Asian Journal of Advanced Research and Reports

An Overview of the Application of Machine Learning and Deep Learning Techniques for Agricultural Crop Yield Prediction in Terms of Methods, Data Inputs and Prospects

V. Vakdevi — 2026-04-17

Correct and timely prediction of crop yields is fundamental to global food security, agricultural policy planning and the equitable management of natural resources in the phase of a rapidly changing climate. The mounting complexity of agro-environmental systems focused by soil variability, extreme weather conditions and environmental interactions has reduced dependency on traditional statistical and process based models. Over the past decade, machine learning (ML) and deep learning (DL) techniques have emerged as transformative alternatives, capable of capturing nonlinear, high-dimensional relationships across heterogeneous data sources. The data sources include remote sensing imagery, meteorological records, soil surveys and crop management accounts. This study examines the application of ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN) along with major DL techniques, including Convolutional Neural Networks (CNN), Long Short Term Memory networks (LSTM), hybrid CNN-LSTM models and emerging transformer based models. Key input features, such as the Normalized Difference Vegetation Index (NDVI), climatic variables, soil parameters and multi-source remote sensing data are evaluated for their influence on predictive performance. Comparisons across different crops including wheat, rice, maize and soybean reveal that ensemble and hybrid DL models consistently provide superior accuracy, with R² values commonly exceeding 0.85 in many investigations. Critical challenges including data scarcity, model interpretability deficits, geographic transferability limitations and computational demands are addressed in detail. Whereas, the role of Explainable Artificial Intelligence (XAI), transfer learning and multimodal data fusion is considered as a borderline to these limitations.

E-Governance and Inclusive Growth: A Political Analysis of India’s Digital Transformation

Rambir Singh — 2026-04-17

The rapid expansion of digital technologies has significantly transformed governance structures across the globe, with India emerging as a leading example of large-scale digital integration in public administration. Under flagship initiatives such as Digital India Programme, Direct Benefit Transfer, and Unified Payments Interface, the Indian state has actively leveraged e-governance to enhance service delivery, transparency, and citizen engagement. This paper examines the role of e-governance in promoting inclusive growth in India from a political science perspective. The study is based on secondary data drawn from government reports, policy documents, and institutional databases such as NITI Aayog, Reserve Bank of India, and Ministry of Electronics and Information Technology. It analyses how digital governance initiatives have contributed to financial inclusion, reduced administrative leakages, and strengthened participatory governance mechanisms. The findings indicate that platforms such as UPI and DBT have expanded access to financial services and welfare schemes, particularly among marginalized populations, thereby fostering inclusive development. However, the paper also critically highlights persistent challenges, including the digital divide, infrastructural disparities, and concerns related to data privacy and state surveillance. These issues raise important questions regarding equity, accountability, and democratic governance in a rapidly digitalizing society. The paper argues that while e-governance has emerged as a powerful instrument for inclusive growth, its success is contingent upon a balanced policy framework that ensures digital access, institutional transparency, and protection of citizen rights. The study concludes that India’s digital transformation represents both an opportunity and a challenge for deepening democratic governance and achieving sustainable inclusive development.

Herbal Therapeutic Approaches for Migraine Management: Pharmacological Mechanisms and Evidence of Medicinal Plants with Clinical Approach

Mohini Patidar — 2026-04-17

Migraine is a complex, debilitating neurological disorder characterized by recurrent episodes of intense, often unilateral headache accompanied by nausea, vomiting, and heightened sensitivity to light and sound. Despite considerable advances in conventional pharmacotherapy, a significant proportion of patients experience inadequate relief, intolerable adverse effects, or contraindications to standard treatments, prompting growing interest in herbal therapeutic alternatives. This narrative review comprehensively examines the pharmacological mechanisms, clinical evidence, and safety profiles of medicinal plants used in migraine prophylaxis and acute management. Key botanical agents investigated include Tanacetum parthenium (feverfew), Petasites hybridus (butterbur), Zingiber officinale (ginger), Mentha × piperita (peppermint), Lavandula angustifolia (lavender), and Cannabis sativa, amongst others. The principal pharmacological mechanisms operative in these plants include inhibition of serotonin release and platelet aggregation, modulation of calcitonin gene-related peptide (CGRP) signaling, cyclooxygenase-2 (COX-2) inhibition, attenuation of cortical spreading depression, and interaction with transient receptor potential (TRP) ion channels. Clinical trial evidence, whilst promising for several agents—particularly feverfew and butterbur—remains variable in methodological rigor. Safety concerns include hepatotoxicity associated with unsaturated pyrrolizidine alkaloids in butterbur and potential drug interactions with herbal preparations. This review highlights the need for standardized phytochemical preparations, robust randomized controlled trials, and regulatory oversight to translate the therapeutic promise of medicinal plants into evidence-based clinical practice for migraine management.

