Deep transfer learning approaches for Monkeypox disease diagnosis.

Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients. Thus, ML could potentially be used to diagnose Monkeypox as well. In this study, we developed a Monkeypox diagnosis model using Generalization and Regularization-based Transfer Learning approaches (GRA-TLA) for binary and multiclass classification. We tested our proposed approach on ten different convolutional Neural Network (CNN) models in three separate studies. The preliminary computational results showed that our proposed approach, combined with Extreme Inception (Xception), was able to distinguish between individuals with and without Monkeypox with an accuracy ranging from 77% to 88% in Studies One and Two, while Residual Network (ResNet)-101 had the best performance for multiclass classification in Study Three, with an accuracy ranging from 84% to 99%. In addition, we found that our proposed approach was computationally efficient compared to existing TL approaches in terms of the number of parameters (NP) and Floating-Point Operations per Second (FLOPs) required. We also used Local Interpretable Model-Agnostic Explanations (LIME) to explain our model's predictions and feature extractions, providing a deeper understanding of the specific features that may indicate the onset of Monkeypox.

Persistence of sleep disturbance among post-COVID patients: Findings from a 2-month follow-up study in a Bangladeshi cohort.

To evaluate the persistence and factors associated with sleep disturbances among COVID-19 patients with a history of sleep disturbances 2 months after discharge from the hospital. A total of 400 patients admitted at Dhaka Medical College Hospital during July and August were diagnosed as suffering from sleep disturbances during their hospital stay using a standardized scale. They were followed up 2 months later through telephone, and a total of 322 participants were interviewed (excluding 63 nonresponders and five deceased) regarding the persistence of disturbances in sleep through a structured questionnaire. Patient demographic, clinical, and epidemiological data including history regarding in-hospital sleep disturbance were retrieved from hospital treatment sheets. Results revealed, 35% of study participants (n = 113) were still experiencing symptoms of sleep disturbances during the interview by telephone. Age (p = 0.015), diabetes mellitus (relative risk [RR]: 1.21; confidence interval [CI]: 1.02-1.42, p = 0.022), on admission SPO2 (p = 0.009), C-reactive protein (CRP) (p = 0.025), serum ferritin (p = 0.014), and d-dimer (p = 0.030) were independently associated with sleep disturbances among participants (p < 0.05). Binary and fitting logistic regression through repeated K folds cross-validation revealed 1.65 (CI: 1.02-2.66), 1.07 (CI: 1.01-1.14), and 1.07 (CI: 1.00-1.15) times higher odds of persistence of sleep disturbances among patients with diabetes mellitus, increased neutrophil, and lymphocyte percentages, respectively. Findings of this study need to be validated and patients should be further followed up with more in-depth studies conducted 6 or 12 months after initial infection, possibly with the help of higher sample size and in-person interview.

A Comprehensive HPTLC-Based Analysis of the Impacts of Temperature on the Chemical Properties and Antioxidant Activity of Honey.

Honeys are commonly subjected to a series of post-harvest processing steps, such as filtration and/or radiation treatment and heating to various temperatures, which might affect their physicochemical properties and bioactivity levels. Therefore, there is a need for robust quality control assessments after honey processing and storage to ensure that the exposure to higher temperatures, for example, does not compromise the honey's chemical composition and/or antioxidant activity. This paper describes a comprehensive short-term (48 h) and long-term (5 months) study of the effects of temperature (40 °C, 60 °C and 80 °C) on three commercial honeys (Manuka, Marri and Coastal Peppermint) and an artificial honey, using high-performance thin-layer chromatography (HPTLC) analysis. Samples were collected at baseline, at 6 h, 12 h, 24 h and 48 h, and then monthly for five months. Then, they were analysed for potential changes in their organic extract HPTLC fingerprints, in their HPTLC-DPPH total band activities, in their major sugar composition and in their hydroxymethylfurfural (HMF) content. It was found that, while all the assessed parameters changed over the monitoring period, changes were moderate at 40 °C but increased significantly with increasing temperature, especially the honeys' HPTLC-DPPH total band activity and HMF content.

Australian Honeypot Ant (Camponotus inflatus) Honey-A Comprehensive Analysis of the Physiochemical Characteristics, Bioactivity, and HPTLC Profile of a Traditional Indigenous Australian Food.

