Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients.

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.

Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the cause of coronavirus disease (COVID-19) that causes a major threat to humanity. As the spread of the virus is probably getting out of control on every day, the epidemic is now crossing the most dreadful phase. Idiopathic pulmonary fibrosis (IPF) is a risk factor for COVID-19 as patients with long-term lung injuries are more likely to suffer in the severity of the infection. Transcriptomic analyses of SARS-CoV-2 infection and IPF patients in lung epithelium cell datasets were selected to identify the synergistic effect of SARS-CoV-2 to IPF patients. Common genes were identified to find shared pathways and drug targets for IPF patients with COVID-19 infections. Using several enterprising Bioinformatics tools, protein-protein interactions (PPIs) network was designed. Hub genes and essential modules were detected based on the PPIs network. TF-genes and miRNA interaction with common differentially expressed genes and the activity of TFs are also identified. Functional analysis was performed using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway and found some shared associations that may cause the increased mortality of IPF patients for the SARS-CoV-2 infections. Drug molecules for the IPF were also suggested for the SARS-CoV-2 infections.

Impacts of COVID-19-Related Non-Pharmaceutical Interventions on Mobility and Accidents in Bangladesh.

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown-including on the transport sector-to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra- and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making.

Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach.

Emerging evidence indicates IBD is a risk factor for the increasing incidence of colorectal cancer (CRC) development. We used a system biology approach to identify common molecular signatures and pathways that interact between IBD and CRC and the indispensable pathological mechanisms. First, we identified 177 common differentially expressed genes (DEGs) between IBD and CRC. Gene set enrichment, protein-protein, DEGs-transcription factors, DEGs-microRNAs, protein-drug interaction, gene-disease association, Gene Ontology, pathway enrichment analyses were conducted to these common genes. The inclusion of common DEGs with bimolecular networks disclosed hub proteins (LYN, PLCB1, NPSR1, WNT5A, CDC25B, CD44, RIPK2, ASAP1), transcription factors (SCD, SLC7A5, IKZF3, SLC16A1, SLC7A11) and miRNAs (mir-335-5p, mir-26b-5p, mir-124-3p, mir-16-5p, mir-192-5p, mir-548c-3p, mir-29b-3p, mir-155-5p, mir-21-5p, mir-15a-5p). Analysis of the interaction between protein and drug discovered ASAP1 interacts with cysteine sulfonic acid and double oxidized cysteine drug compounds. Gene-disease association analysis retrieved ASAP1 also associated with pulmonary and bladder neoplasm diseases.

Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting.

Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the drug discovery process, having enormous benefit to the patients and pharmaceutical company. The traditional wet-lab experiments of DTI is expensive, time-consuming, and labor-intensive. Therefore, many computational techniques have been established for this purpose; although a huge number of interactions are still undiscovered. Here, we present pdti-EssB, a new computational model for identification of DTI using protein sequence and drug molecular structure. More specifically, each drug molecule is transformed as the molecular substructure fingerprint. For a protein sequence, different descriptors are utilized to represent its evolutionary, sequence, and structural information. Besides, our proposed method uses data balancing techniques to handle the imbalance problem and applies a novel feature eliminator to extract the best optimal features for accurate prediction. In this paper, four classes of DTI benchmark datasets are used to construct a predictive model with XGBoost. Here, the auROC is utilized as an evaluation metric to compare the performance of pdti-EssB method with recent methods, applying five-fold cross-validation. Finally, the experimental results indicate that our proposed method is able to outperform other approaches in predicting DTI, and introduces new drug-target interaction samples based on prediction probability scores. pdti-EssB webserver is available online at http://pdtiessb-uestc.com/.

DeepACTION: A deep learning-based method for predicting novel drug-target interactions.