Indigenous Knowledge and the Knowledge Society in Uganda: Insights, Lessons, and Challenges

Mark Kiiza — 2026-04-22

This paper examines the evolving role of indigenous knowledge and the knowledge society, with particular reference to Uganda. The expansion of knowledge societies driven by rapid technological advancement, continuous innovation, and complex knowledge networks has intensified concerns about how knowledge is produced, disseminated, and applied in higher education. While universities have traditionally been central to knowledge creation, these transformations increasingly challenge their conventional roles. This study aims to analyse how universities can respond to these changing demands and whether they can retain their central position in knowledge production. A qualitative research design based on documentary review is employed, drawing on existing literature to synthesize perspectives on institutional change in higher education. The findings indicate that universities face growing pressure to transition towards more socially engaged, technologically responsive, and market-oriented models. Emerging technologies are expanding access to information while reshaping teaching, research, and innovation processes and interdisciplinary and cross-sectoral collaboration is becoming essential. The study concludes that although universities remain pivotal, their continued relevance depends on their ability to adapt to evolving socio-economic and technological conditions. It recommends that universities in Uganda reassess their missions, adopt flexible and technology-driven pedagogies, and strengthen partnerships with industry and communities, while policymakers support innovation, research collaboration, and equitable access in Uganda

AI-Enabled Digital Twin Framework for Predictive Maintenance and Performance Optimisation of Mechatronic Mechanical Systems

Oluwafunmilayo Ifeoluwa Somoye — 2026-04-20

The growing integration of artificial intelligence and digital twin technologies into predictive maintenance has greatly contributed to the optimisation of the performance of mechatronics and industrial systems; yet several challenges, related to model validation, scalability, and real-world deployment, still pose serious concerns. The current paper describes a systematic literature review (SLR) carried out based on the PRISMA approach with the purpose of analysing the latest progress made in AI-based modelling techniques used for predictive purposes. Peer-reviewed academic publications on the use of AI-based techniques published from 2018 to 2025 were chosen from Scopus, Web of Science, ScienceDirect, and SpringerLink online databases. The findings show that there is a prevalent use of machine learning and deep learning approaches such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Support Vector Machines (SVM) and physics-informed models in combination. These algorithms are used mainly for detecting faults, predicting RUL, analysing anomalies, and conducting predictive analytics. The high level of predictive accuracy achieved, nevertheless, is accompanied by insufficient research in real-world implementation, IIoT applications, and system validation. The existing models are mostly experimental and require improvements in terms of robustness, generalizability, data quality, and validation process.

The paper gives an overview of modelling techniques used, approaches to validation, and application areas, highlighting the most relevant problems faced during real-world deployment and the lack of integration with digital twins.

A Machine Learning-Based Framework for Real-Time Environmental Health Risk Prediction and Spatial Air Quality Intelligence in Urban-Industrial Ecosystems

Chijioke George Edeh — 2026-04-04

The high rate of industrialization and urbanization has enhanced the pollution of the environment and the occurrence of occupational health hazards especially in urban-industrial ecosystems where people are subjected to cumulative environmental and occupational hazards. Traditional monitoring is mostly reactive and inhibited in the ability to capture nonlinear, high frequency and spatially heterogeneous exposure patterns. The paper presents and assesses a machine learning-supported framework of real-time predictions of environmental health risks and spatial air quality intelligence.