Despite its cultural and nutritional importance for local Aboriginal people, the unusual insect honey produced by Western Australian honeypot ant (Camponotus inflatus) has to date been rarely investigated. This study reports on the honey's physicochemical properties, its total phenolic, major sugars and 5-hydroxymethylfurfural contents, and its antioxidant activities. The honey's color value is 467.63 mAU/63.39 mm Pfund, it has a pH of 3.85, and its electric conductivity is 449.71 µSiemens/cm. Its Brix value is 67.00, corresponding to a 33% moisture content. The total phenolics content is 19.62 mg gallic acid equivalent/100 g honey. Its antioxidant activity measured using the DPPH* (2,2-diphenyl-1-picrylhydrazyl) and FRAP (ferric reducing-antioxidant power) assays is 1367.67 µmol Trolox/kg and 3.52 mmol Fe+2/kg honey, respectively. Major sugars in the honey are glucose and fructose, with a fructose-to-glucose ratio of 0.85. Additionally, unidentified sugar was found in minor quantities.

Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis.

Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30-40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients' and smokers' tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein-protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein-drug interactions for our identified biomarkers.

Capacity development and safety measures for health care workers exposed to COVID-19 in Bangladesh.

BACKGROUND: The safety of health care workers (HCWs) in Bangladesh and the factors associated with getting COVID-19 have been infrequently studied. The aim of this study was to address this gap by assessing the capacity development and safety measures of HCWs in Bangladesh who have been exposed to COVID-19 and by identifying the factors associated with respondents' self-reported participation in capacity development trainings and their safety practices. METHODS: This cross-sectional study was based on an online survey of 811 HCWs working at 39 dedicated COVID-19 hospitals in Bangladesh. A pretested structured questionnaire consisting of questions related to respondents' characteristics, capacity development trainings and safety measures was administered. Binary logistic regressions were run to assess the association between explanatory and dependent variables. RESULTS: Among the respondents, 58.1% had been engaged for at least 2 months in COVID-19 care, with 56.5% of them attending capacity development training on the use of personal protective equipment (PPE), 44.1% attending training on hand hygiene, and 35% attending training on respiratory hygiene and cough etiquette. Only 18.1% reported having read COVID-19-related guidelines. Approximately 50% of the respondents claimed that there was an inadequate supply of PPE for hospitals and HCWs. Almost 60% of the respondents feared a high possibility of becoming COVID-19-positive. Compared to physicians, support staff [odds ratio (OR) 4.37, 95% confidence interval (CI) 2.25-8.51] and medical technologists (OR 8.77, 95% CI 3.14-24.47) were more exhausted from working in COVID-19 care. Respondents with longer duty rosters were more exhausted, and those who were still receiving infection prevention and control (IPC) trainings were less exhausted (OR 0.54, 95% CI 0.34-0.86). Those who read COVID-19 guidelines perceived a lower risk of being infected by COVID-19 (OR 0.44, 95% CI 0.29-0.67). Compared to the respondents who strongly agreed that hospitals had a sufficient supply of PPE, others who disagreed (OR 2.68, 95% CI 1.31-5.51) and strongly disagreed (OR 5.05, 95% CI 2.15-11.89) had a higher apprehension of infection by COVID-19. CONCLUSION: The findings indicated a need for necessary support, including continuous training, a reasonable duty roster, timely diagnosis of patients, and an adequate supply of quality PPE.

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things.

Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback-Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.

Sugar Profiling of Honeys for Authentication and Detection of Adulterants Using High-Performance Thin Layer Chromatography.

Honey adulteration, where a range of sugar syrups is used to increase bulk volume, is a common problem that has significant negative impacts on the honey industry, both economically and from a consumer confidence perspective. This paper investigates High-Performance Thin Layer Chromatography (HPTLC) for the authentication and detection of sugar adulterants in honey. The sugar composition of various Australian honeys (Manuka, Jarrah, Marri, Karri, Peppermint and White Gum) was first determined to illustrate the variance depending on the floral origin. Two of the honeys (Manuka and Jarrah) were then artificially adulterated with six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrup). The findings demonstrate that HPTLC sugar profiles, in combination with organic extract profiles, can easily detect the sugar adulterants. As major sugars found in honey, the quantification of fructose and glucose, and their concentration ratio can be used to authenticate the honeys. Quantifications of sucrose and maltose can be used to identify the type of syrup adulterant, in particular when used in combination with HPTLC fingerprinting of the organic honey extracts.

