Recent advances in the metamaterial and metasurface-based biosensor in the gigahertz, terahertz, and optical frequency domains.

Recently, metamaterials and metasurface have gained rapidly increasing attention from researchers due to their extraordinary optical and electrical properties. Metamaterials are described as artificially defined periodic structures exhibiting negative permittivity and permeability simultaneously. Whereas metasurfaces are the 2D analogue of metamaterials in the sense that they have a small but not insignificant depth. Because of their high optical confinement and adjustable optical resonances, these artificially engineered materials appear as a viable photonic platform for biosensing applications. This review paper discusses the recent development of metamaterial and metasurface in biosensing applications based on the gigahertz, terahertz, and optical frequency domains encompassing the whole electromagnetic spectrum. Overlapping features such as material selection, structure, and physical mechanisms were considered during the classification of our biosensing applications. Metamaterials and metasurfaces working in the GHz range provide prospects for better sensing of biological samples, THz frequencies, falling between GHz and optical frequencies, provide unique characteristics for biosensing permitting the exact characterization of molecular vibrations, with an emphasis on molecular identification, label-free analysis, and imaging of biological materials. Optical frequencies on the other hand cover the visible and near-infrared regions, allowing fine regulation of light-matter interactions enabling metamaterials and metasurfaces to offer excellent sensitivity and specificity in biosensing. The outcome of the sensor's sensitivity to an electric or magnetic field and the resonance frequency are, in theory, determined by the frequency domain and features. Finally, the challenges and possible future perspectives in biosensing application areas have been presented that use metamaterials and metasurfaces across diverse frequency domains to improve sensitivity, specificity, and selectivity in biosensing applications.

Chalcogen Bonding in Selective Recognition and Liquid-Liquid Extraction of Perrhenate.

Efficient recognition and extraction of hazardous anionic pollutants from water medium is of great significance for environmental concerns, representing a challenging area of research in supramolecular chemistry. In this study, we present, for the first time, a comprehensive demonstration of the ability of chalcogen bonding (ChB) to recognize and remove the ReO4- from 100% water medium. The anion recognition ability is well elucidated through solution phase NMR and ITC studies, which clearly reveal the selective binding of ReO4- over other oxo-anions. Moreover, the selenoimidazolium scaffold effectively engages in Se•••O ChB interaction with ReO4- as confirmed by X-ray crystal structure and XPS analysis. More importantly, the binding of ReO4- with different prolongations of the σ-holes, along with Se•••Se chalcogen bonding interactions, lead to the formation of a 1D supramolecular assembly. Eventually, ChB receptor Se4Me-Br exhibits ~62% ReO4- extraction efficiency through precipitation as the extraction method. Furthermore, in efforts to enhance efficiency, a hydrophobic ChB receptor Se4Do-PF6 has been prepared, achieving an efficiency of up to ~93% at a very low concentration (~5ppm) by liquid-liquid extraction.

COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning.

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.

Nanohole array integrated metal insulator metal (MIM) based structure employing dual mode SPR sensor for detection of Hemoglobin (Hb) in blood.

Surface Plasmon Resonance (SPR) based optical biosensors are recently the most attractive sensing devices that can detect minor changes in refractive index. Multiple methods have been developed to design SPR based biosensors with high-performance and ease of fabrication. This research is about a grating based biosensor that utilizes Silver (Ag) and Titanium (Ti) to produce the SP resonance state. The structure has a resonance wavelength, which displays sensitivity to changes in the surrounding medium of the refractive index. The study has been conducted using numerical simulations, utilizing the finite-difference-time-domain (FDTD) method.The simulation results shows a sharp resonance peaks in the wavelength range of 450-700 nm with a remarkable sensitivity of 172 nm/RIU (for mode 1 at SPR peak 465 nm) and 515 nm/RIU (for mode 2 at SPR peak 585 nm), which is superior to other on-chip device. The investigation involves a comparative analysis of sensing performance, focusing on parameters like transmission, reflection, FWHM and Quality factor to measure the detection accuracy of the proposed material combination. Later, we employed this miniature biosensor device to detect hemoglobin concentrations in the blood. Our findings indicate that this developed structure has great potential for detecting any biomolecule, such as proteins, glucose, fructose, nucleic acids, and cells.