Drug-target interactions (DTIs) play a key role in drug development and discovery processes. Wet lab prediction of DTIs is time-consuming, expensive, and tedious. Fortunately, computational approaches can identify new interactions (drug-target pairs) and accelerate the process of drug repurposing. However, a vast number of interactions remain undiscovered; therefore, we proposed a deep learning-based method (deepACTION) for predicting potential or unknown DTIs. Here, each drug chemical structure and protein sequence are transformed according to structural and sequence information using different descriptors to represent their features correctly. There have been some challenges, such as the high dimensionality and class imbalance of data during the prediction process. To address these problems, we developed the MMIB technique to balance the majority and minority instances in the dataset and utilized a LASSO model to handle the high dimensionality of the data. In addition, we trained the convolutional neural network algorithm with balanced and reduced features for accurate prediction of DTIs. In this study, the AUC is considered a primary evaluation metric for comparing the performance of the deep ACTION model with that of existing methods by a 5-fold cross-validation test. Our experiential dataset obtained from the DrugBank database and our deepACTION model achieved an AUC of 0.9836 for this dataset. The experimental results ensured that the model can predict significant numbers of new DTIs and provide complete information to motivate scientists to develop drugs.

Cost-effectiveness of using a gene expression profiling test to aid in identifying the primary tumour in patients with cancer of unknown primary.

We aimed to investigate the cost-effectiveness of a 2000-gene-expression profiling (GEP) test to help identify the primary tumor site when clinicopathological diagnostic evaluation was inconclusive in patients with cancer of unknown primary (CUP). We built a decision-analytic-model to project the lifetime clinical and economic consequences of different clinical management strategies for CUP. The model was parameterized using follow-up data from the Manitoba Cancer Registry, cost data from Manitoba Health administrative databases and secondary sources. The 2000-GEP-based strategy compared to current clinical practice resulted in an incremental cost-effectiveness ratio (ICER) of $44,151 per quality-adjusted life years (QALY) gained. The total annual-budget impact was $36.2 million per year. A value-of-information analysis revealed that the expected value of perfect information about the test's clinical impact was $4.2 million per year. The 2000-GEP test should be considered for adoption in CUP. Field evaluations of the test are associated with a large societal benefit.

Epidemiology of hepatitis C virus among hemodialysis patients in the Middle East and North Africa: systematic syntheses, meta-analyses, and meta-regressions.

We aimed to investigate hepatitis C virus (HCV) epidemiology among hemodialysis (HD) patients in the Middle East and North Africa (MENA). Our data source was an HCV biological measures database populated through systematic literature searches. Descriptive epidemiologic syntheses, effects meta-analyses and meta-regressions, and genotype analyses were conducted. We analyzed 289 studies, including 106 463 HD patients. HCV incidence ranged between 0 and 100% as seroconversion risk, and between 0 and 14·7 per 1000 person-years as incidence rate. The regional pooled mean estimate was 29·2% (95% CI: 25·6-32·8%) for HCV antibody positive prevalence and 63·0% (95% CI: 55·4-70·3%) for the viremic rate. Region within MENA, country income group, and year of data collection were associated with HCV prevalence; year of data collection adjusted odds ratio was 0·92 (95% CI: 0·90-0·95). Genotype diversity varied across countries with four genotypes documented regionally: genotype 1 (39·3%), genotype 2 (5·7%), genotype 3 (29·6%), and genotype 4 (25·4%). Our findings showed that one-third of HD patients are HCV antibody positive and one-fifth are chronic carriers and can transmit the infection. However, HCV prevalence is declining. In context of growing HD patient population and increasing HCV treatment availability, it is critical to improve standards of infection control in dialysis and expand treatment coverage.

The clinical significance of occult gynecologic primary tumours in metastatic cancer.

