are composed of minimally processed, nutrient dense, local foods, there are notable gaps in meal diversity and compliance, as reflected in provided meals. Caterer compliance to planned menus varied greatly, reflecting recent food price inflation. Upwardly adjusting the current meal allocation of 1.2 cedis (0.22USD) per child per day could enhance access to more affordable, nutritious and diverse foods in school meals.

Abstract Introduction : Nutrition is a very important element of a comprehensive care for people living with HIV/AIDS (PLHIV), especially in resource-constrained settings where malnutrition and food insecurity are common. Dietary diversity is a useful indication of nutritional adequacy (diet quality) in people of all ages. An optimally diverse diet strengthens the body’s immune system. Objective This study aimed to assess diet quality and its associated factors among PLHIV. Methods A facility-based cross-sectional study design was employed to select 440 PLHIV from two hospitals in the Eastern Region of Ghana. Dietary intakes were determined using 24-hour recall. A stadiometer and bioimpedance analysis machine were used to obtain anthropometric and body composition data. Diet quality was assessed using FAO’s individual dietary diversity score (IDDS) as a proxy. SPSS version 20 was used for analysis. Odds ratios and ordinal logistic regression were used to identify factors associated with diet quality among the PLHIV. P-value was set at 0.05. Results Most of the PLHIV (73%) consumed from ‘Starchy staple” food group. Less than 20% of the study sample consumed ‘Fruits’ and ‘Vegetables’ (17% and 14% respectively) a day before the survey. The mean IDDS was 4.11 (SD = 1.29). Overall, most of the PLHIV (56%) had medium IDDS which is equivalent to “diet needing improvement’, 14% had higher IDDS (good diet), whiles about 31% of the participants actually had poor diet (lower IDDS). Associated factors of diet quality were age (AOR = 0.966: 95%CI: 0.936–0.997: p = 0.031), married (AOR = 4.634: 95%CI: 1.329–16.157: p = 0.0016), separated (AOR = 0.0203: 95%CI: .036–0.994: p = 0.049), and daily meal frequency (AOR = 0.441: 95%CI: .478–1.948: p = 0.020). Overall, the model accounts for about 20% of the variation in diet quality of the participants (pseudo-R square = 0.196). Conclusion This study demonstrates that most of the PLHIV did not consume good diet which may have an implication on their immune system, which is already under attack by HIV, and probably emerging infections. Age, marital status, and meal frequency were the variables that predicted diet quality among the study participants.

IntroductionVeterinary antibiotics are essential for maintaining animal health and welfare, however, small-scale farmers in Malawi face challenges in accessing them due to limited availability, affordability, and long distances to rural drug retailers.MethodsThis study mapped the veterinary antibiotic distribution chain, examined the governance structure of the chain, and analyzed access and usage practices among stakeholders in Malawi. Data were collected through focus group discussions (n = 15), key informant interviews (n = 6) and individual interviews (n = 189).ResultsThe key stakeholders identified included regulators, local pharmaceutical manufacturers, wholesalers, veterinary clinics, veterinary retail shops, animal health practitioners, and farmers. The distribution of veterinary antibiotics was characterized by both formal and informal pathways for importing and distributing veterinary medicines. Additionally, there were issues with antibiotic mishandling such as improper storage on open shelves in direct sunlight and disposal in pit latrines. There was a marked lack of proper antibiotic dispensation training among veterinary medicine shop attendants, and in terms of regulation, there were gaps in coordination and overlapping mandates among regulatory authorities hindering effective regulation.DiscussionRegulatory agencies need to strengthen oversight of veterinary antibiotics, conduct trainings on antibiotic stewardship with various stakeholders, and enhance public-private partnerships to better manage the informal pathways for importing and distributing veterinary medicines. This multi-sectoral approach aims to ensure responsible use and improve the pharmacovigilance of veterinary antibiotics.

