Kwara State University

Indoor air quality (IAQ) and thermal comfort are critical for health and productivity in enclosed basements. This study investigates a mechanically ventilated bank vault, comparing the performance of mixing ventilation (MV) and displacement ventilation (DV). An occupant survey captured environmental complaints, while field measurements recorded carbon dioxide (CO2), radon, fine particulates (PM2.5), and thermal parameters. Computational Fluid Dynamics (CFD) and analytical models were used to determine Air Change Efficiency (ACE), Contaminant Removal Effectiveness (CRE), Predicted Mean Vote (PMV), and Predicted Percentage Dissatisfied (PPD). The DV system, supplied at low-level inlets with ceiling exhaust, significantly improved air distribution and pollutant removal. Results showed ACE increased from 0.41 (MV) to 0.68 (DV). CRE values for CO2, radon, and PM2.5 were 1.52, 0.79, and 1.12, respectively, outperforming MV. Comfort also improved, with DV achieving a near-neutral PMV (-0.03) and 5% dissatisfaction, compared to a slightly warm PMV (0.29) and 6.7% dissatisfaction under MV. Additionally, an air curtain at entrances helped reduce particulate ingress. Overall, DV demonstrated superior IAQ and comfort, offering practical guidance for retrofitting HVAC systems in basements and other confined urban workplaces.

Background: Sickle cell anemia (SCA), a form of sickle cell disorder (SCD), is characterized by chronic hemolytic anemia, recurrent acute and persistent pain episodes, and progressive multiorgan complications. Among these, sickle cell nephropathy (SCN) is a significant and severe complication that may advance to chronic kidney disease (CKD), often beginning asymptomatically in childhood. Despite its clinical relevance, data on the early assessment of renal function in patients with SCA remain limited in Nigeria, hindering timely detection and intervention. This study, therefore, investigates the diagnostic utility of urinary kidney injury molecule-1 (KIM-1) as a biomarker for renal dysfunction in patients with steady-state SCA. Objective: This study assessed urinary kidney injury molecule 1 as an early biomarker of nephropathy in patients with sickle cell anemia. Method: This cross-sectional comparative study included ninety participants, comprising forty-five individuals with a normal hemoglobin genotype (HbAA) and forty-five with sickle cell anemia (HbSS). Hemoglobin genotype was determined using cellulose acetate electrophoresis. Serum creatinine levels were measured using the modified Jaffe method, and the estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Urinary kidney injury molecule-1 (KIM-1) concentrations were assessed using the enzyme-linked immunosorbent assay (ELISA) technique. Results: This study observed no significant difference in mean age between the HbAA and HbSS groups (14.16 ± 2.54 vs. 13.52 ± 3.33 years; p = 0.121). However, the mean body mass index (BMI) was significantly higher in the HbAA group (21.40 ± 1.02 kg/m²) compared to the HbSS group (18.69 ± 2.19 kg/m²; p = 0.004). Serum creatinine levels did not differ significantly between the two groups (p = 0.311). In contrast, urinary KIM-1 levels were significantly elevated in the HbSS group relative to the HbAA group (p < 0.001). In addition, a significant negative correlation was observed between urinary KIM-1 and estimated glomerular filtration rate (eGFR) in both groups, with the correlation being stronger in the HbSS group (HbAA: r = –0.64, p = 0.005; HbSS: r = –0.79, p = 0.002). Conclusion: The findings from this study observed no significant difference in serum creatinine levels between individuals with HbAA and HbSS genotypes. However, urinary KIM-1 concentrations were significantly higher in the HbSS group, with a stronger negative correlation with eGFR. These findings suggest that, while serum creatinine may not be effective in detecting early renal impairment in sickle cell anemia, urinary KIM-1 has promising potential for detecting renal dysfunction in this population.

As the global issue surrounding climate change intensifies, businesses face heightened, pressure, competition, and rapidly changing market dynamics, making innovation a critical factor for survival. On this premise, this study investigates the relationship between sustainable innovation and environmental management practices—specifically carbon emission monitoring and energy management among small and medium-sized enterprises (SMEs) in Africa. Utilizing data from the 2023 World Bank Enterprise Survey, the analysis employs probit regression to assess how sustainable innovation and other various factors influence these sustainability practices. The findings reveal that sustainable innovation significantly enhances the likelihood of adopting both carbon monitoring and energy management practices, with marginal effects indicating a 2.5% increase in carbon monitoring and a 7.03% increase in energy management for each unit increase in the innovation index. Other included variables, including access to finance, managerial experience, and foreign ownership, also demonstrate significant effects, highlighting the complex interplay of resources and contextual factors. It is recommended that policies are formulated to enhance sustainable innovative strategies which can foster carbon emission monitoring and energy management among SMEs in Africa. Overall, this study contributes to the growing literature on sustainable practices in developing economies and provides actionable insights for policymakers aiming to promote environmental stewardship.

