Mycobacterium tuberculosis Acetyltransferase Suppresses Oxidative Stress by Inducing Peroxisome Formation in Macrophages.

Mycobacterium tuberculosis (Mtb) inhibits host oxidative stress responses facilitating its survival in macrophages; however, the underlying molecular mechanisms are poorly understood. Here, we identified a Mtb acetyltransferase (Rv3034c) as a novel counter actor of macrophage oxidative stress responses by inducing peroxisome formation. An inducible Rv3034c deletion mutant of Mtb failed to induce peroxisome biogenesis, expression of the peroxisomal ß-oxidation pathway intermediates (ACOX1, ACAA1, MFP2) in macrophages, resulting in reduced intracellular survival compared to the parental strain. This reduced virulence phenotype was rescued by repletion of Rv3034c. Peroxisome induction depended on the interaction between Rv3034c and the macrophage mannose receptor (MR). Interaction between Rv3034c and MR induced expression of the peroxisomal biogenesis proteins PEX5p, PEX13p, PEX14p, PEX11ß, PEX19p, the peroxisomal membrane lipid transporter ABCD3, and catalase. Expression of PEX14p and ABCD3 was also enhanced in lungs from Mtb aerosol-infected mice. This is the first report that peroxisome-mediated control of ROS balance is essential for innate immune responses to Mtb but can be counteracted by the mycobacterial acetyltransferase Rv3034c. Thus, peroxisomes represent interesting targets for host-directed therapeutics to tuberculosis.

Mycobacterium tuberculosis LprE Suppresses TLR2-Dependent Cathelicidin and Autophagy Expression to Enhance Bacterial Survival in Macrophages.

Despite representing a very important class of virulence proteins, the role of lipoproteins in the pathogenesis of Mycobacterium tuberculosis remains elusive. In this study, we investigated the role of putative lipoprotein LprE in the subversion of host immune responses using the M. tuberculosis CDC1551 LprE (LprE Mtb ) mutant (Mtb∆LprE). We show that deletion of LprE Mtb results in reduction of M. tuberculosis virulence in human and mouse macrophages due to upregulation of vitamin D3-responsive cathelicidin expression through the TLR2-dependent p38-MAPK-CYP27B1-VDR signaling pathway. Conversely, episomal expression of LprE Mtb in Mycobacterium smegmatis improved bacterial survival. Infection in siTLR2-treated or tlr2-/- macrophages reduced the survival of LprE Mtb expressing M. tuberculosis and M. smegmatis because of a surge in the expression of cathelicidin. Infection with the LprE Mtb mutant also led to accumulation of autophagy-related proteins (LC3, Atg-5, and Beclin-1) and augmented recruitment of phagosomal (EEA1 and Rab7) and lysosomal (LAMP1) proteins, thereby resulting in the reduction of the bacterial count in macrophages. The inhibition of phago-lysosome fusion by LprE Mtb was found to be due to downregulation of IL-12 and IL-22 cytokines. Altogether, our data indicate that LprE Mtb is an important virulence factor that plays a crucial role in mycobacterial pathogenesis in the context of innate immunity.

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework.

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization.

Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation (K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original, S4-four splits, S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols (K2, K3, K5 and K10) and all types of US data sets (S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%.

Author Correction to: Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization.

The original version of this article unfortunately contained a mistake. The family name of Rui Tato Marinho was incorrectly spelled as Marinhoe.

Preclinical evaluation of engineered L-asparaginase variants to improve the treatment of Acute Lymphoblastic Leukemia.

INTRODUCTION: Escherichia coli l-asparaginase (EcA), an integral part of multi-agent chemotherapy protocols of acute lymphoblastic leukemia (ALL), is constrained by safety concerns and the development of anti-asparaginase antibodies. Novel variants with better pharmacological properties are desirable. METHODS: Thousands of novel EcA variants were constructed using protein engineering approach. After preliminary screening, two mutants, KHY-17 and KHYW-17 were selected for further development. The variants were characterized for asparaginase activity, glutaminase activity, cytotoxicity and antigenicity in vitro. Immunogenicity, pharmacokinetics, safety and efficacy were tested in vivo. Binding of the variants to pre-existing antibodies in primary and relapsed ALL patients' samples was evaluated. RESULTS: Both variants showed similar asparaginase activity but approximately 24-fold reduced glutaminase activity compared to wild-type EcA (WT). Cytotoxicity against Reh cells was significantly higher with the mutants, although not toxic to human PBMCs than WT. The mutants showed approximately 3-fold lower IgG and IgM production compared to WT. Pharmacokinetic study in BALB/c mice showed longer half-life of the mutants (KHY-17- 267.28±9.74; KHYW-17- 167.41±14.4) compared to WT (103.24±18). Single and repeat-doses showed no toxicity up to 2000 IU/kg and 1600 IU/kg respectively. Efficacy in ALL xenograft mouse model showed 80-90 % reduction of leukemic cells with mutants compared to 40 % with WT. Consequently, survival was 90 % in each mutant group compared to 10 % with WT. KHYW-17 showed over 2-fold lower binding to pre-existing anti-asparaginase antibodies from ALL patients treated with l-asparaginase. CONCLUSION: EcA variants demonstrated better pharmacological properties compared to WT that makes them good candidates for further development.

