Abnormal Cell Development Research Articles

Sustainable development of factory aquaculture through automation of ultraviolet parasiticide for the prevention and control of cryptocaryoniasis.

Cryptocaryon irritans infestations on marine teleosts are a considerable burden on factory mariculture. Ultraviolet (UV) light can kill C. irritans under laboratory conditions. However, a rational method for using UV in factory aquaculture to control cryptocaryoniasis has not been developed. This study focused on evaluating the killing effect of UV on protomonts and tomonts of C. irritans and established an automatic UV parasiticide device for the prevention and control of cryptocaryoniasis in marine teleosts. The survival rate of protomonts and tomonts decreased with an increase in the UV irradiation dose. All the protomonts and tomonts died within 14 and 24 min, respectively. The lowest UV lethal doses of protomonts and tomonts of C. irritans were 2.0 × 106 and 3.5 × 106 μWs cm-2 , respectively. Exposure of protomonts and tomonts to lethal doses of UV radiation led to shrinkage and severe dissolution of the protoplasm, causing abnormal development of cells. The survival rate of artificially infected Larimichthys crocea (treatment group, group A) was 83.33% at the end of the test (day 14) after disinfection using the automatic UV parasiticide device, whereas that of the control group (group C) was 90.00% (p < 0.05). However, all artificially infected L. crocea without disinfection using the automatic UV parasiticide device (untreated group, group B) died on day 8. The automation of traditional physical methods conforms to the sustainable development of aquaculture and provides a theoretical reference for the prevention and control of cryptocaryoniasis in mariculture. © 2022 Society of Chemical Industry.

Cyclosporin A Promotes Invasion and Migration of Extravillous Trophoblast Cells Derived from Human-Induced Pluripotent Stem Cells and Human Embryonic Stem Cells.

Extravillous trophoblast (EVT) cells play an essential role in the maternal-fetal interaction. Although abnormal development and function of EVT cells, including impaired migration and invasion capability, are believed to be etiologically linked to severe pregnancy disorders including pre-eclampsia, the associated molecular mechanisms are not clear due to the lack of an appropriate cell model in vitro. Cyclosporin A (CsA) is a macrolide immunosuppressant and also used in clinic to improve pregnancy outcomes. However, whether CsA has any effects on the function of EVT cells has not been well investigated. In this study, we induced differentiation of human-induced pluripotent stem cells (hiPSCs) and human embryonic stem cells (hESCs) into EVT cells (hiPSC-EVT and hESC-EVT cells, respectively) by Y27632, neuregulin-1 (NRG1), A83-01, and matrigel, and collected these derived EVT cells by flow cytometry for sorting cells positive for double human leukocyte antigen-G (HLA-G) and Cytokeratin7 (KRT7), both of which are EVT markers. We then investigated the effects of CsA on the invasion and migration of these derived EVT cells. We found that the hiPSC-EVT and hESC-EVT cells expressed high levels of the EVT markers such as KRT7, integrin alpha 5 (ITGA5), and HLA-G but low levels of OCT4, a stem cell marker, and that CsA significantly promoted the invasion and migration of hiPSC-EVT and hESC-EVT cells compared with HTR-8/SVneo cells. These results represent a possible cell model for studying the function of EVT cells and mechanism of pregnancy-related disorders associated with EVT. In addition, CsA may be used to treat pregnancy complications in clinic associated with deficient EVT function.

Emergence of nutrigenomics and dietary components as a complementary therapy in cancer prevention.

Cancer is an illness characterized by abnormal cell development and the capability to infiltrate or spread to rest of the body. A tumor is the term for this abnormal growth that develops in solid tissues like an organ, muscle, or bone and can spread to other parts of the body through the blood and lymphatic systems. Nutrition is a critical and immortal environmental component in the development of all living organisms encoding the relationship between a person's nutrition and their genes. Nutrients have the ability to modify gene expression and persuade alterations in DNA and protein molecules which is researched scientifically in nutrigenomics. These interactions have a significant impact on the pharmacokinetic properties of bioactive dietary components as well as their site of action/molecular targets. Nutrigenomics encompasses nutrigenetics, epigenetics, and transcriptomics as well as other "omic" disciplines like proteomics and metabolomics to explain the vast disparities in cancer risk among people with roughly similar life style. Clinical trials and researches have evidenced that alternation of dietary habits is potentially one of the key approaches for reducing cancer risk in an individual. In this article, we will target how nutrigenomics and functional food work as preventive therapy in reducing the risk of cancer.

