Abnormal Cell Development Research Articles

Brain Tumor Semantic Segmentation using U-Net and Moth Flame Optimization

Brain tumor is an abnormal development of brain cells that, if left untreated, can have severe consequences. Brain tumour semantic segmentation is the process of determining and distinguishing the impacted brain regions, which is essential for accurate diagnosis, treatment planning, as well as surveillance of the tumor's development over time. This paper presents a model for identifying and segmenting brain tumor using Unet architecture with the optimization of hyper parameters using the Moth Flame Optimization (MFO) algorithm. Due to its capacity to collect spatial information, the Unit architecture is a common choice for picture segmentation tasks. The MFO algorithm is an optimization technique that draws inspiration and replicates from the behavior of moths. Both techniques are developed to improve efficiency. The performance of the model has increased using the MFO method, which led to improved segmentation results. Based on comparative analysis report, the proposed model shows a percentage improvement of approximately 65.16% in MSE, 28.87% in PSNR, and 40.30% in Tversky compared to the Unet and Unet++ models. This method has demonstrated good results in identifying and segmenting brain tumors, which can be helpful in the early identification and treatment of brain tumor.

Brain Tumour Detection Using Resnet 50 and Mobilenet

The scientific community defines a brain tumour as a mass or growth of abnormal cells in the brain. A brain tumour is a development of abnormal cells, some of which may develop into cancer. MRI scans are the most common way to find brain tumours and are used to detect brain cancer. There are different types of tumours exist. They are cancerous(malignant)and non- cancerous(benign) in the brain identification of unchecked tissue growth in MRI may help us diagnose brain cancer. Machine Learning and Deep Learning algorithms are used to identify this tissue growth. When these algorithms are applied to MRI scans, a faster prediction of brain tumours is made, and a better degree of accuracy aids in treating patients. MRI scans allow us to perform rapid analysis and identify the exact location of unwanted tissue growth. Various uses include image recognition and identifying objects, image classification, segmentation, neural network and data processing. The proposed model successfully classified the MRI image into four classes: glioma, meningioma, and pituitary tumour and no tumour, indicating that the given brain MRI has no tumour. In this paper the proposed models are MobileNet and Resnet50 and gives accuracy of 0.98. These models classifies the type of tumour very accurately.

Role of Docking in Anticancer Drug Discovery

Abstract: The computational method is widely used in the field of drug design as well as discovery. It aids the drug discovery and design process by making the procedure faster while also ensuring fewer human errors. Cancer is a condition with the development of abnormal cells expressing features like uncontrolled growth and cell division. This leads to abnormal tissue enlargement and interrupts the normal functioning of the tissue. Computational methods, mainly the molecular docking method, have been utilised extensively in the field of anticancer drug discovery. Docking is a virtual screening method that can be performed on a large database of compounds. Molecular docking helps in identifying the predominant binding modes of a ligand with a protein whose three-dimensional structure is known. The docking process can predict the method of inhibition of the target molecule by the ligand molecule. Utilities of molecular docking include structure-activity relationship studies, lead identification by virtual screening, optimization of the identified lead, combinatorial library design and more. This review discusses the process of docking, its role in anticancer drug discovery, and a comparison of different docking software. Docking programs are used to make the docking process much more quick, efficient, and with fewer human errors, as it mostly depends on computational algorithms. A description of some representative studies in anticancer drug discovery related to selected docking software, Autodock, SwissDock, ICM, GOLD and Glide, are also mentioned. This paper concludes by emphasizing the importance of docking programs in the field of drug discovery and how it influences the modern drug discovery processes.

Novel Insight into the Cellular and Molecular Signalling Pathways on Cancer Preventing Effects of Hibiscus sabdariffa: A Review.

A category of diseases known as cancer includes abnormal cell development and the ability to infiltrate or spread to other regions of the body, making them a major cause of mortality worldwide. Chemotherapy, radiation, the use of cytotoxic medicines, and surgery are the mainstays of cancer treatment today. Plants or products produced from them hold promise as a source of anti-cancer medications that have fewer adverse effects. Due to the presence of numerous phytochemicals that have been isolated from various parts of the Hibiscus sabdariffa (HS) plant, including anthocyanin, flavonoids, saponins, tannins, polyphenols, organic acids, caffeic acids, citric acids, protocatechuic acid, and others, extracts of this plant have been reported to have anti-cancer effects. These compounds have been shown to reduce cancer cell proliferation, induce apoptosis, and cause cell cycle arrest. They also increase the expression levels of the cell cycle inhibitors (p53, p21, and p27) and the pro-apoptotic proteins (BAD, Bax, caspase 3, caspase 7, caspase 8, and caspase 9). This review highlights various intracellular signalling pathways involved in cancer preventive potential of HS.

