DNA Quantity and Quality Comparisons between Cryopreserved and FFPE Tumors from Matched Pan-Cancer Samples.

Personalized cancer care requires molecular characterization of neoplasms. While the research community accepts frozen tissues as the gold standard analyte for molecular assays, the source of tissue for testing in clinical cancer care comes almost universally from formalin-fixed, paraffin-embedded tissue (FFPE). As newer technologies emerge for DNA characterization that requires higher molecular weight DNA, it was necessary to compare the quality of DNA in terms of DNA length between FFPE and cryopreserved samples. We hypothesized that cryopreserved samples would yield higher quantity and superior quality DNA compared to FFPE samples. We analyzed DNA metrics by performing a head-to-head comparison between FFPE and cryopreserved samples from 38 human tumors representing various cancer types. DNA quantity and purity were measured by UV spectrophotometry, and DNA from cryopreserved tissue demonstrated a 4.2-fold increase in DNA yield per mg of tissue (p-value < 0.001). DNA quality was measured on a fragment microelectrophoresis analyzer, and again, DNA from cryopreserved tissue demonstrated a 223% increase in the DNA quality number and a 9-fold increase in DNA fragments > 40,000 bp (p-value < 0.0001). DNA from the cryopreserved tissues was superior to the DNA from FFPE samples in terms of DNA yield and quality.

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

Enhancing rice growth and yield with weed endophytic bacteria Alcaligenes faecalis and Metabacillus indicus under reduced chemical fertilization.

Endophytic bacteria, recognized as eco-friendly biofertilizers, have demonstrated the potential to enhance crop growth and yield. While the plant growth-promoting effects of endophytic bacteria have been extensively studied, the impact of weed endophytes remains less explored. In this study, we aimed to isolate endophytic bacteria from native weeds and assess their plant growth-promoting abilities in rice under varying chemical fertilization. The evaluation encompassed measurements of mineral phosphate and potash solubilization, as well as indole-3-acetic acid (IAA) production activity by the selected isolates. Two promising strains, tentatively identified as Alcaligenes faecalis (BTCP01) from Eleusine indica (Goose grass) and Metabacillus indicus (BTDR03) from Cynodon dactylon (Bermuda grass) based on 16S rRNA gene phylogeny, exhibited noteworthy phosphate and potassium solubilization activity, respectively. BTCP01 demonstrated superior phosphate solubilizing activity, while BTDR03 exhibited the highest potassium (K) solubilizing activity. Both isolates synthesized IAA in the presence of L-tryptophan, with the detection of nifH and ipdC genes in their genomes. Application of isolates BTCP01 and BTDR03 through root dipping and spraying at the flowering stage significantly enhanced the agronomic performance of rice variety CV. BRRI dhan29. Notably, combining both strains with 50% of recommended N, P, and K fertilizer doses led to a substantial increase in rice grain yields compared to control plants receiving 100% of recommended doses. Taken together, our results indicate that weed endophytic bacterial strains BTCP01 and BTDR03 hold promise as biofertilizers, potentially reducing the dependency on chemical fertilizers by up to 50%, thereby fostering sustainable rice production.

Prevalence of and factors associated with zero-dose and under-immunized children in selected areas of Bangladesh: Findings from Lot Quality Assurance Sampling Survey.

BACKGROUND: In the era of Gavi's 5.0 vision of "leaving no one behind with immunization", childhood routine vaccination in missed communities is considered as a priority concern. Despite having a success story at the national level, low uptake of immunization is still persistent in selected pocket areas of Bangladesh. However, prevalence and the associated factors of zero-dose (ZD) and under-immunization (UI) are still unknown at those geo-pockets of Bangladesh. Thus, the study aims to report and identify the factors associated with ZD and UI in selected geographical locations. METHODS: This study used data from a Lot Quality Assurance Sampling (LQAS) survey where 504 households from 18 clusters of four hard to reach (HTR) and one urban slum were included. Caregivers of children aged 4.5 to 23 months were interviewed. Three outcome variables- ZD, UI and ZD/UI were considered and several related attributes were considered as independent variables. Data were analyzed through bivariate analysis, binary logistic regression and dominance analysis. RESULTS: Overall, 32% of the children were either ZD (8%) or UI (26%) in the selected areas. The adjusted odds of ZD/UI for urban slum and haor (wetlands) areas were 5.62 and 3.61 respectively considering coastal areas as reference. However, distance of nearest EPI center, availability of EPI card, age of caregivers, education and occupation of mother and number of earning members in household were influential factors for ZD/UI. According to dominance analysis, availability of EPI card can explain the most of the variation of ZD/UI in this study. CONCLUSION: The study findings highlight the high prevalence ZD/UI in certain geo-pockets of the country. It provided a powerful insight of current situation and associated factors in regards to ZD/UI in the country which will help policy-makers and programme managers in designing programmes to reduce missed communities in Bangladesh.