The GPR176 protein is a cell membrane protein implicated in human diseases, especially cancers. Numerous studies have highlighted its overexpression, which is considered a major driver of tumorigenesis. Reducing its overexpression has been shown by many studies to be a viable pharmacological strategy for cancer therapy, prompting this study to search for drug molecules from existing FDA-approved drugs. We performed homology modeling of the GPR176 protein to obtain its 3D structure, conducted docking simulations of FDA-approved drugs retrieved from ReDO_DB to identify compounds with strong binding affinities, and carried out density functional theory quantum calculations and molecular dynamic simulations to assess stability and compactness. Additionally, pharmacokinetic profiling and drug-likeness analyses were performed to identify molecules capable of inhibiting and potentially deorphanizing GPR176. We identified Fostamatinib and Ticagrelor as both compounds exhibited binding affinities of -11.503 kcal/mol and − 11.882 kcal/mol, which indicates that both compounds effectively interact with the protein; they both had minimal deviations from their natural states as the RMSD values of 0.35 and 0.45 characterize their stability profile; energy gap of -0.1327 eV and − 0.1371 eV, which illuminates their reactivity to the protein and favorable pharmacokinetic profiles compared to the control, Vismodegib. This leads to the identification of Fostamatinib and Ticagrelor as potential inhibitors of the protein, and thus, we recommend further experimental studies to validate these findings.

Background Parasitic infections such as schistosomiasis, lymphatic filariasis, and onchocerciasis remain endemic in parts of rural Nigeria. Assessing seroprevalence of exposure through antibody surveillance can reveal patterns of exposure and inform targeted interventions. Objective The objective is to assess the prevalence and predictors of seroprevalence of prior antibody exposure to selected parasitic infections in urban and rural populations of the Etsako region, northern Edo State, Nigeria. Methods A cross-sectional study was conducted among 500 participants selected using multistage random sampling across urban (Auchi), semiurban (Fugar), and rural (Elele, Iyamho, Agbede) communities. Sociodemographic data and water contact history were collected through structured questionnaires. Serological testing for antibodies to Schistosoma spp., Wuchereria bancrofti , and Onchocerca volvulus was performed using an enzyme-linked immunosorbent assay. Statistical analyses included Chi-square tests, Pearson correlations, and binary logistic regression at P < 0.05 significance. Results Overall antibody prevalence was highest for Schistosoma spp. (47.2%), followed by W . bancrofti (35.0%) and O . volvulus (28.8%). Rural residents and individuals with regular water contact had significantly higher antibody prevalence ( P < 0.05). Regression analysis showed that age (odds ratio [OR] = 1.03, P = 0.020), rural residency (OR = 1.42, P = 0.041), and water contact (OR = 1.58, P = 0.007) were significant predictors of seropositivity, while gender was not ( P = 0.240). Conclusion Seroprevalence of prior exposure to parasitic infections is prevalent in Etsako, with water exposure and rural residency as key risk factors. These findings highlight the importance of tailoring control efforts to local realities and strengthening public health education within affected communities.