Computer assisted diagnosis of skin cancer: A survey and future recommendations

Skin cancer is amid the most frequent types of cancer, accounting for approximately 2 to 3 million cases being diagnosed each year worldwide. Abnormal cell development on the skin causes skin lesions and manual inspection of skin lesions is a difficult, challenging, instinctive, and tedious task. Computer Aided Diagnosis (CAD) techniques can assist doctors to enhance their investigation skills and reduce the time it takes to get a precise diagnosis. Furthermore, the lack of advanced, user-friendly CAD techniques has raised serious concerns about the noninvasive, precise, and rapid identification of diseases. CAD systems can help to make an early diagnosis of skin lesions to plan timely treatment schedules for the patients to increase their survival rates. However, due to the distinctive and complex properties of skin lesion images, examination of skin lesion images still poses significant difficulties. The motivation behind this study is to discuss several preprocessing, segmentation, and classification strategies for analyzing skin lesions to differentiate between cancerous and non-cancerous images. The primary goal is to provide an overview for naïve researchers to commence their research in this field. Moreover, this manuscript will also highlight open challenges and future recommendations which further calls the distinct researchers to begin their research in this domain.

Assessing Pretransplant and Posttransplant Therapy Response in Multiple Myeloma Patients.

Multiple myeloma (MM) is a hematologic cancer defined by an abnormal development of clonal plasma cells in the bone marrow, releasing vast quantities of immunoglobulins and different proteins. In the majority of patients, MM remains incurable despite decades of medical improvement and a number of treatment breakthroughs. Frontline standard-of-care has little long-term success, with the majority of patients eventually relapsing, although the overall progression-free survival (PFS) has improved significantly in the last ten years. Patients who are eligible for a transplant have the highest PFS rate at 5 years, depending on medication response and other various factors that are yet to be discovered. Therefore, the current study aimed to evaluate the response to VCD (bortezomib, cyclophosphamide, dexamethasone) and VTD (bortezomib, thalidomide, dexamethasone) used as pretransplant regimens, as well as to compare responses between thalidomide and lenalidomide used as maintenance therapy posttransplant. This retrospective study was performed on a group of 105 hospitalized patients in the Hematology Department of the Timisoara Municipal Emergency Clinical Hospital between January 2016 and December 2021. Data was collected from the paper records of patients with MM who were under-followed. The treatment regimens used as induction therapy were either VCD or VTD if cyclophosphamide was contraindicated. Of the 105 patients, 27 became eligible for bone marrow transplantation. Furthermore, they received maintenance therapy which was based on either lenalidomide with dexamethasone or thalidomide with dexamethasone. Of the 62 patients treated with VTD, 17.7% were in complete remission before stem cell transplantation. Of the 43 patients treated with VCD, 37.2% were in complete remission. The 5-year mean progression-free survival (PFS) in the entire cohort was better in the group treated with the VTD regimen (31.6 vs. 27.2 months). However, in the 27 patients undergoing maintenance after ASCT, the PFS with thalidomide was 35.5 months (95% CI = 27-42), while the PFS rate in those receiving maintenance treatment with lenalidomide was 46.1 months (95% CI = 20-73). VCD proved to be superior to VTD in inducing complete pretransplant responses. Regarding maintenance therapy, patients from the lenalidomide group had superior responses compared with those under thalidomide.

A research review of experimental animal models with myelodysplastic syndrome.

Myelodysplastic syndrome (MDS) consists of a group of hematologic tumors that are derived from the clonal proliferation of hematopoietic stem cells, featuring abnormal hematopoietic cell development and ineffective hematopoiesis. Animal models are an important scientific research platform that has been widely applied in the research of human diseases, especially tumors. Animal models with MDS can simulate characteristic human genetic variations and tumor phenotypes. They also provide a reliable platform for the exploration of the pathogenesis and diagnostic markers of MDS as well as for a drug efficacy evaluation. This paper reviews the research status of three animal models and a new spontaneous mouse model with MDS.

SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer

Cancer is the unregulated development of abnormal cells in the human body system. Cervical cancer, also known as cervix cancer, develops on the cervix’s surface. This causes an overabundance of cells to build up, eventually forming a lump or tumour. As a result, early detection is essential to determine what effective treatment we can take to overcome it. Therefore, the novel Machine Learning (ML) techniques come to a place that predicts cervical cancer before it becomes too serious. Furthermore, four common diagnosis testing namely, Hinselmann, Schiller, Cytology, and Biopsy have been compared and predicted with four common ML models, namely Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (K-NNs), and Extreme Gradient Boosting (XGB). Additionally, to enhance the better performance of ML models, the Stratified k-fold cross-validation (SKCV) method has been implemented over here. The findings of the experiments demonstrate that utilizing an RF classifier for analyzing the cervical cancer risk, could be a good alternative for assisting clinical specialists in classifying this disease in advance.

Optimal and Efficient Deep Learning Model for Brain Tumor Magnetic Resonance Imaging Classification and Analysis

A brain tumor (BT) is an abnormal development of brain cells that causes damage to the nerves and blood vessels. An accurate and early diagnosis of BT is important to prevent future complications. Precise segmentation of the BT provides a basis for surgical and planning treatment to physicians. Manual detection utilizing MRI images is computationally difficult. Due to significant variation in their structure and location, viz., ambiguous boundaries and irregular shapes, computerized tumor diagnosis is still a challenging task. The application of a convolutional neural network (CNN) helps radiotherapists categorize the types of BT from magnetic resonance images (MRI). This study designs an evolutional algorithm with a deep learning-driven brain tumor MRI image classification (EADL-BTMIC) model. The presented EADL-BTMIC model aims to accurately recognize and categorize MRI images to identify BT. The EADL-BTMIC model primarily applies bilateral filtering (BF) based noise removal and skull stripping as a pre-processing stage. In addition, the morphological segmentation process is carried out to determine the affected regions in the image. Moreover, sooty tern optimization (STO) with the Xception model is exploited for feature extraction. Furthermore, the attention-based long short-term memory (ALSTM) technique is exploited for the classification of BT into distinct classes. To portray the increased performance of the EADL-BTMIC model, a series of simulations were carried out on the benchmark dataset. The experimental outcomes highlighted the enhancements of the EADL-BTMIC model over recent models.

Early-Stage Hepatic Tumor Detection Segmentation and Prediction Using Deep Learning Models

Abstract: Hepatocellular Carcinoma (HCC), which accounts for 75% of instances of liver cancer, is the most prevalent form of the disease. Liver cancer, also known as hepatic cancer, is caused by abnormal cell development in the liver. It is challenging to detect this tumor, which is typically discovered at an advanced stage and poses a serious threat to life. Thus, it is crucial to find the tumor at an early stage. Therefore, the primary goal of this study is to use image processing to detect liver cancer at an earlier stage. Liver cancer contributes to the increasing mortality rate in the world. Therefore, early detection may lead to a decrease in morbidity and increase the chance of survival rate. This research offers a computer-aided diagnosis system, which uses computed tomography scans to categorize hepatic tumors as benign or malignant.

CNN-Based Deep Learning Technique for the Brain Tumor Identification and Classification in MRI Images

A brain tumor is an abnormal development of cells in the brain that are either benign or malignant. Magnetic resonance imaging (MRI) is used to identify tumors. Manual evaluation of brain tumors from MRI images by a radiologist is a challenging task. Hence, this paper proposes VGG-19 Convolutional Neural Networks (CNN)-based deep learning model for the classification of brain tumors. Initially, in the proposed model, contrast stretching technique is employed for noises removal. Next, a deep neural network is employed for rich feature extract. Further, these learning features are combined with classifier models of CNN for training and validation. performance analysis of the proposed methodology and experiments have been carried out using publicly available MRI images in Figshare dataset of 3064 slices from 233 subjects. The proposed model has achieved 99.83% accuracy. Moreover, the proposed model obtained precision 96.32%, 98.26%, and 98.56%, recall of 97.82%, 98.62%, 98.87%, and specificity of 98.72%, 99.51%, and 99.43% for the Glioma, Meningioma, and Pituitary tumors respectively.