Occurrence of microplastics in fish gastrointestinal tracts belongs to different feeding habits from the Bangladesh coast of the Bay of Bengal.

The Bay of Bengal (BoB) is home to a range of commercially important species with different food habits and feeding features. Microplastic (MP) contamination in the fish of BoB, like in many other marine environments, is a significant environmental concern. The study aimed to investigate the presence of microplastics (MPs) in the gastrointestinal tracts (GITs) of selected commercial marine fishes from the Bangladesh coast of the BoB. Six fish species (Escualosa thoracata, Tenualosa ilisha, Johnius belangerii, Trichiurus lepturus, Planiliza parsia, and Mystus gulio) were investigated (n = 120) following hydrogen peroxide digestion, and floatation (saline solution) protocols. After analyses, a total number of 696 MPs (dimension 0.3 to 5 mm) were identified. Moreover, the highest occurrence of MPs in fish GITs was found in planktivorous fish (average of 7.7 items/individual), followed by omnivorous (average of 5.2 items/individual), and carnivorous fish (average of 4.6 items/individual) (p < 0.001). However, planktivorous E. thoracata showed the highest number of MPs per g of GIT (average of 30.99 items/g GIT), whereas T. ilisha showed the lowest count (average of 0.77 items/g GIT). Different types of MPs (fibers (19 to 76%), fragments (6 to 61%), films (8 to 35%), microbeads (0 to 5%), and foams (0 to 2%)) were also observed. In terms of the color of MPs, the transparent, black, green, and blue types were the most common. Polymers were found as polyethylene (35 to 43%), polyethylene terephthalate (28 to 35%), polyamide (20 to 31%), and polystyrene (0 to 7%). The study provides a significant incidence of MPs in fish from the Bangladesh part of the BoB, which is very concerning. Therefore, long-term research is indispensable to ascertain the variables affecting the presence of MPs in fish, their origins, and their potential effects on the BoB fisheries. Stringent policies on plastic use and disposal should be strongly urged in this coastal region.

Characterization of textile effluent treatment plant sludge and its industrial application in fired clay bricks with health risk assessment.

The textile industry in Bangladesh faces environmental and health challenges due to the disposal of solid waste from Effluent Treatment Plants (ETPs). To address this issue, a study was conducted using soil from a brick industry near Dhaka, amending it with varying amounts of dry sludge to create clay bricks. The original soil had a loam texture and medium plasticity. The research found that adding 9 wt% of sludge resulted in Grade A commercial bricks with a compressive strength of 15.33 MPa and water absorption of 13.33 wt%, meeting BDS 208 standards. However, these sludge-incorporated bricks experienced more shrinkage during the burning process due to organic content, requiring additional soil to maintain conventional dimensions. Also, to assess the health hazards of these sludge-incorporated bricks, a leaching test was performed, revealing that no toxic heavy metals (Pb, Cd, Cr, Cu, Ni, and Zn) in the leachate exceeded the limits set by the United States Environmental Protection Agency (USEPA). The study indicates that textile ETP sludge can serve as a sustainable raw material for bricks, potentially reducing the environmental burden caused by textile sludge disposal by 28.75%. This innovative approach offers a promising solution to both environmental and health concerns associated with textile waste in Bangladesh's industrial sector.

Development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images.

Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement. In this regard, early diagnosis of KOA is necessary for better treatment. Nevertheless, manual KOA detection is time-consuming and error-prone for large data hubs. In contrast, an automated detection system aids the specialist in diagnosing KOA grades accurately and quickly. So, the main objective of this study is to create an automated decision system that can analyze KOA and classify the severity grades, utilizing the extracted features from segmented X-ray images. In this study, two different datasets were collected from the Mendeley and Kaggle database and combined to generate a large data hub containing five classes: Grade 0 (Healthy), Grade 1 (Doubtful), Grade 2 (Minimal), Grade 3 (Moderate), and Grade 4 (Severe). Several image processing techniques were employed to segment the region of interest (ROI). These included Gradient-weighted Class Activation Mapping (Grad-Cam) to detect the ROI, cropping the ROI portion, applying histogram equalization (HE) to improve contrast, brightness, and image quality, and noise reduction (using Otsu thresholding, inverting the image, and morphological closing). Besides, the focus filtering method was utilized to eliminate unwanted images. Then, six feature sets (morphological, GLCM, statistical, texture, LBP, and proposed features) were generated from segmented ROIs. After evaluating the statistical significance of the features and selection methods, the optimal feature set (prominent six distance features) was selected, and five machine learning (ML) models were employed. Additionally, a decision-making strategy based on the six optimal features is proposed. The XGB model outperformed other models with a 99.46 % accuracy, using six distance features, and the proposed decision-making strategy was validated by testing 30 images.

The pectinolytic activity of Burkholderia cepacia and its application in the bioscouring of cotton knit fabric.

BACKGROUND: Enzymatic catalysis in different industrial applications is often preferred over chemical methods due to various advantages, such as higher specificity, greater efficiency, and less environmental footprint. Pectinases are a group of enzymes that catalyze the degradation of pectic compounds, the key components of plant middle lamella and the primary cell wall. Pectinases have found applications in multiple industrial processes, including cotton bioscouring, fruit juice extraction and its clarification, plant fiber degumming, paper making, plant biomass liquefaction, and saccharification, among others. The purpose of this study was to taxonomically characterize a bacterial species exhibiting pectinolytic activities and assess its pectinolytic activity qualitatively and quantitatively, as well as test its bioscouring potential. RESULTS: Here, we report that Burkholderia cepacia, a previously unknown species with pectinolytic activity, exerts such activity comparable to commercially used pectinase enzymes in the textile industry, but requires less temperature for activity. CONCLUSION: Quantitative evaluation of enzyme activity indicates the potential of the bacterial species for use in the bioscouring of cotton knit fabric.

Investigations of hemato-biochemical and histopathological parameters, and growth performance of walking catfish (Clarias batrachus) exposed to PET and LDPE microplastics.

Fish inhabiting various trophic levels are affected differently as the presence of microplastic (MP) in the water column and their ingestion by fish varies. Walking catfish (Clarias batrachus) inhabits the bottom of the water bodies. To understand the effects of MP, we exposed C. batrachus to two types of MP - polyethylene terephthalate (PET) and low-density polyethylene (LDPE) for 60 days. After exposure, hematological indices, mainly red blood cells and hemoglobin levels decreased, and white blood cells increased significantly compared to the control group (p < 0.05). A significant increase in the levels of blood urea and glucose was observed, and serum glutamic pyruvate transaminase and serum glutamyl oxaloacetic transaminase activity remained elevated (p < 0.05). Histopathological examination of the liver, kidney, intestine, and gills showed morphological alterations. Moreover, MP exposure caused growth retardation (p < 0.05) in C. batrachus. Widespread pollution of water bodies by MP may impose serious ecological risks to bottom-feeding fish in Bangladesh.

Disease-associated metabolic pathways affected by heavy metals and metalloid.