Buckwheat is a pseudo‐cereal with chemical, functional and application comparable to wheat but possesses higher antinutrient contents, which limit their digestibility and broader utilisation. The study investigated the effects of germination and nixtamalisation on the quality characteristics of buckwheat flour. Germination and nixtamalisation processes were carried out using limewater and potassium hydroxide. Whole, germinated buckwheat flour (GBW), organically nixtamalised buckwheat flour and synthetically nixtamalised buckwheat flour (SNBW) were produced and analysed for nutritional composition (proximate and mineral contents), antinutrients (phytate, oxalate, tannin and saponin) and antioxidant properties (phenol, flavonoids, ferric‐reducing antioxidant power and total antioxidant), physicochemical and functional properties (water absorption capacity [WAC], oil absorption capacity (OAC), swelling capacity (SC) and bulk density (BD), and data were analysed using ANOVA at α 0.05 . Germination and nixtamalisation processes significantly increased the moisture content (5.67%–8.67%; p < 0.05, ash (1.67%–4.30%; p value p < 0.05), crude protein (11.43%–14.91%; p < 0.05), fibre (10.20%–13.20%) and fat (5.0%–15.0%) but reduced the carbohydrate (61.52%–50.43%) of buckwheat flour. Protein digestibility of buckwheat flour was significantly improved (65.11%–78.14%). Similar trends were observed for the mineral content and antioxidant properties of the treated flours. The antinutritional properties of flour samples were reduced by both germination and nixtamalisation. Germinated buckwheat showed higher lightness ( L ), redness ( a ) and light intensity ( E ) compared with nixtamalised ones. Germination and nixtamalisation significantly influenced the pH, WAC, OAC, SC, solubility and bulk density of the buckwheat flour, which ranged between (4.94–8.91), (74.67–190.33 mL/g), (72.00–84.00 mL/g), (6.28–9.87 mL/g), (15.67–52.67 mL/g) and (0.8–0.85 g/mL), respectively. The application of germination and nixtamalisation processes significantly improves the protein digestibility, mineral content, and acidity of buckwheat flour as compared to whole buckwheat. Thus, these methods of processing have been proven to further enhance the qualitative attributes of buckwheat flour, promoting its expanded application in the food sector.

Urban infrastructure systems are increasingly vulnerable to the impacts of climate change, particularly in developing countries where resilience planning is limited. Cities across Nigeria, including Lagos, Abuja, Enugu, Maiduguri, Kano, and Port Harcourt, are experiencing significant climate stressors such as extreme rainfall and rising temperatures that contribute to flood events and infrastructure deterioration. Understanding how these climatic variables influence urban infrastructure is vital for proactive decision making and effective adaptation strategies. This study presents a multi-model analysis that integrates machine learning and statistical techniques to evaluate the relationship between climate indicators and infrastructure performance across these six cities. Historical climate and infrastructure data from 2000 to 2024 were collected, processed, and analyzed. Exploratory data analysis and visualization were performed to understand variable relationships, followed by preprocessing such as scaling and encoding. Multiple regression models including linear regression, support vector regression, and multilayer perceptron were implemented using a pipeline framework to predict infrastructure conditions. Additionally, Ordinary Least Squares (OLS) regression was used for interpretability and statistical validation, including evaluation of multicollinearity using the Variance Inflation Factor (VIF). The study found a strong correlation between rainfall patterns and flood events, significantly affecting infrastructure quality. Model evaluation revealed that machine learning methods offered higher predictive accuracy, while statistical models provided greater insight into variable significance. This combined approach bridges the gap between prediction and interpretation, supporting data-informed urban planning and policy making. The study contributes to the body of knowledge on climate-resilient infrastructure and provides a framework adaptable to other regions facing similar challenges.

Background In Africa, deaths from non-infectious causes, including cancer, have been rising. In 2022, over a million new cancer cases were reported, and projections indicate that this number could double by 2040 without significant interventions. To improve cancer management, combination treatments often including systemic therapies, radiotherapy, and surgery are employed, however, they pose the risk of cardiotoxicity. Given the growing burden of cancer and the associated cardiovascular complications, it is essential to evaluate the cardiovascular outcomes of combination cancer therapies in African populations, identify challenges faced by healthcare systems, and propose strategies to mitigate these risks. Main body Several anti-cancer agents, including anthracyclines, HER2 inhibitors, immune checkpoint inhibitor myocarditis, VEGF inhibitors, 5-fluorouracil, etc., have been linked to cardiovascular complications. These include left ventricular dysfunction, immune myocarditis, coronary spasms, and oxidative stress-induced cardiomyocyte death amongst others. The field of cardio-oncology has emerged to address these risks and improve patient outcomes. African health systems face unique challenges in managing cardiovascular risks associated with cancer therapies. These include delayed diagnosis and limited screening, resource constraints, underrepresentation in clinical trials, comorbidities, and socioeconomic barriers. These factors hinder early detection and management of cardiovascular complications, exacerbating the burden of treatment-related morbidity and mortality. Conclusion As the burden of cancer continues to rise in Africa, addressing cardiovascular complications associated with cancer therapies is critical. Strengthening cardio-oncology programs, improving early screening, and increasing access to cardiovascular care within oncology settings are essential steps toward better patient outcomes. By addressing existing gaps and resource limitations, African healthcare systems can enhance cancer treatment while minimizing the attending cardiovascular risks.

