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Background Lassa fever (LF) is a zoonotic infectious disease of public concern in Nigeria. The infection dynamics of the disease is not well elucidated in Nigeria. This study was carried out to describe the pattern of infection, case fatality rate and spread of lassa virus (LASV) from 2017 to 2020. Methods Weekly epidemiological data on LF from December, 2016 to September, 2020 were obtained from Nigeria Centre for Disease Control. The number of confirmed cases and deaths were computed according to months and states. Descriptive statistics was performed and case fatality rate was calculated. Distribution and spread maps of LF over the four years period was performed on ArcMap 10.7. Results A total of 2787 confirmed cases and 516 deaths were reported in Nigeria from December, 2016 to September, 2020. Increase in number of cases and deaths were observed with 298, 528, 796 and 1165 confirmed cases and 79, 125, 158 and 158 deaths in 2017, 2018, 2019 and 2020 respectively. Over 60% of the cases were reported in two states, Edo and Ondo states. The LF cases spread from 19 states in 2017 to 32 states and Federal Capital Territory (FCT) in 2020. Ondo state (25.39%) had the highest of deaths rate from LF over the four years. Case fatality rate (CFR) of LF was highest in 2017 (26.5%) with CFR of 23.7, 19.6 and 13.4% in 2018, 2019 and 2020 respectively. The peak of infection was in the month of February for the four years. Infections increases at the onset of dry season in November and decline till April when the wet season sets-in. Conclusion There is an annual increase in the number of LASV infection across the states in Nigeria. There is need to heighten control strategies through the use of integrated approach, ranging from vector control, health education and early diagnosis.

Urinary tract infections (UTIs) are common among pregnant women and are frequently caused by uropathogenic Escherichia coli (UPEC). This cross-sectional study investigated the distribution of key virulence genes, antimicrobial resistance patterns, and their relationship with phylogenetic groups in UPEC isolates obtained from pregnant women with asymptomatic bacteriuria in Gorgan, Iran. Out of 913 urine samples collected between 2018 and 2023, 360 non-duplicate UPEC isolates were confirmed using standard biochemical methods. Nineteen virulence genes, including neuC , fimH , papC , sfaS , sfa/focDE , focG , kpsMTII , ecp A, iutA , ibeA , rfc , fyuA , traT , iroN , papGII , hlyA , cnf1 , cdtB , and cvaC , were detected by PCR, while phylogenetic grouping was performed through triplex PCR targeting chuA , yjaA , and tspE4.C2 . Antimicrobial susceptibility testing showed that imipenem (96.3%) and nitrofurantoin (93.0%). The capsule gene kpsMTII was the most prevalent, and fimH was the most frequent adhesin gene (89.8%). Phylogenetic group B2 exhibited the highest diversity and frequency of virulence genes. Multidrug resistance (MDR) was observed in 51.4% of isolates, and although 58.3% of B2 strains were MDR, the association between phylogeny and MDR was not statistically significant ( p  > 0.05). Strong biofilm formation correlated significantly with resistance to ciprofloxacin and ampicillin ( p  < 0.01). This study documents a high prevalence of antimicrobial resistance and ESBL production among UPEC isolates from pregnant women with asymptomatic bacteriuria. The coexistence of multiple virulence traits with MDR and strong biofilm formation suggests potential biological interactions that warrant further investigation.

Accurate prediction of oil production rates through wellhead chokes is critical for optimizing crude oil production and operational efficiency in the petroleum industry. The central thrust of this investigation involves the systematic creation of machine learning (ML) paradigms for the robust prediction of choke flow performance. This endeavor is rigorously informed by comprehensive data acquired from an operational petroleum production facility in the Middle East. Within the dataset, produced gas-oil ratio (GOR), choke size, basic sediment and water (BS&W), wellhead pressure (THP), and crude oil API stand out as key parameters. Each plays a vital role in forecasting the oil production rate. To ensure reliability, robust data preprocessing was conducted using the Monte Carlo outlier detection (MCOD) method to recognize and manage data outliers. The models were trained using 198 data points, employing K-fold cross-validation (five folds) to ensure generalization. Gradient boosting machine (GBM) models were optimized using advanced algorithms like self-adaptive differential evolution (SADE), evolution strategy (ES), Bayesian probability improvement (BPI), and Batch Bayesian optimization (BBO). Among these, SADE demonstrated superior performance based on metrics such as average absolute relative error (AARE%), R 2 , and mean squared error (MSE). Furthermore, SHAP (SHapley Additive exPlanations) analysis was used to interpret the models and highlight the dominant influence of choke size and THP on the predictions. Overall, this research work presents a data-driven framework for highly accurate and interpretable predictions, significantly contributing to production optimization initiatives in the oil and gas sector.