Promoting all efforts that centres around environmental sustainability is a collective responsibility. This paper recognizes the axiom that embracing green computing principles across all sectors, not just in corporations, leads to low-carbon development strategies and contributes positively to our planet. Hinged on this perspective, this study was poised to succinctly identify what drives the adoption of green computing policies, justify them, and recommend strategic policies based on the existing experience of other developed countries. The present study employed convenience sampling, a widely used non-probability sampling method, where participants are selected based on their availability and willingness to participate in the study. Limitations that hinder the adoption of green computing policies in some Nigerian organizations were examined. Primary data was collected from 52 participants, specifically top managers on the awareness, adoption, or practice of green computing policies in their affiliated organizations. Most respondents claimed awareness of green computing policies, while the use of power management features was considered the most adopted green initiative. However, when respondents were asked whether their organizations were doing enough to reduce their carbon footprint and operational cost through green computing initiatives, most respondents (61.5%) claimed their organization was not doing enough in this regard. It is important to note that this study considered several factors that are critical for the adoption of green computing, namely knowledge transfer, financing, availability of basic infrastructure (energy supply), presence of investors and the increasing number of recycling centres. Additionally, this paper reasoned that achieving a climate-resilient society, particularly through the adoption of green computing principles, necessitates ongoing and deliberate efforts focused on public awareness, green investment, and stringent corporate green policy guidelines, with a focused training approach for every member of the organization.

IntroductionDigital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector.MethodsThis article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation.ResultsA short case study is presented to demonstrate the viability of the proposed simulation architecture.DiscussionThe proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians’ bedside decision-making.

Objective: To describe the characteristics of COVID-19 patients with pneumothorax and pneumomediastinum (PTX/PM) and their association with patient outcomes. Patients and methods: Adults admitted to five Mayo Clinic hospitals with COVID-19 between 03/2020–01/2022 were evaluated. PTX/PM was defined by imaging. Descriptive analyses and a matched (age, sex, admission month, COVID-19 severity) cohort comparison was performed. Hospital mortality, length of stay (LOS), and predisposing factors were assessed. Results: Among 6663 patients, 197 had PTX/PM (3 %) (75 PM, 40 PTX, 82 both). The median age was 59, with 71 % males. Exposure to invasive and non-invasive mechanical ventilation and high-flow nasal cannula before PTX/PM were 42 %, 17 %, and 20 %, respectively. Among isolated PTX and PM/PTX patients 70 % and 53.7 % underwent an intervention, respectively, while 96 % of the PM-only group was followed conservatively.A total of 171 patients with PTX/PM were compared to 171 matched controls. PTX/PM patients had more underlying lung disease (40.9 vs. 23.4 %, p

Objective: To use SMOTE to enhance class balance and compare the performance of different classification methods before and after applying SMOTE. Methods: The study used a dataset from Kaggle. Consisted of several health-related features linked to obesity prediction. Checking for class imbalance within the dataset affected initial model performance. SMOTE was applied to synthetically increase the representation of minority classes, reducing the class imbalance. It was conducted in two stages: 1. Training and testing the classification algorithms before applying SMOTE. 2. Training and testing the same models after applying SMOTE to enhance class balance. The performance of all models was evaluated based on metrics before and after the SMOTE application. Results: Models Logistic Regression and Naive Bayes struggled with low sensitivity and specificity, and KNN (k=5) showed poor specificity. Significant improvements were observed across all models after applying SMOTE. Logistic Regression, despite a decrease in accuracy(-8.8), sensitivity and specificity increased substantially(+56.7%), with balanced accuracy improving(+16.6%). Naive Bayes saw a modest accuracy increase(+2.3%), with sensitivity and specificity improving(+47.9%). The KNN classifier exhibited a transformative enhancement with sensitivity and specificity increasing(+96.0%) and balanced accuracy(+28.3%). Deep Learning showed a significant increase in sensitivity (+69.8%), balanced accuracy (+29.4%), and an improvement in precision and F1-score despite a slight decrease in specificity(-10.9%). Conclusion: The results demonstrate that while there might be slight trade-offs, the overall improvements in key metrics such as sensitivity, specificity, balanced accuracy, precision, and F1-score affirm the utility of SMOTE in enhancing model performance for imbalanced datasets

Street foods are one of the highest contributors to foodborne illness in most developing economies around the world. In Ghana, diarrhoeal diseases, which are usually food or waterborne, are among the top ten causes of death. Most street food safety risks are avoidable when all food safety regulations are complied with. This paper identified and examined the barriers to the implementation of street food safety regulations in Ghana. A qualitative research approach was adopted by collecting data from nine focus group discussion sessions involving a total of 94 participants and five key informant interviews. The research uncovered three broad but interconnected categories of challenges to ensuring and sustaining street food safety: street vendor anonymity as a central challenge; poor trust in the regulatory system as root challenges; and vendor practices that risk the safety of street foods as consequential challenges. These findings have tangible policy implications. To best serve their purpose, policymakers need to understand these food safety challenges and ensure that food safety policies are responsive to the challenges.