A PKS-fired 5-10 kW micro power plant has been designed, modeled and simulated to produce superheated steam for domestic and SMEs consumption. The target is to sustain power output at optimal design parameters. Design, modeling and simulation analyses of plant’s components were carried and then integrated to a unit micro power plant. These analyses were done to enhance the efficiency of whole power plant through reduction of heat losses and optimal sizing of components. Modeling results indicated that running the plants at the lowest possible temperatures (pressures) of 235 oC (0.35 MPa) and 235 oC (0.35 MPa) would be sufficient to enhance safety with acceptable steam mass flow rates (0.00357 kg/s, 0.00178) and steam flow velocities (14.87 m/s, 7.43 m/s). Design results showed that angular mild steel of diameter ranging 5 -10 mm were adequate for the plant stand. Simulation results revealed that the designed parameters (stress, strain, deflection, and thermal resistance) were within the acceptable standards, this portrayed design adequacy. Designed temperature of 0- 400oC was within the acceptable range and far from maximum tolerable material temperature of 1200oC. Heat flux (2.31 W/mm2) obtained from the design was conveniently within the simulated range, which shows that the design is workable. Improvement efficiency of 12% was obtained as compared to past micro plant design; this is a remarkable achievement.

Inconsistent feeding behaviour in rabbits remains a major challenge in livestock management, often leading to poor feeding efficiency, variable growth, and unstable environmental conditions within rearing facilities. This study focuses on the development and validation of a Controlled Feeding System (CFS) that integrates acoustic frequency modulation (AFM) and bioactive dosing using a vitamin B complex (HD) to assess its operational reliability and environmental responsiveness under pilot conditions. Twenty crossbred rabbits were distributed across nine test configurations and one control group following a 3² factorial design that combined three AFM levels (5 Hz, 10 Hz, and 20 Hz) with three HD (5 mL, 7 mL, and 10 mL). The CFS employed a microcontroller-operated MOSFET circuit to deliver auditory cues and regulate feeding intervals, thereby simulating an adaptive biosystem interface. Key performance parameters, growth rate, temperature, and relative humidity, were continuously monitored over 27 days. The combination of 10 Hz AFM and 7 mL bioactive dosing yielded the most stable synchronization between animal response and environmental control. These findings confirm the system’s functional viability and control precision, establishing the CFS as a validated model platform that integrates physiological feedback with engineered regulation. Overall, this study serves as a proof of concept demonstrating that sensory modulation and biochemical cues can be effectively coupled within a controlled framework to guide future large-scale biological applications and intelligent livestock process automation.

Introduction It has been established by various researchers that the type of coagulants affects the quality of Tofu. Still, no work has been published on how the chemical composition and textural attributes influence the sensory acceptability of Tofu. This study aims to assess the relationship between the chemical composition, textural attributes, and sensory acceptability of Tofu. Methods Soymilk was produced from soybeans, with soymilk protein denatured by heat, and curdled using different coagulants like vinegar, lime juice, alum solution, and steeped ogi water to get different samples of Tofu. The Tofu samples were evaluated for chemical composition, textural attributes, and sensory acceptability using standard methods. Results and discussion The results showed that the vinegar-coagulated Tofu significantly possesses the highest fat, ash, crude fiber, total carbohydrate, and total energy contents, and the steeped Ogi water-coagulated Tofu had the highest protein content. The calcium, magnesium, and zinc contents were higher in the vinegar-coagulated Tofu, while the sodium content was higher in the alum solution-coagulated Tofu. Total phenolics and total flavonoids were higher in the vinegar-coagulated Tofu, while the steeped Ogi water-coagulated Tofu had the highest DPPH value. The lime-coagulated Tofu had the lowest of all the chemical compositions. The adhesiveness, chewiness, cohesiveness, and gumminess were higher in the vinegar-coagulated Tofu, while the fracturability and hardness were higher in the lime juice-coagulated Tofu. Steeped Ogi water was shown to be the most effective coagulant in improving the sensory aspects of Tofu, followed by vinegar, providing a tasty and aesthetically beautiful product, while lime was the least popular choice. The calcium and zinc contents, total flavonoid and phenolic contents, and DPPH may have also contributed to the fracturability of the steeped Ogi water-coagulated Tofu, and the protein content may have contributed to the springiness and subsequent overall acceptability of the steeped Ogi water-coagulated Tofu. Conclusion Therefore, steeped Ogi water could be used to produce quality Tofu that will balance the chemical composition, textural attributes and sensory acceptability.

The study presents a transformer-based approach for sentiment classification in the Yoruba language using the multilingual Bidirectional Encoder Representations from Transformers (mBERT) model. Yoruba, a tonal and morphologically rich language, presents unique challenges for computational modeling due to its diacritical orthography and limited digital resources. A manually annotated corpus of 2,000 Yoruba movie reviews was developed, containing balanced positive and negative sentiments. Preprocessing involved Unicode normalization to preserve tonal diacritics and maintain orthographic integrity, avoiding unidecode stripping. The mBERT model, fine-tuned using the Hugging Face Transformers and PyTorch framework, was evaluated against traditional machine learning and deep learning baselines including Naïve Bayes, Support Vector Machine, Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Experimental results revealed that the diacritic-preserving mBERT model achieved 89.4% accuracy, outperforming all baseline models. The inclusion of Unicode normalization improved classification accuracy by 4.5% compared with the diacritic-stripped variant. Macro-averaged metrics confirmed balanced model performance across sentiment classes, while grouped movie-based splitting validated robustness by eliminating data leakage. The findings demonstrate that transformer-based multilingual models can effectively capture contextual and tonal nuances in low-resource African languages. The study highlights the importance of diacritic-sensitive preprocessing and transfer learning in advancing natural language understanding for Yoruba and other underrepresented African languages.

Terephthalic acid (TPA) was successfully extracted from polyethylene terephthalate (PET) plastic bottles through alkaline hydrolysis. Coordination Polymers ([Zn(TPA)2(H2O)2], and [Co(TPA)2(H2O)2]), and [Ni(TPA)2(H2O)2] (TPA = Terepthalic acid) were synthesized by grinding terepthalic acid extracted from the plastic waste with metal acetate salt via green synthetic method (ball-milling). These were characterised using FTIR, PXRD, BET, Melting Point analysis, UV–Vis spectroscopy, XRF and SEM techniques to investigate the functional groups, structural crystallinity, surface area, thermal stability, electronic transition, elemental composition and morphology of the material, respectively. Adsorption studies for the removal of endocrine-disrupting chemicals (Atrazine) were carried out to understand their applicability in the removal of pesticide residues from a polluted environment. The study also shows that the synthesized coordination polymers were favorable for Atrazine removal, for which [Co(TPA)2(H2O)2], [Ni(TPA)2(H2O)2], and [Zn(TPA)2(H2O)2] showed a maximum adsorption capacity of 41.322, 52.91 and 60.241 mg/g, respectively. The kinetics study shows that Atrazine adsorption followed a pseudo-first-order kinetics model. The thermodynamic parameters were calculated, and the results showed that the adsorption of Atrazine on the coordination polymers is a spontaneous and exothermic process. Therefore, all the synthesised coordination polymers are promising adsorbents for the removal of Atrazine pesticide in a polluted environment.

The growing environmental impact of tire waste necessitates innovative recycling methods. With global tire production expected to reach 2.67 billion units by 2027, repurposing tire-derived materials in concrete offers a sustainable solution. This study investigates the effects of steel fibers from waste tires on concrete incorporating coconut shells (CSs), palm kernel shells (PKSs), and recycled aggregates. The research examines how different fiber percentages influence fresh properties and strength. A concrete mix ratio of 1:2.19:2.46 (Cement: Fine Aggregate: Coarse Aggregate) was used, with an admixture at 1% by cement weight. Natural aggregates were replaced with recycled concrete aggregates (RCA), CSs, and PKS at 25%, 50%, 75%, and 100%, while steel fibers were added at 0.25%, 0.5%, 0.75%, and 1% by cement weight. Slump, split tensile, and compressive strength tests were conducted. Results showed that steel fibers enhanced cohesion but reduced slump due to increased stiffness. After 28 days of curing, the optimal mix contained 1% steel fibers and 25% aggregate replacements, achieving a balance between workability and strength. In addition, a life cycle and economic assessment indicated that these sustainable mixes can lower CO 2 emissions by 15%-25% and reduce costs by 2%-4%, confirming their potential for affordable, low-carbon construction in Kwara state, Nigeria.

Class-imbalanced learning presents critical challenges in machine learning, largely because data in most domains are naturally imbalanced. Although resampling techniques have been widely applied to address this issue, their effectiveness has been inconsistent and sometimes flawed, owing to artificial assumptions. In this study, we move beyond hype surrounding resampling methods by exploring alternative strategies, such as ensemble learning, cost-sensitive algorithms, and one-class classification techniques. Through rigorous experimentation across extreme, moderate, and mild imbalance levels, our findings reveal that these alternatives often outperform traditional resampling in terms of F1-scores, with ensemble SVM and one-class logistic regression achieving notable values of 1.00 and 0.90, respectively. In addition, we introduce a knowledge-based recommender system designed to help practitioners choose the most appropriate techniques for addressing class imbalance. This research argues that resampling is not always the optimal solution for all instances, thereby advocating a more balanced framework that leverages advanced methods for superior performance in imbalanced learning tasks. Our study advances the field by offering a pragmatic, data-driven approach to overcoming class imbalances, contributing valuable insights for both researchers and practitioners.

Fanconi syndrome is named after Guido Fanconi, a pediatrician who described a child with glucosuria albuminuria, rickets, and dwarfism in 1931. Two years later, de Toni added hypophosphatemia to the clinical picture; soon after, they found large amounts of organic acids in the urine of an 11-year-old girl (Harrison 1958). Fanconi’s further contribution to the subject came in 1936, when he recognized the similarities between these cases, added two new patients to the list, named the disease nephrotic-glucosuric dwarfism with hypophosphatemic rickets, and suggested that the organic acids found in the urine may be amino acids. Fanconi’s findings were confirmed in 1943 by McCune et al. and in 1947 by Dent, who established that the organic acids originated in the kidneys (McCune et al. 1943).

Background Malaria remains a serious public health challenge in sub-Saharan Africa, and Nigeria accounts for almost 30% of global child malaria deaths. This study employs machine learning (ML) to improve prediction efficiency and identify the most significant risk factors associated with childhood malaria in high-burden populations. Methods A cross-sectional study was conducted among 693 under-5 children from Nigerian Internally Displaced Persons (IDP) camps. Sociodemographic data, household living conditions, and malaria knowledge were collected in addition to Rapid Diagnostic Test (RDT) outcomes. The dataset was split 70:30 to train and evaluate four ML models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Machine (GBM). The performance of the models was measured by AUC, precision, recall, F1-score, and variable importance. Results Malaria prevalence was 68.5%. Key risk factors were a caregiver with no education (aOR = 3.23, p = 0.026), while female caregivers were significantly associated (aOR = 0.53, p = 0.024). The Random Forest model performed best (AUC = 0.892), where caregiver occupation and residential camp were the most significant predictors. A vast knowledge-practice gap was observed, where 60.3% of caregivers had knowledge of prevention but low bed net usage (2%). Conclusion Random Forest machine learning greatly improves the precision of malaria risk prediction. The results underscore the importance of extensive, modifiable factors such as caregiver occupation and education. Integration of these ML models into surveillance can enable precision public health interventions, including enhanced vector control and focused health education, to combat malaria effectively in high-burden populations.

Introduction Illicit drug use remains a significant public health concern, particularly among young adults, with growing evidence linking substance use to immune dysregulation. Sociodemographic factors such as age, gender, and socioeconomic status may influence immune responses in drug users, yet data from Nigerian populations remain limited. Aims This study investigates the association between sociodemographic characteristics and immune dysregulation among illicit drug users at Osun State Polytechnic, Iree, providing insights for targeted public health interventions. Material and Methods A cross-sectional study was conducted among 180 participants, comprising 90 illicit drug users and 90 nonusers. Data on sociodemographics were collected through structured questionnaires, while blood samples were analyzed for inflammatory markers (interleukin-6 [IL-6] and C-reactive protein [CRP]) using Enzyme-linked immunosorbent assay and cellular immune responses (cluster of differentiation 4 positive [CD4+] T lymphocyte [T-cell] counts) using flow cytometry. Statistical analyses, including Chi-square tests and Mann–Whitney U-tests, were performed to determine associations, with P < 0.05 considered statistically significant. Results Illicit drug users exhibited significantly higher inflammatory marker levels (IL-6: 18.5 ± 4.2 pg/mL vs. 10.2 ± 3.1 pg/mL; CRP: 8.9 ± 2.5 mg/L vs. 4.3 ± 1.8 mg/L; P < 0.001) and lower CD4+ T-cell counts (320 ± 75 cells/mm³ vs. 550 ± 100 cells/mm³; P < 0.001) compared to nonusers. Male drug users and individuals from low socioeconomic backgrounds showed more pronounced immune dysregulation ( P < 0.01). Conclusions These findings highlight the impact of sociodemographic factors on immune function among drug users, emphasizing the need for targeted public health interventions. Gender-specific and socioeconomically tailored prevention programs are crucial to mitigating the health risks associated with illicit substance use. Further longitudinal studies are recommended to explore causality and long-term immune consequences.