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.

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.

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.

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.

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.

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.

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.

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.

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.

Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma.

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein-protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

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).

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.

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services.

The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the "HAM10000" dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model's classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

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.

Hyponatremia in COVID-19 patients: Experience from Bangladesh.

Background: The purpose of the study was to measure the prevalence of hyponatremia and its association with clinical and laboratory characteristics of hospitalized coronavirus disease 2019 (COVID-19) patients at Dhaka Medical College and Hospital (DMCH). Methods: This retrospective study was conducted in COVID-19 dedicated wards at DMCH from June to August 2020. Demographic, clinical, and laboratory data were collected from patient treatment sheets. Two groups of COVID-19 patients were retrospectively screened on the basis of plasma sodium level at admission: hyponatremic (sodium < 135 mM, n = 84) or normonatremic (sodium ≥ 135 mM, n = 48) patients. Severity was assessed using World Health Organization classification for COVID-19 disease severity. To compare the two groups, Pearson's χ 2 (qualitative variables) and Student's T tests (quantitative variables) were applied. The link between patients' clinical data and outcomes was investigated using logistic regression model. Results: A total of 132 patients were included in the study, with a mean age of 51.41 (±14.13) years. Hyponatremia was found in 84 patients (63.6%) and the remaining 48 patients (36.4%) had normal plasma Na+ values. Among them, 74 (56.06%) presented with severe disease and 53 (40.15%) with moderate disease. At presentation, patients with moderate COVID-19 disease had 2.15 (1.04-4.5) times higher odds of suffering from hyponatremia. Besides, hyponatremia was independently associated with on admission SpO2 (p = 0.038), hemoglobin (p = 0.004), and C-reactive protein (p = 0.001). Conclusions: The authors suggest that patients' serum electrolytes be measured during initial hospital admission and then monitored throughout the hospital stay to predict the probability for referral for invasive ventilation and for better management.

An investigation of the suitability of melissopalynology to authenticate Jarrah honey.

This study reports on the analysis of eleven Jarrah (Eucalyptus marginata) honeys, of which nearly half (n = 5) were re-classified as Blackbutt (E. patens) honey on the grounds of the predominant flower pollen identified by melissopalynology. Based on a comprehensive analysis of the honeys' physico- and phytochemical characteristics and antioxidant activity data, taking into account pH, electrical conductivity, refractive index and Brix values as well as moisture content, individual fructose and glucose content and derived fructose to glucose ratio alongside total phenolic content and antioxidant activity determined by the DPPH assay, no statistically significant difference was found amongst the eleven honeys classified by pollen analysis into two honey groups, 'Jarrah' or 'Blackbutt'. This study therefore draws into question the value of melissopalynology as an analysis tool to authenticate Jarrah honey.

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers.

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

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.

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.

A 2-month post-COVID-19 follow-up study on patients with dyspnea.

BACKGROUND AND AIMS: Dyspnea is one of the most common symptoms associated with the COVID-19 caused by novel coronavirus SARS-CoV-2. This study aimed to assess the prevalence of dyspnea, observe co-variables, and find predictors of dyspnea after 2 months of recovery from COVID-19. METHODS: A total of 377 patients were included in the study based on their responses and clinical findings during initial admission to the hospital with COVID-19. After excluding five deceased patients, a total of 327 patients were interviewed through telephone using a 12-point dyspnea scale and using relevant questions to gauge the patient clinically. Interviews were carried out by trained physicians, and responses were recorded and stored. All analyses were carried out using the statistical programming language R. RESULTS: Of the total 327 participants in the study, 34% had stated that they were suffering from respiratory symptoms even after 2 months of COVID-19. The study demonstrated that patient oxygen saturation level SpO2 (P = .03), D-dimer (P = .001), serum ferritin (P = .006), and the presence and severity of dyspnea are significantly correlated. In addition to that, patient smoking history (P = .012) and comorbidities such as chronic obstructive pulmonary disease (COPD) (P = .021) were found to be statistically significant among groups. CONCLUSION: These findings of this study can be useful for predicting and managing long-term complications of COVID-19.