Mud loss during drilling operations poses a significant problem in the oil and gas industry due to its contributions to increased costs and operational risks. This study aims to develop a reliable predictive model for mud loss volume using machine learning techniques to improve drilling efficiency and reduce non-productive time. The dataset consists of 949 field records from Middle Eastern drilling sites, incorporating variables such as borehole diameter, drilling fluid viscosity, mud weight, solid content, and pressure differential. Initial data analysis included statistical evaluation, outlier detection using leverage diagnostics, and data normalization to ensure validity and consistency. A Gradient Boosting Machine (GBM) served as the core predictor, with its hyperparameters fine-tuned using four optimization strategies: Evolution Strategies (ES), Batch Bayesian Optimization (BBO), Bayesian Probability Improvement (BBI), and Gaussian Process Optimization (GPO). Model performance was evaluated using k-fold cross-validation, with metrics including R², mean squared error and average absolute relative error percentage. Results demonstrated that the GBM-BPI achieved the strongest test performance (R² = 0.926, MSE = 1208.77, AARE% = 26.73), outperforming other approaches in accuracy and stability. Feature importance assessed through SHAP analysis revealed that hole size, formation type, and pressure differential were the most influential variables, while solid content had minimal effect.

The efficient formulation of drilling fluids is critical for maintaining stability and performance in demanding wellbore environments. In this study, a novel nanocomposite material (TiO 2 /Saponin/Zr) was synthesized and introduced into drilling fluid formulations to enhance rheological behavior, filtration control, and thermal stability. The synthesis involved sol-gel methods, FTIR, TGA, and SEM analyses, confirming the material’s successful functionalization and nanoscale structure. Rheological measurements demonstrated significant improvements in viscosity and shear stress with nanoparticle concentrations up to 500 ppm, where the optimal performance was achieved. Filtration tests revealed reductions in fluid loss by up to 50%, ensuring better wellbore stability. Statistical modeling with the Bingham Plastic and Herschel-Bulkley approaches revealed superior predictability for these nanocomposite-enhanced fluids. Overall, this innovative nanocomposite provides a promising avenue for addressing challenges in modern drilling operations, offering technical and operational benefits.

Optimizing oil production in wells employing gas lift systems is a critical challenge due to the complex interplay of operational and reservoir parameters. This study aimed to develop robust predictive models for estimating oil production rates using a comprehensive dataset from oil fields in south-eastern Iraq, leveraging advanced machine learning techniques. The dataset, comprised of 169 rigorously validated samples, includes key features such as basic sediment and water content, choke size, pressures, gas injection characteristics, gas lift valve depth, oil density, and temperature. Input and output variables were normalized and split into training and test sets to ensure fairness and reliability. Multiple machine learning models (Decision Tree, AdaBoost, Random Forest, Ensemble Learning, CNN, SVR, MLP-ANN, and Lasso Regression) were trained and evaluated using 5-fold cross-validation and key statistical metrics (R², MSE, AARE%). The Random Forest model demonstrated superior performance, achieving a test R² of 0.867 and the lowest prediction errors (MSE: 18502 and AARE: 8.76%) for the testing phase, while other models were prone to overfitting or underfitting. Sensitivity analysis and SHAP interpretability methods revealed that basic sediment and water content, choke size, and upstream pressure had the greatest influence on oil output. These findings underscore the importance of both statistical rigor and model interpretability in oil production forecasting and provide actionable insights for optimizing gas lift operations in oil wells.