The architecture integrates the multi-sources of data (environment, weather, work-related exposures, demographic risk factors, etc.) into a single predictive pipeline. A high-resolution, 30-day, controlled simulation (5 minutes each) was created in five representative areas (industrial, high-traffic urban, residential, suburban, and rural) and produced 43,200 spatiotemporal records. There was an implementation of ensemble models (Random Forest, Gradient Boosting, XGBoost) and a Long Short-Term Memory (LSTM) network with regard to pollutant prediction and Health Risk Index (HRI) estimation. The regression output reported a high predictive ability, XGBoost (R² = 0.92), Random Forest (R² = 0.91), and Gradient Boosting (R² = 0.89), and Gradient Boosting (R²), and LSTM exhibiting the best performance in terms of temporal modelling accuracy, which indicates the significance of the consideration of the sequential dependency. The application of Risk classification (Low, Moderate, High, Critical) was found to have 93 percent accuracy with F1-scores of over 0.90 and AUC values of between 0.91 and 0.95. Temporal realism was observed by characteristic bimodal peaks in PM2.5 day by day simulations, which were in agreement with the cycles of traffic and industry. Interpretability and policy relevance were also added by incorporating explainable Artificial Intelligence methods, especially SHAP. The developed framework takes the current environmental analytics a step forward and integrates predictive modeling, spatial risk mapping, and stakeholder-oriented intelligence and offers the proactive governance of the whole community of health-related issues and sustainable urban-industrial growth. The current text fits into the developing literature of combining environmental science, population health, and artificial intelligence. The study offers a novel method to forecast the environmental health risks of urban-industrial ecosystems by creating a machine learning-based model to integrate the environmental, occupational, meteorological, and demographic factors. It (Integration of ensemble learning models and Long Short-Term Memory (LSTM) networks with a composite Health Risk Index (HRI)) presents a viable tool of real-time environmental risk assessment and decision support. Also, the transparency and policy relevance are improved by the addition of explainable artificial intelligence (XAI) methods, thus the framework is useful to researchers, environmental authorities, and urban planners working on the development of smart cities sustainably and with health considerations.

Comparative Histomorphometric Analysis of Short and Flat Bones in Human Skeletal Remains

Clinton David Orupabo — 2026-04-06

Background: Bone histomorphometry provides valuable microstructural information for forensic identification, even when skeletal remains are fragmented or morphologically compromised.

Aims: The study aims to evaluate and compare histomorphometric parameters of short and flat bones in human skeletal remains.

Study Design: The study used a descriptive cross-sectional design that employed comparative anatomy.

Place & duration of study: The study was carried out using skeletal collections from the Department of Human Anatomy, Faculty of Basic Medical Sciences, Rivers State University, within a period of one year, spanning Feb.2025 and Feb.2026.

Methodology: 30 adult bone samples comprising ribs, sternum, and calcaneus were used. Undecalcified bone sections were prepared and analyzed using digital microscopy and image analysis software. Quantitative parameters measured included primary and secondary osteons, osteon fragments, Haversian canal diameter, and maximum osteon diameter. Statistical analyses involved descriptive statistics, independent-samples t tests, and analysis of remodeling and preservation indices.

Results: Ribs exhibited the highest osteon population and remodeling activity, with a mean secondary osteon count of 8.00 ± 1.36 and the highest OS/OF ratio (0.58). Sternum bones demonstrated intermediate remodeling patterns, while calcaneus bones displayed minimal osteon population but significantly larger Haversian canals and osteon diameters. Osteon Fragmentation Index (OFI) analysis indicated superior histological preservation in ribs and sternum compared with calcaneus. These findings demonstrate statistically significant (p≤0.05) histomorphometric differences between short and flat bones, highlighting the importance of bone-specific reference data in forensic histology.

Conclusion: The ribs appear to provide the most reliable microstructural indicators for histomorphometric assessment in fragmented skeletal remains.

Digital and Intelligent Empowerment for Precision Teaching: A Process-oriented Evaluation Model for Higher Mathematics Blended Learning

Wang Na — 2026-04-07

Aims: To solve problems like strong subjectivity, unbalanced weights, and lagging evaluation dimensions in blended teaching of Higher Mathematics, and build a process-oriented comprehensive evaluation model empowered by digital intelligence for precision teaching.

Study Design: Constructed an evaluation system and comprehensive evaluation model based on dual-drive logic of Delphi method and Analytic Hierarchy Process (AHP).

Place and Duration of Study: Shenyang Normal University, School of Mathematics and Systems Science, between December 2025 and February 2026.

Methodology: Established a 15-member interdisciplinary expert consultation pool; screened and optimized indicators through two rounds of Delphi surveys; used AHP to determine indicator weights and conduct consistency tests.

Results: The results of AHP weight determination and consistency tests indicate that the "Teaching Support" dimension holds a dominant position, among which "Automated Learning Early Warning and Intervention Response Efficiency" has the highest composite weight (0.112), emerging as a core variable driving precision teaching.

