Fluids and Electrolytes under Confinement in Single-Digit Nanopores.

Confined fluids and electrolyte solutions in nanopores exhibit rich and surprising physics and chemistry that impact the mass transport and energy efficiency in many important natural systems and industrial applications. Existing theories often fail to predict the exotic effects observed in the narrowest of such pores, called single-digit nanopores (SDNs), which have diameters or conduit widths of less than 10 nm, and have only recently become accessible for experimental measurements. What SDNs reveal has been surprising, including a rapidly increasing number of examples such as extraordinarily fast water transport, distorted fluid-phase boundaries, strong ion-correlation and quantum effects, and dielectric anomalies that are not observed in larger pores. Exploiting these effects presents myriad opportunities in both basic and applied research that stand to impact a host of new technologies at the water-energy nexus, from new membranes for precise separations and water purification to new gas permeable materials for water electrolyzers and energy-storage devices. SDNs also present unique opportunities to achieve ultrasensitive and selective chemical sensing at the single-ion and single-molecule limit. In this review article, we summarize the progress on nanofluidics of SDNs, with a focus on the confinement effects that arise in these extremely narrow nanopores. The recent development of precision model systems, transformative experimental tools, and multiscale theories that have played enabling roles in advancing this frontier are reviewed. We also identify new knowledge gaps in our understanding of nanofluidic transport and provide an outlook for the future challenges and opportunities at this rapidly advancing frontier.

Empirical social triad statistics can be explained with dyadic homophylic interactions.

The remarkable robustness of many social systems has been associated with a peculiar triangular structure in the underlying social networks. Triples of people that have three positive relations (e.g., friendship) between each other are strongly overrepresented. Triples with two negative relations (e.g., enmity) and one positive relation are also overrepresented, and triples with one or three negative relations are drastically suppressed. For almost a century, the mechanism behind these very specific ("balanced") triad statistics remained elusive. Here, we propose a simple realistic adaptive network model, where agents tend to minimize social tension that arises from dyadic interactions. Both opinions of agents and their signed links (positive or negative relations) are updated in the dynamics. The key aspect of the model resides in the fact that agents only need information about their local neighbors in the network and do not require (often unrealistic) higher-order network information for their relation and opinion updates. We demonstrate the quality of the model on detailed temporal relation data of a society of thousands of players of a massive multiplayer online game where we can observe triangle formation directly. It not only successfully predicts the distribution of triangle types but also explains empirical group size distributions, which are essential for social cohesion. We discuss the details of the phase diagrams behind the model and their parameter dependence, and we comment on to what extent the results might apply universally in societies.

Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathologic Predictive Power of RhoB in Rectal Cancer Biopsy.

RhoB protein belongs to the Rho GTPase family, which plays an important role in governing cell signaling and tissue morphology. Its expression is known to have implications in pathologic processes of diseases. In particular, the role of RhoB in rectal cancer is not well understood. Investigation in the regulation and communication of this protein, detected by immunohistochemical staining on the microscope, can help gain insightful information leading to optimal disease treatment options. Herein, deep learning-based image analysis and the decomposition of multiway arrays were used to study the predictive factor of RhoB in two cohorts of patients with rectal cancer having survival rates of <5 and >5 years. The results show distinctions between the tensor decomposition factors of the two cohorts.

Depletion of lipid storage droplet-1 delays endoreplication progression and induces cell death in Drosophila salivary gland.

Perilipins are evolutionarily conserved from insects to mammals. Drosophila lipid storage droplet-1 (LSD-1) is a lipid storage droplet membrane surface-binding protein family member and a counterpart to mammalian perilipin 1 and is known to play a role in lipolysis. However, the function of LSD-1 during specific tissue development remains under investigation. This study demonstrated the role of LSD-1 in salivary gland development. Knockdown of Lsd-1 in the salivary gland was established using the GAL4/UAS system. The third-instar larvae of knockdown flies had small salivary glands containing cells with smaller nuclei. The null mutant Drosophila also showed the same phenotype. The depletion of LSD-1 expression induced a delay of endoreplication due to decreasing CycE expression and increasing DNA damage. Lsd-1 genetically interacted with Myc in the third-instar larvae. These results demonstrate that LSD-1 is involved in cell cycle and cell death programs in the salivary gland, providing novel insight into the effects of LSD-1 in regulating salivary gland development and the interaction between LSD-1 and Myc.

