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1.
Front Biosci (Landmark Ed) ; 26(11): 1312-1339, 2021 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-34856770

RESUMO

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

2.
J Diabetes Complications ; : 108074, 2021 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-34774416

RESUMO

BACKGROUND: Circulatory Fetuin-A has been well reported to elevate the risk for Diabetic Nephropathy (DN) and is associated with many vascular complications. Compelling reports have well documented that the circulatory levels of Fetuin-A directly have an impact on its AHSG (α2- Heremans- Schmid Glycoprotein) gene single nucleotide polymorphisms (SNP). Thus, in this study among the South Indian T2DM population, we aim to explore the association of AHSG Thr256Ser (rs4918) SNP in subjects with DN and correlate with the circulatory levels of Fetuin-A at various stages of DN patients. METHODS: A total of 975 subjects were recruited, such as Group-I, consisting of Controls (n = 300), Group-II, with normoalbuminuria (n = 300), Group-IIIa, with incipient microalbuminuria (n = 195), Group-IIIb, with persistent macroalbuminuria (n = 180)] and were subjected for genotyping using PCR-Restriction Fragment Length Polymorphism (RFLP). Circulatory Fetuin-A was measured using sandwich enzyme-linked immunosorbent assay (ELISA). RESULTS: The 'G' allele of AHSG exon-7 (C/G) SNP is significantly concomitant and conferred significant risk for normoalbuminuria subjects. In the DN subjects, the 'G' allele showed the risk for persistent macroalbuminuria. A noticeable stepwise decrease was evidenced in the circulatory Fetuin-A among persistent macroalbuminuria over incipient microalbuminuria from normoalbuminuria. Further, the circulatory Fetuin-A was lowered in DN subjects with mutant GG genotype than the wild CC. CONCLUSION: AHSG Thr256Ser (rs4918) SNP was associated with renal complications among South Indian T2DM subjects.

3.
Eur Respir J ; 2021 Oct 28.
Artigo em Inglês | MEDLINE | ID: mdl-34711538

RESUMO

Pre-treatment IL-6 is a biomarker for unfavorable tuberculosis treatment outcomes independent of disease severity and, improves the performance of risk-prediction models comprising of established clinical predictors. BACKGROUND: Biomarkers of unfavorable tuberculosis treatment outcomes are needed to accelerate new drug and regimen development. Whether plasma cytokine levels can predict unfavorable tuberculosis treatment outcomes is unclear. METHODS: We identified and internally validated the association between 20 a-priori selected plasma inflammatory markers and unfavorable treatment outcomes of failure, recurrence and all-cause mortality among adults with drug-sensitive pulmonary tuberculosis in India. We externally validated these findings in two independent cohorts of predominantly diabetic and HIV coinfected tuberculosis patients in India and South Africa, respectively. RESULTS: Pre-treatment IFN-γ, IL-13 and IL-6 were associated with treatment failure in the discovery analysis. Internal validation confirmed higher pre-treatment IL-6 concentrations among failure cases compared to controls. External validation among predominantly diabetic tuberculosis patients found an association between pre-treatment IL-6 concentrations and subsequent recurrence and death. Similarly, external validation among predominantly HIV coinfected tuberculosis patients found an association between pre-treatment IL-6 concentrations and subsequent treatment failure and death. In a pooled analysis of 363 tuberculosis cases from the Indian and South African validation cohorts, high pre-treatment IL-6 concentrations were associated with higher risk of failure (adjusted odds ratio [aOR]=2.16, 95%CI 1.08-4.33, p=0.02), recurrence (aOR=5.36, 95%CI 2.48-11.57, p<0.001) and death (aOR=4.62, 95%CI 1.95-10.95, p<0.001). Adding baseline IL-6 to a risk-prediction model comprising of low BMI, high smear grade and cavitation improved model performance by 15 percent (C-statistic of 0.66 versus 0.76, p=0.02). CONCLUSION: Pre-treatment IL-6 is a biomarker for unfavorable tuberculosis treatment outcomes. Future studies should identify optimal IL-6 concentrations for point-of-care risk prediction.

4.
Int J Low Extrem Wounds ; : 15347346211047098, 2021 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-34617810

RESUMO

Diabetic foot ulcers, with worldwide prevalence ranging from 12%-25%, are an important cause of nontraumatic lower limb amputation. Evidence-based assessment of early infection can help the clinician provide the right first line treatment thus helping improve the wound closure rate. Illuminate®, a novel point of care device working on multispectral autofluorescence imaging, helps in the rapid identification and classification of bacteria. This study was aimed to evaluate the diagnostic accuracy of the device in detecting bacterial gram type against standard culture methods. A total of 178 patients from a tertiary care center for diabetes was recruited and 203 tissue samples were obtained from the wound base by the plastic surgeon. The device was handled by the trained investigator to take wound images. The tissue samples were taken from the color-coded infected region as indicated by the device's Artificial Intelligence algorithm and sent for microbial assessment. The results were compared against the Gram type inferred by the device and the device was found to have an accuracy of 89.54%, a positive predictive value of 86.27% for detecting Gram-positive bacteria, 80.77% for Gram-negative bacteria, and 91.67% for no infection. The negative predictive value corresponded to 87.25% for Gram-positive, 92% for Gram-negative, and 96.12% for no infection. The Results exhibited the accuracy of this novel autofluorescence device in identifying and classifying the gram type of bacteria and its potential in significantly aiding clinicians towards early infection assessment and treatment.

5.
Ann Transl Med ; 9(14): 1206, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34430647

RESUMO

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

6.
Diabetes Metab Syndr ; 15(4): 102199, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34265492

RESUMO

BACKGROUND AND AIMS: Glucagon levels and glucagon suppression in response to oral glucose load has not been elucidated at different stages of glucose intolerance in India. METHODS: A total of 81 subjects underwent OGTT and were classified into three groups as having normal glucose tolerance (NGT) (n = 23), prediabetes (PreDM) (n = 33), newly diagnosed diabetes (NDM) (n = 25). Insulin and glucagon at fasting, 30 and 120 min was measured by ELISA. HOMA-IR, measures of insulin sensitivity, early, late and overall glucagon suppression during OGTT was calculated. RESULTS: Plasma glucagon levels were higher at all-time points in the PreDM and NDM groups. Fasting glucagon levels were higher than post glucose load glucagon in all groups. There was a significant difference in the fasting(p = 0.001), 30 min(p = 0.004) and 120 min(p = 0.032) glucagon between the groups. HOMA-IR increased and insulin sensitivity decreased with worsening of glucose intolerance(p < 0.0001). The groups did not differ in terms of early glucagon suppression(p = 0.094). NDM group suppressed glucagon more than NGT from 30 to 120 min after glucose intake. CONCLUSION: This study demonstrated higher fasting glucagon levels. Prediabetes and newly diagnosed diabetes individuals had higher glucagon levels, high insulin resistance and lower insulin sensitivity. Hyperglucagonemia may contribute to type 2 diabetes.

8.
PLoS One ; 16(7): e0254921, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34293021

RESUMO

AIM: Diabetic foot ulcer (DFU) is a major concern in diabetes and its control requires in-depth molecular investigation. The present study aimed to screen the expression of microRNA-210 (miR-210) and its association in hypoxic pathway in DFU patients. METHODS: The study consists of 3 groups of circulation samples (50 in each group of: healthy volunteers, T2DM and T2DM with DFU) and 2 groups of tissue samples (10 in each group of: control and T2DM with DFU). Expression of miR-210 and hypoxia inducible factor-1 alpha (HIF-1α), and its responsive genes such as VEGF, TNF-α, IL-6, BCl2, Bax and Caspase 3 were analyzed by RT-PCR, Western blot and ELISA analyses. RESULTS: The HIF-1α expression decreased in DFU patients with increased miR-210 expression in both circulation and tissue biopsies. The circulatory IL-6 and inflammatory gene TNF-α expression was increased in DFU compared to healthy controls and T2DM subjects. Further, we found there was no alteration in the angiogenic marker, VEGF expression. In comparison, anti-apoptotic BCl2 was decreased and Bax and Caspase 3 was increased in DFU tissues relative to control. CONCLUSIONS: The study showed that there was an inverse relationship between miR-210 and HIF-1α expression in patients with DFU, indicating that miR-210 may regulate the expression of the hypoxic gene.


Assuntos
Diabetes Mellitus Tipo 2/metabolismo , Pé Diabético/metabolismo , Regulação da Expressão Gênica , MicroRNAs/biossíntese , Cicatrização , Adulto , Proteínas Reguladoras de Apoptose/biossíntese , Proteínas Reguladoras de Apoptose/genética , Citocinas/biossíntese , Citocinas/genética , Diabetes Mellitus Tipo 2/genética , Pé Diabético/genética , Feminino , Humanos , Masculino , MicroRNAs/genética , Pessoa de Meia-Idade
9.
J Digit Imaging ; 34(3): 581-604, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-34080104

RESUMO

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.


Assuntos
Espessura Intima-Media Carotídea , Acidente Vascular Cerebral , Inteligência Artificial , Artérias Carótidas/diagnóstico por imagem , Humanos , Acidente Vascular Cerebral/diagnóstico por imagem , Ultrassonografia
10.
Int J Low Extrem Wounds ; : 15347346211020985, 2021 May 28.
Artigo em Inglês | MEDLINE | ID: mdl-34047626

RESUMO

People with diabetes have a higher risk of lower-limb amputations than people without diabetes. The risk of avoidable lower-limb amputations has increased in the coronavirus disease 2019 (COVID-19) lockdown period. Hence, we conducted a retrospective, single-centered study on major amputations during the prepandemic period (March 25, 2019-December 31, 2019) and pandemic period (March 25, 2020-December 31, 2020). During the prepandemic period, 24 major amputations (below-knee and above-knee amputations) were performed and during the pandemic period, 37 major amputations were performed. There was a 54.1% increase in major amputations noted in the pandemic period more than the prepandemic period. This increase may also be due to irregular/missed hospital visits, improper diet, nonadherence to the medications, and physical inactivity. This study shows the indirect effect of the COVID-19 pandemic on people with diabetes, resulting in the increased incidence of lower-extremity amputations (below-knee and above-knee amputations) which might cause a drastic impact on their quality of life. This study also emphasizes the importance of easy and routine access to foot-care specialists to prevent avoidable amputations.

11.
J Clin Tuberc Other Mycobact Dis ; 23: 100237, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33997311

RESUMO

Objectives: To study the association of Tissue inhibitors of matrix metalloproteinases (TIMP) levels with tuberculosis-diabetes comorbidity (TB-DM) comorbidity at baseline and in response to anti-TB treatment (ATT). Methods: We examined the levels of TIMP-1, -2, -3 and -4 in pulmonary tuberculosis alone (TB) or TB-DM at baseline and after ATT. Results: TIMP-1, -3 and -4 were significantly increased in TB-DM compared to TB at baseline and after ATT. ATT resulted in a significant reduction in TIMP-2 and -3 levels and a significant increase in TIMP-1 in both TB and TB-DM. TIMP-1, -3 and -4 were also significantly increased in TB-DM individuals with bilateral, cavitary disease and also exhibited a positive relationship with bacterial burden in TB-DM and HbA1c in all TB individuals. Within the TB-DM group, those known to be diabetic before incident TB (KDM) exhibited higher levels of TIMP-1, -2, -3 and -4 at baseline and TIMP-2 at post-treatment compared to those newly diagnosed with DM (NDM). KDM individuals on metformin treatment exhibited lower levels of TIMP-1, -2 and -4 at baseline and of TIMP-4 at post-treatment. Conclusions: TIMP levels were elevated in TB-DM, associated with disease severity and bacterial burden, correlated with HbA1c levels and modulated by duration of DM and metformin treatment.

12.
Diabetes Metab Syndr Obes ; 14: 1703-1728, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33889005

RESUMO

Diabetes and obesity are both increasing at a fast pace and giving rise to a new epidemic called diabesity. Lifestyle interventions including diet play a major role in the treatment of diabetes, obesity and diabesity. There are many guidelines on dietary management of diabetes or obesity globally and also from South Asia. However, there are no global or South Asian guidelines on the non-pharmacological management of diabesity. South Asia differs from the rest of the world as South Asians have different phenotype, cooking practices, food resources and exposure, medical nutrition therapy (MNT) practices, and availability of trained specialists. Therefore, South Asia needs its own guidelines for non-pharmacological management of diabesity in adults. The aim of the Consensus on Medical Nutrition Therapy for Diabesity (CoMeND) in Adults: A South Asian Perspective is to recommend therapeutic and preventive MNT in the South-Asians with diabesity.

13.
World J Diabetes ; 12(3): 215-237, 2021 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-33758644

RESUMO

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".

14.
Int J Comput Assist Radiol Surg ; 16(3): 423-434, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33532975

RESUMO

BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. METHODOLOGY: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. RESULTS: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. CONCLUSIONS: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.


Assuntos
Inteligência Artificial , COVID-19/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Pneumonia/diagnóstico por imagem , Aprendizado Profundo , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , SARS-CoV-2 , Tomografia Computadorizada por Raios X/métodos
15.
Comput Biol Med ; 130: 104210, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33550068

RESUMO

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.


Assuntos
Inteligência Artificial , COVID-19/diagnóstico por imagem , Pulmão/diagnóstico por imagem , SARS-CoV-2 , Índice de Gravidade de Doença , Tomografia Computadorizada por Raios X , Humanos
16.
Med Biol Eng Comput ; 59(3): 511-533, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33547549

RESUMO

Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.


Assuntos
Inteligência Artificial , Degeneração Hepatolenticular , Encéfalo/diagnóstico por imagem , Degeneração Hepatolenticular/diagnóstico por imagem , Humanos , Imageamento por Ressonância Magnética , Reprodutibilidade dos Testes
19.
Cell Stress Chaperones ; 26(2): 311-321, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33161510

RESUMO

Increasing evidence in substantiating the roles of endoplasmic reticulum stress, oxidative stress, and inflammatory responses and their interplay is evident in various diseases. However, an in-depth mechanistic understanding of the crosstalk between the intracellular stress signaling pathways and inflammatory responses and their participation in disease progression has not yet been explored. Progress has been made in our understanding of the cross talk and integrated stress signaling network between endoplasmic reticulum stress and oxidative stress towards the pathogenesis of diabetic nephropathy. In this present study, we studied the crosstalk between the endoplasmic reticulum stress and oxidative stress by understanding the role of protein disulfide isomerase and endoplasmic reticulum oxidase 1α, a key player in redox protein folding in the endoplasmic reticulum. We had recruited a total of 90 subjects and divided into three groups (control (n = 30), type 2 diabetes mellitus (n = 30), and diabetic nephropathy (n = 30)). We found that endoplasmic reticulum stress markers, activating transcription factor 6, inositol-requiring enzyme 1α, protein kinase RNA-like endoplasmic reticulum kinase, C/EBP homologous protein, and glucose-regulated protein-78; oxidative stress markers, thioredoxin-interacting protein and cytochrome b-245 light chain; and the crosstalk markers, protein disulfide isomerase and endoplasmic reticulum oxidase-1α, were progressively elevated in type 2 diabetes mellitus and diabetic nephropathy subjects. The association between the crosstalk markers showed a positive correlation with endoplasmic reticulum stress and oxidative stress markers. Further, the interplay between endoplasmic reticulum stress and oxidative stress was investigated in vitro using a human leukemic monocytic cell line under a hyperglycemic environment and examined the expression of protein disulfide isomerase and endoplasmic reticulum oxidase-1α. DCFH-DA assay and flow cytometry were performed to detect the production of free radicals. Further, phosphorylation of eIF2α in high glucose-exposed cells was studied using western blot. In conclusion, our results shed light on the crosstalk between endoplasmic reticulum stress and oxidative stress and significantly contribute to the onset and progression of diabetic nephropathy and therefore represent the major therapeutic targets for alleviating micro- and macrovascular complications associated with this metabolic disturbance. Graphical abstract.

20.
Int Angiol ; 40(2): 150-164, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33236868

RESUMO

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD.


Assuntos
Doenças Cardiovasculares , Insuficiência Renal Crônica , Acidente Vascular Cerebral , Inteligência Artificial , Doenças Cardiovasculares/diagnóstico por imagem , Taxa de Filtração Glomerular , Humanos , Fenótipo , Insuficiência Renal Crônica/complicações , Insuficiência Renal Crônica/diagnóstico , Medição de Risco , Fatores de Risco , Ultrassom
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