International journal of science academic research最新文献

{"title":"APPLICATION OF AI AND MACHINE LEARNING TO ANALYZE PROTEIN CONTENT IN U.S. COMMERCIAL BABY FOODS.","authors":"Malick T Yedjou, Kevine M Makoudjou, Ingrid K Tchakoua, Solange S Tchounwou, Clement G Yedjou","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Proteins are essential macronutrients that support the growth, development, and maintenance of tissues in children. Nutrient requirements vary with age, weight, and physiological needs, making age-specific dietary planning critical. Adequate protein intake promotes both physical growth and cognitive development, while diverse sources such as lean meats, dairy, legumes, and nuts help meet varying nutritional needs and encourage lifelong healthy eating habits. This study analyzed a nutritional dataset of 244 baby foods using artificial intelligence (AI) and machine learning to assess protein content, categorizing items into three groups based on protein content: low (0.0-5.9 g/day), moderate (6.0-10.9 g/day), and high (11.0-15.0 g/day). The majority (n = 202) fell into the low-protein range, followed by 22 in the moderate range and 20 in the high range. Age-specific protein requirements, expressed in grams per kilogram of body weight (g/kg), were assessed for four age groups: 0-6 months (1.52 g/kg; 12.6-15.8 g/day; 5.5-6.0 kg), 7-9 months (1.20 g/kg; 9.0-10.2 g/day; 7.5-8.5 kg), 10-12 months (1.00 g/kg; 8.5-9.5 g/day; 8.5-9.5 kg), and 1-3 years (1.05 g/kg; 12.6-15.8 g/day; 12.0-15.0 kg). Low-protein foods may be insufficient for infants with reduced breastmilk or formula intake, while high-protein foods often rich in meat, dairy, or fortified products can help meet upper-range requirements. These findings underscore the need for careful alignment of complementary food protein levels with age-specific nutritional guidelines to support optimal growth and development in early childhood.</p>","PeriodicalId":93454,"journal":{"name":"International journal of science academic research","volume":"6 8","pages":"10485-10487"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12435971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NEED FOR INTENSIVE CARE UNIT ADMISSION, MORTALITY RATE AND ASSOCIATED FACTORS USING PAEDIATRIC ADVANCED WARNING SCORE (PAWS) ON ARRIVAL AT HOSPITAL: HOSPITAL BASED PROSPECTIVE STUDY.","authors":"Matei Mselle, Bladina Mmbaga, Aisa Shayo, Jeffrey Perlman, Esther Majaliwa, Grace Kinabo","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Pediatric intensive care units (PICUs) are special areas of service to save the lives of children with life-threatening conditions/or critically ill children. Pediatric early warning scores can accurately identify up to 85% of children who will experience deterioration as early as 11 hours before, with specificity up to 95%. Delayed ICU admission has been reported to be associated with increase in mortality.</p><p><strong>Objective: </strong>To determine the need for ICU admission, mortality rate and associated factors using pediatric advanced warning score (PAWS) on arrival at Hospital.</p><p><strong>Methodology: </strong>This was a prospective observational cohort study, conducted among pediatric patients aged 1month-14yrs arriving through Emergency department at Kilimanjaro Christian medical center. Initial PAW score was done by trained senior resident then grouped into two groups critical score of ≥4, and none critical <4. Each patient was followed up until discharge where the outcome was recorded and matched with the PAW scored.Data were analyzed through SPSS version 25; logistic and poison regression model were used to measure the association and comparison between groups was done by non-parametric 2 independent sample.</p><p><strong>Results: </strong>310 were enrolled during study period,35.4%(n=110) were admitted in ICU. The median age was 29 and 21 months for all participants and for those admitted to ICU, respectively. 80% of ICU admission had critical PAW score on arrival, with significant association (p<0.001).There was statistically significant difference between critical score and non-criticalin need for admission Mann-Whitney test (p<0.001). Overall ICU mortality rate was 52.7%(n=58). Those who died, 86.2%(n=50) had critical score on admission, and there was statistically significance difference between critical score and non-critical in terms of mortality, Mann-Whitney test (p<0.001). Otherwise, those with critical PAW score were likely to stay longer in ICU.</p><p><strong>Conclusion: </strong>PAW score right on arrival at emergency can identify up to 80% of pediatric patient requiring ICU admission, thus maybe incorporated in ICU admission criteria in low and middle income countries. Critical PAW score on arrival have increased chance of death and staying longer in ICU.</p>","PeriodicalId":93454,"journal":{"name":"International journal of science academic research","volume":"6 5","pages":"9925-9935"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification.","authors":"Clement G Yedjou,&nbsp;Solange S Tchounwou,&nbsp;Richard A Aló,&nbsp;Rashid Elhag,&nbsp;BereKet Mochona,&nbsp;Lekan Latinwo","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Breast cancer continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in females worldwide despite remarkable progress in early diagnosis, screening, and patient management. All breast lesions are not malignant, and all the benign lesions do not progress to cancer. However, the accuracy of diagnosis can be increased by a combination or preoperative tests such as physical examination, mammography, fine-needle aspiration cytology, and core needle biopsy. Despite some limitations, these procedures are more accurate, reliable, and acceptable, when compared with a single adopted diagnostic procedure. Recent studies have shown that breast cancer can be accurately predicted and diagnosed using machine learning (ML) technology. The objective of this study was to explore the application of ML approaches to classify breast cancer based on feature values generated from a digitized image of a fine-needle aspiration (FNA) of a breast mass. To achieve this objective, we used ML algorithms, collected a scientific dataset of 569 breast cancer patients from Kaggle (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data), analyze and interpreted the data based on ten real-valued features of a breast mass FNA including the radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. Among the 569 patients tested, 63% were diagnosed with benign breast cancer and 37% were diagnosed with malignant breast cancer. Benign tumors grow slowly and do not spread while malignant tumors grow rapidly and spread to other parts of the body.</p>","PeriodicalId":93454,"journal":{"name":"International journal of science academic research","volume":"2 1","pages":"3081-3086"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8612371/pdf/nihms-1752600.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39927568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}