Increased exposure to environmental heavy metals and metalloids and their associated toxicities has become a major threat to human health. Hence, the association of these metals and metalloids with chronic, age-related metabolic disorders has gained much interest. The underlying molecular mechanisms that mediate these effects are often complex and incompletely understood. In this review, we summarize the currently known disease-associated metabolic and signaling pathways that are altered following different heavy metals and metalloids exposure, alongside a brief summary of the mechanisms of their impacts. The main focus of this study is to explore how these affected pathways are associated with chronic multifactorial diseases including diabetes, cardiovascular diseases, cancer, neurodegeneration, inflammation, and allergic responses upon exposure to arsenic (As), cadmium (Cd), chromium (Cr), iron (Fe), mercury (Hg), nickel (Ni), and vanadium (V). Although there is considerable overlap among the different heavy metals and metalloids-affected cellular pathways, these affect distinct metabolic pathways as well. The common pathways may be explored further to find common targets for treatment of the associated pathologic conditions.

A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional Transformer and Low-Resolution Images to Reduce Computation Time.

Diabetic retinopathy (DR) is the foremost cause of blindness in people with diabetes worldwide, and early diagnosis is essential for effective treatment. Unfortunately, the present DR screening method requires the skill of ophthalmologists and is time-consuming. In this study, we present an automated system for DR severity classification employing the fine-tuned Compact Convolutional Transformer (CCT) model to overcome these issues. We assembled five datasets to generate a more extensive dataset containing 53,185 raw images. Various image pre-processing techniques and 12 types of augmentation procedures were applied to improve image quality and create a massive dataset. A new DR-CCTNet model is proposed. It is a modification of the original CCT model to address training time concerns and work with a large amount of data. Our proposed model delivers excellent accuracy even with low-pixel images and still has strong performance with fewer images, indicating that the model is robust. We compare our model's performance with transfer learning models such as VGG19, VGG16, MobileNetV2, and ResNet50. The test accuracy of the VGG19, ResNet50, VGG16, and MobileNetV2 were, respectively, 72.88%, 76.67%, 73.22%, and 71.98%. Our proposed DR-CCTNet model to classify DR outperformed all of these with a 90.17% test accuracy. This approach provides a novel and efficient method for the detection of DR, which may lower the burden on ophthalmologists and expedite treatment for patients.

Zinc trafficking: 1,10-phenanthroline, glutathione, and other metal binding ligands form adducts with proteomic Zn2.

Hypotheses were tested that the proteome of pig kidney LLC-PK1 cells (i) contains Zn-proteins that react with a diversity of native and pharmacologically active metal-binding ligands to form ternary complexes and (ii) includes proteins that bind Zn2+ nonspecifically and together form ternary adducts with a variety of metal-binding agents. The method to observe ternary complex formation with Zn-proteins and proteome•Zn involved preformation of fluorescent TSQ [6-Methoxy-(8-p-toluenesulfonamido)quinoline]-Zn-proteins and/or proteome•Zn-TSQ adducts followed by competitive reaction with selected ligands. The loss of TSQ-dependent fluorescence signaled the replacement of TSQ by the competing ligand in the starting adducts. In vitro, 1,10-phenanthroline competed effectively with TSQ for binding to Zn-proteins in the proteome. The successful competition of 1,10-phenanthroline with TSQ-Zn-proteins was also observed in cells. Similarly, 1,10-phenanthroline was shown to bind to a sizable fraction of Zn2+ associated adventitiously with proteome (proteome•Zn). Other synthetic ligands that bind to Zn-proteins and proteome•Zn include 2,2-bipyridyl, 8-hydroxyquinoline, 2,2'-dicarboxypyridine, and pyrithione. Such results suggest that ligand binding to such sites may play a role in the observed biological effects of these and other metal-binding molecules. Although cysteine does not significantly compete with TSQ, glutathione displaces TSQ from Zn-proteins and proteome•Zn at concentrations well below those found in cells, implying that ternary complex formation involving glutathione may be physiologically significant.

Groundwater potential mapping in Jashore, Bangladesh.

