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All Journal African Multidisciplinary Journal of Sciences and Artificial Intelligence African Journal of Biochemistry and Molecular Biology Research Open Access DRIVERset Kwaghe International Journal of Sciences and Technology Open Access DRIVERset African Journal of Clinical Medicine and Pharmacy Research African Journal of Medicine, Surgery and Public Health Research
Umaru, Hauwa A.
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences

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Journal : Open Access DRIVERset

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Chronic Disease Management: Integrating Occupational Risk Evaluation with Predictive Prevention and Diagnostics Umaru, Isaac John; Asare, Solomon O.; Danjuma, Tyem Lawal; Eno, Gomba Victoria; Akem, Ingwu Joseph; Chukwudi, Eluu Emmanuel; Salman, Julius Ishaya; Umaru, Hauwa A.; Ahmed, Maryam Usman
Kwaghe International Journal of Sciences and Technology Vol 3 No 1 (2026): Kwaghe International Journal of Sciences and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijst.v3i1.9304

Abstract

Occupational health has become an increasingly important dimension of public health, particularly in efforts to prevent chronic disease; however, the application of artificial intelligence (AI) in this area is constrained by the lack of standardized occupational exposure metrics, limited representation of diverse work environments, and fragmented data systems. This study aimed to evaluate the impact of occupational exposure variables on chronic disease risk prediction, assess the performance of Random Forest, XGBoost, and Deep Neural Network (DNN) models across workplace contexts, and propose a framework for interoperable platforms that integrate health and occupational data to strengthen predictive analytics and early diagnostics. Using a cross-sectional dataset of 5,000 workers from the manufacturing, agriculture, healthcare, and service sectors, the study analyzed demographic characteristics, clinical biomarkers, occupational exposure logs, and psychosocial assessments. Model performance was evaluated using ROC-AUC, precision, recall, and F1-score, while feature importance analysis quantified the contribution of occupational variables; in addition, a prototype interoperable platform was developed to demonstrate real-time integration between electronic health records and workplace monitoring systems. The findings showed that the DNN model outperformed the other algorithms, achieving a ROC-AUC of 0.89, precision of 0.85, recall of 0.88, and F1-score of 0.86. Occupational exposure variables contributed 27% to predictive power, with chemical exposure and psychosocial stressors showing the strongest associations with chronic disease markers. Among high-risk individuals, 54% were identified with subclinical conditions, including elevated C-reactive protein and HbA1c levels, while personalized interventions based on model outputs reduced risk scores by 22% and improved biometric indicators. The interoperable platform also successfully synchronized health and exposure data, enabling real-time analytics and targeted alerts. These findings demonstrate that integrating standardized occupational exposure metrics with interoperable data platforms substantially enhances the accuracy and practical utility of AI-driven chronic disease prediction, while supporting more equitable and proactive occupational health surveillance across diverse industries.
The Legalization of Abortion and Its Influence on Youth Sexual Behavior and the Catholic Church: A Case Study of Adamawa State Umaru, Isaac John; Akem, Ingwu Joseph; Asare, Solomon O.; Danjuma, Tyem Lawal; Yeboah, Genevieve A.; Salman, Julius Ishaya; Umaru, Hauwa A.; Ahmed, Maryam Usman
Kwaghe International Journal of Sciences and Technology Vol 3 No 1 (2026): Kwaghe International Journal of Sciences and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijst.v3i1.9401

