The Potential Health Benefits of Papaya Seeds

Papaya (Carica papaya) is a tropical fruit having commercial importance because of its high nutritive and medicinal value. Papaya seeds are traditionally used by human beings from centuries to heal many illnesses. Papaya seeds contains antioxidants and antimicrobial virtues which gives protection against harmful free radicals and reduce rate of cancer and heart disease. Papaya seeds contain a variety of essential macro and micronutrients. The papaya seeds contain large amounts of nutrients, including fibre which is effective for combating constipation and other digestive problems. Seeds can be used to treat health complications such as urinary tract infection, candidiasis and sepsis. Papaya seeds are sometimes used in salads and can even be used as a substitute for black pepper. Products developed from papaya seeds are considered to be nutritionally rich with diverse sources of all nutrients. Papaya seeds have the potential to produce oil with nutritional and functional properties highly similar to olive oil. The papaya seed, and products may be useful for bio-fuel, medicinal and industrial purposes. Therefore, Papaya seeds are usually thrown away but their proper utilization can lead to the development of novel drug in medicine and considered as safe for consumption. ‘In this review subtopics covered are nutritional and Phytochemicals composition, antioxidant effect, antibacterial Activity, anthelminthic effect, Phytochemicals with Anticancer activity, Lycopene and isothiocyanate applications in papaya, papaya seed products and in vivo animal studies.’

Exploring Barriers to Food Security Among Immigrants: A Critical Role for Public Health Nutrition

Upon moving to a new country and new food environment, 2 important public health issues may be experienced by immigrants as they adapt to their new country of residence, namely a higher prevalence of food insecurity and/or a decline in overall health over time postimmigration. Therefore, improving the food environment experienced by new migrants may be an effective strategy to reduce long-term health complications and improve well-being postimmigration. The aim of this paper is to discuss the potential barriers experienced by new immigrants in the access, availability, and utilization of familiar culturally appropriate foods and the subsequent impact on their food security status. Culturally appropriate foods are foods commonly consumed as part of cultural food traditions and are often staples within the diet; however, limited availability of and/or access to these foods can reduce food security. By understanding the barriers to food security and challenges that may be faced by immigrants and refugees, dietitians will be better equipped to assist these individuals in accessing culturally familiar foods and improve quality of life. In this capacity, dietitians can play a critical public health nutrition role by serving as a conduit for new immigrants to access community resources and navigate a new food environment.

Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair

Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advantage of the increased range of motion offered by the dynamic chairs. Building a system that identifies users’ postures in real time, as well as forecasts the next few postures, can bring awareness to the sitting behavior of each user. In this study, machine learning algorithms have been implemented to automatically classify users’ postures and forecast their next motions. The random forest, gradient decision tree, and support vector machine algorithms were used to classify postures. The evaluation of the trained classifiers indicated that they could successfully identify users’ postures with an accuracy above 90%. The algorithm can provide users with an accurate report of their sitting habits. A 1D-convolutional-LSTM network has also been implemented to forecast users’ future postures based on their previous motions, the model can forecast a user’s motions with high accuracy (97%). The ability of the algorithm to forecast future postures could be used to suggest alternative postures as needed.

Exoskeleton Training and Trans-Spinal Stimulation for Physical Activity Enhancement After Spinal Cord Injury (EXTra-SCI): An Exploratory Study

After spinal cord injury (SCI) physical activity levels decrease drastically, leading to numerous secondary health complications. Exoskeleton-assisted walking (EAW) may be one way to improve physical activity for adults with SCI and potentially alleviate secondary health complications. The effects of EAW may be limited, however, since exoskeletons induce passive movement for users who cannot volitionally contribute to walking. Trans-spinal stimulation (TSS) has shown the potential to enable those with even the most severe SCI to actively contribute to movements during EAW. To explore the effects of EAW training on improving secondary health complications in persons with SCI, participants with chronic (n = 8) were enrolled in an EAW program 2–3 times per week for 12 weeks. Anthropometrics (seated and supine waist and abdominal circumferences (WC and AC), body composition assessment (dual exposure x-ray absorptiometry-derived body fat percent, lean mass and total mass for the total body, legs, and trunk), and peak oxygen consumption (VO2 during a 6-minute walk test [6MWT]) were assessed before and after 12 weeks of EAW training. A subset of participants (n = 3) completed EAW training with concurrent TSS, and neuromuscular activity of locomotor muscles was assessed during a 10-m walk test (10MWT) with and without TSS following 12 weeks of EAW training. Upon completion of 12 weeks of training, reductions from baseline (BL) were found in seated WC (−2.2%, P = 0.036), seated AC (−2.9%, P = 0.05), and supine AC (−3.9%, P = 0.017). Percent fat was also reduced from BL for the total body (−1.4%, P = 0.018), leg (−1.3%, P = 0.018), and trunk (−2%, P = 0.036) regions. No effects were found for peak VO2. The addition of TSS for three individuals yielded individualized responses but generally increased knee extensor activity during EAW. Two of three participants who received TSS were also able to initiate more steps without additional assistance from the exoskeleton during a 10MWT. In summary, 12 weeks of EAW training significantly attenuated markers of obesity relevant to cardiometabolic health in eight men with chronic SCI. Changes in VO2 and neuromuscular activity with vs. without TSS were highly individualized and yielded no overall group effects.

Water extract of Ferula lehmanni Boiss. prevents high-fat diet-induced overweight and liver injury by modulating intestinal microbiota in mice

Obesity, often accompanied by hepatic steatosis, has been associated with an increased risk of health complications such as fatty liver disease and certain cancers. Ferula lehmannii Boiss. a food and...

The Modeling of the Capacity of the Moroccan Healthcare System in the Context of COVID-19

The outbreak of SARS-Cov2 in China and its subsequent spread has caused a global pandemic. The authors conducted a simple susceptible-infected (SI) model of the spread of COVID-19 in Moroccan population. The model is based on combining the average contact rate (µmax) extracted from the early exponential phase of the outbreak with a logistic simulation over time. Interestingly, this modeling approach showed a perfect fit with a strong correlation between real confirmed and estimated cases when calibrated on the Chinese declining outbreak. Subsequently, the model was applied for studying the ongoing COVID-19 outbreak in Morocco to determine the needed time for reaching 10,000 confirmed cases whose 15% (1,500) are at risk of developing health complications requiring patient care in hospitals. The latter total capacity does not exceed 1,640 beds according to the authorities. Incorporating these parameters in the logistic model, they predicted that the Moroccan healthcare system will be at 27%, 50%, 76%, and 90% of saturation on April 11, 16, 23, and May 4, 2020, respectively.

Automatic Detection and Classification of Diabetes Using Artificial Intelligence

Diabetes is characterized as a chronic disease that may cause many health complications. Artificial intelligence techniques are adopted diagnose diabetes more accurately. This paper presents an artificial intelligence technique for diabetes diagnosis. Efficacy of the technique is evaluated using diabetes database. Experimental results show that the back propagation neural network algorithm yields the highest classification rate compared to k-nearest neighbourhood classifier. Additionally, the back propagation neural network provides error with the highest area under curve of 90 %.

Applying Machine Learning Algorithms to Predict Endometriosis Onset

Endometriosis is a commonly occurring progressive gynecological disorder, in which tissues similar to the lining of the uterus grow on other parts of the female body, including ovaries, fallopian tubes, and bowel. It is one of the primary causes of pelvic discomfort and fertility challenges in women. The actual cause of the endometriosis is still undetermined. As a result, the objective of the chapter is to identify the drivers of endometriosis’ diagnoses via leveraging selected advanced machine learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a greater extent if a likelihood of endometriosis could be predicted well in advance. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Several direct and indirect features were identified as important to an accurate prediction of the condition onset, including selected diagnosis and procedure codes. Creating analytical tools based on the model results that could be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers might aid the objective of improving the diagnostic processes and result in a timely and precise diagnosis, ultimately increasing patient care and quality of life.

Social Grievances and Drugs Rummage-Sale as Performance Enhancement Attitude of Mature Athletes – An Inferential Approach

Objective: The central objective of the present study was to determine the relationship between social grievances and drugs rummage-sale (performance enhancement attitude) of the mature athletes of Pakistan and examine their difference as well. Methodology: The targeted population was mature national athletes belonging to national sports departments. The respondents were recruited through snowball and convenient sampling methods. Total 105 questionnaires were posted at the given addresses of athletes. However, 73 athletes (69.5%) returned the questionnaires. For achieving study objectives, quantitative approach was adopted wherein several statistical tests were performed including descriptive statistics, Pearson’s correlation, and t-test analyses and implemented to draw the required study findings. Results: The findings revealed that a strong association exists between social grievances and drugs rummage use. Therefore, the relationship was considered positive and highly significant among the constructs. On the other hand, the findings revealed no difference between social grievances and drugs rummage-use. The findings revealed that social grievance and drug rummage-sale positively contribute to enhance the sports performance of athletes. Conclusion: It was concluded that athletes use drugs for several purposes such as enhancing performance, competing for highest sports level, gaining fame, and jobs opportunities. However, with these benefits, the athletes fell in diverse health complications as a side effect of drugs being taken for above said purposes.