The development of gonads in females of the common perch Perca fluviatilis from the lower Dnister in the vegetative period

The present paper presents the scientific results regarding the particularities of the oogenesis of the common perch in the lower sector of the Dniester river. In the mentioned species, the development of the oocytes is synchronous, which determines a unitary mode of spawning. Differences in the character of oocyte development were established in breeders of different sizes, indicating earlier spawning by breeders of perch in smaller gravimetric groups. In sevenyearold females with ovaries in developmental stages II and II-III, resorption of oocytes was found in the phase of protoplasmic growth and in the initial phases of vacuolation of the cytoplasm, which indicates the attenuation of reproductive function in higher age groups. In May, an individual hermaphrodite perch with an abnormal development of germ cells was captured.

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.

The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model's sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network's core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.

7, 12- Dimethylbenz[a]anthracene: A potent and multivariant Chemical carcinogen

Cancer is currently the second major cause of mortality globally. It is a collection of disorders categorized as abnormal cell development which can infiltrate and can spread to countless sections of the body. It has been essentially brought about by genetic and environmental sources mainly, countless patients have impacted by ecological factors like polycyclic aromatic hydrocarbons (PAHs). However, many PAHs in the ecosystem are generated from natural factors like direct combustion, petroleum and volcanic activity. Among these, 7,12 dimethylbenz[a]anthracene (DMBA) has been widely recognized for its capability to induce carcinogenesis in animals and humans. The carcinogenic activity of DMBA indicates the impact of well-differentiated tumors and it is recognized for generating DNA-reactive species that increase the oxidative damage in cells through their metabolism. Additionally, the configuration of DNA adducts and oxidative intermediates acquired from DMBA metabolism impairs prominent cellular activities by harming lipid and protein barriers. DNA adduct is made up of highly reactive intermediates. DMBA-induced cancer displayed higher expression of aryl hydrocarbon receptor (AhR), c-Myc, cyclinD1, activation of upstream regulatory mechanisms and frequent detection of NF-κB pathway components. In this study, we emphasized the growth of several cancers and the kinetic impact of DMBA to probable chemical carcinogenic activities.

Anticancer Activity of The Ethanol Fraction of Gnetum gnemon L. Seeds on HeLa Cell Lines

Cervical cancer results from abnormal cell development in the cervix and is caused by the Human Papillomavirus (HPV). Cancer treatment with chemotherapy drugs has dangerous side effects because it can attack normal cells. Gnetum gnemon L. seeds are known to possess anticancer potential. Therefore, natural ingredients are necessary to develop new cancer drug strategies with minimal side effects. This study aims to determine the anticancer activity of the ethanol fraction of Gnetum gnemon L. Seeds (EFGS) against HeLa cells. The research method used in this study was an identification test to detect compounds from EFGS using the TLC method and the HeLa cell cytotoxic test using the MTT Assay method. The results showed that EFGS contained stilbenoid group compounds based on TLC-Densitometry, with an Rf value of 0.22 cm. The cytotoxic test results on HeLa cells indicated an IC50 value of 784 µg/mL, classifying it as having the potential as a moderate category anticancer agent. Therefore, this study indicates that EFGS can be utilized as a chemopreventive agent.

Immunosensor for breast cancer CD44 biomarker detection based on exfoliated graphene quantum dots integrated gold nanoparticles