Substrate-Related Factors Affecting Cellulosome-Induced Hydrolysis for Lignocellulose Valorization.

Cellulosomes are an extracellular supramolecular multienzyme complex that can efficiently degrade cellulose and hemicelluloses in plant cell walls. The structural and unique subunit arrangement of cellulosomes can promote its adhesion to the insoluble substrates, thus providing individual microbial cells with a direct competence in the utilization of cellulosic biomass. Significant progress has been achieved in revealing the structures and functions of cellulosomes, but a knowledge gap still exists in understanding the interaction between cellulosome and lignocellulosic substrate for those derived from biorefinery pretreatment of agricultural crops. The cellulosomic saccharification of lignocellulose is affected by various substrate-related physical and chemical factors, including native (untreated) wood lignin content, the extent of lignin and xylan removal by pretreatment, lignin structure, substrate size, and of course substrate pore surface area or substrate accessibility to cellulose. Herein, we summarize the cellulosome structure, substrate-related factors, and regulatory mechanisms in the host cells. We discuss the latest advances in specific strategies of cellulosome-induced hydrolysis, which can function in the reaction kinetics and the overall progress of biorefineries based on lignocellulosic feedstocks.

Physicochemical Properties, Antioxidant Activity, and High-Performance Thin-Layer Chromatography Profiling of Propolis Samples from Western Australia.

This study reports on the physicochemical and antioxidant properties of propolis samples from various regions across Western Australia and identifies some phenolic constituents using high-performance thin-layer chromatography (HPTLC). Total phenolic content (TPC) was determined using a modified Folin-Ciocalteu assay, and antioxidant activity was investigated with the Ferric Reducing Antioxidant Power (FRAP) assay and also visualised and semi-quantified by HPTLC-DPPH analysis. TPC values ranged from 9.26 to 59.3 mg gallic acid equivalent/g of raw propolis and FRAP assay data from 4.34 to 53.8 mmol Fe2+ mmol/kg of raw propolis, although some of these variations might be related to differences in extraction yields obtained with 70% ethanol. The presence of luteolin, taxifolin, naringenin, and 4-hydroxyphenylacetic acid was confirmed based on a comprehensive, validated matching approach against an HPTLC-derived database. The findings of the study highlight the importance of future research on the chemical composition and bioactivity of Western Australian propolis.

Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh.

Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model's predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model's transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field.

Regulation of regeneration in Arabidopsis thaliana.

We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. Searching for TF-binding sites in the promoters of the co-expressed and co-regulated (CCGs) genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each subnetwork. Finally, six potential candidate TFs-WOX9A, LEC2, PGA37, WIP5, PEI1, and AIL1 from subnetwork 1-were identified, and their roles in somatic embryogenesis (GO:0010262) and regeneration (GO:0031099) were discussed, so were the TFs in Subnetwork 12 and 17 associated with regeneration. The TFs identified were also assessed using the CIS-BP database and Expression Atlas. Our analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to regeneration. The tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-023-00121-9.

Antioxidant Activity and Phenolic Compound Identification and Quantification in Western Australian Honeys.