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems.

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.

Intrinsic threshold plasticity: cholinergic activation and role in the neuronal recognition of incomplete input patterns.

Activity-dependent changes in membrane excitability are observed in neurons across brain areas and represent a cell-autonomous form of plasticity (intrinsic plasticity; IP) that in itself does not involve alterations in synaptic strength (synaptic plasticity; SP). Non-homeostatic IP may play an essential role in learning, e.g. by changing the action potential threshold near the soma. A computational problem, however, arises from the implication that such amplification does not discriminate between synaptic inputs and therefore may reduce the resolution of input representation. Here, we investigate consequences of IP for the performance of an artificial neural network in (a) the discrimination of unknown input patterns and (b) the recognition of known/learned patterns. While negative changes in threshold potentials in the output layer indeed reduce its ability to discriminate patterns, they benefit the recognition of known but incompletely presented patterns. An analysis of thresholds and IP-induced threshold changes in published sets of physiological data obtained from whole-cell patch-clamp recordings from L2/3 pyramidal neurons in (a) the primary visual cortex (V1) of awake macaques and (b) the primary somatosensory cortex (S1) of mice in vitro, respectively, reveals a difference between resting and threshold potentials of ∼15 mV for V1 and ∼25 mV for S1, and a total plasticity range of ∼10 mV (S1). The most efficient activity pattern to lower threshold is paired cholinergic and electric activation. Our findings show that threshold reduction promotes a shift in neural coding strategies from accurate faithful representation to interpretative assignment of input patterns to learned object categories. KEY POINTS: Intrinsic plasticity may change the action potential threshold near the soma of neurons (threshold plasticity), thus altering the input-output function for all synaptic inputs 'upstream' of the plasticity location. A potential problem arising from this shared amplification is that it may reduce the ability to discriminate between different input patterns. Here, we assess the performance of an artificial neural network in the discrimination of unknown input patterns as well as the recognition of known patterns subsequent to changes in the spike threshold. We observe that negative changes in threshold potentials do reduce discrimination performance, but at the same time improve performance in an object recognition task, in particular when patterns are incompletely presented. Analysis of whole-cell patch-clamp recordings from pyramidal neurons in the primary somatosensory cortex (S1) of mice reveals that negative threshold changes preferentially result from electric stimulation of neurons paired with the activation of muscarinic acetylcholine receptors.

Effects of ketogenic diet on health outcomes: an umbrella review of meta-analyses of randomized clinical trials.

BACKGROUND: Systematic reviews and meta-analyses of randomized clinical trials (RCTs) have reported the benefits of ketogenic diets (KD) in various participants such as patients with epilepsy and adults with overweight or obesity. Nevertheless, there has been little synthesis of the strength and quality of this evidence in aggregate. METHODS: To grade the evidence from published meta-analyses of RCTs that assessed the association of KD, ketogenic low-carbohydrate high-fat diet (K-LCHF), and very low-calorie KD (VLCKD) with health outcomes, PubMed, EMBASE, Epistemonikos, and Cochrane database of systematic reviews were searched up to February 15, 2023. Meta-analyses of RCTs of KD were included. Meta-analyses were re-performed using a random-effects model. The quality of evidence per association provided in meta-analyses was rated by the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) criteria as high, moderate, low, and very low. RESULTS: We included 17 meta-analyses comprising 68 RCTs (median [interquartile range, IQR] sample size of 42 [20-104] participants and follow-up period of 13 [8-36] weeks) and 115 unique associations. There were 51 statistically significant associations (44%) of which four associations were supported by high-quality evidence (reduced triglyceride (n = 2), seizure frequency (n = 1) and increased low-density lipoprotein cholesterol (LDL-C) (n = 1)) and four associations supported by moderate-quality evidence (decrease in body weight, respiratory exchange ratio (RER), hemoglobin A1c, and increased total cholesterol). The remaining associations were supported by very low (26 associations) to low (17 associations) quality evidence. In overweight or obese adults, VLCKD was significantly associated with improvement in anthropometric and cardiometabolic outcomes without worsening muscle mass, LDL-C, and total cholesterol. K-LCHF was associated with reduced body weight and body fat percentage, but also reduced muscle mass in healthy participants. CONCLUSIONS: This umbrella review found beneficial associations of KD supported by moderate to high-quality evidence on seizure and several cardiometabolic parameters. However, KD was associated with a clinically meaningful increase in LDL-C. Clinical trials with long-term follow-up are warranted to investigate whether the short-term effects of KD will translate to beneficial effects on clinical outcomes such as cardiovascular events and mortality.