Role of EGFR and FASN in breast cancer progression.

Breast cancer (BC) emerged as one of the life-threatening diseases among females. Despite notable improvements made in cancer detection and treatment worldwide, according to GLOBACAN 2020, BC is the fifth leading cancer, with an estimated 1 in 6 cancer deaths, in a majority of countries. However, the exact cause that leads to BC progression still needs to be determined. Here, we reviewed the role of two novel biomarkers responsible for 50-70% of BC progression. The first one is epidermal growth factor receptor (EGFR) which belongs to the ErbB tyrosine kinases family, signalling pathways associated with it play a significant role in regulating cell proliferation and division. Another one is fatty acid synthase (FASN), a key enzyme responsible for the de novo lipid synthesis required for cancer cell development. This review presents a rationale for the EGFR-mediated pathways, their interaction with FASN, communion of these two biomarkers with BC, and improvements to overcome drug resistance caused by them.

Detection of Peroxisomal Proteins During Mycobacterial Infection.

Peroxisomes are ubiquitous organelles with essential roles in lipid and reactive oxygen species (ROS) metabolism. They are involved in modulating the immune responses during microbial infection, thus having major impact on several bacterial and viral infectious diseases including tuberculosis. Intracellular pathogens such as Mycobacterium tuberculosis (M. tb) employ various strategies to suppress the host oxidative stress mechanisms to avoid killing by the host. Peroxisome-mediated ROS balance is crucial for innate immune responses to M. tb. Therefore, peroxisomes represent promising targets for host-directed therapeutics to tuberculosis. Here, we present protocols used in our laboratory for the culture of M. tb and detection of peroxisomal proteins in M. tb infected macrophages.

Exploring the antimicrobial potential of isoniazid loaded Cu-based metal-organic frameworks as a novel strategy for effective killing of Mycobacterium tuberculosis.

Tuberculosis (TB) remains one of the most infectious pathogens with the highest human mortality and morbidity. Biofilm formation during Mycobacterium tuberculosis (Mtb) infection is responsible for bacterial growth, communication, and, most essentially, increased resistance/tolerance to antibiotics leading to higher bacterial persistence. Thus, biofilm growth is presently considered a key virulence factor in the case of chronic disease. Metal-Organic Frameworks (MOFs) have recently emerged as a highly efficient system to improve existing antibiotics' therapeutic efficacy and reduce adverse effects. In this regard, we have synthesized Cu-MOF (IITI-3) using a solvothermal approach. IITI-3 was well characterized by various spectroscopic techniques. Herein, IITI-3 was first encapsulated with isoniazid (INH) to form INH@IITI-3 with 10 wt% loading within 1 hour. INH@IITI-3 was well characterized by PXRD, TGA, FTIR, and BET surface area analysis. Furthermore, the drug release kinetics studies of INH@IITI-3 have been performed at pH 5.8 and 7.4 to mimic the small intestine and blood pH, respectively. The results show that drug release follows first-order kinetics. Furthermore, the antimycobacterial activity of INH@IITI-3 demonstrated significant bacterial killing and altered the structural morphology of the bacteria. Moreover, INH@IITI-3 was able to inhibit the mycobacterial biofilm formation upon treatment and showed less cytotoxicity toward the murine RAW264.7 macrophages. Thus, this work significantly opens up new possibilities for the applications of INH@IITI-3 in biofilm infections in Mtb and further contributes to TB therapeutics.

Multifaceted Carbon Dots: toward pH-Responsive Delivery of 5-Fluorouracil for In Vitro Antiproliferative Activity.