Real-Time Obstructive Sleep Apnea Detection from Raw ECG and SpO2 Signal Using Convolutional Neural Network.

Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Recently, there have been numerous studies demonstrating the application of artificial intelligence to detect apnea in real time. But the majority of these studies apply data pre-processing and feature extraction techniques resulting in a longer inference time that makes the real-time detection system inefficient. This study proposes a single convolutional neural network architecture that can automatically extract spatial features and detect apnea from both electrocardiogram (ECG) and blood-oxygen saturation (SpO2) signals. Using segments of 10s, the network classified apnea with an accuracy of 94.2% and 96% for ECG and SpO2 respectively. Moreover, the overall performance of both models was consistent with an AUC score of 0.99.

Designing and development of agricultural rovers for vegetable harvesting and soil analysis.

To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover's hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN's SDG 2 Zero Hunger goals.

Post-Acute Cardiovascular Outcomes of COVID-19 in Children and Adolescents: An EHR Cohort Study from the RECOVER Project.

Background: The risk of cardiovascular outcomes in the post-acute phase of SARS-CoV-2 infection has been quantified among adults and children. This paper aimed to assess a multitude of cardiac signs, symptoms, and conditions, as well as focused on patients with and without congenital heart defects (CHDs), to provide a more comprehensive assessment of the post-acute cardiovascular outcomes among children and adolescents after COVID-19. Methods: This retrospective cohort study used data from the RECOVER consortium comprising 19 US children's hospitals and health institutions between March 2020 and September 2023. Every participant had at least a six-month follow-up after cohort entry. Absolute risks of incident post-acute COVID-19 sequelae were reported. Relative risks (RRs) were calculated by contrasting COVID-19-positive with COVID-19-negative groups using a Poisson regression model, adjusting for demographic, clinical, and healthcare utilization factors through propensity scoring stratification. Results: A total of 1,213,322 individuals under 21 years old (mean[SD] age, 7.75[6.11] years; 623,806 male [51.4%]) were included. The absolute rate of any post-acute cardiovascular outcome in this study was 2.32% in COVID-19 positive and 1.38% in negative groups. Patients with CHD post-SARS-CoV-2 infection showed increased risks of any cardiovascular outcome (RR, 1.63; 95% confidence interval (CI), 1.47-1.80), including increased risks of 11 of 18 post-acute sequelae in hypertension, arrhythmias (atrial fibrillation and ventricular arrhythmias), myocarditis, other cardiac disorders (heart failure, cardiomyopathy, and cardiac arrest), thrombotic disorders (thrombophlebitis and thromboembolism), and cardiovascular-related symptoms (chest pain and palpitations). Those without CHDs also experienced heightened cardiovascular risks after SARS-CoV-2 infection (RR, 1.63; 95% CI, 1.57-1.69), covering 14 of 18 conditions in hypertension, arrhythmias (ventricular arrhythmias and premature atrial or ventricular contractions), inflammatory heart disease (pericarditis and myocarditis), other cardiac disorders (heart failure, cardiomyopathy, cardiac arrest, and cardiogenic shock), thrombotic disorders (pulmonary embolism and thromboembolism), and cardiovascular-related symptoms (chest pain, palpitations, and syncope). Conclusions: Both children with and without CHDs showed increased risks for a variety of cardiovascular outcomes after SARS-CoV-2 infection, underscoring the need for targeted monitoring and management in the post-acute phase.

Supramolecular chemistry of liquid-liquid extraction.