OBJECTIVE: We estimated the frequency of occult gynecologic primary tumours (gpts) in patients with metastatic cancer from an uncertain primary and evaluated the effect on disease management and overall survival (os). METHODS: We used Manitoba administrative health databases to identify all patients initially diagnosed with metastatic cancer during 2002-2011. We defined patients as having an "occult" primary tumour if the primary was classified at least 6 months after the initial diagnosis. Otherwise, we considered patients to have "obvious" primaries. We then compared clinicopathologic and treatment characteristics and 2-year os for women with occult and with obvious gpts. We used Cox regression adjustment and propensity score methods to assess the effect on os of having an occult gpt. RESULTS: Among the 5953 patients diagnosed with metastatic cancer, occult primary tumours were more common in women (n = 285 of 2552, 11.2%) than in men (n = 244 of 3401, 7.2%). In women, gpts were the most frequent occult primary tumours (n = 55 of 285, 19.3%). Compared with their counterparts having obvious gpts, women with occult gpts (n = 55) presented with similar histologic and metastatic patterns but received fewer gynecologic diagnostic examinations during diagnostic work-up. Women with occult gpts were less likely to undergo surgery, waited longer for radiotherapy, and received a lesser variety of chemotherapeutic agents. Having an occult compared with an obvious gpt was associated with decreased os (hazard ratio: 1.62; 95% confidence interval: 1.2 to 2.35). Similar results were observed in adjusted analyses. CONCLUSIONS: In women with metastatic cancer from an uncertain primary, gpts constitute the largest clinical entity. Accurate diagnosis of occult gpts early in the course of metastatic cancer might lead to more effective treatment decisions and improved survival outcomes.

The effects of power toothbrushing on periodontal inflammation in a Canadian nursing home population: A randomized controlled trial.

OBJECTIVES: The aim of this study was to investigate whether twice-daily use of a rotating-oscillating power toothbrush (Oral-B Professional Care 1000™ ) in nursing home (NH) residents over a 6-week period, compared to usual care (UC), would reduce periodontal inflammation. METHODS: In this repeated measures single-blinded randomized controlled trial, 59 residents of one NH in Winnipeg, Canada, were randomized to receive either twice-daily tooth brushing with a rotating-oscillating power toothbrush (PB) or UC by caregivers. Consent was obtained from residents or their proxies. Participants had some natural teeth, periodontal inflammation, non-aggressive behaviour, no communicable diseases, were non-smokers and non-comatose. Outcomes were measured at baseline and 6 weeks, which included: inflammation (MGI, Lobene), bleeding (PBI, Loesche) and Plaque (Turesky). Comparisons of group changes in outcomes were analysed using an ANOVA with a repeated measure. RESULTS: Of 59 original study participants, one withdrew, one died prior to study commencement and three died before study completion. All oral parameters improved significantly for the remaining 54 residents over time (P<.0001), with no differences between groups. CONCLUSIONS: These results demonstrate that it is possible for caregivers to improve periodontal inflammation of residents over a 6-week period. Despite no significant group differences, periodontal inflammation of all study participants improved significantly, particularly in the reduction of bleeding, a direct measure of periodontal inflammation, which is a unique finding.

Intracellular Delivery of Synthetic dsRNA to Leukemic Cells Induces Apoptotic and Necrotic Cell Death.

The type of tumour cell death dictates the type of adaptive immune response mounted against the tumours. In haematological malignancies such as acute myeloid leukaemia (AML), immune evasion due to the poor immunogenicity of leukemic cells is a major hurdle in generating an effective immune response. Transfection of synthetic dsRNA, poly I:C, into leukemic cells to trigger tumour cell death and enhance immunogenicity of the tumour is a promising immunotherapeutic approach. However, the temporal cell death kinetics of poly I:C-electroporated AML cells has not been thoroughly investigated. Electroporation of U937 cells, a human AML cell line, with a high dose of poly I:C resulted in cytotoxicity as early as 1 h post-transfection. Flow cytometric analysis revealed the temporal switch from early apoptosis to late apoptosis/secondary necrosis in poly I:C-electroporated cells in which the nuclear morphology at later time points was consistent with necrotic cell death. Our brief findings demonstrated the temporal cell death kinetics of dsRNA-transfected leukemic cells. This finding is an important development in the field of dsRNA immunotherapy for leukaemia as understanding the type of cell death elicited by transfected dsRNA will dictate the type of immune response to be directed against leukemic cells.

Structural Variations of Nose and Paranasal Sinuses in Various Sinonasal Pathologies: Tomographic Study of 50 Cases in Bangladeshi People.