Conclusion: The research demonstrates that the focus of evaluation has achieved a paradigm reconstruction from "end-product output" to "process intervention". This model provides decision-making support for the precise implementation of Higher Mathematics teaching reform and offers an operational framework for quality assessment in the digital transformation of higher education.

Evaluation of Protein Quality of Pap-based Complementary Food Enriched with the Edible Grasshopper Zonocerus variegatus

Nkemjika Nnedinso Umerah — 2026-04-08

Background: Protein quality is an index of how well a protein meets the requirements of essential amino acids, as well as the physiological needs, of the organism.

Aim: To evaluate the effect of “otujo ant” on the protein quality of complementary food.

Methods: A Completely Randomized Design (CRD) was carried out among 50 male weanling rats at Enugu State University of Science and Technology. Rats were randomly assigned to a 5-week clinical study (intervention, n=40) and (control, n-10). Urinary sample with dried diet and feacal samples were analyzed and used to calculate true digestibility (TD), nitrogen balance (NB), net protein utilization (NPU), protein efficiency ratio (PER) and biological value (BV). Between-group means of quantitative clinical indices were compared using Duncan New Multiple range test and ANOVA test.

Result: The results of the nitrogen balance showed that the BV, NPU and retained nitrogen for the experimental diets were between 67-68, 67-69 and 2.86-2.93%, respectively, against the casein diet 75, 76 and 3.10%. The PER for the experimental diets were 1.62-2.22 against the casein diet 2.98. The results also showed that the weight gain by the experimental diets was 2.74-3.24 g weight while the casein diet gained 3.50 g.

Conclusion: The study concluded that enriching pap with “otujo ant” will improve the protein quality of pap, hence a vehicle for the prevention of protein-energy malnutrition.

Topographic Control of Temperature Distribution and Thermal Comfort: A Comparative Study of Hill and Plain Stations in North-East India

Farhana Islam — 2026-04-12

This study investigates the role of topography in controlling temperature distribution and thermal comfort across selected hill (Shillong, Aizawl and Kohima) and plain (Guwahati, Silchar and Agartala) stations in North-East India using Indian Meteorological Department (IMD) climatological normal data for the period 1991–2020.The analysis incorporates key thermal parameters, including mean maximum and minimum temperature, wet bulb and dry bulb temperature, and derived indices such as diurnal temperature range (DTR) and Discomfort Index (DI).The results reveal a clear altitudinal variation in temperature, with hill stations such as Shillong, Aizawl, and Kohima recording lower temperature ranges (maximum: 15.2°C–28.8°C; minimum: 5.1°C–20.2°C), while plain stations such as Guwahati, Silchar, and Agartala exhibit significantly higher values (maximum: 23.9°C–33.3°C; minimum: 10.8°C–25.9°C). A statistically significant inverse relationship between elevation and temperature is observed (p < 0.05), supported by correlation and regression analysis. The diurnal temperature range (DTR) varies from 14.7°C to 17.1°C across the study area, with lower values observed in hill stations and relatively higher values in plains, indicating greater thermal variability in lowland regions. Thermal comfort analysis based on the Discomfort Index (DI) shows that hill stations remain within the comfortable to slightly uncomfortable range (DI < 24°C), whereas plain stations frequently experience moderate to severe discomfort, with DI values reaching up to ~29°C during the monsoon season. Furthermore, higher wet bulb temperatures and smaller dry–wet bulb differences in plain regions indicate increased atmospheric moisture and humidity-induced discomfort. The findings highlight the dominant role of topography in shaping regional thermal regimes and emphasize the importance of elevation in influencing temperature variability and human thermal comfort. These insights are crucial for climate assessment, environmental planning, and sustainable regional development in North-East India.