BERBERINE: A POTENT ADJUVANT IN PSORIASIS NANOTECHNOLOGY

Psoriasis is a multi-factorial disease represented by complex atomic scenes and cellular pathways that lead to abnormal cell development. The normal mixture has a clear target and limited cytotoxicity; in this sense, it can support the further development of new therapies for the treatment of this flexible disease. Berberine is an individual of the original berberine alkaloid family. It mainly exists in the roots, trunks and barks of various trees and has anti-psoriatic activity. In any case, according to the berberine organization, limited bioavailability and low assimilation rate are the two main obstacles, because only 0.5% of the ingested berberine is consumed in the small digestive tract, and this rate is when it enters the intestines. It is further reduced to 0.35%. Nano-based details are seen as the best way to increase the assimilation rate because substances accumulated at the nano-level can be quickly absorbed in the intestine. Useful methods based on nanotechnology have been developed to solve these problems to ultimately achieve greater sufficiency in the treatment of various diseases. The investigation introduced and essentially examined the anti-proliferative effects of berberine and nanotechnology-based repair technology for nano-scale transportation of berberine. Finally, we will thoroughly analyze and examine the current methods and promising viewpoints of the recent transportation of this alkaloid.

Analysis of Genes Associated with Both Neural Tube Defects and Neuroectodermal Tumors.

BackgroundPrevious studies have demonstrated that embryo development and the occurrence of tumors are closely related, as key genes, pathways, miRNAs, and other biological mechanisms are involved in both processes. Extensive research has found that abnormal development of nerve ectodermal cells not only leads to neural tube defects (NTDs), but also neuroectodermal tumors.Material/MethodsGenes associated with both NTDs and neuroectodermal tumors were obtained from the DisGeNET database. The STRING database was used to construct the protein–protein interaction (PPI) network and the hub genes were visualized using Cytoscape. Additionally, we predicted the miRNAs targeting the identified genes. Sequencing data obtained from an NTDs mouse model and human samples were used to confirm the bioinformatics results. Moreover, a dual-luciferase report assay was used to validate the targeting relationship between the miRNA-gene pairs identified.ResultsA total of 104 intersection genes of NTDs-related and neuroectodermal tumors-related genes were obtained; 20 of these genes were differentially expressed in NTDs samples and had very close interactions. Among 10 hub genes, we identified 3 important susceptibility genes differentially expressed both in RA-induced NTDs mice and human glioblastoma samples: Ncam1, Shh, and Ascl1. Among these, we found that the Ncam1 expression level was regulated by mmu-miR-30a-5p, and the Ascl1 expression level was regulated by mmu-miR-375-3p.ConclusionsIn conclusion, we identified differentially expressed genes and a potential miRNA-mediated regulation mechanism shared between NTDs and neuroectodermal tumors that may guide future studies aiming to find novel therapeutic targets for NTDs or neuroectodermal tumors.

MiR-185-5p suppresses acute myeloid leukemia by inhibiting GPX1.

Acute myeloid leukemia (AML) has been characterized by the swift development of abnormal cells in the bone marrow. This research aimed to examine the impacts of the miR-185-5p-GPX1 axis on AML progression and differentiation. Findings indicated that miR-185-5p and GPX1 levels were significantly reduced and elevated, respectively. The upregulation of miR-185-5p was observed to restrict the proliferation and invasion abilities of AML cells, and promote differentiate and apoptosis. Moreover, the overexpression of GPX1 was noticed to enhance the growth of AML cells. In conclusion, this research suggested that by targeting GPX1, miR-185-5p inhibited AML progression and downregulated AML cells' proliferation and invasion.

The Role Of “Il 10 (-2 1082 A/G)” Promoter Polymorphism In Ovarian Epithelial Cancer Pathogenesis

Ovarian cancer (ovarian cancer) is the abnormal development of cells in one or two ovaries. Many cytokine functions are related to the development of immunological and inflammatory responses that play essential roles in cancer pathogenesis. Among the cytokines, interleukin 10 (IL-10) is an anti-inflammatory cytokine involved in the downregulation of cytotoxic and inflammatory cell-mediated responses. This study aims to determine the relationship between the Gene Promoter Polymorphism Interleukin 10 (-1082 A/G) with the incidence of ovarian epithelial cancer. This research is an observational analytical study conducted at the Molecular Biology Laboratory in Palembang City in October-December 2021. The number of samples was 60 respondents consisting of 30 case groups and 30 control groups. Data were obtained by genotype and allele using PCR-RFLP, which was then analyzed by Chi-Square test to determine whether there was a relationship. The results showed no significant relationship between the promoter of the IL 10 gene polymorphism (-1082 A/G) and the incidence of ovarian epithelial cancer.