The global groundwater crisis is a perplexing issue, and for its resolution, it is of the utmost importance for delineating groundwater potential zones. This research aims to create a precise groundwater potential map for Bangladesh's Jashore district by combining geospatial approach and an analytical hierarchy process. Fourteen parameters, namely, lineament density, drainage density, land use and land cover, slope, curvature, topographic position index, topographic wetness index, rainfall, geology, roughness, fractional impervious surface, topsoil texture, soil permeability, and general soil types, were considered for the study after an extensive literature review. The weights of these parameters were determined using an analytical hierarchy process, and the scores of each sub-parameter were assigned based on published literature. The final groundwater potential map was then generated using the weighted overlay analysis tool in ArcGIS 10.3 and categorized into five classes. The analysis reveals that very high, high, moderate, low, and very low groundwater potential zones cover 3.96 km2 (0.16%), 444.75 km2 (17.72%), 1615.51 km2 (64.37%), 441.79 km2 (17.60%), and 3.59 km2 (0.14%) of the study area, respectively. The map removal sensitivity analysis shows that geology is the most significant element in groundwater potential zoning, followed by land use and land cover (LULC), slope, and topsoil texture as moderately sensitive elements. Since the groundwater potentiality zones of the study region are clearly delineated, this research may be valuable for implementing an appropriate water resource management strategy.

Microplastics pollution in the river Karnaphuli: a preliminary study on a tidal confluence river in the southeast coast of Bangladesh.

Bangladesh is a deltaic country in Asia, and its riverine systems ultimately drain into the Bay of Bengal. Plastic is a severe environmental issue for coastal-marine ecosystems due to the indiscriminate usage and discarding of plastic items in the upstream river that eventually find their route into the Bay of Bengal. Microplastics (MPs) are widespread pollutants in almost all environmental compartments, including aquatic environments. This study aimed to quantify and understand the distribution of microplastics in surface water and sediments of the river Karnaphuli, a tidal confluence river adjacent to the Chattogram seaport city of Bangladesh, a highly inhabited and industrial area on the southeast coast of the Bay of Bengal. A manta trawl net (300-µm mesh size) was used to collect surface water samples, while an Ekman dredge was used to collect sediment samples. The concentrations of microplastics in the surface water of the river Karnaphuli during late monsoon, winter, and early summer were recorded to be 120,111.11, 152,222.22, and 164,444.44 items/km2, respectively, while in sediments, those were recorded to be 103.83, 137.50, and 103.67 items/kg, respectively. A higher abundance of microplastics was observed in downstream surface water (228,888.88 items/km2) and sediments (164.17 items/kg). Smaller sizes (0.3 to 0.5 mm) of microplastics were predominant, fibers or threads were the frequent types, and black was the most common color in the river Karnaphuli. The Fourier transform infrared analysis revealed that polyethylene terephthalate (surface water: 22%, sediments: 19%), polyamide (surface water: 15%, sediments: 13%), polyethylene (surface water: 12%, sediments: 18%), polystyrene (surface water: 13%, sediments: 11%), and alkyd resin (surface water: 13%, sediments: 10%) were the most prevalent polymers in the river Karnaphuli. Moreover, there was a moderate positive correlation between MPs abundance in surface water and sediments. Therefore, improved long-term research (in different seasons with horizontal and vertical monitoring) is necessary in order to accurately determine the flux of microplastics from the river Karnaphuli to the Bay of Bengal.

Socio-economic inequalities in minimum dietary diversity among Bangladeshi children aged 6-23 months: a decomposition analysis.

This study aimed to measure the socio-economic inequalities in having minimum dietary diversity (MDD) among Bangladeshi children aged 6-23 months as well as to determine the factors that potentially contribute to the inequity. The Bangladesh Demographic and Health Survey (BDHS) 2017-2018 data were used in this study. A sample of 2405 (weighted) children aged 6-23 months was included. The overall weighted prevalence of MDD was 37.47%. The concentration index (CIX) value for inequalities in MDD due to wealth status was positive and the concentration curve lay below the line of equality (CIX: 0.1211, p < 0.001), where 49.47% inequality was contributed by wealth status, 25.06% contributed by the education level of mother, and 20.41% contributed by the number of ante-natal care (ANC) visits. Similarly, the CIX value due to the education level of mothers was also positive and the concentration curve lay below the line of equality (CIX: 0.1341, p < 0.001), where 52.68% inequality was contributed by the education level of mother, 18.07% contributed by wealth status, and 14.69% contributed by the number of ANC visits. MDD was higher among higher socioeconomic status (SES) groups. Appropriate intervention design should prioritize minimizing socioeconomic inequities in MDD, especially targeting the contributing factors of these inequities.