Liquid-Liquid Extraction (LLE) is a venerable and widely used method for the separation of a targeted solute between two immiscible liquids. In recent years, this method has gained popularity in the supramolecular chemistry community due to the development of various types of synthetic receptors that effectively and selectively bind specific guests in an aqueous medium through different supramolecular interactions. This has eventually led to the development of state-of-the-art extraction technologies for the removal and purification of anions, cations, ion pairs, and small molecules from one liquid phase to another liquid phase, which is an industrially viable method. The focus of this perspective is to furnish a vivid picture of the current understanding of supramolecular interaction-based LLE chemistry. This will not only help to improve separation technology in the chemical, mining, nuclear waste treatment, and medicinal chemistry sectors but is also useful to address the purity issue of the extractable species, which is otherwise difficult. Thus, up-to-date knowledge on this subject will eventually provide opportunities to develop large-scale waste remediation processes and metallurgy applications that can address important real-life problems.

Infection Prevention and Control: Baseline Knowledge and Practices of TBAs in Rural Kano, Northwestern Nigeria.

BACKGROUND: Maternal infections remain a significant contributor to maternal mortality worldwide. Majority of births in northern Nigeria occur at home and are attended by Traditional Birth Attendants (TBAs). Little has been documented about their knowledge and practice on infection prevention and control practices in Kano, northern Nigeria. OBJECTIVES: This study evaluated the level as well as factors associated with TBAs' infection prevention and control knowledge and practices. METHODS: The study is the baseline phase of a quasi-experimental study, conducted in a rural LGA in Kano State, Nigeria. Using an adapted tool, 163 eligible TBAs were surveyed. Knowledge and practice of IPC were scored, aggregated, and dichotomized into good or poor. Binary logistic regression analysis was used to predict knowledge and practice of IPC. RESULTS: Majority (79.1%) of the TBAs exhibited poor IPC knowledge but many (78.5%) reported good practice. Good knowledge of IPC was predicted by the TBAs' age: a six-fold increased likelihood (AOR=6.25, 95% CI: 1.02- 38.53) and almost five-fold increased likelihood (AOR=4.75, 95% CI: 1.39- 16.24) for those in their second and fourth decades of life. TBAs who reported poor practice of IPC were 83% less likely (AOR=0.17, 95% CI: 0.03- 0.92) to have good knowledge of IPC. TBAs' practice was only linked to previous training (AOR=0.17, 95% CI: 0.04- 0.76). CONCLUSION: TBAs knowledge of IPC was low although reported practice was good. The need for tailored training interventions to enhance knowledge and skills for safe delivery care is paramount to improve maternal and neonatal outcomes.

CONTEXTE: Les infections maternelles restent une contribution significative à la mortalité maternelle dans le monde. La majorité des accouchements dans le nord du Nigeria ont lieu à domicile et sont assistés par des TBA. Peu de choses ont été documentées sur leurs connaissances et leurs pratiques en matière de prévention et de contrôle des infections à Kano, dans le nord du Nigeria. OBJECTIFS: Cette étude a évalué le niveau de connaissances et de pratiques des TBA en matière de prévention et de contrôle des infections, ainsi que les facteurs associés. MÉTHODES: L'étude est la phase de base d'une étude quasiexpérimentale, menée dans une LGA rurale de l'État de Kano, au Nigeria. En utilisant un outil adapté, 163 TBA éligibles ont été interrogés. Les connaissances et les pratiques en matière de PCI ont été évaluées, agrégées et dichotomisées en bonnes ou mauvaises. Une analyse de régression logistique binaire a été utilisée pour prédire les connaissances et les pratiques en matière de PCI. RÉSULTATS: La majorité (79,1 %) des TBA présentaient des connaissances médiocres en PCI, mais beaucoup (78,5 %) ont déclaré avoir de bonnes pratiques. De bonnes connaissances en PCI étaient prédites par l'âge des TBA : une probabilité multipliée par six (AOR=6,25, IC à 95 % : 1,02-38,53) et presque multipliée par cinq (AOR=4,75, IC à 95 % : 1,39-16,24) pour ceux dans leur deuxième et quatrième décennies de vie. Les TBA qui ont déclaré une mauvaise pratique de la PCI étaient 83 % moins susceptibles (AOR=0,17, IC à 95 % : 0,03-0,92) d'avoir de bonnes connaissances en PCI. La pratique des TBA était uniquement liée à une formation antérieure (AOR=0,17, IC à 95 % : 0,04­0,76). CONCLUSION: Les connaissances des TBA en matière de PCI étaient faibles bien que les pratiques déclarées étaient bonnes. La nécessité d'interventions de formation sur mesure pour améliorer les connaissances et les compétences en matière de soins de l'accouchement sécurisés est primordiale pour améliorer les résultats maternels et néonatals. MOTS-CLÉS: Accoucheuses Traditionnelles, Mortalité Maternelle, Infection Maternelle, Nigeria.