Diagnostic Value of 1H NMR-Based Metabolomics in Acute Lymphoblastic Leukemia, Acute Myeloid Leukemia, and Breast Cancer.

Cancer refers to a massive number of diseases distinguished by the development of abnormal cells that divide uncontrollably and have the capability of infiltration and destroying the normal body tissue. It is critical to detect biomarkers that are early detectable and noninvasive to save millions of lives. The aim of the present work is to use NMR as a noninvasive diagnostic tool for cancer diseases. This study included 30 plasma and 21 urine samples of patients diagnosed with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), 25 plasma and 17 urine samples of patients diagnosed with breast cancer (BC), and 9 plasma and urine samples obtained from healthy individuals as controls. They were prepared for NMR measurements; then, the metabolites were identified and the data were analyzed using multivariate statistical procedures. The OPLS-DA score plots clearly discriminated ALL, AML, and BC from healthy controls. Plots of the PLS-DA loadings and S-line plots showed that all metabolites in plasma were greater in BC than in the healthy controls, whereas lactate, O-acetylcarnitine, pyruvate, trimethylamine-N-oxide (TMAO), and glucose were higher in healthy controls than in ALL and AML. On the other hand, urine samples showed lower amounts of lactate, melatonin, pyruvate, and succinate in all of the studied types of cancer when compared to those of healthy controls. 1H NMR can be a successful and noninvasive tool for the diagnosis of different types of cancer.

Quality of life among caregivers of thalassaemic children, attending at Thalassaemia control unit of a rural tertiary care hospital, in a sub-Himalayan district of West Bengal

Thalassaemia is a disease of abnormal development of red blood cells which manifests as anaemia. This chronic disease may cause mental, social, financial burdens on the families, care givers and also on health care system.To assess the quality of life (QOL) of the caregivers of thalassaemic children and to identify the predictors of quality of their physical and mental health.Institution based descriptive cross-sectional study conducted in the Thalassaemia Control Unit (TCU) of North Bengal Medical College (NBMC) from December 2018 to April 2019. Total 136 caregivers of children (≤12 years) with thalassaemia were included by complete enumeration method. Physical and mental health of the caregivers were reported in Short Form-36 (SF-36) Health Survey. Collected data were entered into MS-Excel, analysed with the help of SPSS (Version 22).Mean age of caregivers was 34.3 (SD ± 1.4 years). Most of caregivers were female (89%) and had educational qualification up to Primary school (45.6%). Majority (70.6%) of the caregivers were the mothers of the children. Most of the study participants (60.3%) had favourable Physical health Component Summary (PCS) but 64.7% of the caregivers had unfavourable Mental health component Summary (MCS). Educational status was found to be the predictor for PCS but age and relationship with the child were the predictors for MCS.Counselling, psychotherapy, social support for family members or caregivers of the thalassaemic children and community involvement with their full participation should be emphasized to reduce stigma related to thalassaemia.

Triclosan induced zebrafish immunotoxicity by targeting miR-19a and its gene socs3b to activate IL-6/STAT3 signaling pathway

As a broad-spectrum antibacterial agent, triclosan (TCS) has been confirmed to possess potential immunotoxicity to organisms, but the underlying mechanisms remains unclear. Herein, with the aid of transgenic zebrafish strains Tg (coro1A: EGFP) and Tg (rag2: DsRed), we intuitively observed acute TCS exposure caused the drastic differentiation, abnormal development and distribution of innate immune cells, as well as barriers to formation of adaptive immune T cells. These abnormalities implied occurrence of the cytokine storm, which was further evidenced by expression changes of immune-related genes, and functional biomarkers. Based on transcriptome deep sequencing, target gene prediction and dual luciferase validation, the highly conservative and up-regulated miR-19a was chosen as the research target. Under TCS exposure, miR-19a up-regulation triggered down-regulation of its target gene socs3b, and simultaneously activated the downstream IL-6/STAT3 signaling pathway. Artificial over-expression and knock-down of miR-19a was realized by microinjecting agomir and antagomir, respectively, in 1–2-cell embryos. The miR-19a up-regulation inhibited socs3b expression to activate IL-6/STAT3 pathway, and yielded abnormal changes in the functional cytokine biomarkers, along with the sharp activation of immune responses. These findings disclose the molecular mechanisms regarding TCS-induced immunotoxicity, and offer important theoretical guidance for healthy safety evaluation and disease early warning from TCS pollution.