The aim of this study was to determine the frequency of structural variations in nose & paranasal sinuses in computed tomography in Bangladeshi people. This retrospective study was done at the Sir Salimullah Medical College Mitford Hospital and Apollo Hospitals, Dhaka, Bangladesh. Fifty (50) CT scan of Nose and Para nasal sinuses were collected from the patients presented with different sinonasal pathologies in OPD, IPD of both hospitals from July 2013 to June 2014. The scans were reviewed for the presence of different structural variations of nose and paranasal sinuses. The age range of the patients was 25 to 65 years. The most common anatomical variation in this study was hypertrophied inferior turbinate (82%) followed by ethmoidal bulla (70%), deviated nasal septum (64%), agar nasi cell (40%), concha bullosa (38%). In most of the patients we found more than one variation. There is wide range of anatomical variations in nose and paranasal sinuses which might be regarded as the aetiological factors of different sinonasal pathologies. To maximize patients' benefit and to avoid unexpected situations during surgeries as well as dreadful complications, individualized pre-planning through tomographic study should be considered.

The risk of colorectal cancer is not increased after a diagnosis of urothelial cancer: a population-based study.

BACKGROUND: The data about whether patients with a prior urothelial cancer (uca) are at increased risk of colorectal cancer (crc) are conflicting. We used a competing risks analysis to determine the risk of crc after uca. METHODS: Historical cohorts were assembled by record linkage of Manitoba Cancer Registry and Manitoba Health databases. The incidence of crc for individuals with uca as their first cancer between 1987 and 2009 was compared with the incidence for randomly selected age-and sex-matched individuals without a cancer diagnosis at the index date (uca diagnosis date). Three competing outcomes (crc, another primary cancer, and death) were evaluated by competing risks proportional hazards models with adjustment for relevant confounders. RESULTS: The cohorts of 4591 patients with uca and 22,312 without uca were followed for a total of 179,287 person-years (py). After uca, the rate of subsequent colon cancer in uca patients was 4.5 per 1000 py compared with 3.6 per 1000 py in the non-cancer cohort. In the multivariable analysis, no overall increase in crc risk was observed for patients first diagnosed with uca (hazard ratio: 0.88; 95% confidence interval: 0.70 to 1.1; p = 0.26). CONCLUSIONS: Because of similar crc risk, a similar crc screening strategy should be applied for individuals with and without uca.

Quality of life of cervical cancer patients after completion of treatment - A study among Bangladeshi women.

Cervical cancer is the main cause of malignancy-related death among women living in developing countries. The aim of this study is to evaluate the quality of life (QOL) among Bangladeshi cervical cancer survivors and its relationships with demographic and disease related factors A cross-sectional study was carried out onlone hundred nine consecutive cervical cancer survivors in National Institute of cancer Research and Hospital, Dhaka from September 2014 to february 2015 using European organization-for Research and treatment of cancer core Questionnaires (QOL-C30 and QOL- CX24). Demographic condition like education level, occupation and disease related factors like stages, treatment modality and duration of follow-up time were taken as investigating factors against functional scales. Cronbach's alpha was calculated to asses' internal consistency among items. Cervical cancer survivors stated a moderate QOL. Sub-domains of QOL score and global health status were significantly associated with physical function(PF) scales (p=.000), fatigue (p=.045), nausea and vomiting (p=.000), Appetite loss (p=.001), constipation (p=.005), symptom experience (p=.005) and menopausal symptoms (p=.015). QOL mean score were negatively associated with emotional function(EF) scales, pain, fatigue, nausea, appetite loss and financial problems. Education level showed significant association with physical function(PF) (p=.001), emotional function(EF) (p=.027), Cognitive function(CF) (p=.000) and sexual function (p=.001). Duration (Follow-up) time was significance association with PF (p=.005), EF (p=.012), symptoms experience (p=.001). Although, the QOL in cervical cancer survivors was moderate, treatment of related symptoms and improvement of demographic condition can influence the QOL and survivors improve the care of cervical cancer. So, improve the QOL among cervical cancer survivors.

Emergency department and 'Google flu trends' data as syndromic surveillance indicators for seasonal influenza.

We evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data - total ED visits attributed to influenza-like illness (ILI) ('ED ILI volume') and percentage of visits attributed to ILI ('ED ILI percent') - and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.

Color Doppler sonography and resistivity index in the differential diagnosis of hepatic neoplasm.