Metasurface absorber based single junction thin film solar cell exceeding 30% efficiency.

In this article, we report, as per our knowledge, for the first time, a thin film single junction solar cell with a metasurface absorber layer directly incorporated. We have used an interconnected dual inverted split ring resonator pattern in the InAsP absorber layer. The structure eliminated patterns of conventional metals, such as silver, aluminum, and gold, from the active layer, a common drawback in conventional solar absorbers, hindering their direct integration into solar cells. Optical simulation results show a peak ideal short circuit current density of 76.23mA/cm2 for the meta-absorber structure under solar illumination. This current is the highest among previously reported absorbers based on Group IV materials and III-V compounds, overcoming the low solar absorption of such metasurfaces. The final proposed solar cell structure combines this meta-absorber layer with traditional efficiency enhancement methods namely anti-reflecting coating, textured back reflector, and transparent top electrode. This novel single junction structure shows a solar absorption efficiency of 97.86% and a power conversion efficiency of 30.87%, the highest for III-V solar cells. Our device proves the ability of metasurface absorber layers to produce high-efficiency solar cells and is expected to pave the way for integrating novel meta-devices into state-of-the-art photovoltaic devices, aiding the global transition towards clean energy sources.

Design optimization and validation of UV-C illumination chamber for filtering facepiece respirators.

In this study, we constructed an UV-C illumination chamber using commercially available germicidal lamps and other locally available low-cost components for general-purpose biological decontamination purposes. The illumination chamber provides uniform illumination of around 1 J/cm2 in under 5 min across the chamber. The control mechanism was developed to automate the on/off process and make it more secure minimizing health and other electrical safety. To validate the decontamination efficacy of the UV-C Illumination Chamber we performed the Geobacillus spore strip culture assay. Additionally, we performed the viral load measurement by identifying the COVID-19-specific N-gene and ORF1 gene on surgical masks. The gold standard RT-qPCR measurement was performed to detect and quantify the COVID-19-specific gene on the mask sample. The biochemical assay was conducted on the control and test group to identify the presence of different types of bacteria, and fungi before and after exposure under the illumination chamber. The findings of our study revealed satisfactory decontamination efficacy test results. Therefore, it could be an excellent device in healthcare settings as a disinfection tool for biological decontamination such as SAR-CoV-2 virus, personal protection equipment (PPE), (including n95, k95 respirators, and surgical masks), and other common pathogens.

Label-free quantification of gold nanoparticles at the single-cell level using a multi-column convolutional neural network (MC-CNN).

Gold nanoparticles (AuNPs) are extensively used in cellular imaging, single-particle tracking, disease diagnosis, studying membrane protein interaction, and drug delivery. Understanding the dynamics of AuNP uptake in live cells is crucial for optimizing their efficacy and safety. Traditional manual methods for quantifying AuNP uptake are time-consuming and subjective, limiting their scalability and accuracy. The available fluorescence-based techniques are limited to photobleaching and photoblinking. Optical microscopy techniques are limited by diffraction limits. Electron microscopy-based imaging techniques are destructive and unsuitable for live cell imaging. Furthermore, the resulting images may contain hundreds of particles with varied intensities, blurring, and substantial occlusion, making it difficult to manually quantify AuNP uptake. To overcome this issue and measure AuNP uptake by live cells, we annotated a dataset of dark-field images of 50 nanometer-radius AuNPs at different incubation durations. Then, to count the number of particles present in a cell, we created a customized multi-column convolutional neural network (MC-CNN). The customized MC-CNN outperformed typical particle counting architectures when compared to spectroscopy-based counting. This will allow researchers to gain a better understanding of AuNP behavior and interactions with cells, paving the way for advancements in nanomedicine, drug delivery, and biomedical research. The code for this paper is available at the following link: https://github.com/Namerlight/LabelFree_AuNP_Quantification.

