Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks

Data Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data.Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model’s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model’s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.This research received no external funding

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

This paper introduces Hk-medoids, a modified version of the standard k-medoids algorithm. The modification extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series. We first proposed an intermediary fusion approach to calculate fused similarities between objects, SMF, taking into account the similarities between the component elements of the objects using appropriate similarity measures. The fused approach entails uncertainty for incomplete objects or for objects which have diverging distances according to the different component. Our implementation of Hk-medoids proposed here works with the fused distances and deals with the uncertainty in the fusion process. We experimentally evaluate the potential of our proposed algorithm using five datasets with different combinations of data types that define the objects. Our results show the feasibility of the our algorithm, and also they show a performance enhancement when comparing to the application of the original SMF approach in combination with a standard k-medoids that does not take uncertainty into account. In addition, from a theoretical point of view, our proposed algorithm has lower computation complexity than the popular PAM implementation

Microarray analysis revealed different gene expression patterns in HepG2 cells treated with low and high concentrations of the extracts of Anacardium occidentale shoots

In this study, the effects of low and high concentrations of the Anacardium occidentale shoot extracts on gene expression in liver HepG2 cells were investigated. From MTT assays, the concentration of the shoot extracts that maintained 50% cell viability (IC50) was 1.7 mg/ml. Cell viability was kept above 90% at both 0.4 mg/ml and 0.6 mg/ml of the extracts. The three concentrations were subsequently used for the gene expression analysis using Affymetrix Human Genome 1.0 S.T arrays. The microarray data were validated using real-time qRT–PCR. A total of 246, 696 and 4503 genes were significantly regulated (P < 0.01) by at least 1.5-fold in response to 0.4, 0.6 and 1.7 mg/ml of the extracts, respectively. Mutually regulated genes in response to the three concentrations included CDKN3, LOC100289612, DHFR, VRK1, CDC6, AURKB and GABRE. Genes like CYP24A1, BRCA1, AURKA, CDC2, CDK2, CDK4 and INSR were significantly regulated at 0.6 mg/ml and 1.7 mg but not at 0.4 mg/ml. However, the expression of genes including LGR5, IGFBP3, RB1, IDE, LDLR, MTTP, APOB, MTIX, SOD2 and SOD3 were exclusively regulated at the IC50 concentration. In conclusion, low concentrations of the extracts were able to significantly regulate a sizable number of genes. The type of genes that were expressed was highly dependent on the concentration of the extracts used

Mechanisms and treatment of ischaemic stroke: insights from genetic associations

The precise pathophysiology of ischaemic stroke is unclear, and a greater understanding of the different mechanisms that underlie large-artery, cardioembolic and lacunar ischaemic stroke subtypes would enable the development of more-effective, subtype-specific therapies. Genome-wide association studies (GWASs) are identifying novel genetic variants that associate with the risk of stroke. These associations provide insight into the pathophysiological mechanisms, and present opportunities for novel therapeutic approaches. In this Review, we summarize the genetic variants that have been linked to ischaemic stroke in GWASs to date and discuss the implications of these associations for both our understanding and treatment of ischaemic stroke. The majority of genetic variants identified are associated with specific subtypes of ischaemic stroke, implying that these subtypes have distinct genetic architectures and pathophysiological mechanisms. The findings from the GWASs highlight the need to consider whether therapies should be subtype-specific. Further GWASs that include large cohorts are likely to provide further insights, and emerging technologies will complement and build on the GWAS findings

Big Data Fusion Model for Heterogeneous Financial Market Data (FinDF)

The dawn of big data has seen the volume, variety, and velocity of data sources increase dramatically. Enormous amounts of structured, semi-structured and unstructured heterogeneous data can be garnered at a rapid rate, making analysis of such big data a herculean task. This has never been truer for data relating to financial stock markets, the biggest challenge being the 7 Vs of big data which relate to the collection, pre-processing, storage and real-time processing of such huge quantities of disparate data sources. Data fusion techniques have been adopted in a wide number of fields to cope with such vast amounts of heterogeneous data from multiple sources and fuse them together in order to produce a more comprehensive view of the data and its underlying relationships. Research into the fusing of heterogeneous financial data is scant within the literature, with existing work only taking into consideration the fusing of text-based financial documents. The lack of integration between financial stock market data, social media comments, financial discussion board posts and broker agencies means that the benefits of data fusion are not being realised to their full potential. This paper proposes a novel data fusion model, inspired by the data fusion model introduced by the Joint Directors of Laboratories, for the fusing of disparate data sources relating to financial stocks. Data with a diverse set of features from different data sources will supplement each other in order to obtain a Smart Data Layer, which will assist in scenarios such as irregularity detection and prediction of stock prices

Covalent modification of reduced graphene oxide with piperazine as a novel nanoadsorbent for removal of H2S gas

In the present research, piperazine grafted-reduced graphene oxide RGO-N-(piperazine) was synthesized through a three-step reaction and employed as a highly efficient nanoadsorbent for H2S gas removal. Temperature optimization within the range of 30–90 °C was set which significantly improved the adsorption capacity of the nanoadsorbent. The operational conditions including the initial concentration of H2S (60,000 ppm) with CH4 (15 vol%), H2O (10 vol%), O2 (3 vol%) and the rest by helium gas and gas hour space velocity (GHSV) 4000–6000 h−1 were examined on adsorption capacity. The results of the removal of H2S after 180 min by RGO-N-(piperazine), reduced graphene oxide (RGO), and graphene oxide (GO) were reported as 99.71, 99.18, and 99.38, respectively. Also, the output concentration of H2S after 180 min by RGO-N-(piperazine), RGO, and GO was found to be 170, 488, and 369 ppm, respectively. Both chemisorption and physisorption are suggested as mechanism in which the chemisorption is based on an acid–base reaction between H2S and amine, epoxy, hydroxyl functional groups on the surface of RGO-N-(piperazine), GO, and RGO. The piperazine augmentation of removal percentage can be attributed to the presence of amine functional groups in the case of RGO-N-(piperazine) versus RGO and GO. Finally, analyses of the equilibrium models used to describe the experimental data showed that the three-parameter isotherm equations Toth and Sips provided slightly better fits compared to the three-parameter isotherms