Assessment of Groundwater Quality in a Selected Residential Area of Ibadan

Oluwatunmise Peter Abolarin — 2026-04-15

Groundwater is a vital source of drinking water in Nigeria, particularly in peri-urban areas where public water supply is limited. This study evaluated groundwater quality in Awotan Asunle, Ibadan, for domestic use by analysing physicochemical parameters and selected heavy metals (Fe, Cu, Zn) from ten sampling points consisting of five wells (W1–W5) and five boreholes (B1–B5). Key parameters analysed included pH, turbidity, alkalinity, hardness, chloride, sulphate, dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD), and the results were compared with standards established by the World Health Organization (WHO), Nigerian Standard for Drinking Water Quality (NSDWQ), and the European Union (EU).Most parameters were within permissible limits, indicating generally good water quality with minimal organic and metal contamination. Low turbidity confirmed effective natural filtration, while low BOD and COD suggested negligible organic pollution. Metal concentrations were below guideline values, indicating no significant health risk. However, pH values (5.65–6.65) revealed slightly acidic conditions that may corrode plumbing systems, and elevated chloride in one well suggested localized anthropogenic influence. Dissolved oxygen levels were generally acceptable with minor variations among sampling points. The study concludes that groundwater in Awotan Asunle is largely suitable for domestic use but remains vulnerable to localized contamination and acidity. Regular monitoring, improved sanitation, and appropriate treatment measures are recommended to safeguard water quality and support sustainable groundwater management in rapidly developing peri-urban communities.

Selected Forest Resources and Their Role in Traditional Healthcare Delivery in Nigeria

Ebikapade Amasuomo — 2026-04-17

Background: Forests and vegetation resources form a critical background for human sustenance by supplying timber, non-timber products, and essential ecological support. They also provide a wide variety of medicinal plants that are fundamental to primary healthcare practices. In a country like Nigeria, the use of forest resources as medicinal plants for the management of ill health remains a major type of healthcare system among a section of the populace, especially rural dwellers.

Aim: The current study investigates traditional healthcare delivery and their reliance on forest resources for the treatment and management of ailments. The essence is to emphases the need for forest conservation in Nigeria by investigating common types of medicinal plants in Nigeria and their perceived effectiveness compared to conventional medicine.

Study Design: The current research employed a cross-sectional questionnaire survey carried out in two urban markets (Lagos and Abuja).

Place and Duration of Study: Data were collected between January and May in the two cities.

Methodology: The study employed a mixed method approach. One hundred and twenty (120) questionnaires were administered in two large markets across the country. The markets were purposively selected in two big cities where residents come from across the country.

Results: Data showed that 79% used medicinal plants as a result of affordability (65%) and accessibility (17%). The most commonly used medicinal plants include Bitter Leaf (Vernonia amygdalina), Pawpaw Leaf (Carica papaya), Sour Sop (Annona muricata), Scent Leaf (Ocimum gratissimum) and Dogon Yaro (Azadirachta indica). Illnesses most treated with herbs are ulcers, diabetes, malaria, digestive disorders and hypertension. Respondents were of the opinion that medicinal plants were effective for treatment. The study showed the importance of preserving the natural environment for enhanced healthcare delivery.

Conclusion: The study concludes that there is a need to develop policies and strategies for the protection of forest ecosystems to safeguard the constant supply of medicinal plants and related traditional practices.

Leadership Style and Teacher Work Commitment: A Quantitative Correlational Study

Regine L. Galaraga — 2026-04-18

Background: School leadership plays a pivotal role in shaping teacher commitment, as it directly influences motivation, professional engagement, and workplace climate. Leadership style, in particular, determines how school heads guide, support, and inspire teachers. Transformational and innovative leadership styles have been found to positively affect teachers’ organizational commitment, motivation, and work behavior, reinforcing the importance of supportive and development-oriented leadership in educational institutions.

Aims: This study examined the relationship between leadership style and teacher work commitment among public junior high school teachers in the Division of Digos City. Specifically, it determined the levels of transformational, transactional, and laissez-faire leadership, assessed teachers’ work commitment in terms of affective, continuance, and normative dimensions, and identified the significant relationship and predictive influence of leadership styles on teacher commitment.

Study Design: This study utilized a quantitative, non-experimental correlational research design.

Place and Duration of Study: The study was conducted at public junior high schools in Region XI, Philippines during school year 2025-2026.

Methodology: A total of 169 teachers were selected through stratified random sampling. Data were collected using the Multifactor Leadership Questionnaire (MLQ) and the Organizational Commitment Questionnaire (OCQ). Statistical tools included mean, standard deviation, Pearson correlation, and multiple regression analysis.

Results: Findings revealed that transformational leadership was practiced at a very high level (M = 4.31), transactional leadership at a high level (M = 3.97), and laissez-faire leadership at a low level (M = 2.38). Teachers’ work commitment was generally high (M = 4.13), with affective (M = 4.33) and normative (M = 4.20) commitment rated very high, and continuance commitment rated high (M = 3.85). Correlation analysis showed that transformational leadership had a strong positive relationship with teacher commitment (r = 0.68, p < .001), transactional leadership had a moderate positive relationship (r = 0.45, p < .001), and laissez-faire leadership had a weak negative relationship (r = –0.21, p = .012). Regression analysis indicated that leadership styles significantly predicted teacher work commitment (R² = .52), with transformational leadership emerging as the strongest predictor (β = .59, p < .001).