Selective recognition and extraction of iodide from pure water by a tripodal selenoimidazol(ium)-based chalcogen bonding receptor.

A selenium-based tripodal chalcogen bond (ChB) donor TPI-3Se is demonstrated for the recognition and extraction of I- from 100% water medium. NMR and ITC studies with the halides reveal that the ChB donor selectively binds with the large, weakly hydrated I-. Interestingly, I- crystallizes out selectively in the presence of other halides supporting the superiority of the selective recognition of I-. The X-ray structure of the ChB-iodide complex manifests both the µ1 and µ2 coordinated interactions, which is rare in the C-Se···I chalcogen bonding. Furthermore, to validate the selective I- binding potency of TPI-3Se in pure water, comparisons are made with its hydrogen and halogen bond donor analogs. The computational analysis also provides the mode of I- recognition by TPI-3Se. Importantly, this receptor is capable of extracting I- from pure water through selenium sigma-hole and I- interaction with a high degree of efficiency (∼70%).

Implementation of bubble continuous positive airway pressure for children with severe pneumonia and hypoxemia in intensive care unit of Dhaka Hospital, Bangladesh-Effect on pneumonia mortality.

BACKGROUND: After the completion of a randomized trial at Dhaka Hospital in 2013, bubble continuous positive airway pressure (BCPAP) oxygen therapy was incorporated as the part of the standard treatment for children with severe pneumonia with hypoxemia in an intensive care unit at Dhaka Hospital in August 2013 instead of World Health Organization (WHO) standard low flow oxygen therapy. OBJECTIVE: To understand the long-term effectiveness of the introduction of bCPAP oxygen therapy by comparing pneumonia mortality in the post-trial period (August 2013 to December 2017) with the pre-trial (February 2009 to July 2011) and trial periods (August 2011 to July 2013). METHODS: It was a retrospective analysis of prospectively collected hospital data of all admissions. Mortality rates of all children with WHO-defined pneumonia, and the subset of children with severe pneumonia and hypoxemia (oxygen saturation <90%) were evaluated. RESULTS: The analysis covered 10,107 children with pneumonia: 2523 in the pre-trial (414 with severe pneumonia and hypoxemia; none of them received bCPAP), 2959 during the trial (376 with severe pneumonia and hypoxemia; 79 received bCPAP), and 4625 in the post-trial period (1208 with severe pneumonia and hypoxemia; 1125 had bCPAP). The risk of death from pneumonia in the post-trial period was lower than in pre-trial (adjusted risk ratio [RR] = 0.73, 95% confidence interval [CI] = 0.58-0.92; p = 0.007), among children with severe pneumonia and hypoxemia, the risk of death was lower in the post-trial period than in the pre-trial (adjusted RR = 0.46, 95% CI = 0.37-0.58, p < 0.001), and the trial period (adjusted RR = 0.70, 95% CI = 0.51-0.95; p = 0.023). CONCLUSION: After the introduction of bCPAP oxygen therapy as part of the routine management of severe pneumonia and hypoxemia in the ICU of the Dhaka hospital, we observed significantly lower mortality, even after accounting for measurable confounding.

Pd-Catalyzed Tandem Pathway for Stereoselective Synthesis of (E)-1,3-Enyne from ß-Nitroalkenes by Using a Sacrificial Directing Group.