The synthesis of smart hybrid material to assimilate diagnosis and treatment is crucial in nanomedicine. Herein, we present a simple and facile method to synthesize multitalented blue-emissive nitrogen-doped carbon dots N@PEGCDs. The as-prepared carbon dots N@PEGCDs show enhanced biocompatibility, small size, high fluorescence, and high quantum yield. The N@PEGCDs are used as a drug carrier for 5-fluorouracil (5-FU) with more release at acidic pH. Furthermore, the mode of action of drug-loaded CD (5FU-N@PEGCDs) has also been explored by performing wound healing assay, DCFDA assay for ROS generation, and Hoechst staining. The drug loaded with carbon dots showed less toxicity to normal cells compared to cancer cells, making it a perfect candidate to be studied for designing next-generation drug delivery systems.

Structure-based design and synthesis of a novel long-chain 4''-alkyl ether derivative of EGCG as potent EGFR inhibitor: in vitro and in silico studies.

Herein, we report the discovery of a novel long-chain ether derivative of (-)-epigallocatechin-3-gallate (EGCG), a major green tea polyphenol as a potent EGFR inhibitor. A series of 4''-alkyl EGCG derivatives have been synthesized via regio-selectively alkylating the 4'' hydroxyl group in the D-ring of EGCG and tested for their antiproliferative activities against high (A431), moderate (HeLa), and low (MCF-7) EGFR-expressing cancer cell lines. The most potent compound, 4''-C14 EGCG showed the lowest IC50 values across all the tested cell lines. 4''-C14 EGCG was also found to be significantly more stable than EGCG under physiological conditions (PBS at pH 7.4). Further western blot analysis and imaging data revealed that 4''-C14 EGCG induced cell death in A431 cells with shrunken nuclei, nuclear fragmentation, membrane blebbing, and increased population of apoptotic cells where BAX upregulation and BCLXL downregulation were observed. In addition, autophosphorylation of EGFR and its downstream signalling proteins Akt and ERK were markedly inhibited by 4''-C14 EGCG. MD simulation and the MM/PBSA analysis disclosed the binding mode of 4''-C14 EGCG in the ATP-binding site of EGFR kinase domain. Taken together, our findings demonstrate that 4''-C14 EGCG can act as a promising potent EGFR inhibitor with enhanced stability.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

Machine learning paradigm for dynamic contrast-enhanced MRI evaluation of expanding bladder.

Delineation of the bladder under a dynamic contrast enhanced (DCE)-MRI protocol requires robust segmentation. However, this method is subject to errors due to variations in the content of fluid within the bladder, as well as presence of air and similarity of signal intensity in adjacent organs. Introduction of the contrast media into the bladder also causes signal errors due to alterations in the shape of the bladder. To circumvent such errors, and to improve the accuracy, we adapted a machine learning paradigm that utilizes the global bladder shape. The ML system first uses the combination of low level image processing tools such as filtering, and mathematical morphology as preprocessing step. We use neural network for training the network using extracted features and application of trained model on test slices to compute the delineated bladder shapes. This ML-based integrated system has an accuracy of 90.73% and time reduction of 65.2% in over manual delineation and can be used in clinical settings for IC/BPS patient care. Finally, we apply Jaccard Similarity Measure which we report to have a mean score of 0.933 (95% Confidence Interval 0.923, 0.944).

Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment.

MOTIVATION: The early screening of cardiovascular diseases (CVD) can lead to effective treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque measurements are crucial. Current measurement methods are time-consuming and do not utilize the power of knowledge-based paradigms such as artificial intelligence (AI). We present an AI-based methodology for the joint automated detection and measurement of wall thickness and carotid plaque (CP) in the form of carotid intima-media thickness (cIMT) and total plaque area (TPA), a class of AtheroEdge™ system (AtheroPoint™, CA, USA). METHOD: The novel system consists of two stages, and each stage comprises an independent deep learning (DL) model. In Stage I, the first DL model segregates the common carotid artery (CCA) patches from ultrasound (US) images into the rectangular wall and non-wall patches. The characterized wall patches are integrated to form the region of interest (ROI), which is then fed into Stage II. In Stage II, the second DL model segments the far wall region. Lumen-intima (LI) and media-adventitial (MA) boundaries are then extracted from the wall region, which is then used for cIMT and PA measurement. RESULTS: Using the database of 250 carotid scans, the cIMT error using the AI model is 0.0935±0.0637 mm, which is lower than those of all previous methods. The PA error is found to be 2.7939±2.3702 mm2. The system's correlation coefficient (CC) between AI and ground truth (GT) values for cIMT is 0.99 (p < 0.0001), which is higher compared with the CC of 0.96 (p < 0.0001) shown by the earlier DL method. The CC for PA between AI and GT values is 0.89 (p < 0.0001). CONCLUSION: A novel AI-based strategy was applied to carotid US images for the joint detection of carotid wall thickness (cWT) and plaque area (PA), followed by cIMT and PA measurement. This AI-based strategy shows improved performance using the patch technique compared with previous methods using full carotid scans.

