25 research outputs found
Integrating Prevention of Mother-to-Child HIV Transmission Programs to Improve Uptake: A Systematic Review
BACKGROUND: We performed a systematic review to assess the effect of integrated perinatal prevention of mother-to-child transmission of HIV interventions compared to non- or partially integrated services on the uptake in low- and middle-income countries. METHODS: We searched for experimental, quasi-experimental and controlled observational studies in any language from 21 databases and grey literature sources. RESULTS: Out of 28 654 citations retrieved, five studies met our inclusion criteria. A cluster randomized controlled trial reported higher probability of nevirapine uptake at the labor wards implementing HIV testing and structured nevirapine adherence assessment (RRR 1.37, bootstrapped 95% CI, 1.04-1.77). A stepped wedge design study showed marked improvement in antiretroviral therapy (ART) enrolment (44.4% versus 25.3%, p<0.001) and initiation (32.9% versus 14.4%, p<0.001) in integrated care, but the median gestational age of ART initiation (27.1 versus 27.7 weeks, p = 0.4), ART duration (10.8 versus 10.0 weeks, p = 0.3) or 90 days ART retention (87.8% versus 91.3%, p = 0.3) did not differ significantly. A cohort study reported no significant difference either in the ART coverage (55% versus 48% versus 47%, p = 0.29) or eight weeks of ART duration before the delivery (50% versus 42% versus 52%; p = 0.96) between integrated, proximal and distal partially integrated care. Two before and after studies assessed the impact of integration on HIV testing uptake in antenatal care. The first study reported that significantly more women received information on PMTCT (92% versus 77%, p<0.001), were tested (76% versus 62%, p<0.001) and learned their HIV status (66% versus 55%, p<0.001) after integration. The second study also reported significant increase in HIV testing uptake after integration (98.8% versus 52.6%, p<0.001). CONCLUSION: Limited, non-generalizable evidence supports the effectiveness of integrated PMTCT programs. More research measuring coverage and other relevant outcomes is urgently needed to inform the design of services delivering PMTCT programs
Genomic analysis of codon usage shows influence of mutation pressure, natural selection, and host features on Marburg virus evolution
Citizenship pressure and job performance : roles of citizenship fatigue and continuance commitment
Electromagnetic-guided neuronavigation for safe placement of intraventricular catheters in pediatric neurosurgery
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Predictive modeling of compressive strength for additively manufactured PEEK spinal fusion cages using machine learning techniques
The current study delves into the utilization of Machine Learning (ML) algorithms to evaluate the mechanical properties of additively manufactured PEEK spinal fusion cages. In this research, a range of ML models, including Linear Regression (LiR), Lasso Regression (LaR), Decision tree (DT), and K-Nearest Neighbor (KNN) are harnessed to enhance compressive strength prediction. Ensemble learning techniques such as bagging, boosting, and stacking are applied to identify the most accurate ML model in terms of achieving heightened accuracy and minimized errors. To facilitate this, spinal fusion cages are 3D printed using the Fused Filament Fabrication (FFF) technique and subsequently tested using a Universal Testing Machine (UTM). The development of ML models involves the exploration of independent material-extrusion factors, encompassing layer height (0.1 mm, 0.2 mm, 0.3 mm), printing temperature (370℃, 390℃, 410℃), printing speed (10 mm/sec, 30 mm/sec, 50 mm/sec), infill density (40%, 70%, 100%), build orientation (0º, 45º, 90º), and line width (0.1 mm, 0.2 mm, 0.3 mm). The robustness and effectiveness of the developed ML models in predicting compressive strength properties are optimized through comprehensive error metric analysis. The results indicate that the LiR model, particularly when implemented under the boosting ensemble technique, demonstrates the highest accuracy with a Mean Absolute Error (MAE) of 0.657, Root Mean Square Error (RMSE) of 0.758, and Median Absolute Error (MedAE) of 0.634. This underscores the potential of LiR for precise compressive strength prediction in 3D-printed PEEK spinal fusion cages for spinal and maxillomandibular reconstruction