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images.

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

Singular Nuclei Segmentation for Automatic HER2 Quantification Using CISH Whole Slide Images.

Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for HER2 quantification in which at least 20 cancer-affected singular nuclei are quantified for HER2 grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for HER2 quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic HER2 quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic HER2 quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate HER2 grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic HER2 quantification during a comparison with the SVM-based approach.

The Perception of Health Professionals in Bangladesh toward the Digitalization of the Health Sector.

Bangladesh is undertaking a major transformation towards digitalization in every sector, and healthcare is no exception. Digitalization of the health sector is expected to improve healthcare services while reducing human effort and ensuring the satisfaction of patients and health professionals. However, for practical and successful digitalization, it is necessary to understand the perceptions of health professionals. Therefore, we conducted a cross-sectional survey in Bangladesh to investigate health professionals' perceptions in relation to various socio-demographic variables such as age, gender, location, profession and institution. We also evaluated their competencies, as digital health-related competencies are required for digitalization. Additionally, we identified major digitalization challenges. Quantitative survey data were analyzed with Python Pandas, and qualitative data were classified using Valence-Aware Dictionary and Sentiment Reasoner (VADER). This study found significant relationships between age χ2(12,N=701)=82.02,p<0.001; location χ2(4,N=701)=18.78,p<0.001; and profession χ2(16,N=701)=71.02,p<0.001; with technical competency. These variables also have similar influences on psychological competency. According to VADER, 88.1% (583/701) of respondents have a positive outlook toward digitalization. The internal consistency of the survey was confirmed by Cronbach's alpha score (0.746). This study assisted in developing a better understanding of how professionals perceive digitalization, categorizes professionals based on competency, and prioritizes the major digitalization challenges.

Navigating the genomic instability mine field of osteosarcoma to better understand implications of non-coding RNAs.

Osteosarcoma is one of the most genomically complex cancers and as result, it has been difficult to assign genomic aberrations that contribute to disease progression and patient outcome consistently across samples. One potential source for correlating osteosarcoma and genomic biomarkers is within the non-coding regions of RNA that are differentially expressed. However, it is unsurprising that a cancer classification that is fraught with genomic instability is likely to have numerous studies correlating non-coding RNA expression and function have been published on the subject. This review undertakes the formidable task of evaluating the published literature of noncoding RNAs in osteosarcoma. This is not the first review on this topic and will certainly not be the last. The review is organized with an introduction into osteosarcoma and the epigenetic control of gene expression before reviewing the molecular function and expression of long non-coding RNAs, circular RNAs, and short non-coding RNAs such as microRNAs, piwi RNAs, and short-interfering RNAs. The review concludes with a review of the literature and how the biology of non-coding RNAs can be used therapeutically to treat cancers, especially osteosarcoma. We conclude that non-coding RNA expression and function in osteosarcoma is equally complex to understanding the expression differences and function of coding RNA and proteins; however, with the added lens of both coding and non-coding genomic sequence, researchers can begin to identify the patterns that consistently associate with aggressive osteosarcoma.

Circling back to epigenetic regulation in osteosarcoma: Comment on 'Hsa_circ_0088212-mediated miR-520h/APOA1 axis inhibits the osteosarcoma progression' by ' Hao Peng'.

Osteosarcoma is a genetically complex cancer, thus there are increasing efforts to identify biomarkers and regulators within the epigenome that can better predict patient outcomes and provide new therapeutic targets. In this commentary, we have evaluated the work of Peng and colleagues on the identification and function of the signaling axis circ_0088212/miR_520 h/APOA1 in osteosarcoma. We provide the context of how novel it is to demonstrate the anti-tumor functions of APOA1 and not just correlative gene expression with patient outcome. We further postulate why some studies involving circRNAs in osteosarcoma contradict one another. We conclude that the study performed by Peng and colleagues was performed with enough rigor to give confidence that circ_0088212 is a bona fide tumor suppressor in osteosarcoma.