The involvement of nitroalkenes instead of minimal one alkyne motif for (E)-1,3-enynes synthesis through a palladium catalyzed stereoselective bond forming pathway at room temperature is presented. Implication of nitro group as a sacrificial directing group, formation of magical alkyne on a newly developed Csp 3 -Csp 3 bond with initial palladium-MBH adduct make this methodology distinctive. This protocol features an unprecedented sequential acetate addition, carbon-carbon bond formation, isomerization of double bond and nitromethane degradation in a tandem catalytic walk via dancing hybridization. Mechanistic understanding through identification of intermediates and computational calculations furnishes complete insight into the tandem catalytic pathway. Broad substrates scope and functional groups tolerance make this synthetic methodology magnificent and dynamic. This represents the first example of stereoselective 1,3-enyne synthesis exclusively from alkene substrates by introducing the concept of sacrificial directing group.

Efficiency improvement of thin film solar cell using silver pyramids array and antireflective layer.

In recent years, plasmonics has been widely employed to improve light trapping in solar cells. Silver nanospheres have been used in several research works to improve the capability of solar absorption. In this paper, we use silver pyramid-shaped nanoparticles, a noble plasmonic nanoparticle, inside thin-film silicon and InP solar cells to increase light absorption compared to previously published topologies. The proposed structure consists of a TiO2 pyramid structure placed at the top of the surface working as an anti-reflective layer, silicon/indium phosphate as an absorption layer, silver pyramid-shaped nanoparticles incorporated inside the absorption layer, and an aluminum reflecting layer at the bottom. In this research, we used finite difference time domain (FDTD) simulation to model the thin-film solar cell (TFSC). Optimizing the shape and placement of the silver pyramids, we have achieved an efficiency of 17.08% and 18.58% using silicon and InP as the absorbing layers respectively, which is significantly better than previously reported studies. The open-circuit voltages are 0.58 V and 0.92 V respectively, which is the highest among other configurations. To conclude, the findings of this study laid the foundation to create an efficient thin-film solar cell utilizing the light-trapping mechanism of noble plasmonic nanoparticles.

Enrichment of a Data Lake to Support Population Health Outcomes Studies Using Social Determinants Linked EHR Data.

The integration of electronic health records (EHRs) with social determinants of health (SDoH) is crucial for population health outcome research, but it requires the collection of identifiable information and poses security risks. This study presents a framework for facilitating de-identified clinical data with privacy-preserved geocoded linked SDoH data in a Data Lake. A reidentification risk detection algorithm was also developed to evaluate the transmission risk of the data. The utility of this framework was demonstrated through one population health outcomes research analyzing the correlation between socioeconomic status and the risk of having chronic conditions. The results of this study inform the development of evidence-based interventions and support the use of this framework in understanding the complex relationships between SDoH and health outcomes. This framework reduces computational and administrative workload and security risks for researchers and preserves data privacy and enables rapid and reliable research on SDoH-connected clinical data for research institutes.

Factors Associated With Receipt of Molecular Testing and its Impact on Time to Initial Systemic Therapy in Metastatic Non-Small Cell Lung Cancer.

BACKGROUND: Despite recommendations for molecular testing irrespective of patient characteristics, differences exist in receipt of molecular testing for oncogenic drivers amongst metastatic non-small cell lung cancer (mNSCLC) patients. Exploration into these differences and their effects on treatment is needed to identify opportunities for improvement. PATIENTS AND METHODS: We conducted a retrospective cohort study of adult patients diagnosed with mNSCLC between 2011 and 2018 using PCORnet's Rapid Cycle Research Project dataset (n = 3600). Log-binomial, Cox proportional hazards (PH), and time-varying Cox regression models were used to ascertain whether molecular testing was received, and time from diagnosis to molecular testing and/or initial systemic treatment in the context of patient age, sex, race/ethnicity, and multiple comorbidities status. RESULTS: The majority of patients in this cohort were ≤ 65 years of age (median [25th, 75th]: 64 [57, 71]), male (54.3%), non-Hispanic white individuals (81.6%), with > 2 comorbidities in addition to mNSCLC (54.1%). About half the cohort received molecular testing (49.9%). Patients who received molecular testing had a 59% higher probability of initial systemic treatment than patients who were yet to receive testing. Multiple comorbidity status was positively associated with receipt of molecular testing (RR, 1.27; 95% CI 1.08, 1.49). CONCLUSION: Receipt of molecular testing in academic centers was associated with earlier initiation of systemic treatment. This finding underscores the need to increase molecular testing rates amongst mNSCLC patients during a clinically relevant period. Further studies to validate these findings in community centers are warranted.