Fines migration is a major cause of formation damage in clay‑rich sandstones, particularly under low‑salinity waterflooding where electrostatic double‑layer expansion destabilizes clay–mineral adhesion and causes severe permeability loss. This study investigates, at the laboratory scale, the potential of a Fe₃O₄@saponin/Cu nanocomposite to mitigate fines release and permeability loss during simulated low-salinity water flooding of clay-rich sandstone. The nanocomposite was synthesized by co‑precipitating magnetite nanoparticles, coating them with a saponin biopolymer via hydrogen bonding and hydrophobic interactions, and introducing Cu2⁺ through coordination with surface hydroxyl and glycosidic groups. Physicochemical characterization (FT‑IR, TGA, SEM, DLS) confirmed successful functionalization, enhanced thermal stability, controlled particle size, and colloidal stability up to 500 ppm loading. Zeta potential analysis revealed that the nanocomposite shifts mineral surface charge toward neutral or slightly positive values, reducing electrostatic repulsion between fines and the pore matrix. Core flooding of clay‑rich sandstone showed that untreated low‑salinity water caused catastrophic permeability loss (− 69.9%) and tripled injection pressure, whereas adding 500 ppm nanocomposite reduced impairment to − 4.1%, comparable to high‑salinity conditions without treatment. The mitigation mechanism involves synergistic electrostatic neutralization, steric stabilization from the saponin corona, and magnetic/coordination bridging that anchor fines to pore surfaces without plugging. These results demonstrate that Fe₃O₄@saponin/Cu nanofluid enables low‑salinity flooding while suppressing fines migration, providing a practical alternative to high‑salinity injection for enhanced oil recovery.

Drilling operations increasingly face challenges related to poor rheology control, excessive fluid loss, and inefficient cuttings transport, especially in HPHT conditions. This study aimed to address these issues by developing a sustainable nanoparticle based additive synthesized through a green route. A biogenic extract from Pinus nigra pollen was used to produce selenium doped silver zinc oxide nanocomposites (Se@Ag/AgO–ZnO) via a single step co precipitation method. The resulting heterostructured material exhibited nanoscale crystallite size and a textured morphology confirmed by SEM. The nanocomposite was incorporated into water based drilling fluids at concentrations of 0 to 5000 ppm. Rheological behavior was evaluated using Bingham Plastic modeling, filtration performance was measured under standard and HPHT conditions, and cuttings transport was assessed through rolling oven tests. Results showed that the optimal concentration of 1000 ppm increased yield point to 12.12 Pa and plastic viscosity to 48.7 cP while maintaining high model accuracy with R2 values of at least 0.991. Filtrate volumes decreased by up to 68.5 percent in standard tests and 69.2 percent in HPHT tests due to formation of a compact and low permeability filter cake. Quartz and shale cuttings recovery reached 88 percent and 79 percent respectively at 1000 ppm. At 5000 ppm, mild performance decline was linked to particle agglomeration. These findings demonstrate that pollen derived nanocomposites can enhance drilling fluid behavior and offer a sustainable approach for improving rheology, filtration control, and hole cleaning efficiency.

Dendrimers are nanosized, symmetrical molecules in which a small atom or group of atoms is surrounded by the symmetric branches known as dendrons. The structure of dendrimers possesses the greatest impact on their physical and chemical properties. They grow outwards from the core-shell which further reacts with monomers having one reactive or two dormant molecules. Dendrimers’ unique characteristics such as hyperbranching, well-defined spherical structure, and high compatibility with the biological systems are responsible for their wide range of applications including medical and biomedical areas. Particularly, the dendrimers’ three-dimensional structure can incorporate a wide variety of drugs to form biologically active drug conjugates. In this review, we focus on the synthesis, mechanism of drug encapsulations in dendrimers, and their wide applications in drug delivery.

Curcuma longa is very well-known medicinal plant not only in the Asian hemisphere but also known across the globe for its therapeutic and medicinal benefits. The active moiety of Curcuma longa is curcumin and has gained importance in various treatments of various disorders such as antibacterial, antiprotozoal, cancer, obesity, diabetics and wound healing applications. Several techniques had been exploited as reported by researchers for increasing the therapeutic potential and its pharmacological activity. Here, the dictum is the new room for the development of physicochemical, as well as biological, studies for the efficacy in target specificity. Here, we discussed nanoformulation techniques, which lend support to upgrade the characters to the curcumin such as enhancing bioavailability, increasing solubility, modifying metabolisms, and target specificity, prolonged circulation, enhanced permeation. Our manuscript tried to seek the attention of the researcher by framing some solutions of some existing troubleshoots of this bioactive component for enhanced applications and making the formulations feasible at an industrial production scale. This manuscript focuses on recent inventions as well, which can further be implemented at the community level.