The purpose of this study was to determine the usefulness of color doppler sonography and resistivity index (RI) in differentiating liver tumors. The study was carried out in the Department of Radiology and Imaging, Mymensingh Medical College Hospital, and Institute of Nuclear Medicine and Allied Sciences (INMAS), Mymensingh, Bangladesh, during the period of July 2009 to June 2011. Total 50 consecutive cases were studied. Among them 27 were hepatocellular carcinomas, 19 were metastatic tumors, 03 were hemangiomas and 01 was hepatic adenoma. Doppler sonographic findings were then correlated, case by case, with final diagnosis- either pathologically by USG guided Fine-needle aspiration or by other imaging modalities (e.g., CT scan and RBC liver scan for hepatic hemangioma). The RI value of hepatocellular carcinoma was 0.69±0.096 and in metastatic tumors 0.73±0.079. The results showed no significant difference between the RI of hepatocellular carcinomas and metastatic liver tumors but it was significantly higher than benign lesions (p<0.05). RI of hemangiomas was 0.49±0.64 and in one hepatic adenoma was 0.65. When RI was <0.6 for benign liver tumors and ≥0.6 for malignant tumors we calculated a sensitivity of 89.14%, specificity of 66.7%, accuracy of 85.71% positive predictive value of 97.62% and negative predictive value of 28.57% in differentiating benign and malignant tumors. Thirty four of 46(73.9%) malignant lesions had intratumoral flow and 25% of benign lesions also showed intratumoral flow. The difference of intratumoral flow between malignant and benign lesions was significant (p<0.01). Two of 4 benign lesions (50%) had peritumoral vascularity where 6% of the malignant tumors showed peritumoral vascularity. In conclusion, combined studies of the type of intra-and peri-tumoral flow signals in CDFI and the parameter of RI would be more helpful in the differential diagnosis of benign and malignant liver tumors.

Correlation of Body Mass Index with Serum Lipid Profile Level in Adolescent Students of Bangladesh.

Obesity is associated with metabolic disorders such as dyslipidaemia, diabetes mellitus (DM), hypertension (HTN) and cardiovascular disease (CVD). There has been rising burden of childhood and adolescent obesity in most developing countries in recent years. Changes in dietary habits, junk and fast food, physical inactivity and smoking habits increases among adolescent students, which causes obesity and simultaneously increases risk of metabolic diseases. The objective of the study is to determine the correlation between Body Mass Index (BMI) and lipid profile among adolescent students of Bangladesh. This cross-sectional observational study was conducted among 79 undergraduate healthy adolescent students, aged 10-18 years who were selected through purposive sampling. The study was conducted from July 2022 to June 2023 in urban and rural areas of Dhaka, Narayanganj and Rangpur. Data was collected using a semi-structured questionnaire. Correlation of dyslipidemia and BMI was analyzed by Pearson Coefficient. Data was analyzed using SPSS version 22.0 with level of statistical significance at p<0.05. Mean age of the respondents was 14.9±4.5 years. Male and female ratio was 2.16:1. Among respondents, 46.8% had BMI 18.5-23.0 (normal), 31.6% had BMI 23.1-25.0 (overweight) and 21.5% had BMI >25.0 (obese). Prevalence of dyslipidaemia was 34.1%. Overweight and obese respondents had raised total cholesterol (TC) level 209.51±48.6 mg/dl and 218.36±80.0 mg/dl respectively. Mean high density lipoprotein (HDL) cholesterol level was 38.91±10.51 mg/dl in overweight and 36.54±10.04 mg/dl in obese. Mean low density lipoprotein (LDL) cholesterol level was 135.23±44.5 mg/dl in overweight and 143.61±56.0 mg/dl in obese. Among obese cases, 94.1% respondents had borderline triglyceride (TG) with mean 164.46±111.0 mg/dl. Among the study respondents, overweight and obesity (higher BMI) tend to have abnormal lipid profile. It is recommended that assessment of BMI should be incorporated into school health programme and those with overweight and obesity should be subjected to routine lipogram in order to apply timely preventive as well as therapeutic measures to save lives.

Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.