This study reports on the total phenolic content and antioxidant activity as well as the phenolic compounds that are present in Calothamnus spp. (Red Bell), Agonis flexuosa (Coastal Peppermint), Corymbia calophylla (Marri) and Eucalyptus marginata (Jarrah) honeys from Western Australia. The honey's total phenolic content (TPC) was determined using a modified Folin-Ciocalteu assay, while their total antioxidant activity was determined using FRAP and DPPH assays. Phenolic constituents were identified using a High Performance Thin-Layer Chromatography (HTPLC)-derived phenolic database, and the identified phenolic compounds were quantified using HPTLC. Finally, constituents that contribute to the honeys' antioxidant activity were identified using a DPPH-HPTLC bioautography assay. Based on the results, Calothamnus spp. honey (n = 8) was found to contain the highest (59.4 ± 7.91 mg GAE/100 g) TPC, followed by Eucalyptus marginata honey (50.58 ± 3.76 mg GAE/100 g), Agonis flexuosa honey (36.08 ± 4.2 mg GAE/100 g) and Corymbia calophylla honey (29.15 ± 5.46 mg GAE/100 g). In the FRAP assay, Calothamnus spp. honey also had the highest activity (9.24 ± 1.68 mmol Fe2+/kg), followed by Eucalyptus marginata honey (mmol Fe2+/kg), whereas Agonis flexuosa (5.45 ± 1.64 mmol Fe2+/kg) and Corymbia calophylla honeys (4.48 ± 0.82 mmol Fe2+/kg) had comparable FRAP activity. In the DPPH assay, when the mean values were compared, it was found that Calothamnus spp. honey again had the highest activity (3.88 ± 0.96 mmol TE/kg) while the mean DPPH antioxidant activity of Eucalyptus marginata, Agonis flexuosa, and Corymbia calophylla honeys were comparable. Kojic acid and epigallocatechin gallate were found in all honeys, whilst other constituents (e.g., m-coumaric acid, lumichrome, gallic acid, taxifolin, luteolin, epicatechin, hesperitin, eudesmic acid, syringic acid, protocatechuic acid, t-cinnamic acid, o-anisic acid) were only identified in some of the honeys. DPPH-HPTLC bioautography demonstrated that most of the identified compounds possess antioxidant activity, except for t-cinnamic acid, eudesmic acid, o-anisic acid, and lumichrome.

Integrated bioinformatics and statistical approach to identify the cmmon mlecular mchanisms of oesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder.

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.

TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization.

Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver. The synthetic data generated in a general setting was used to test four TF-TG identification methods, ENET-based methods performed better than Lasso-based methods. Synthetic data generated from two network settings was used to test Huber-Net and MSE-Net, which outperformed all other methods. The TF-TG identification methods were also tested with SND1 and gl3 overexpression transcriptomic data, Huber-ENET and MSE-ENET outperformed all other methods when genome-wide predictions were performed. The TF-TG identification methods fill the gap of lacking a method for genome-wide TG prediction of a TF, and potential for validating ChIP/DAP-seq results, while the two Net-based methods are instrumental for predicting pathway GRNs.

Trends and inequity in improved sanitation facility utilisation in Bangladesh: Evidence from Bangladesh Demographic and Health Surveys.

Improved sanitation is indispensable to human health. However, lack of access to improved sanitation remains one of the most daunting public health challenges of the twenty-first century in Bangladesh. The aim of the study was to describe the trends in access to improved sanitation facilities following the inequity gap among households in different socioeconomic groups in Bangladesh. Data from the Bangladesh Demographic and Health Survey (BDHS) 2007, 2011, 2014, and 2017-18 were extracted for this study. Inequity in access to improved sanitation was calculated using rich-poor ratio and concentration index to determine the changes in inequity across the time period. In Bangladesh, the proportion of households with access to improved sanitation increased steadily from 25.4% to 45.4% between 2007 and 2014, but slightly decreased to 44.0% in 2017-18. Age, educational status, marital status of household head, household wealth index, household size, place of residence, division, and survey year were significantly associated with the utilisation of improved sanitation. There is a pro-rich situation, which means that utilisation of improved sanitation was more concentrated among the rich across all survey years (Concentration Index ranges: 0.40 to 0.27). The government and other relevant stakeholders should take initiatives considering inequity among different socioeconomic groups to ensure the use of improved sanitation facilities for all, hence achieving universal health coverage.

Evaluation of feasibility phase of adaptive version of locally made bubble continuous positive airway pressure oxygen therapy for the treatment of COVID-19 positive and negative adults with severe pneumonia and hypoxaemia.

