Methotrexate Induced Lung Injury in a Patient with Primary CNS Lymphoma: a Case Report

Methotrexate is an antimetabolite commonly used in clinical practice for a variety of indications ranging from rheumatoid arthritis and other connective tissue disorders to high dose regimens in many malignancies. This folate antagonist has got a spectrum of toxicities among which gastrointestinal effects predominate. Lung injury is a well described but rare event and has been reported most often in patients who have been on long term oral therapy for rheumatic disorders. Acute lung injury in a patient receiving a high dose regimen for haematological malignancies has not been reported previously. We present one such case of methotrexate related acute lung injury in a patient of primary CNS lymphoma receiving high dose methotrexate

Prognostic Stratification of GBMs Using Combinatorial Assessment of IDH1 Mutation, MGMT Promoter Methylation, and TERT Mutation Status: Experience from a Tertiary Care Center in India

AbstractThis study aims to establish the best and simplified panel of molecular markers for prognostic stratification of glioblastomas (GBMs). One hundred fourteen cases of GBMs were studied for IDH1, TP53, and TERT mutation by Sanger sequencing; EGFR and PDGFRA amplification by fluorescence in situ hybridization; NF1expression by quantitative real time polymerase chain reaction (qRT-PCR); and MGMT promoter methylation by methylation-specific PCR. IDH1 mutant cases had significantly longer progression-free survival (PFS) and overall survival (OS) as compared to IDH1 wild-type cases. Combinatorial assessment of MGMT and TERT emerged as independent prognostic markers, especially in the IDH1 wild-type GBMs. Thus, within the IDH1 wild-type group, cases with only MGMT methylation (group 1) had the best outcome (median PFS: 83.3 weeks; OS: not reached), whereas GBMs with only TERT mutation (group 3) had the worst outcome (PFS: 19.7 weeks; OS: 32.8 weeks). Cases with both or none of these alterations (group 2) had intermediate prognosis (PFS: 47.6 weeks; OS: 89.2 weeks). Majority of the IDH1 mutant GBMs belonged to group 1 (75%), whereas only 18.7% and 6.2% showed group 2 and 3 signatures, respectively. Interestingly, none of the other genetic alterations were significantly associated with survival in IDH1 mutant or wild-type GBMs.Based on above findings, we recommend assessment of three markers, viz., IDH1, MGMT, and TERT, for GBM prognostication in routine practice. We show for the first time that IDH1 wild-type GBMs which constitute majority of the GBMs can be effectively stratified into three distinct prognostic subgroups based on MGMT and TERT status, irrespective of other genetic alterations

Evaluation of Gamma glutamyl-transferase (GGT) levels in COVID-19: A retrospective analysis in tertiary care centre

Many recent studies have reported that patients infected with novel coronavirus 2019 or SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) might have a liver injury. However, few studies have focussed on the levels of Gamma glutamyl-transferase (GGT) alone and the variations associated with it. We retrospectively analysed the GGT levels of 476 admitted patients with confirmed COVID-19 in a tertiary care centre, PGIMER (Post Graduate Institute of Medical Education and Research), Chandigarh. Out of the total 476 COVID-19 patients studied, 35% had elevated GGT levels. ICU care was required for 51.19% (P <0.0001) of these patients and their hospital stay was of longer duration as compared to the patients with normal GGT levels. The incidence of GGT elevation was found to be more pronounced in males and elderly patients. The male population displayed higher GGT levels with 52% having raised levels compared to females where only 21.6% had elevated GGT levels. Although the number of COVID-19 cases was majorly from young age groups, the elevation in GGT levels has been reported more in elderly patients. GGT levels can therefore serve as a predictor for the extent of liver injury and severity in COVID-19 patients

Evaluation of Gamma glutamyl-transferase (GGT) levels in COVID-19: A retrospective analysis in tertiary care centre

681-686Many recent studies have reported that patients infected with novel coronavirus 2019 or SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) might have a liver injury. However, few studies have focussed on the levels of Gamma glutamyl-transferase (GGT) alone and the variations associated with it. We retrospectively analysed the GGT levels of 476 admitted patients with confirmed COVID-19 in a tertiary care centre, PGIMER (Post Graduate Institute of Medical Education and Research), Chandigarh. Out of the total 476 COVID-19 patients studied, 35% had elevated GGT levels. ICU care was required for 51.19% (P <0.0001) of these patients and their hospital stay was of longer duration as compared to the patients with normal GGT levels. The incidence of GGT elevation was found to be more pronounced in males and elderly patients. The male population displayed higher GGT levels with 52% having raised levels compared to females where only 21.6% had elevated GGT levels. Although the number of COVID-19 cases was majorly from young age groups, the elevation in GGT levels has been reported more in elderly patients. GGT levels can therefore serve as a predictor for the extent of liver injury and severity in COVID-19 patients

Validation of a noninvasive aMMP-8 point-of-care diagnostic methodology in COVID-19 patients with periodontal disease

Objectives: The aim of this study was to validate an active matrix metalloproteinase (MMP-8) point-of-care diagnostic tool in COVID-19 patients with periodontal disease. Subjects, Materials, and Methods: Seventy-two COVID-19-positive and 30 COVID-19-negative subjects were enrolled in the study. Demographic data were recorded, periodontal examination carried out, and chairside tests run for evaluating the expression of active MMP-8 (aMMP-8) in the site with maximum periodontal breakdown via gingival crevicular fluid sampling as well as via a mouth rinse-based kit for general disease activity. In COVID-19-positive patients, the kits were run again once the patients turned COVID-19 negative. Results: The overall (n = 102) sensitivity/specificity of the mouthrinse-based kits to detect periodontal disease was 79.41%136.76% and that of site-specific kits was 64.71%/55.88% while adjusting for age, gender, and smoking status increased the sensitivity and specificity (82.35%/76.47% and 73.53%/88.24, respectively). Receiver operating characteristic (ROC) analysis for the adjusted model revealed very good area under the ROC curve 0.746-0.869 (p < .001) and 0.740-0.872 (p < .001) (the aMMP-8 mouth rinse and site-specific kits, respectively). No statistically significant difference was observed in the distribution of results of aMMP-8 mouth rinse test (p = .302) and aMMP-8 site-specific test (p = .189) once the subjects recovered from COVID-19. Conclusions: The findings of the present study support the aMMP-8 point-of-care testing (PoCT) kits as screening tools for periodontitis in COVID-19 patients. The overall screening accuracy can be further increased by utilizing adjunctively risk factors of periodontitis. The reported noninvasive, user-friendly, and objective PoCT diagnostic methodology may provide a way of stratifying risk groups, deciding upon referrals, and in the institution of diligent oral hygiene regimens.Peer reviewe

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

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

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

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

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

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

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients