Improved Methods Of Diagnosis Research Articles

AUTOMATIC SEGMENTATION OF COLON CANCER USING SAM AI

The third most prevalent form of cancer globally in both men and women is colon cancer which affects the digestive tract and occurs in the human body's greater intestine develops from certain polyps are tiisues that grow inside the colon of various sizes which at later stage can develop into a cancer cell. The tissues are taken from colon using biopsy method and cured. Under a microscope, histopathology images are categorised as a manual screening of colon the study tissue. Size of the nucleus and form of the glands are accepted standards for identifying colon cancer cells. The images obtaimed using colonscopy are converted into gray scale images where feature extraction done by conventional techniques and classified. To improve diagnosis methods for processing images and AI characteristics are also used. The automatic thresholding approach known as Otsu's Method, image thresholding, image enhancement, and edge detection techniques are used in the many processing procedures for an image. The SAM AI model is utilised to extract the malignancy characteristic of colon cancer. For the diagnostic images, metrics like resolution and peak signal to noise ratio are acquired.

An improved method for diagnosis of Parkinson’s disease using deep learning models enhanced with metaheuristic algorithm

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.

Proposal and Validation of a Diagnosis Method of Fuel Failures in Pressurized Water Reactor Nuclear Power Plants

Based on research into the diagnosis methods for fuel failures in pressurized water reactor nuclear power plants and an analysis of existing problems, this paper proposes an enhanced fuel failure diagnosis method, addressing three key improvements: identification of the occurrence of fuel failure, diagnosis of the number of failed fuel rods, and the size of fuel rod failure. Other factors that may influence the diagnosis results are also identified and discussed. The proposed method is then verified using operational data from in‐service nuclear power plants in China. The verification results demonstrate that the improved diagnosis method accurately identifies fuel failures and exhibits broader applicability.

Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information.

The expression of FLNA and CLU in PBMCs as a novel screening marker for hepatocellular carcinoma

Early detection improves survival and increases curative probability in hepatocellular carcinoma (HCC). Peripheral blood mononuclear cells (PBMCs) can provide an inexpensive, less-invasive and highly accurate method. The objective of this study is to find the potential marker for HCC screening, utilizing gene expression of the PBMCs. Data from the NCBI GEO database of gene expression in HCC patients and healthy donor's PBMCs was collected. As a result, GSE 49515 and GSE 58208 were found. Using both, a statistical significance test was conducted in each gene expression of each data set which resulted in 187 genes. We randomized three selected genes (FLNA, CAP1, and CLU) from the significant p-value group (p-values < 0.001). Then, a total of 76 healthy donors, 153 HCC, 20 hepatic fibrosis, 20 non-alcoholic fatty liver were collected. Quantitative RT-PCR (qRT-PCR) was performed in cDNA from all blood samples from the qRT-PCR, The Cycle threshold (Ct) value of FLNA, CLU, CAP1 of HCC group (28.47 ± 4.43, 28.01 ± 3.75, 29.64 ± 3.90) were lower than healthy group (34.23 ± 3.54, 32.90 ± 4.15, 32.18 ± 5.02) (p-values < 0.0001). The accuracy, sensitivity and specificity of these genes as a screening tool were: FLNA (80.8%, 88.0%, 65.8%), CLU (63.4%, 93.3%, 31.3%), CAP1 (67.2%, 83.3%, 39.1%). The tests were performed in two and three gene combinations. Results demonstrated high accuracy of 86.2%, sensitivity of 85% and specificity of 88.4% in the FLNA and CLU combination. Furthermore, after analyzed using hepatic fibrosis and non-alcoholic fatty liver as a control, the FLNA and CLU combination is shown to have accuracy of 76.9%, sensitivity of 77.6% and specificity of 75%. Also, we founded that our gene combination performs better than the current gold standard for HCC screening. We concluded that FLNA and CLU combination have high potential for being HCC novel markers. Combined with current tumor markers, further research of the gene’s expression might help identify more potential markers and improve diagnosis methods.

Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor

This paper proposes an improved diagnosis method for early detection and localization of Inter-Turn Short Circuit (ITSC) faults in the stator winding of the induction motor (IM). The main advantages of the method are the simplicity, low cost, and accurate diagnosis of these types of faults such that it can detect and localize even a low number of shorted turns faults in the stator winding of the motor. This is achieved by using a novel indicator that is based on the Discrete Wavelet Energy Ratio (DWER) of three stator currents, with Artificial Neural Network (ANN). Three different models of typical neural networks, namely, Multi-Layer perceptron (MLP), radial basis function (RBF), and Elman Neural Network (ENN) based on Bayesian Regularized (BR) training algorithm are proposed for ITSC classification based on fault feature extraction using discrete wavelet transform. To test the effectiveness of the proposed method, several experimental tests were carried out under different operating conditions of the IM, which contains the healthy and the ITSC faults cases that have experimented under various loads and different numbers of shorted turns in the three phases of the motor. The obtained results proved that the DWER is an accurate and robust indicator to diagnose the ITSC fault, this is confirmed by ANN results which gave the best results with the Bayesian regularized Elman network model that has the highest performance with minimal error rate is 10−9.Consequently, the combination DWER-ENN has assured its ability to accurately detect high and even low numbers of the shorted turns and localize the defective phase even within various loads in the IM.

Identification of hub genes in peripheral blood mononuclear cells for the diagnosis of hepatocellular carcinoma using a weighted gene co-expression network analysis.

Human hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive tract that is prevalent worldwide. Improving diagnosis methods for HCC helps to improve patient survival rate. The present study aimed to identify novel HCC biomarkers for the diagnosis of HCC through analyzing gene changes on peripheral blood mononuclear cells (PBMCs) and verifying these in additional samples. The gene expression profiles GSE49515 (including 10 specimens from normal patients and 10 specimens from patients with HCC) and GSE58208 (including 5 specimens from normal patients and 10 specimens from patients with HCC) were downloaded from the online Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) in PBMCs between healthy controls and patients with HCC were identified using R software. A total of 935 DEGs, including 686 upregulated DEGs and 249 downregulated DEGs, were identified in the present study. In order to identify any internal associations, these DEGs were used to construct weighted gene co-expression networks (WGCNA). Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of genes in each module were conducted using the online database DAVID. Furthermore, hub genes with high module membership were identified in a co-expression network and receiver operating characteristic curves were used to verify the diagnostic values of these eight hub genes. Furthermore, the expression and diagnosis value of the eight hub genes were also verified in additional samples. The results of the present study suggested that secreted protein acidic and cysteine rich(SPARC), transmembrane protein 40 (TMEM40), solute carrier family 25 member 44, formyl peptide receptor 2 (FPR2), complement C8 β chain, N-myristoyltransferase 1, protein kinase C δ(PRKCD) and protein phosphatase, Mg2+/Mn2+ dependent 1M(PPM1M) were hub genes. SPARC, TMEM40, FPR2, PRKCD and PPM1M had prominent diagnostic value according to the results from the GEO data and the additional samples. The present study demonstrated that these hub genes may help to improve the diagnosis of HCC.

Research and Improvement of Diagnosis Method of Fuel Failures for Pressurized Water Reactor Nuclear Power Plant

The diagnosis methods in the world for the fuel failures in PWR and the existing problems of diagnosis methods are analyzed and improved from three aspects, which are the number of failed fuel rods, fuel failure size and burnup of failed fuel. The factors that may affect the diagnosis result are also identified and discussed. The operating data of Chinese in-service nuclear power plants are used to verify the improved diagnosis method, and the results show that, the improved diagnosis method for fuel failures can accurately diagnose the fuel failure and has a wider applicability.

Pancreatic cancer incidence trends: evidence from the Surveillance, Epidemiology and End Results (SEER) population-based data.

Annual pancreatic cancer incidence rates have been increasing. We examine pancreatic cancer incidence trends by demographics and histologic type. Data from the Surveillance, Epidemiology and End Results (SEER) registries were available to assess temporal trends and pancreatic cancer rates from 1974 to 2013. Pancreatic cancer incidence rates declined between the 1970s and 1990s but increased from 1994 to 2013 among White males. Among non-Hispanic White and Hispanic males, the annual percent change (APC) in incidence between 1992 and 2013 was 0.84% and 0.73%, respectively. Rates also rose among White non-Hispanic, Hispanic and Asian females (APC = 0.81%, 0.56% and 1.23%, respectively) and even more rapidly among females aged 25-34 years (APC > 2.5%). Rates among Black males and females remained unchanged, but higher compared with the other racial/ethnic groups. By histologic type, the increases were greatest for non-secretory endocrine cancers ( > 6%), followed by ductal adenocarcinomas (∼5%) and adenocarcinoma, NOS (∼1.4%)-the largest histologic subgroup of pancreatic cancer. Rates for mucinous adenocarcinomas and poorly specified pancreatic cancer decreased. Overall, incidence rates during 2000-13 were higher among males than females [MF incidence rate ratio (IRR) = 1.28]. The IRR was >1.00 at all ages ≥ 35, but rates among females were higher at younger ages (IRRs 15-24: 0.66, 25-34: 0.81). The MF IRRs for most of the histologic types were elevated among males apart from solid pseudopapillary adenocarcinoma and cystic carcinomas (IRR = 0.22, confidence interval: 0.14-0.34 and 0.52, 0.41-0.65, respectively). Pancreatic cancer has been increasing overall, but patterns differ by demographic group and histologic type. Many of the trends parallel changing prevalence of lifestyle risk factors such as smoking, overweight and obesity, and diabetes in the USA, particularly for pancreatic adenocarcinoma, and improved diagnosis methods during the past 40 years.

Wind Turbine Diagnosis under Variable Speed Conditions Using a Single Sensor Based on the Synchrosqueezing Transform Method.

The gearbox is one of the key components in wind turbines. Gearbox fault signals are usually nonstationary and highly contaminated with noise. The presence of amplitude-modulated and frequency-modulated (AM-FM) characteristics compound the difficulty of precise fault diagnosis of wind turbines, therefore, it is crucial to develop an effective fault diagnosis method for such equipment. This paper presents an improved diagnosis method for wind turbines via the combination of synchrosqueezing transform and local mean decomposition. Compared to the conventional time-frequency analysis techniques, the improved method which is performed in non-real-time can effectively reduce the noise pollution of the signals and preserve the signal characteristics, and hence is suitable for the analysis of nonstationary signals with high noise. This method is further validated by simulated signals and practical vibration data measured from a 1.5 MW wind turbine. The results confirm that the proposed method can simultaneously control the noise and increase the accuracy of time-frequency representation.

A miRNA expression based diagnostic tool for breast cancer using random forests

Breast cancer is the leading cause of death by cancer among women and there is a need to improve diagnosis methods. MicroRNAs (miRNAs) are noncondig RNAs that regulate gene expression and many have been implicated in breast cancer. Here, we show that an accurate diagnostic tool for breast cancer can be built based on the expression levels of 8 circulating miRNAs (out of 188 probed miRNAs) and the use of the Random forests classification algorithm. Introduction

Multi-Stage Health Diagnosis for Concrete Cable-Stayed Bridge Based on EWMA Control Chart

To overcome the huge difficulties in constructing an accurately finite element model in health diagnosis of bridge, this paper presented an improved diagnosis method via exponential weighted moving average (EWMA) control chart. Firstly the characteristic of the live-load effect of main girder strain in a bridge was analyzed as a theoretical basis of the proposed strategy. Then wavelet analysis was employed to extract the live-load effect of strain from the total measurement as sensitive feature for structural health diagnosis. Finally the EWMA control chart was designed to complete the structural health diagnosis of bridge, in which the multi-stage control limits are estimated by reliability analysis. The results from application in a concrete cable-stayed bridge show significant effectiveness of the proposed method in health diagnosis of bridge.

FLOTAC: An improved method for diagnosis of lungworm infections in sheep

The FLOTAC techniques involve the spinning of faecal samples onto the surface of counting chambers to permit enumeration of parasitic elements (eggs, larvae, oocysts and cysts) to an accuracy of one parasitic element per gram of faeces. In the present study it is demonstrated that FLOTAC provides a rapid and very sensitive method for counting of lungworm larvae of sheep. The optimum flotation solution for lungworm larvae is zinc sulphate and mercury II iodide (s.g. 1.45) although zinc sulphate (s.g. 1.20 or 1.35) on its own also gave good results. Samples preserved in 5% formalin gave the highest counts but fresh, frozen and samples in 10% formalin also gave higher counts than McMaster and simple flotation. Larval counts of 307 field samples gave up to 1.27x more positives samples than use of Baermann funnels and up to 4.18x more larvae per sample. As FLOTAC is faster than Baermannisation of samples it offers a better method of counting larvae in ruminant faecal samples.

A novel neuropsychological assessment to discriminate between ischemic and nonischemic dementia

Introduction In the present study we have elaborated a multivariate–multifactor diagnostic method for diagnosis of ischemic and nonischemic dementia, and validated it using different human populations. Our purpose was to improve dementia diagnosis by means of comprehensive neuropsychological assessment and the control of intervening variables. Methods Data were obtained from 114 healthy volunteers to 101 patients consecutively referred for suspected dementia. On the basis of neuromorphological criteria, the patient sample was subdivided into those with ischemic lesions (IL: N = 12) and without ischemic lesions (non-IL: N = 89). The patient groups and the healthy subjects (HS) were matched according to age, education, sex ratio and handedness. A comprehensive neuropsychological battery was administered to all the participants. Results The two patient groups differed in their Hachinski ischemic score, but not in terms of demographic variables, the Blessed rating scale, Mini Mental State, Geriatric Depression Scale, or other measures. Discriminant analysis selected fifteen neuropsychological variables from the comprehensive battery, and these were found to provide accuracy of classification in 98% of HS and 87% of the patients considered as a whole. Considering the three groups, those variables provided accuracy of classification in 98% of HS, 82% of IL patients and 82% of non-IL patients. Subtests were internally consistent (Cronbach's alpha: 0.87) and a positive association between ischemic lesions and cognitive impairment was observed when the 15 dependent variables were added. Discussion It is possible to select a combination of cognitive tests that discriminated HS from dementia patients and, within this category, patients with and without ischemic lesions. Analysis of the 15 variables provides an improved method for diagnosis of ischemic vs. nonischemic lesion dementia in humans.

A simple and improved method for diagnosis of tuberculosis using hypertonic saline and sodium hydroxide to concentrate and decontaminate sputum

We report the use of hypertonic saline and sodium hydroxide (HS-SH) for concentration-decontamination of sputum samples for the diagnosis of tuberculosis. This method increases detection of mycobacteria in smears compared to examination of direct smears, is comparable and reduces contamination in cultures compared to NALC-NaOH.

Rapid molecular cytogenetic analysis of X‐chromosomal microdeletions: Fluorescence in situ hybridization (FISH) for complex glycerol kinase deficiency

Diagnosis of X-chromosomal microdeletions has relied upon the traditional methods of Southern blotting and DNA amplification, with carrier identification requiring time-consuming and unreliable dosage calculations. In this report, we describe rapid molecular cytogenetic identification of deleted DNA in affected males with the Xp21 contiguous gene syndrome (complex glycerol kinase deficiency, CGKD) and female carriers for this disorder. CGKD deletions involve the genes for glycerol kinase, Duchenne muscular dystrophy, and/or adrenal hypoplasia congenita. We report an improved method for diagnosis of deletions in individuals with CGKD and for identification of female carriers within their families, using fluorescence in situ hybridization (FISH) with a cosmid marker (cosmid 35) within the glycerol kinase gene. When used in combination with an Xq control probe, affected males demonstrate a single signal from the control probe, while female carriers demonstrate a normal chromosome with two signals, as well as a deleted chromosome with a single signal from the control probe. FISH analysis for CGKD provides the advantages of speed and accuracy for evaluation of submicroscopic X-chromosomal deletions, particularly in identification of female carriers. In addition to improving carrier evaluation, FISH will make prenatal diagnosis of CGKD more readily available.

An improved method for diagnosis of gaucher’s disease using skin fibroblasts and a chromogenic substrate for glucocerebrosidase

The glucosylceramide analogue, 2-N-hexadecanoylamino-4-nitrophenyl-β-D-glucopyranosidc (HNGIu) has been shown to be a suitable glucocerebrosidase substrate for the diagnosis of Gaucher’s disease. We report a procedure giving up to 20-fold more activity in normal skin fibroblasts than the reported procedure, but still showing negligible activity in Gaucher’s disease fibroblasts. The assay involves the incubation of the fibroblast extract with HNGIu (1.08 mM) at pH 5.0 in the presence of sodium taurocholate (9.3 mM) and oleic acid (1.06 mM) as activators. Normal fibroblasts showed an activity of 3.0-9.3 U/g protein, with a Km (apparent) of 0.15 mM. Fibroblasts from a patient with infantile Gaucher’s disease showed minimal activity from pH 4.0 6.4, indicating the absence of any other acid β glucosidase activity towards HNGIu. At pH 5.0, the patient’s activity was 0.3 U/g protein, while his parents had activities of 3.5 U/g protein and 1.5 U/g protein. This reduced activity resulted from a marked decrease in V max with no change in Km (apparent). The assay appears suitable for the diagnosis of Gaucher’s disease and, possibly, for Gaucher’s disease hcterozygotes.