Breast cancer is a fatal disease caused by the abnormal development of cells in the breast, which forms tumors, and whose progression results in metastatic behavior. In this study, we present a novel electrochemical immunosensor based on graphene quantum dots decorated gold nanoparticles (GQDs-GNPs) for the early detection of breast cancer antigen Cluster of Differentiation-44 (CD44). The GQDs have been synthesized through electrochemical exfoliation of waste dry cell batteries and a simple chemical process was employed for the synthesis of GQDs-GNPs nanocomposite. It has been characterized for its electrochemical properties. The immunosensor was created utilizing GQDs-GNPs nanocomposite cross-linked via EDC/NHS coupling procedure to immobilize anti-CD44 antibodies. The resultant electrode surface was characterized by Transmission Electron Microscopy (TEM), Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV), and Electrochemical Impedance Spectroscopy (EIS). TEM analysis revealed an average spherical GQD size of 3 nm and a GNP size of 15 nm. In phosphate buffer saline (PBS), the immunosensor had a low detection limit of 1.17 fg/mL. The findings suggest that the generated GQDs-GNPs nanocomposite exhibits favorable properties for usage as a successful nanomaterial in biosensing and other applications. According to these findings, our newly built immunosensor has a lot of potential to be a valuable alternative diagnostic tool for identifying various cancer biomarkers, particularly CD44 that exist in biological materials. The results indicate that the synthesized GQDs-GNPs nanocomposite possesses desirable properties for use as an effective nanomaterial in biosensing and other applications.

Comparison Analysis of Brain Image Classification Based on Thresholding Segmentation With Convolutional Neural Network

Brain tumor is one of the most fatal diseases that can afflict anyone regardless of gender or age necessitating prompt and accurate treatment as well as early discovery of symptoms. Brain tumors can be identified using Magnetic Resonance Imaging (MRI) to detect abnormal tissue or cell development in the brain and surrounding the brain. Biopsy is another option, but it takes approximately 10 to 15 days after the inspection, so technology is required to classify the image. The goal of this study is to conduct a comparative analysis of the greatest accuracy value attained while classifying using segmentation with thresholding versus segmentation without thresholding on the CNN method. Images are assigned threshold values of 150, 100, and 50. The dataset consists of 7023 MRI scans of four types of brain tumors: glioma, notumor, meningioma, and pituitary. Without utilising thresholding segmentation, the classification yielded the highest degree of accuracy, 92%. At the threshold of 100, classification by segmentation received the highest score of 88%. This demonstrates that thresholding segmentation during CNN model preprocessing is less effective for brain image classification

Susceptibility of PCSK2 Polymorphism to Hirschsprung Disease in Southern Chinese Children.

Hirschsprung's disease (HSCR) is a developmental defect of the enteric nervous system (ENS), which is caused by abnormal development of enteric neural crest cells. Its occurrence is caused by genetic factors and environmental factors. It has been reported that single nucleotide polymorphisms (SNPs) of proprotein convertase subtilisin/kexin type 2 (PCSK2) gene are associated with HSCR. However, the correlation of HSCR in southern Chinese population is still unclear. We assessed the association of rs16998727 with HSCR susceptibility in southern Chinese children using TaqMan SNP genotyping analysis of 2943 samples, including 1470 HSCR patients and 1473 controls. The association test between rs16998727 and phenotypes was performed using multivariable logistic regression analysis. We got an unexpected result, PCSK2 SNP rs16998727 was not significantly different from HSCR and its HSCR subtypes: S-HSCR (OR = 1.08, 95% IC: 0.93~1.27, P_adj = 0.3208), L-HSCR (OR = 1.07, 95% IC: 0.84~1.36, P_adj = 0.5958) and TCA (OR = 0.94, 95% IC: 0.61~1.47, P_adj = 0.8001). In summary, we report that rs16998727 (PCSK2 and OTOR) is not associated with the risk of HSCR in southern Chinese population.

Targeting hPKM2 in cancer: A bio isosteric approach for ligand design

The term cancer refers to a plethora of diseases characterized by the development of abnormal cells that divide uncontrollably and can infiltrate further proximal or distal body tissues. Each type of cancer can be defined by aggressiveness, localization, metabolism, and response to available treatments. Among the most common hallmarks of cancer is a more acidic intracellular microenvironment. Offset pH values are due to an excess of lactate and an increased hypoxia-inducible factor (HIF) expression, which leads to a hypoxic state and a metabolic shift towards glycolysis to produce adenosine-5′-triphosphate (ATP) necessary for cellular metabolism. Warburg's hypothesis underpins this concept, making glycolysis and its central enzyme pyruvate kinase (hPKM2), an ideal target for drug development. Using molecular docking and extensive molecular dynamics (MD) simulations we investigated the binding mode of phosphoenolpyruvate (PEP) inside the hPKM2 active site, and then evaluated a set of known bio-isosteric inhibitors to understand the differences caused by their substitutions on their binding mode. Ultimately, we propose a new molecular entity to hamper hPKM2, unbalance cellular energy, and possibly trigger autophagic mechanisms.