Recent developments in the synthesis and anti-cancer activity of acridine and xanthine-based molecules

Abstract Cancer is the uncontrolled growth and development of abnormal cells which is a major cause of death in both advanced and emerging countries. Although currently chemotherapy is most broadly used among an extensive range of anti-cancer therapies, it includes many demerits, such as highly toxic, side-effects, expensive and partial lack of targeting specificity. So the design and synthesis of new molecules that perform specifically on target proteins in tumor cells is a focus of contemporary research. So many researchers aim for new drugs that will be more efficient, more selective, and less toxic. Because of the interesting structures and significant biological profile, naturally occurring acridines and xanthines as well as their analogues have attracted considerable interest in researchers and technologists. Natural and synthetic acridine derivatives form a significant category of heterocycles having nitrogen that is of considerable interest for organic chemists and biological communities due to their attractive anti-cancer activity. Another important class of therapeutic agents with diverse biological properties including cytotoxic effects is xanthine derivatives which are collectively called xanthines (a group of alkaloids). Among many significant molecules based on the structure of the purine, there is a group of natural xanthines, involving theobromine, caffeine, and theophylline and analogues of xanthine display anti-cancer activity. Hence the present chapter wishes to concentrate the attention on the synthesis and anti-cancer activity of acridine and xanthine-based compounds brilliantly.

A comparative study of fine-tuning deep learning models for MRI Images

A brain tumor is an abnormal development of cells that reproduce uncontrollably and without any external stimulation. If tumors are not found early enough, brain tumors can be fatal to one’s health. Specialists and neurosurgeons employ magnetic resonance imaging (MRI) scans to diagnose brain tumors. Several deep learning methods for detecting the existence of brain tumors have been developed to overcome these constraints. The accurate detection of the size and location of a brain tumor is crucial in the diagnosis of the tumor. Medical image processing is a highly complex and tough discipline in which image processing and its methods are an active research topic. There are various technical deep learning and machine learning algorithms which are used to detect brain tumor. We used CNN architecture, ResNet, VGG16 and inception network in this paper and did a comparative study to find out the maximum accuracy for detecting brain tumor. When these algorithms are imposed to MRI images, the prediction of brain tumours is done quickly, and the higher accuracy aids in the treatment of patients. In this paper, after complete procedure and analysis of four different algorithms, we found out that CNN architecture is the most suitable with highest accuracy.

Neutrosophic set with Adaptive Neuro-Fuzzy Inference System for Liver Tumor Segmentation and Classification Model

Lung cancer is the abnormal development of cells in the lung causes serious risk to the health since lung has an interconnected system of blood vessel and lymphatic channel exposed to metastasis. The survival rate of lung cancer depends greatly on the earlier diagnosis and staging of the lung cancer. Computed Tomography (CT) image is commonly employed for lung cancer diagnosis since they offer data regarding distinct portions of the lung. The exactness of finding tumor location, volume and shape acting a major role in positive treatment and diagnosis of tumor. This article designs a novel neutrosophic set with adaptive neuro-fuzzy inference system for liver tumor segmentation and classification (NSANFIS-LTSC) model. The presented NSANFIS-LTSC model aims to identify and classify the presence of liver tumor from medical images. The presented NSANFIS-LTSC model primarily undergoes pre-processing to eradicate the noise. Followed by, the neutrosophic set (NS) based segmentation is applied to identify the affected tumor regions in the CT images. Besides, DenseNet-169 model is utilized to create feature vectors and dragonfly algorithm (DFA) is applied to tune the hyper parameters of the DenseNet-169 model. Finally, ANFIS classifier is exploited for the occurrence and classification of liver tumor. The simulation analysis of the NSANFIS-LTSC model is experimented using benchmark dataset and the results are investigated under several aspects. The simulation outcome reported the betterment of the NSANFIS-LTSC model over the recent methodologies.