Abstract

Although debates on abortion reform have intensified in Nigeria, limited attention has been paid to how partial legalization shapes youth perspectives and sexual and reproductive behaviors within specific sociocultural and religious contexts. This study examines how the limited legalization of abortion influences youth attitudes and behaviors in Adamawa State, with particular attention to demographic, cultural, and institutional factors, as well as the Catholic Church’s response to these changes. A qualitative approach was employed through interviews with health workers, clergy, and youths aged 18–25 from urban and rural areas of Adamawa State. Supplementary data were obtained from community forums, church outreach programs, and health service records, and the data were analyzed thematically to identify patterns in attitudes, behaviors, and institutional responses. The findings indicate a marked urban–rural divide, with urban youths reporting higher sexual activity, greater contraceptive use, and stronger support for reproductive autonomy. The study also found that the Catholic Church’s moral campaigns have had limited influence, particularly among urban youths who perceive religious teachings as less relevant to contemporary realities. In contrast, health workers were viewed as more trusted sources of guidance, while peers and media played a substantial role in shaping youth behavior. The study concludes that the evolving legal and cultural landscape in Adamawa State reflects a broader generational shift toward autonomy and informed decision-making among youths. These findings contribute to understanding the changing relationship between legal reform, religious influence, and youth reproductive behavior, and suggest that more balanced strategies integrating moral guidance, comprehensive education, and supportive policies are needed to promote healthy youth development in a changing society.
Impact of Distinct Carbon Substrates on the Proliferation of Antimicrobial-Producing Microbes Umaru, Isaac John; Danjuma, Tyem Lawal; Akem, Ingwu Joseph; Salman, Julius Ishaya; Ahmed, Maryam Usman; Umaru, Hauwa A.
Kwaghe International Journal of Sciences and Technology Vol 3 No 2 (2026): Kwaghe International Journal of Sciences and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijst.v3i2.9474

Abstract

Microbial production of antimicrobial compounds remains a fundamental area of biotechnology and pharmaceutical development, and its efficiency is strongly influenced by the carbon source available in the growth medium, which functions not only as an energy substrate but also as a regulator of microbial metabolism and secondary metabolite synthesis. This study aimed to evaluate the effects of five carbon sources—glucose, lactose, sucrose, starch, and glycerol—on microbial biomass yield, strain-specific growth preferences, metabolic compatibility, antimicrobial potency, inhibition zones, and bioactive metabolite production. Seven microbial strains, including Streptomyces, Bacillus, Pseudomonas, Actinomyces, and Clostridium, were cultured in media supplemented with each carbon source. Biomass yield was measured gravimetrically, growth rate indices were calculated on a scale of 0–10, antimicrobial potency was assessed using zone of inhibition assays against S. aureus, E. coli, P. aeruginosa, and K. pneumoniae, and metabolite yield was quantified in mg/L using spectrophotometric analysis. All experiments were conducted in triplicate. The findings showed that glucose produced the highest biomass yield and growth rates across all strains, with Bacillus and Pseudomonas each scoring 10. Lactose demonstrated selective effectiveness, particularly for Streptomyces, whereas sucrose supported moderate growth and selective antimicrobial activity. In contrast, starch and glycerol consistently resulted in low biomass production and minimal antimicrobial potency. The zone of inhibition results further confirmed that glucose and lactose were the most effective substrates, with inhibition zones exceeding 20 mm. Similarly, metabolite yield was highest with glucose (120 ± 5.4 mg/L) and lactose (115 ± 4.9 mg/L), while glycerol produced the lowest yield (30 ± 1.5 mg/L). The study concludes that carbon source selection plays a critical role in microbial proliferation and antimicrobial compound production, with glucose and lactose emerging as the most suitable substrates for broad-spectrum antimicrobial activity and high metabolite yield. These findings contribute practical evidence for optimizing fermentation strategies according to microbial metabolic profiles to enhance antimicrobial synthesis.
Co-Authors Ahmed, Fasihuddin Badruddin Ahmed, Maayam Usman Ahmed, Maryam Usman Akem, Ingwu Joseph Akpan, Usenobong Morgan Akumia, Prince Osei Amoah, Kwabena Owusu Asare, Solomon O. Ayi, Atsu Chukwudi, Eluu Emmanuel Danjuma, Tyem Lawal Eno, Gomba Victoria Essien, Inemesit Samuel Hassan, George Okra Jack, Precious Chioma Omanchi, Emmanuel Olofu Salman, Julius Ishaya Shadrach, Katchin Evelyn Tony-Enwin, Evelyn Ovenomo Umaru, Isaac John Yeboah, Genevieve A.