IDH2 regulates U2AF1 expression and hydroxymethylation in MDS patients

ABSTRACT The expression of some genes regulated by their DNA methylation is involved in pathogenesis and disease progression of myelodysplastic syndrome (MDS), which is characterised by abnormal differentiation and development of myeloid cells. Therefore, it is significant for us to work on investigating what factors regulate U2AF1 expression and hydroxymethylation in MDS patients. However, the members of TET protein family can change 5-methylcytosine (5mC) into 5-hydroxymethylcytosine5-methyl cytosine (5hmC). In general, 5mC and 5hmC levels maintain dynamic equilibrium, and their imbalance is associated with the onset and progression of some tumors. In this study, the expression and 5mC and 5hmC levels of U2AF1 gene decreased significantly after the treatment by decitabine in Mutz-1 cells. The decreased degree of 5hmC is far greater than that of 5mC. IDH2 expression decreased significantly followed by U2AF1 5hmC levels. However, the expression of other hydroxymethylation-related genes such as IDH1, TET1 and TET2 also decreased, but the difference did not achieve significance. Compared with IDH2 or U2AF1 wild-type MDS patients, U2AF1 expression and 5hmC level in patients with these two gene mutations were both significantly reduced.

Inhibition of Expression of the Circadian Clock Gene Cryptochrome 1 Causes Abnormal Glucometabolic and Cell Growth in Bombyx mori Cells.

Cryptochrome is the earliest discovered photoreceptor protein in organisms. However, the effect of CRY (BmCRY), the clock protein in Bombyx mori, on the body or cell metabolism remains unclear. In this study, we continuously interfered with the expression of the BmCry1 gene (Cry1-KD) in the silkworm ovary cell line (BmN), and the BmN cells developed abnormally, with accelerated cell growth and a smaller nucleus. Metabolomics was used to identify the cause of the abnormal development of Cry1-KD cells based on gas chromatography/liquid chromatography-mass spectrometry. A total of 56 differential metabolites including sugars, acids, amino acids, and nucleotides were identified in wild-type and Cry1-KD cells. KEGG enrichment analysis showed that BmCry1 knockdown resulted in significantly upregulated glycometabolism in BmN cells, indicated by glucose-6-phosphate, fructose-6-phosphate, and pyruvic acid levels. The activities of key enzymes BmHK, BmPFK, and BmPK as well as their mRNA levels further confirmed that the glycometabolism level of Cry1-KD cells was significantly increased. Our results show that a possible mechanism of BmCry1 knockdown leading to abnormal cell development is the elevated level of glucose metabolism in cells.

A104 INVISIBLE COLONIC MALIGNANCY AND POSSIBLE IDIOSYNCRATIC DRUG-INDUCED LIVER INJURY FROM VEDOLIZUMAB IN A PATIENT WITH ULCERATIVE COLITIS, PRIMARY SCLEROSING CHOLANGITIS AND AUTOIMMUNE HEPATITIS OVERLAP SYNDROME

