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Leukemia is a type of cancer characterized by the exponential growth of abnormal blood cells, which damages white blood cells and disrupts the function of the human body’s bone marrow. It is very challenging to classify because blood smear images are complicated, and there is a lot of variation between each class. Acute Lymphoblastic Leukemia (B-ALL) is one of the subtypes of leukemia. It is a rapidly progressing cancer that originates in B lymphocytes, characterized by the overproduction of immature B lymphoblasts. The purpose of this work is to classify different types of B-ALL subtypes such as Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B from the peripheral blood smear images effectively. To accomplish this task, a novel deep-learning technique based on a fine-tuned ResNet-50 model has been developed. Our fine-tuned ResNet-50 model integrates several additional customized fully connected layers, including dense and dropout layers. Various data augmentation techniques such as flipping, rotation, and zooming have been applied to mitigate the risk of overfitting. In addition, a five-fold cross-validation technique has been employed to enhance the model’s generalization. The performance of our proposed technique is compared with several other methods, including VGG-16, DenseNet-121, and EfficientNetB0, as well as existing baselines, using different performance metrics. Experimental results demonstrate the superiority of the fine-tuned ResNet-50 model, achieving the highest accuracy and an F1-score of 99.38%. It also outperforms existing state-of-the-art approaches by a significant margin. The proposed fine-tuned ReNet-50 model achieves such performance without the need for microscopic image segmentation which indicates its potential utility in healthcare sectors in enhancing precise leukemia diagnosis.

The alliance of NRF3 expression with human cancer patient survival and the underlying function of this gene in liaison to various cancers progression has not been comprehensively studied. In addition, the molecular mechanism of the NRF3 gene and its relationship to cancer patient survival and prognosis are still poorly understood. To elucidate the carcinogenic activity of NRF3, we systematically analyzed the NRF3 expression and its prognostic relevance in diverse cancer patients to decipher the oncogenic function of NRF3. Therefore, we systematically investigated the expression level of NRF3 and its relationship with patient survival as well as the affected pathways in breast, colorectal adenocarcinoma, renal cancer, pancreatic cancer, and thymoma cancer. Relative expression levels of NRF3 were analyzed using the Oncomine, GEPIA2, GENT, UALCAN, cBioPortal, UCSC Xena and The Cancer Genome Atlas (TCGA) databases. Gene copy number of NRF3 was analyzed using cBioPortal, and the subsequent patient prognosis and risk factors were analyzed by obtaining the data from GEPIA2, GENT, and PrognoScan databases. Besides, gene ontology and pathway analysis of co-expressed genes, and transcriptome data analysis were performed using the Enrichr, which revealed NRF3-related pathways in diverse malignancies. Overall, this open database analysis shows a link between NRF3 expression and cancer patient survival and identifies potential NRF3-mediated pathways that could lead to NRF3 being used as a therapeutic target for a variety of human cancers.

Abstract Presence of natural fractures in sub-surface makes an oil well drilling operation very challenging. As one of the major functions of drilling mud is to maintain bottomhole pressure inside a wellbore to avoid any invasion of unwanted high-pressure influx (oil/gas/water), drilling a well through these fractures can cause severe mud loss into the formation and subsequent danger of compromising the wellbore pressure integrity. The aim of this paper is to carry out a Computational Fluid Dynamics (CFD) study of drilling fluid flow through natural fractures to improve comprehensive understanding of the flow in fractured media. The study was carried out by creating a three-dimensional steady-state CFD model using ANSYS (Fluent). For simplicity and validation purpose, the model defines fracture as an empty space between two circular disks. Moreover, it is considered that single-phase fluid is flowing through fractures. By solving the flow equations in the model, correlations to determine the fracture width and invasion radius have been developed for specific mud rheological properties. Prior to onset of drilling and at the end of lost circulation, similar correlations can be developed by knowing rheological properties of drilling fluid which will be very much helpful to take an instantaneous action during lost circulation, i.e., determining lost circulation material particle size and also be useful in the well development stage to determine the damaged area to be treated.

This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.

Water-lubricated flow technology is an environmentally friendly and economically beneficial means of transporting unconventional viscous crudes. The current research was initiated to investigate an engineering model suitable to estimate the frictional pressure losses in water-lubricated pipelines as a function of design/operating parameters such as flow rates, water content, pipe size, and liquid properties. The available models were reviewed and critically assessed for this purpose. As the reliability of the existing models was not found to be satisfactory, a new two-parameter model was developed based on a phenomenological analysis of the dataset available in the open literature. The experimental conditions for these data included pipe sizes and oil viscosities in the ranges of 25–260 mm and 1220–26,500 mPa·s, respectively. A similar range of water equivalent Reynolds numbers corresponding to the investigated flow conditions was 103–106. The predictions of the new model agreed well with the experimental results. The respective values of the coefficient of determination (R2) and the root mean square error (RMSE) were 0.90 and 0.46. The current model is more refined, easy-to-use, and adaptable compared to other existing models.