The Role of Food Insecurity and Dietary Diversity on Recovery from Wasting among Hospitalized Children Aged 6-23 Months in Sub-Saharan Africa and South Asia.

Background: Current guidelines for the management of childhood wasting primarily focus on the provision of therapeutic foods and the treatment of medical complications. However, many children with wasting live in food-secure households, and multiple studies have demonstrated that the etiology of wasting is complex, including social, nutritional, and biological causes. We evaluated the contribution of household food insecurity, dietary diversity, and the consumption of specific food groups to the time to recovery from wasting after hospital discharge. Methods: We conducted a secondary analysis of the Childhood Acute Illness Network (CHAIN) cohort, a multicenter prospective study conducted in six low- or lower-middle-income countries. We included children aged 6−23 months with wasting (mid-upper arm circumference [MUAC] ≤ 12.5 cm) or kwashiorkor (bipedal edema) at the time of hospital discharge. The primary outcome was time to nutritional recovery, defined as a MUAC > 12.5 cm without edema. Using Cox proportional hazards models adjusted for age, sex, study site, HIV status, duration of hospitalization, enrollment MUAC, referral to a nutritional program, caregiver education, caregiver depression, the season of enrollment, residence, and household wealth status, we evaluated the role of reported food insecurity, dietary diversity, and specific food groups prior to hospitalization on time to recovery from wasting during the 6 months of posthospital discharge. Findings: Of 1286 included children, most participants (806, 63%) came from food-insecure households, including 170 (13%) with severe food insecurity, and 664 (52%) participants had insufficient dietary diversity. The median time to recovery was 96 days (18/100 child-months (95% CI: 17.0, 19.0)). Moderate (aHR 1.17 [0.96, 1.43]) and severe food insecurity (aHR 1.14 [0.88, 1.48]), and insufficient dietary diversity (aHR 1.07 [0.91, 1.25]) were not significantly associated with time to recovery. Children who had consumed legumes and nuts prior to diagnosis had a quicker recovery than those who did not (adjusted hazard ratio (aHR): 1.21 [1.01,1.44]). Consumption of dairy products (aHR 1.13 [0.96, 1.34], p = 0.14) and meat (aHR 1.11 [0.93, 1.33]), p = 0.23) were not statistically significantly associated with time to recovery. Consumption of fruits and vegetables (aHR 0.78 [0.65,0.94]) and breastfeeding (aHR 0.84 [0.71, 0.99]) before diagnosis were associated with longer time to recovery. Conclusion: Among wasted children discharged from hospital and managed in compliance with wasting guidelines, food insecurity and dietary diversity were not major determinants of recovery.

ECG and SpO2 Signal-Based Real-Time Sleep Apnea Detection Using Feed-Forward Artificial Neural Network.

Sleep apnea (SA) is a common sleep disorder characterized by respiratory disturbance during sleep. Polysomnography (PSG) is the gold standard for apnea diagnosis, but it is time-consuming, expensive, and requires manual scoring. As an alternative to PSG, we investigated a real-time SA detection system using oxygen saturation level (SpO2) and electrocardiogram (ECG) signals individually as well as a combination of both. A series of R-R intervals were derived from the raw ECG data and a feed-forward deep artificial neural network is employed for the detection of SA. Three different models were built using 1-minute-long sequences of SpO2 and R-R interval signals. The 10-fold cross-validation result showed that the SpO2-based model performed better than the ECG-based model with an accuracy of 90.78 ± 10.12% and 80.04 ± 7.7%, respectively. Once combined, these two signals complemented each other and resulted in a better model with an accuracy of 91.83 ± 1.51%.