Conclusion: Leadership style significantly influences teacher work commitment, with transformational leadership as the most effective approach. Strengthening transformational leadership practices is essential to enhance teacher commitment, retention, and overall school effectiveness.

A Bayesian Framework for Estimating the Shape Parameter of an Exponential Poisson-lindley Distribution

Omale Aisha — 2026-04-20

A Bayesian analysis of the shape parameter of an Exponential Poisson Lindley Distribution (ExPLinD) is presented in this study. This study examined the Bayesian estimation of the shape parameters of an ExPLinD using both informative and non-informative priors. Uniform and Jeffrey Priors were used as informative priors, while a gamma prior was adopted as a non-informative prior. These priors were combined with different error loss functions (Squared Error Loss Function (SELF), Precautionary Loss Function (PLF) and Quadratic Loss Function (QLF)) to allow for the possibility of different combinations and scenarios of prior and loss function that produced the best estimate of the shape parameter of an ExPLinD. Simulation study was conducted using Mean Squared Errors (MSE) as a metric, the Quadratic loss function produced the best estimator of the shape parameter of an ExPLinD compared to estimates from the Maximum Likelihood Estimation (MLE), SELF and PLF having the lowest estimated value of this metric except for when α is increased from 0.7 to 3.7 and “a” is increased from 1.0 to 3.5, where the PLF under Gamma prior presented less MSE than the QLF. The result also revealed that the values of the other parameters have no effect on the estimators of the shape parameter, as changing the values of these other parameters alone does not affect the MSE. This makes the QLF the best loss function to be used in obtaining a Bayesian estimate of an ExPLinD, as it will have minimal influence on inference to be made.

Conceptualizing a Smart Maintenance Maturity Model for Commercial Facilities in Emerging Markets

Felix Oluwalomola — 2026-04-23

Maintenance management in commercial buildings has evolved from a repair after failure approach to a proactive, tech-enabled strategy. Yet in emerging markets, practices often stay stuck in reactive mode, hampered by an infrastructure gap and institutional hurdles. This study bridges that divide by introducing the Smart Maintenance Maturity Model (SMMM), custom-built for commercial facilities in developing economies.

Using a design science approach, maintenance literature was considered, smart technology like Internet of Things (IoT), Artificial Intelligence (AI), and Building Information Modelling (BIM), plus established maturity models. Thematic analysis uncovers core capability areas such as technology adoption, data and analytics capability, human capital and skills, process integration, strategic alignment, and governance and risk management, which were organised into clear maturity stages, from a basic reactive approach to advanced predictive and prescriptive strategies.

Tailored to real-world challenges in emerging economies like power instability, infrastructural deficiencies, and skill shortages, the SMMM model is practical and ready to scale. It advances theory by adapting maturity models to emerging-market facility management and hands practitioners a roadmap to evaluate, compare, and upgrade their operations toward smarter, more sustainable maintenance.

Hypolipidemic Effect of Avicennia marina Fruit Extract in High-fat Diet and Cigarette Smoke-induced Hyperlipidemic Rats

Sulistyowati — 2026-04-23

Background: Hyperlipidemia, exacerbated by high-fat diet and cigarette smoke exposure, is a key risk factor for cardiovascular diseases, and Avicennia marina fruit extract may offer promising hypolipidemic benefits as a natural therapeutic agent.

Aims: To evaluate the hypolipidemic effect of ethanol extract of Avicennia marina fruit (EEAF) in hyperlipidemic rats induced by a combination of a high-fat diet and cigarette smoke exposure, based on total cholesterol and triglyceride levels.

Study Design: In vivo experimental study.

Place and Duration of Study: STIFAR Yayasan Pharmasi Semarang, between October and November 2025.

Methodology: An in vivo experimental study was conducted using 25 male Wistar rats. Hyperlipidemia was induced by administering a high-fat diet consisting of quail egg yolk and lard (1:1; 15 mL/kg body weight) combined with cigarette smoke exposure (30 cigarettes/10 rats/day) for 17 days. Rats were randomly divided into five groups (n = 5): negative control (0.5% Na-CMC), positive control (atorvastatin 2.52 mg/kg body weight), and EEAF-treated groups (30, 60, and 120 mg/kg body weight). Treatments were administered orally once daily for 28 days. Serum total cholesterol and triglyceride levels were measured using enzymatic colorimetric methods.