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.

Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm.

BACKGROUND: Stroke is in the top three leading causes of death worldwide. Non-invasive monitoring of stroke can be accomplished via stenosis measurements. The current conventional image-based methods for these measurements are not accurate and reliable. They do not incorporate shape and intelligent learning component in their design. METHODS: In this study, we propose a deep learning (DL)-based methodology for accurate measurement of stenosis in common carotid artery (CCA) ultrasound (US) scans using a class of AtheroEdge system from AtheroPoint, USA. Three radiologists manually traced the lumen-intima (LI) for the near and the far walls, respectively, which served as a gold standard (GS) for training the DL-based model. Three DL-based systems were developed based on three types of GS. RESULTS: IRB approved (Toho University, Japan) 407 US scans from 204 patients were collected. The risk was characterized into three classes: low, moderate, and high-risk. The area-under-curve (AUC) corresponding to three DL systems using receiver operating characteristic (ROC) analysis computed were: 0.90, 0.94 and 0.86, respectively. CONCLUSIONS: Novel DL-based strategy showed reliable, accurate and stable stenosis severity index (SSI) measurements.

Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk.

Manual ultrasound (US)-based methods are adapted for lumen diameter (LD) measurement to estimate the risk of stroke but they are tedious, error prone, and subjective causing variability. We propose an automated deep learning (DL)-based system for lumen detection. The system consists of a combination of two DL systems: encoder and decoder for lumen segmentation. The encoder employs a 13-layer convolution neural network model (CNN) for rich feature extraction. The decoder employs three up-sample layers of fully convolution network (FCN) for lumen segmentation. Three sets of manual tracings were used during the training paradigm leading to the design of three DL systems. Cross-validation protocol was implemented for all three DL systems. Using the polyline distance metric, the precision of merit for three DL systems over 407 US scans was 99.61%, 97.75%, and 99.89%, respectively. The Jaccard index and Dice similarity of DL lumen segmented region against three ground truth (GT) regions were 0.94, 0.94, and 0.93 and 0.97, 0.97, and 0.97, respectively. The corresponding AUC for three DL systems was 0.95, 0.91, and 0.93. The experimental results demonstrated superior performance of proposed deep learning system over conventional methods in literature. Graphical abstract ᅟ.

State-of-the-art review on deep learning in medical imaging.

Deep learning (DL) is affecting each and every sphere of public and private lives and becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the activities of neurons in the neocortex of human brain where the thought process takes place. Therefore, like the brain, it tries to learn and recognize patterns in the form of digital images. This power is built on the depth of many layers of computing neurons backed by high power processors and graphics processing units (GPUs) easily available today. In the current scenario, we have provided detailed survey of various types of DL systems available today, and specifically, we have concentrated our efforts on current applications of DL in medical imaging. We have also focused our efforts on explaining the readers the rapid transition of technology from machine learning to DL and have tried our best in reasoning this paradigm shift. Further, a detailed analysis of complexities involved in this shift and possible benefits accrued by the users and developers.

Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms.

OBJECTIVE: A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth. This study presents an exhaustive comparative study of different machine learning (ML) systems which serves two major purposes: (a) identification of high risk differential genes using statistical tests and (b) development of a ML strategy for predicting cancer genes. METHODS: Four statistical tests namely: Wilcoxon sign rank sum (WCSRS), t test, Kruskal-Wallis (KW), and F-test were adapted for cancerous gene identification using their p-values. The extracted gene set was used to classify cancer patients using ten classifiers namely: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), naïve Bayes (NB), Gaussian process classification (GPC), support vector machine (SVM), artificial neural network (ANN), logistic regression (LR), decision tree (DT), Adaboost (AB), and random forest (RF). Performance was then evaluated using cross-validation protocols and standardized metrics viz. accuracy (ACC) and area under the curve (AUC). RESULTS: The colon cancer dataset consists of 2000 genes from 62 patients (40 cancer vs. 22 control). The overall mean ACC of our ML system using all four statistical tests and all ten classifiers was 90.50%. The ML system showed an ACC of 99.81% using a combination WCSRS test and RF-based classifier. This is an improvement of 8% over previously published values in literature. CONCLUSIONS: RF-based model with statistical tests for detection of high risk genes showed the best performance for accurate cancer classification in multi-center clinical trials.