Abstract Background Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract. Due to an increased risk of developing colorectal cancer, regular surveillance for dysplastic lesions via colonoscopy is recommended. Invisible dysplasia is the abnormal development of cells noted on pathology with no visible lesion seen during colonoscopy. Primary sclerosing cholangitis (PSC) is an immune-mediated liver disease leading to progressive stricturing and fibrosis of the bile ducts. There is significant association between PSC and IBD, as up to 80% of patients with PSC having underlying IBD. Drug induced liver injury (DILI) is a common cause of acute liver failure in most Western countries. Most cases of DILI are self-limited, with resolution of laboratory and clinical findings after cessation of the offending agent. Purpose To describe a case of invisible colonic malignancy and possible idiosyncratic drug-induced liver injury from vedolizumab (VDZ) in a patient with ulcerative colitis (UC), PSC and autoimmune hepatitis (AIH) overlap syndrome. Method Patient consent was obtained. Information from electronic records was extracted, including admission notes and procedural reports. A literature review was performed using Pubmed. Result(s) A 32-year-old Caucasian female with UC on VDZ with PSC and AIH had multiple colonoscopies performed demonstrating multifocal low-grade dysplasia. However, she remained resistant to surgery. She presented to clinic with tea-coloured urine, fatigue, and scleral icterus. Investigations revealed conjugated hyperbilirubinemia with elevation in hepatocellular liver enzymes. Imaging was consistent with large duct PSC, and liver biopsy showed grade 2 chronic hepatitis raising the possibility of large bile duct obstruction. She underwent liver transplant assessment and VDZ was held. Her bilirubin and liver enzymes recovered. Given multiple colonoscopies showing multifocal dysplastic changes and the patient declining other biologics, she would ultimately undergo total proctocolectomy with end ileostomy. Pathology demonstrated mucinous adenocarcinoma with a signet ring component. Conclusion(s) Both mucinous adenocarcinoma and signet ring carcinoma are more aggressive malignancies associated with poor prognosis. Found more often in younger patients, they are often diagnosed in later stages with lymphovascular invasion. Here, there was no evidence of metastases in any of the 40 lymph nodes examined, which indicated more favourable prognosis. The mechanism by which VDZ potentially causes liver injury is unknown. Multiple therapies for PSC-AIH overlap and IBD were entertained as causative agents. However, significant improvement in clinical symptoms and serologic parameters following cessation of VDZ was temporally suggestive. This case simultaneously highlights the importance of both timely colectomy in IBD patients with high-risk features during surveillance and early recognition and cessation of potential causative medications which induce liver injury. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

Iterative Reflect Perceptual Sammon and Machine Learning-Based Bagging Classification for Efficient Tumor Detection

A tumor is an abnormal development of cells in the human body. A tumor develops when cells divide without any control. Tumors change their size from a small to large lump. Tumors appear anywhere in the body. The early stage of diagnosis is an essential one in disease treatment. Many researchers carried out their research on different tumor detection methods. However, the tumor detection accuracy level was not improved and tumor detection time consumption not minimized. In order to address these problems, an Iterative Reflect Perceptual Sammon Bagging Classification (IRPS-BAC) Method is introduced. The aim is to accurately detect brain tumors as early as possible and make the method suitable for real-time applications. The IRPS-BAC Method comprises two processes, namely, feature selection and classification using the iterative reflect perceptual sammon feature selection process and bagging classification process. In the IRPS-BAC Method, an input of medical data are gathered from the Epileptic Seizure Recognition Data Set and Cervical Cancer Risk Classification database. After that, iterative reflect perceptual sammon feature selection process is carried out to select the relevant features. Iterative reflect perceptual divergence computes the variation between two features. After that, sammon mapping projects the similar and dissimilar features into feature space. By this manner, the relevant features get selected using the IRPS-BAC Method. With the help of selected relevant features, bagging classification process is carried out. In bagging classification process, internal node processes the selected features and leaf node to make the tumor decision as normal or cancerous one based on information gain. This, in turn, helps to reduce the time complexity and error rate. The performance of the proposed IRPS-BAC Method is determined by two benchmark datasets through comparing the parameter such as tumor detection time, tumor detection accuracy and error rate with the existing approaches. In the Epileptic Seizure Recognition Data Set, the proposed IRPS-BAC Method improves tumor detection accuracy by 16%, with minimum time period and the error rate of 41 ms and 58% for tumor detection as compared to existing methods. By using Cervical Cancer Risk Classification, the proposed IRPS-BAC Method exhibited higher classification performance measures, including accuracy (14%), time (46 ms), and error rate (61%), than the current conventional approaches.

Determination of sarcosine in urine as a predictor of prostate cancer using Enzyme linked immunosorbent method

Prostate cancer is a type of malignancy that is defined by abnormal development of cells in the prostate tissue. Prostate cancer needs early intervention since its incidence and prevalence is high across the world leading to high morbidity and mortality. Prostatic specific antigen test which is the commonly used screening test in Kenya and across the world is nonspecific, expensive and inaccessible to many people in rural setting who are in need. The definitive histological test is invasive and requires specialized facilities and personnel. This study sought to investigate sarcosine in urine as a predictor of prostate cancer to supplement prostatic specific antigen test in the diagnosis of prostate carcinoma. Cross sectional study design was employed in this study for all suspected prostate cancer identified according to clinical assessment during the study period. Midstream urine samples of about 30mls was collected in plastic tubes, centrifuged and supernatant collected and analyzed using ELISA method for sarcosine. Raw data obtained was tabulated in excel and transferred to statistical package for social science. Differences in means and standard deviation from various age groups was analyzed using one-way Anova and Independent t test. The Bonferroni was used as post Hoc to test the means that were significant from others. Significance level was set at 95%. The concentration of sarcosine (4.30±0.11nmol/ml) in prostate cancer participants was significantly higher than the concentration (0.47±0.06nmol/ml) of control participants using ELISA (p<0.001;). Hence Sarcosine in urine needs to be analyzed for the testing of prostate tumor since it is raised in confirmed prostate carcinoma participants as compared to negative control units. The age groups of the prostate tumor participants had no significant variation in sarcosine concentration using ELISA method (p=0.57). Similarly, the age groups of the control individuals were not significantly different in sarcosine concentration (p=0.17). Future studies need to dwell in incorporating sarcosine metabolite in urine.