Results: Induction significantly increased total cholesterol and triglyceride levels by 159.66 ± 64.30% and 121.88 ± 92.84%, respectively (p < 0.05). EEAF significantly reduced total cholesterol at all doses, with reductions of 47.72 ± 11.20%, 50.90 ± 2.78%, and 58.51 ± 8.69% (p < 0.05 for 30 and 60 mg/kgBW; p < 0.01 for 120 mg/kgBW). Triglyceride levels were reduced by 31.61 ± 27.57%, 46.87 ± 9.18%, and 59.16 ± 13.40%, with significant reductions observed at doses of 60 mg/kgBW (p < 0.05) and 120 mg/kgBW (p < 0.01) compared to the negative control.

Conclusion: EEAF demonstrated significant hypolipidemic activity by reducing total cholesterol and triglyceride levels in hyperlipidemic rats. These findings suggest its potential as a natural agent for improving lipid profiles; however, further studies are required to confirm its safety and underlying mechanisms.Top of Form

Raising a Child with Autism: Expenditures and Financial Management among Parents in a Highly Urbanized Community

Shine Alynna Cuyong — 2026-04-25

Introduction: This study examines the financial challenges experienced by parents raising children with Autism, particularly the increasing costs associated with healthcare, therapy, education, and daily needs. It aimed to determine the expenditures and financial management utilized by parents raising children with autism in Iloilo City.

Methods: A descriptive quantitative-comparative research design was employed, involving selected parents residing in Iloilo City, with data collected through a validated researcher-made questionnaire; ethical clearance was sought prior to data gathering.

Results: The results showed that parents faced a moderate financial burden, mainly due to healthcare and developmental costs, and coped by prioritizing their child’s needs, adjusting expenditures, and finding extra income, though support access was limited. These findings indicate that although parents demonstrate resilience and proactive financial management, gaps in external support systems contribute to continued financial strain.

Recommendation: It is recommended that parents enhance financial literacy and planning through budgeting, private institutions provide financial support, and future researchers broaden the scope and conduct long-term studies to improve understanding and support systems. The findings provide valuable insights for improving policies, services, and support for families raising children with autism.

Conclusion: Overall, the study concludes that while parents raising children with autism demonstrate effective financial management strategies and resilience despite high demands, there remains a clear need for improved financial and institutional support systems. The results also show no significant differences in the strategies used based on educational attainment, employment status, family income, and financial assistance, indicating that parents use similar approaches regardless of their background.

Sero-Prevalence of Toxoplasma gondii in Camels (Camelus dromedarius) in Kassala State –Eastern Sudan: A Comparison of Indirect ELISA and Latex Agglutination Test

Saeed, E. Elnour — 2026-04-25

This cross-sectional study aimed to estimate the seroprevalence of Toxoplasma gondii infection among camels in Kassala State, Eastern Sudan, and to evaluate the diagnostic agreement between the latex agglutination test (LAT) and indirect enzyme-linked immunosorbent assay (iELISA). A total of 322 camel serum samples were collected from different localities in Kassala State (Eastern, Western, Northern, and Southern Kassala) and initially screened using LAT, after which LAT-positive samples were subjected to confirmatory testing by iELISA. The association between seropositivity and selected risk factors, including age, sex, breeds, migration history, owner awareness and geographical variations, was statistically assessed.

LAT screening identified 129 seropositive camels, corresponding to an apparent seroprevalence of 40.1%. Sex-specific analysis revealed seropositivity rates of 36.0% in males (89/247) and 53.3% in females (40/75), with females exhibiting a significantly higher odds of infection (P = 0.007). Confirmatory testing using iELISA detected T. gondii antibodies in 37 camels, representing 28.7% of the LAT-positive samples and an overall confirmed seroprevalence of 11.5%.

The results demonstrate substantial exposure of camels to T. gondii in Kassala State, indicating endemic transmission within the study area. The marked discrepancy between LAT and iELISA outcomes underscores the necessity of confirmatory serological assays to improve diagnostic accuracy in epidemiological surveillance. These findings highlight the potential role of camels as reservoirs of zoonotic toxoplasmosis and emphasize the need for integrated control strategies within a One Health framework.