Homophily-Based Social Group Formation in a Spin Glass Self-Assembly Framework.

Homophily, the tendency of humans to attract each other when sharing similar features, traits, or opinions, has been identified as one of the main driving forces behind the formation of structured societies. Here we ask to what extent homophily can explain the formation of social groups, particularly their size distribution. We propose a spin-glass-inspired framework of self-assembly, where opinions are represented as multidimensional spins that dynamically self-assemble into groups; individuals within a group tend to share similar opinions (intragroup homophily), and opinions between individuals belonging to different groups tend to be different (intergroup heterophily). We compute the associated nontrivial phase diagram by solving a self-consistency equation for "magnetization" (combined average opinion). Below a critical temperature, there exist two stable phases: one ordered with nonzero magnetization and large clusters, the other disordered with zero magnetization and no clusters. The system exhibits a first-order transition to the disordered phase. We analytically derive the group-size distribution that successfully matches empirical group-size distributions from online communities.

Endosomal escape of nucleic acids from extracellular vesicles mediates functional therapeutic delivery.

Extracellular vesicles hold great promise as a drug delivery platform for RNA-based therapeutics. However, there is a lack of experimental evidence for the intracellular trafficking of nucleic acid cargos, specifically, whether they are capable of escaping from the endolysosomal confinement in the recipient cells to be released into the cytosol and hence, interact with their cytoplasmic targets. Here, we demonstrated how red blood cell-derived extracellular vesicles (RBCEVs) release their therapeutic RNA/DNA cargos at specific intracellular compartments characteristic of late endosomes and lysosomes. The released cargos were functional and capable of knocking down genes of interest in recipient cells, resulting in tumor suppression in vitro and in an acute myeloid leukemia murine model without causing significant toxicity. Notably, surface functionalization of RBCEVs with an anti-human CXCR4 antibody facilitated their specific uptake by CXCR4+ leukemic cells, leading to enhanced gene silencing efficiency. Our results provide insights into the cellular uptake mechanisms and endosomal escape routes of nucleic acid cargos delivered by RBCEVs which have important implications for further improvements of the RBCEV-based delivery system.

A new era of the Asian clinical research network: a report from the ATLAS international symposium.

This report summarizes the presentations and discussions in the first Asian Clinical Trials Network for Cancers (ATLAS) international symposium that was held on 24 April 2022, in Bangkok, Thailand, and hosted by the National Cancer Center Hospital (NCCH), co-hosted by the Pharmaceuticals and Medical Devices Agency (PMDA), Clinical Research Malaysia (CRM) and the Thai Society of Clinical Oncology (TSCO), and supported by Embassy of Japan in Thailand. Since 2020, the NCCH has conducted the ATLAS project to enhance research environments and infrastructures to facilitate international clinical research and cancer genomic medicine in the Asian region. The purpose of the symposium was to discuss what we can achieve under the ATLAS project, to share the latest topics and common issues in cancer research and to facilitate mutual understanding. Invitees included stakeholders from academic institutions, mainly at ATLAS collaborative sites, as well as Asian regulatory authorities. The invited speakers discussed ongoing collaborative research, regulatory perspectives to improve new drug access in Asia, the status of phase I trials in Asia, the introduction of research activities at the National Cancer Center (NCC) and the implementation of genomic medicine. As the next steps after this symposium, the ATLAS project will foster increased cooperation between investigators, regulatory authorities and other stakeholders relevant to cancer research, and establish a sustainable pan-Asian cancer research group to increase the number of clinical trials and deliver novel drugs to patients with cancer in Asia.

Noncontact vibration measurement using three spatial-frequency shifting coherent digital holography.

A new digital coherent holographic system that works as a spatial-frequency shifter for measuring three-dimensional (3D) vibration of an object is proposed. The spatial-frequency shifter is constructed by a system of three mirrors inclined with different small angles to shift the object wave to three different frequencies in the spatial-frequency domain. By applying the Fourier transform method and appropriate filters to the hologram recorded by the camera of the system, a three-phase set of object waves corresponding to three shifted frequencies was obtained. From the relation between the phases and the relative position of the object, the position of each point on the surface of the object along the x, y, and z directions was extracted from each hologram. The same process was repeatedly applied to a series of holograms recorded by a fast camera, allowing the 3D vibration of the object to be precisely observed.

Experiences and perceptions of dementia in Vietnam and among the Vietnamese diaspora: a systematic review of qualitative studies.

Objectives: This paper aimed to review and synthesise the qualitative research evidence on the experiences and perceptions of dementia in Vietnam and among the Vietnamese diaspora.Methods: Systematic searches were conducted in June 2019 using Medline, Embase, Emcare, PsycINFO and Cochrane electronic databases, as well as grey literature. Keywords and Medical Subject Headings [MeSH terms] for dementia and associated terms were combined with keywords for Vietnam and its provinces. Qualitative research articles published in English or Vietnamese were included to examine evidence on the life experiences of Vietnamese people with dementia using thematic analysis.Results: Our searches resulted in 3,940 papers, from which 21 qualitative research studies were included for final analysis. The majority of research has not been undertaken in Vietnam but with the Vietnamese diaspora in Western countries and has taken a cultural perspective to analyses. Research in Western countries has focused on the need for culturally adapted and culturally sensitive models of care. Emerging themes about the life experiences of Vietnamese people with dementia identified from the studies included: many people do not have diagnostic terms for dementia but use the descriptive language of symptoms; stigma was a reported problem and on occasions can be observed in the descriptive language used for people with dementia; cultural and traditional values create both an opportunity and a barrier, supporting compassion, family care and relaxation, but creating barriers to accessing health services or long-term residential care.Conclusions: This is the first systematic review reporting qualitative evidence on the life experiences of people with dementia in Vietnam and among the Vietnamese diaspora. Future research is needed on the voice of people with dementia themselves and their caregivers particularly in Vietnam, and low and middle-income countries with regards to living with dementia, pathways to care from diagnosis, treatment, care and support, additional social care and preparedness for end of life care for people with dementia.

Crystallographic Effects of GaN Nanostructures in Photoelectrochemical Reaction.

Tuning the surface structure of the photoelectrode provides one of the most effective ways to address the critical challenges in artificial photosynthesis, such as efficiency, stability, and product selectivity, for which gallium nitride (GaN) nanowires have shown great promise. In the GaN wurtzite crystal structure, polar, semipolar, and nonpolar planes coexist and exhibit very different structural, electronic, and chemical properties. Here, through a comprehensive study of the photoelectrochemical performance of GaN photocathodes in the form of films and nanowires with controlled surface polarities we show that significant photoelectrochemical activity can be observed when the nonpolar surfaces are exposed in the electrolyte, whereas little or no activity is measured from the GaN polar c-plane surfaces. The atomic origin of this fundamental difference is further revealed through density functional theory calculations. This study provides guideline on crystal facet engineering of metal-nitride photo(electro)catalysts for a broad range of artificial photosynthesis chemical reactions.

Anger treatment via CBAT delivered remotely: Outcomes on psychometric and self-monitored measures of anger.

