Search

BackgroundChildhood remains a vulnerable period and a key determiner for adult health. Various illnesses experienced by children in their early years determine future performance and contribution to society. Acute and chronic infectious diseases, undernutrition, and early childhood non-communicable diseases have greatly been linked to intellectual disability, poor childhood development, increased morbidity, and household and healthcare economic costs. In most developing countries, infections contribute to a larger burden of disease. Despite this being the case, most developing countries have a limited range of diagnostic capacity and access to a wide range spectrum of WHO Access, Watch and Reserve antibiotics. This leads to overuse and misuse of the available antibiotics and a wide range spread of resistance strains. In this study, we evaluated common childhood presentations and indications for antibiotic prescriptions at a community hospital in Malawi.ObjectiveThis study analyzed common childhood Clinical Presentations and antibiotic prescription patterns at the pediatric outpatient department (OPD) at St. Gabriel Community Mission Hospital in Malawi.MethodsA retrospective search of all outpatient routinely corrected data from St. Gabriel Community Mission Hospital between January to December 2022 was carried out. Manual screening was done on all appropriate routines under 14 medical records, and prespecified variables were extracted. Data collected consisted of total OPD patient number, age, sex, diagnosis and prescription.ResultsA total of 2711 children under 15 years of age were included, with 53.9% being males. The majority of them were below the age of 5 (59.5%). 30% of the cases seen in the department were attributable to respiratory presentation, representing the majority of the cases seen. Sepsis and enteric diseases also constituted the majority of the cases seen and contributed 18% and 7% respectively. 68% per cent of the children seen during the period of the study had an antibiotic prescription, with the majority having only one antibiotic prescribed (31.7%). Overall, amoxicillin constituted the most commonly prescribed antibiotic for the whole system, while metronidazole was the most commonly prescribed antibiotic among enteric illnesses. Being under five was associated with a higher likelihood of antibiotic prescription (p

Abstract Here, we describe surface functionalized, superparamagnetic iron oxide nanocrystals (IONCs) for ultra-high PFAS sorption and precise, low energy (magnetic) separation, considering perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS). As a function of surface coating, sorption capacities described are considerably higher than previous studies using activated carbon, polymers, and unmodified metal/metal oxides, among others. In particular, positively charged polyethyleneimine (PEI) coated IONCs demonstrate extreme sorption capacities for both PFOA and PFOS due to electrostatic and hydrophobic interactions, along with high polymer grafting densities, while remaining stable in water, thus maintaining available surface area. Further, through a newly developed method using a quart crystal microbalance with dissipation (QCM-D), we present real-time, interfacial observations (e.g., sorption kinetics). Through this method, we explore underpinning mechanism(s) for differential PFAS (PFOA vs PFOS) sorption behavior(s), demonstrating that PFAS functional head group strongly influence molecular orientation on/at the sorbent interface. The effects of water chemistry, including pH, ionic composition of water, and natural organic matter on sorption behavior are also evaluated and along with material (treatment) demonstration via bench-scale column studies.

With the ever-growing reliance on Artificial Intelligence (AI) across diverse domains, there is an increasing concern surrounding the possibility of biases and unfairness inherent in AI systems. Fairness problems in automatic interview assessment systems, especially video-based automated interview assessments, have less been addressed despite their prevalence in the recruiting field. In this paper, we propose a method that resolves fairness problems in an automated interview assessment system that uses multimodal data as input. This is mainly done by minimizing the Wasserstein distance between two sensitive groups by introducing a regularization term. Subsequently, we employ a hyperparameter that can control the trade-off between fairness and accuracy. To test our method in various data settings, we suggest a preprocessing method that can manually adjust the underlying degree of unfairness in the training data. Experimental results show that our method presents state-of-the-art results in terms of fairness compared to previous methods.