In Silico Study on the Interactions, Molecular Docking, Dynamics and Simulation of Potential Compounds from Withania somnifera (L.) Dunal Root against Cancer by Targeting KAT6A.

Cancer is characterized by the abnormal development of cells that divide in an uncontrolled manner and further take over the body and destroy the normal cells of the body. Although several therapies are practiced, the demand and need for new therapeutic agents are ever-increasing because of issues with the safety, efficacy and efficiency of old drugs. Several plant-based therapeutics are being used for treatment, either as conjugates with existing drugs or as standalone formulations. Withania somnifera (L.) Dunal is a highly studied medicinal plant which is known to possess immunomodulatory activity as well as anticancer properties. The pivotal role of KAT6A in major cellular pathways and its oncogenic nature make it an important target in cancer treatment. Based on the literature and curated datasets, twenty-six compounds from the root of W. somnifera and a standard inhibitor were docked with the target KAT6A using Autodock vina. The compounds and the inhibitor complexes were subjected to molecular dynamics simulation (50 ns) using Desmond to understand the stability and interactions. The top compounds (based on the docking score of less than -8.5 kcal/mol) were evaluated in comparison to the inhibitor. Based on interactions at ARG655, LEU686, GLN760, ARG660, LEU689 and LYS763 amino acids with the inhibitor WM-8014, the compounds from W. somnifera were evaluated. Withanolide D, Withasomniferol C, Withanolide E, 27-Hydroxywithanone, Withanolide G, Withasomniferol B and Sitoindoside IX showed high stability with the residues of interest. The cell viability of human breast cancer MCF-7 cells was evaluated by treating them with W. Somnifera root extract using an MTT assay, which showed inhibitory activity with an IC50 value of 45 µg/mL. The data from the study support the traditional practice of W. somnifera as an anticancer herb.

An Overview of Cancer

Cancer is defined as one of the large groups of diseases characterized by the development of abnormal cells that grow beyond their boundaries, which can invade adjoining tissues via circulation, and spread to other organs in the body. Cancer can be initiated anywhere in the body, where damaged cells grow and multiply where they shouldn't. These cells form tumors, also called neoplasm- an abnormal mass of cells. Tumors can be non-cancerous (benign) and cancerous (malignant). Cancers are grouped according to their origin of tissue or organ. Four major types of cancers- • Carcinomas – are malignancies that begin in the epithelial cells, which make up the skin , and tissues that line other internal organs. Examples of carcinomas involved are prostate cancer, breast cancer, lung cancer, and colorectal cancer. • Sarcomas- sarcoma is a type of cancer that starts in tissues like bone or soft tissues and connective tissues . • Leukemias- leukemias are cancer of the white blood cells, which begins in the bone marrow as the bone marrow produces an excessive amount of abnormal white blood cells, that do not function properly. There are 4 major types of leukemia - acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myelogenous leukemia, and chronic myelogenous leukemia . • Lymphoma- a cancer that begins in the lymphatic system, the lymphatic system includes the lymph nodes, spleen, thymus gland, and other network of vessels. There are main types of lymphomas- Hodgkin’s lymphoma and non-Hodgkin's lymphomas. • And there are also other types of cancer • In overview, cancer is a condition where cells multiply abnormally over time. The cells divide and grow rebelliously, they invade surrounding tissues and spread to distant parts of the body. In the course of time, the mass of the cancer cells can get massive enough to make lumps(tumors) that can be felt or seen. But not all tumors are cancer.

An Effective Diagnosis System for Brain Tumor Detection and Classification

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a Support Vector Machine (SVM). Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.