The role of TRIM24 in HCC and the mechanisms underlying the dysregulated expression of TRIM24 is poorly understood. Here, we systematically analyzed TRIM24 expression and its prognostic role in HCC using various open databases. More precisely, we found an up-regulated gene expression profile of TRIM24 in hepatocellular carcinoma by using Oncomine, cBioPortal, TCGA, and the Human Protein Atlas. The higher expression of TRIM24 is allied with various clinicopathological conditions such as cancer stages, tumor grade, nodal metastasis status, histological subtypes, and also mutant TP53 status based on the TCGA database. In addition to the mRNA expression profile, the correlation between mRNA expression and TRIM24 promoter methylation is entrenched using TCGA datasets from the UALCAN web and subsequently, we examined the liaison of TRIM24 promoter methylation with different clinicopathological conditions in HCC patients. We also clarified a negative correlation of the TRIM24 mRNA expression with antioxidant responsive genes using the TCGA cBioPortal database. Data mining via Kaplan-Meier Plotter and the Human Protein Atlas symbolized a positive tie-up among the higher TRIM24 gene expression, protein expression, and poor survival of HCC patients. Besides, we also identified a highly co-expressed gene, SMARCC1 with TRIM24 in the most predominant correlation fashion in HCC using UALCAN, cBioPortal, UCSC Xena database. Finally, we identified the co-expressed genes with gene oncological features, signaling pathways, and ENCODE ChIP-seq coordination in HCC development. Altogether, this study establishes that elevated TRIM24 expression might accelerate the cancer progression and poor prognosis in HCC patients in a concerted fashion with SMARCC1 expression, which would allow them as a potential therapeutic biomarker in HCC cases.

The human pathogen Mycobacterium tuberculosis (MTB) is indeed one of the renowned, important, longtime infectious diseases, tuberculosis (TB). Interestingly, MTB infection has become one of the world’s leading causes of human death. In trehalose synthase, the protein NCGM 946K2 146 found in MTB has an important role. For carbohydrate transport and metabolism, trehalose synthase is required. The protein is not clarified yet, though. In this research, an in silico approach was, therefore, formulated for functional and structural documentation of the uncharacterized protein NCGM946K2_146.Three distinct servers, including Modeller, Phyre2, and Swiss Model, were used to evaluate the predicted tertiary structure. The top materials are selected using structural evaluations conducted with the analysis of Ramachandran Plot, Swiss-Model Interactive Workplace, ProSA-web, Verify 3D, and Z scores. This analysis aimed to uncover the value of the NCGM946K2_146 protein of MTB. This research will, therefore, improve our pathogenesis awareness and give us a chance to target the protein compound.

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Background: Besides maintaining health precautions, vaccination has been the only prevention from SARS-CoV-2, though no clinically proved 100% effective vaccine has been developed till date. At this stage, to withhold the debris of this pandemic-experts need to know the impact of the vaccine efficacy rates, the threshold level of vaccine effectiveness and how long this pandemic may extent with vaccines that have different efficacy rates. In this article, a mathematical model study has been done on the importance of vaccination and vaccine efficiency rate during an ongoing pandemic. Methods: We simulated a five compartment mathematical model to analyze the pandemic scenario in both California, and whole U.S. We considered four vaccines, Pfizer (95%), Moderna (94%), AstraZeneca (79%), and Johnson & Johnson (72%), which are being used rigorously to control the SARS-CoV-2 pandemic, in addition with two special cases: a vaccine with 100% efficacy rate and no vaccine under use. SARS-CoV-2 related data of California, and U.S. were used in this study. Findings: Both the infection and death rates are very high in California. Our model suggests that the pandemic situation in California will be under control in the last quartile of the year 2023 if vaccination program is continued with the Pfizer vaccine. During this time, six waves may happen from the beginning of the immunization where the case fatality and recovery rates will be 1.697% and 98.30%, respectively. However, according to the considered model, this period might be extended to the mid of 2024 when vaccines with lower efficacy rates are used. On the other hand, the daily cases and deaths in the U.S. will be under control at the end of 2026 with multiple waves. Although the number of susceptible people will fall down to none in the beginning of 2027, there is less chance to stop the vaccination program if vaccinated with a vaccine other than a 100% effective vaccine or Pfizer, and at that case vaccination program must run till the mid of 2028. According to this study, the unconfirmed-infectious and infected cases will be under control at the end of 2027 and at the mid of 2028, respectively. Interpretation: The more effective a vaccine is, the less people suffer from this malign infection. Vaccines which are less than 90% effective do not have notable contribution to control the pandemic besides hard immunity. Furthermore, specific groups of people are getting prioritized initially, mass vaccination and quick responses are required to control the spread of this disease.

In this paper, we study a more general diffusive spatially dependent vaccination model for infectious disease. In our diffusive vaccination model, we consider both therapeutic impact and nonlinear incidence rate. Also, in this model, the number of compartments of susceptible, vaccinated and infectious individuals are considered to be functions of both time and location, where the set of locations (equivalently, spatial habitats) is a subset of $ \mathbb {R}^n $ with a smooth boundary. Both local and global stability of the model are studied. Our study shows that if the threshold level $ \mathcal {R}_0 \le 1, $ the disease-free equilibrium $ E_0 $ is globally asymptotically stable. On the other hand, if $ \mathcal {R}_0> 1 $ then there exists a unique stable disease equilibrium $ E^* $ . The existence of solutions of the model and uniform persistence results are studied. Finally, using finite difference scheme, we present a number of numerical examples to verify our analytical results. Our results indicate that the global dynamics of the model are completely determined by the threshold value $ \mathcal {R}_0 $ .

Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak’s pace, health needs, and disaster preparedness. Methodology and Findings: To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number (R0) has been estimated to be 0.7563. Conclusions: Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario.