Examining the implementation of facility-based integrated management of childhood illness and insecticide treated nets in Bangladesh: lessons learned through implementation research.

BACKGROUND: Bangladesh significantly reduced under-5 mortality (U5M) between 2000 and 2015, despite its low economic development and projected high mortality rates in children aged under 5 years. A portion of this success was due to implementation of health systems-delivered evidence-based interventions (EBIs) known to reduce U5M. This study aims to understand how Bangladesh was able to achieve this success between 2000 and 2015. Implementation science studies such as this one provide insights on the implementation process that are not sufficiently documented in existing literature. METHODS: Between 2017 and 2020, we conducted mixed methods implementation research case studies to examine how six countries including Bangladesh outperformed their regional and economic peers in reducing U5M. Using existing data and reports supplemented by key informant interviews, we studied key implementation strategies and associated implementation outcomes for selected EBIs and contextual factors which facilitated or hindered this work. We used facility-based integrated management of childhood illnesses and insecticide treated nets as examples of two EBIs that were implemented successfully and with wide reach across the country to understand the strategies put in place as well as the facilitating and challenging contextual factors. RESULTS: Strategies which contributed to the successful implementation and wide coverage of the selected EBIs included community engagement, data use, and small-scale testing, important to achieving implementation outcomes such as effectiveness, reach and fidelity, although gaps persisted including in quality of care. Key contextual factors including a strong community-based health system, accountable leadership, and female empowerment facilitated implementation of these EBIs. Challenges included human resources for health, dependence on donor funding and poor service quality in the private sector. CONCLUSION: As countries work to reduce U5M, they should build strong community health systems, follow global guidance, adapt their implementation using local evidence as well as build sustainability into their programs. Strategies need to leverage facilitating contextual factors while addressing challenging ones.

Multifunctional Siloxene-Decorated Laser-Inscribed Graphene Patch for Sweat Ion Analysis and Electrocardiogram Monitoring.

Potentiometric detection in complex biological fluids enables continuous electrolyte monitoring for personal healthcare; however, the commercialization of ion-selective electrode-based devices has been limited by the rapid loss of potential stability caused by electrode surface inactivation and biofouling. Here, we describe a simple multifunctional hybrid patch incorporating an Au nanoparticle/siloxene-based solid contact (SC) supported by a substrate made of laser-inscribed graphene on poly(dimethylsiloxane) for the noninvasive detection of sweat Na+ and K+. These SC nanocomposites prevent the formation of a water layer during ion-to-electron transfer, preserving 3 and 5 µV/h potential drift for the Na+ and K+ ion-selective electrodes, respectively, after 13 h of exposure. The lamellar structure of the siloxene sheets increases the SC area. In addition, the electroplated Au nanoparticles, which have a large surface area and excellent conductivity, further increased the electric double-layer capacitance at the interface between the ion-selective membranes and solid-state contacts, thus facilitating ion-to-electron transduction and ultimately improving the detection stability of Na+ and K+. Furthermore, the integrated temperature and electrocardiogram sensors in the flexible patch assist in monitoring body temperature and electrocardiogram signals, respectively. Featuring both electrochemical ion-selective and physical sensors, this patch offers immense potential for the self-monitoring of health.

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment.

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning.

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods.

One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this observation, four different convolutional neural network (CNN) meta-architectures, including InceptionV3, InceptionResnetV2, Xception, and DenseNet121, were applied by using the TensorFlow object detection framework. By using InceptionResnetV2, we were able to attain the avant-garde in cataract disease detection. This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset. This model also has a sensitivity of 96.55% and a specificity of 100%. In addition, the model greatly minimizes training loss while boosting accuracy.

SCN8A Mutation in Infantile Epileptic Encephalopathy: Report of Two Cases.

Early infantile epileptic encephalopathy type 13 is a severe form of epilepsy caused by mutations in the sodium channel 8 alpha (SCN8A) gene. This gene encodes the neuronal voltage-gated sodium channel which plays vital role in neuronal excitability. Here we present two cases with SCN8A encephalopathy. Both cases had mutation in p.Arg1872Gin the SCN8A gene, which was detected by targeted next generation sequencing. Case 1 was a 14-month old boy, who had a normal birth history with normal development up to 6 months and then developed repeated generalized seizure, which was nonresponsive to multiple antiepileptic drugs. He also had neuroregression and dystonia. His electroencephalogram (EEG) showed progressive background abnormality with burst suppression pattern. His metabolic panel was normal and had partial response to carbamazepine. The second case was for an 11-month old boy with the onset of seizure at the age of 7 months. Seizure was generalized, resistant to multiple antiepileptic drugs. He had developmental delay from beginning, no movement disorder. EEG showed focal discharge from left temporal and occipital region. He showed partial response to oxcarbazepine. Our cases had similarities with the previously reported cases. The detailed discussion of our cases would contribute to early detection and targeted treatment of SCN8A encephalopathy. This also gives special emphasis on a genetic test in infants with intractable epilepsy, movement disorder and developmental delay.

Cell morphology of extrusion foamed poly(lactic acid) using endothermic chemical foaming agent.

Poly(lactic acid) (PLA) was foamed with an endothermic chemical foaming agent (CFA) through an extrusion process. The effects of polymer melt flow index, CFA content, and processing speed on the cellular structures, void fraction, and cell-population density of foamed PLA were investigated. The apparent melt viscosity of PLA was measured to understand the effect of melt index on the cell morphology of foamed PLA samples. The void fraction was strongly dependent on the PLA melt index. It increased with increasing melt index, reaching a maximum value, after which it decreased. Melt index showed no significant effect on the cell-population density of foamed samples within the narrow range studied. A gas containment limit was observed in PLA foamed with CFA. Both the void fraction and cell-population density increased with an initial increase in CFA content, reached a maximum value, and then decreased as CFA content continued to increase. The processing speed also affected the morphology of PLA foams. The void fraction reached a maximum value as the extruder's screw speed increased to 40 rpm and a further increase in the processing speed tended to reduce the void fraction of foamed samples. By contrast, cell-population density increased one order of magnitude by increasing the screw speed from 20 to 120 rpm. The experimental results indicate that a homogeneous and finer cellular morphology could be successfully achieved in PLA foamed in an extrusion process with a proper combination of polymer melt flow index, CFA content, and processing speed.