Abstract Numerous research methodologies have been used to examine food environments. Existing reviews synthesizing food environment measures have examined a limited number of domains or settings and none have specifically targeted Canada. This rapid review aimed to 1) map research methodologies and measures that have been used to assess food environments; 2) examine what food environment dimensions and equity related-factors have been assessed; and 3) identify research gaps and priorities to guide future research. A systematic search of primary articles evaluating the Canadian food environment in a real-world setting was conducted. Publications in English or French published in peer-reviewed journals between January 1 2010 and June 17 2021 and indexed in Web of Science, CAB Abstracts and Ovid MEDLINE were considered. The search strategy adapted an internationally-adopted food environment monitoring framework covering 7 domains (Food Marketing; Labelling; Prices; Provision; Composition; Retail; and Trade and Investment). The final sample included 220 articles. Overall, Trade and Investment (1%, n = 2), Labelling (7%, n = 15) and, to a lesser extent, Prices (14%, n = 30) were the least studied domains in Canada. Among Provision articles, healthcare (2%, n = 1) settings were underrepresented compared to school (67%, n = 28) and recreation and sport (24%, n = 10) settings, as was the food service industry (14%, n = 6) compared to grocery stores (86%, n = 36) in the Composition domain. The study identified a vast selection of measures employed in Canada overall and within single domains. Equity-related factors were only examined in half of articles (n = 108), mostly related to Retail (n = 81). A number of gaps remain that prevent a holistic and systems-level analysis of food environments in Canada. As Canada continues to implement policies to improve the quality of food environments in order to improve dietary patterns, targeted research to address identified gaps and harmonize methods across studies will help evaluate policy impact over time.

Health services at the community level are organized and financed in such a way that men need access but encounter barriers to care such as poor service design, lack of insurance, and the absence of health literacy. Community health delivery systems may not be appropriate, effective, fit, or able to meet the needs they are charged to fill. Community-based health services, including health departments, are underfunded, understaffed, and unable to carry out their mission in a way that protects the health of the community. The current design for funding and delivering health care services excludes poor men, particularly men of color. Improving the health of men requires modifications in the way health care is financed, delivered, and managed.

Health services at the community level are organized and financed in such a way that men need access but encounter barriers to care such as poor service design, lack of insurance, and the absence of health literacy. Community health delivery systems may not be appropriate, effective, fit, or able to meet the needs they are charged to fill. Community-based health services, including health departments, are underfunded, understaffed, and unable to carry out their mission in a way that protects the health of the community. The current design for funding and delivering health care services excludes poor men, particularly men of color. Improving the health of men requires modifications in the way health care is financed, delivered, and managed.

Current prognostic tools for pneumonia predominantly focus on mortality, often neglecting other crucial outcomes such as the need for advanced respiratory support. The objective of this study was to develop and validate a tool that predicts the early risk of non-occurrence of respiratory deterioration or mortality. We conducted a single-center, retrospective cohort study involving hospitalized adult patients with community-acquired pneumonia (CAP) and acute hypoxic respiratory failure from January 2009 to December 2019 (n = 4379). We employed the gradient boosting machine (GBM) learning to create a model that estimates the likelihood of patients requiring advanced respiratory support (high flow nasal cannula [HFNC], non-invasive mechanical ventilation [NIMV], and invasive mechanical ventilation [IMV]) or facing mortality during hospitalization. This model utilized readily available data including demographic, physiologic, and laboratory data, sourced from electronic health records and obtained within the first six hours of admission. Out of the cohort, 890 patients (25.2%) either required advanced respiratory support or died during their hospital stay. Our predictive model displayed superior discrimination and higher sensitivity (cross-validation C-statistic = 0.71; specificity = 0.56; sensitivity = 0.72) compared to the pneumonia severity index (PSI) (C-statistic = 0.65; specificity = 0.91; sensitivity = 0.24; P value < 0.001), while maintaining a negative predictive value (NPV) of approximately 0.85. These data demonstrate that our machine learning model predicted the non-occurrence of respiratory deterioration or mortality among hospitalized CAP patients more accurately than the PSI. The enhanced sensitivity of this model holds potential for reliably excluding low-risk patients from pneumonia clinical trials.