Background: Bubble continuous positive airway pressure (bCPAP) oxygen therapy has been shown to be safe and effective in treating children with severe pneumonia and hypoxaemia in Bangladesh. Due to lack of adequate non-invasive ventilatory support during coronavirus disease 2019 (COVID-19) crisis, we aimed to evaluate whether bCPAP was safe and feasible when adapted for use in adults with similar indications. Methods: Adults (18-64 years) with severe pneumonia and moderate hypoxaemia (80 to <90% oxygen saturation (SpO2) in room air) were provided bCPAP via nasal cannula at a flow rate of 10 litres per minute (l/min) oxygen at 10 centimetres (cm) H2O pressure, in two tertiary hospitals in Dhaka, Bangladesh. Qualitative interviews and focus group discussions, using a descriptive phenomenological approach, were performed with patients and staff (n = 39) prior to and after the introduction (n = 12 and n = 27 respectively) to understand the operational challenges to the introduction of bCPAP. Results: We enrolled 30 adults (median age 52, interquartile range (IQR) 40-60 years) with severe pneumonia and hypoxaemia and/or acute respiratory distress syndrome (ARDS) irrespective of coronavirus disease 2019 (COVID-19) test results to receive bCPAP. At baseline mean SpO2 on room air was 87% (±2) which increased to 98% (±2), after initiation of bCPAP. The mean duration of bCPAP oxygen therapy was 14.4 ± 24.8 hours. There were no adverse events of note, and no treatment failure or deaths. Operational challenges to the clinical introduction of bCPAP were lack of functioning pulse oximeters, difficult nasal interface fixation among those wearing nose pin, occasional auto bubbling or lack of bubbling in water-filled plastic bottle, lack of holder for water-filled plastic bottle, rapid turnover of trained clinicians at the hospitals, and limited routine care of patients by hospital clinicians particularly after official hours. Discussion: If the tertiary hospitals in Bangladesh are supplied with well-functioning good quality pulse oximeters and enhanced training of the doctors and nurses on proper use of adapted version of bCPAP, in treating adults with severe pneumonia and hypoxaemia with or without ARDS, the bCPAP was found to be safe, well tolerated and not associated with treatment failure across all study participants. These observations increase the confidence level of the investigators to consider a future efficacy trial of adaptive bCPAP oxygen therapy compared to WHO standard low flow oxygen therapy in such patients. Conclusion: s Although bCPAP oxygen therapy was found to be safe and feasible in this pilot study, several challenges were identified that need to be taken into account when planning a definitive clinical trial.

COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans.

With the increase in severity of COVID-19 pandemic situation, the world is facing a critical fight to cope up with the impacts on human health, education and economy. The ongoing battle with the novel corona virus, is showing much priority to diagnose and provide rapid treatment to the patients. The rapid growth of COVID-19 has broken the healthcare system of the affected countries, creating a shortage in ICUs, test kits, ventilation support system. etc. This paper aims at finding an automatic COVID-19 detection approach which will assist the medical practitioners to diagnose the disease quickly and effectively. In this paper, a deep convolutional neural network, 'COV-RadNet' is proposed to detect COVID positive, viral pneumonia, lung opacity and normal, healthy people by analyzing their Chest Radiographic (X-ray and CT scans) images. Data augmentation technique is applied to balance the dataset 'COVID 19 Radiography Dataset' to make the classifier more robust to the classification task. We have applied transfer learning approach using four deep learning based models: VGG16, VGG19, ResNet152 and ResNext 101 to detect COVID-19 from chest X-ray images. We have achieved 97% classification accuracy using our proposed COV-RadNet model for COVID/Viral Pneumonia/Lungs Opacity/Normal, 99.5% accuracy to detect COVID/Viral Pneumonia/Normal and 99.72% accuracy to detect COVID and non-COVID people. Using chest CT scan images, we have found 99.25% accuracy to classify between COVID and non-COVID classes. Among the performance of the pre-trained models, ResNext 101 has shown the highest accuracy of 98.5% for multiclass classification (COVID, viral pneumonia, Lungs opacity and normal).

A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks.

Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of K-means with CNN are the six modules examined and evaluated. Experiments are carried out on various types of images such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, and computed tomography (CT) scan images for lungs. After combining all of the datasets, we have constructed five subsets of data, each of which had a different number of images: 50, 100, 500, 1000, and 2000. Each of the models was executed and trained on the selected subset of the datasets. From the experimental analysis, it is observed that the performance of K-means with CNN is better than others and achieved 96.45% segmentation accuracy with an average time of 9.09 seconds.

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks.

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.