Studies of anger treatment have often reported on reappraisal and relaxation techniques delivered in person to forensic and psychiatric samples. The present study evaluated an integrative programme of cognitive-behavioural affective therapy (CBAT) delivered remotely to chronic pain sufferers with comorbid anger. Volunteers (N = 54) were randomly assigned to either CBAT or an Emotional Education (EE) group, both receiving hour-long videorecorded sessions twice a week for 4 weeks plus weekly calls by telephone. At 1-month post-treatment, follow-up was conducted. Outcomes were measured using the Anger Parameters Scale (APS) and its five subscales (frequency, duration, intensity, latency and threshold) as well as daily self-monitored anger logs. As hypothesized, pre- to post-treatment decline in APS total scores was significant for CBAT, Hedges' g = 0.65, 95% CI [0.14, 1.16] but nonsignificant and small for EE, g = 0.17, CI [-0.41, 0.75]. At the primary endpoint (post-treatment), APS total scores were significantly lower for CBAT than for EE. Treatment gains were maintained till follow-up. On all five APS subscales, pre-post effect sizes were medium-sized for CBAT and generally small for EE. This picture was mirrored in the self-monitored frequency, duration and intensity of anger. Findings support the efficacy of CBAT over time, its comparative efficacy over EE and its ecological generalizability. Future research could expand sample size, reduce sample imbalance, extend follow-up and strengthen treatment potency with more sessions. The present study renews enthusiasm for teletherapy and is the first to demonstrate CBAT efficacy in treating maladaptive anger in the chronic pain population.

Rectified and Salt Concentration Dependent Wetting of Hydrophobic Nanopores.

Nanopores lined with hydrophobic groups function as switches for water and all dissolved species, such that transport is allowed only when applying a sufficiently high transmembrane pressure difference or voltage. Here we show a hydrophobic nanopore system whose wetting and ability to transport water and ions is rectified and can be controlled with salt concentration. The nanopore we study contains a junction between a hydrophobic zone and a positively charged hydrophilic zone. The nanopore is closed for transport at low salt concentrations and exhibits finite current only when the concentration reaches a threshold value that is dependent on the pore opening diameter, voltage polarity and magnitude, and type of electrolyte. The smallest nanopore studied here had a 4 nm diameter and did not open for transport in any concentration of KCl or KI examined. A 12 nm nanopore was closed for all KCl solutions but conducted current in KI at concentrations above 100 mM for negative voltages and opened for both voltage polarities at 500 mM KI. Nanopores with a hydrophobic/hydrophilic junction can thus function as diodes, such that one can identify a range of salt concentrations where the pores transport water and ions for only one voltage polarity. Molecular dynamics simulations together with continuum models provided a multiscale explanation of the observed phenomena and linked the salt concentration dependence of wetting with an electrowetting model. Results presented are crucial for designing next-generation chemical and ionic separation devices as well as understanding fundamental properties of hydrophobic interfaces under nanoconfinement.

Development of a photoelectrochemically self-improving Si/GaN photocathode for efficient and durable H2 production.

Development of an efficient yet durable photoelectrode is of paramount importance for deployment of solar-fuel production. Here, we report the photoelectrochemically self-improving behaviour of a silicon/gallium nitride photocathode active for hydrogen production with a Faradaic efficiency approaching ~100%. By using a correlative approach based on different spectroscopic and microscopic techniques, as well as density functional theory calculations, we provide a mechanistic understanding of the chemical transformation that is the origin of the self-improving behaviour. A thin layer of gallium oxynitride forms on the side walls of the gallium nitride grains, via a partial oxygen substitution at nitrogen sites, and displays a higher density of catalytic sites for the hydrogen-evolving reaction. This work demonstrates that the chemical transformation of gallium nitride into gallium oxynitride leads to sustained operation and enhanced catalytic activity, thus showing promise for oxynitride layers as protective catalytic coatings for hydrogen evolution.

Coherent fibre link for synchronization of delocalized atomic clocks.

Challenging experiments for tests in fundamental physics require highly coherent optical frequency references with suppressed phase noise from hundreds of kHz down to µHz of Fourier frequencies. It can be achieved by remote synchronization of many frequency references interconnected by stabilized optical fibre links. Here we describe the path to realize a delocalized optical frequency reference for spectroscopy of the isomeric state of the nucleus of Thorium-229 atom. This is a prerequisite for the realization of the next generation of an optical clock - the nuclear clock. We present the established 235 km long phase-coherent stabilized cross-border fibre link connecting two delocalized metrology laboratories in Brno and Vienna operating highly-coherent lasers disciplined by active Hydrogen masers through optical frequency combs. A significant part (up to tens of km) of the optical fibre is passing urban combined collectors with a non-negligible level of acoustic interference and temperature changes, which results in a power spectral density of phase noise over 105 rad2· Hz-1. Therefore, we deploy a digital signal processing technique to suppress the fibre phase noise over a wide dynamic range of phase fluctuations. To demonstrate the functionality of the link, we measured the phase noise power spectral density of a remote beat note between two independent lasers, locked to high-finesse stable resonators. Using optical frequency combs at both ends of the link, a long-term fractional frequency stability in the order of 10-15 between local active Hydrogen masers was measured as well. Thanks to this technique, we have achieved reliable operation of the phase-coherent fibre link with fractional stability of 7 × 10-18 in 103 s.

Convolutional Blur Attention Network for Cell Nuclei Segmentation.

Accurately segmented nuclei are important, not only for cancer classification, but also for predicting treatment effectiveness and other biomedical applications. However, the diversity of cell types, various external factors, and illumination conditions make nucleus segmentation a challenging task. In this work, we present a new deep learning-based method for cell nucleus segmentation. The proposed convolutional blur attention (CBA) network consists of downsampling and upsampling procedures. A blur attention module and a blur pooling operation are used to retain the feature salience and avoid noise generation in the downsampling procedure. A pyramid blur pooling (PBP) module is proposed to capture the multi-scale information in the upsampling procedure. The superiority of the proposed method has been compared with a few prior segmentation models, namely U-Net, ENet, SegNet, LinkNet, and Mask RCNN on the 2018 Data Science Bowl (DSB) challenge dataset and the multi-organ nucleus segmentation (MoNuSeg) at MICCAI 2018. The Dice similarity coefficient and some evaluation matrices, such as F1 score, recall, precision, and average Jaccard index (AJI) were used to evaluate the segmentation efficiency of these models. Overall, the proposal method in this paper has the best performance, the AJI indicator on the DSB dataset and MoNuSeg is 0.8429, 0.7985, respectively.

Effects of Cadmium and Lead on Muscle and Liver Glycogen Levels of Climbing Perch (Anabas testudineus).

The aim of this study was to assess the glycogen content in the muscle and liver tissues of the climbing perch (Anabas testudineus) exposed to sublethal concentrations of Cd and Pb over 28 days of exposure and 14 days of depuration. Muscle and liver glycogen levels in A. testudineus after Pb or Cd treatment were significantly lower (p < 0.05) than that of A. testudineus in the control group during the exposure phase. In the recovery phase, muscle, and liver glycogen levels in A. testudineus increased in all Pb treatment groups, whereas they continuously decreased in all Cd treatment groups. Fish affected by Cd had obvious difficulties recovering from the stress response. It was concluded that exposure to the tested concentrations of Pb and Cd could be a potent endocrine activity disruptor, which may lead to adverse impacts on the health of A. testudineus.

Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images.

Literature survey shows that convolutional neural network (CNN)-based pretrained models have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using chest X-ray (CXR) and computed tomography (CT) datasets. However, most of the methods have used a smaller number of data samples for both CT and CXR datasets for training, validation, and testing. As a result, the model might have shown good performance during testing, but this type of model will not be more effective on unseen COVID-19 data samples. Generalization is an important term to be considered while designing a classifier that can perform well on completely unseen datasets. Here, this work proposes a large-scale learning with stacked ensemble meta-classifier and deep learning-based feature fusion approach for COVID-19 classification. The features from the penultimate layer (global average pooling) of EfficientNet-based pretrained models were extracted and the dimensionality of the extracted features reduced using kernel principal component analysis (PCA). Next, a feature fusion approach was employed to merge the features of various extracted features. Finally, a stacked ensemble meta-classifier-based approach was used for classification. It is a two-stage approach. In the first stage, random forest and support vector machine (SVM) were applied for prediction, then aggregated and fed into the second stage. The second stage includes logistic regression classifier that classifies the data sample of CT and CXR into either COVID-19 or Non-COVID-19. The proposed model was tested using large CT and CXR datasets, which are publicly available. The performance of the proposed model was compared with various existing CNN-based pretrained models. The proposed model outperformed the existing methods and can be used as a tool for point-of-care diagnosis by healthcare professionals.