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

Bronchiectasis in India:results from the European Multicentre Bronchiectasis Audit and Research Collaboration (EMBARC) and Respiratory Research Network of India Registry

BACKGROUND: Bronchiectasis is a common but neglected chronic lung disease. Most epidemiological data are limited to cohorts from Europe and the USA, with few data from low-income and middle-income countries. We therefore aimed to describe the characteristics, severity of disease, microbiology, and treatment of patients with bronchiectasis in India. METHODS: The Indian bronchiectasis registry is a multicentre, prospective, observational cohort study. Adult patients ( 6518 years) with CT-confirmed bronchiectasis were enrolled from 31 centres across India. Patients with bronchiectasis due to cystic fibrosis or traction bronchiectasis associated with another respiratory disorder were excluded. Data were collected at baseline (recruitment) with follow-up visits taking place once per year. Comprehensive clinical data were collected through the European Multicentre Bronchiectasis Audit and Research Collaboration registry platform. Underlying aetiology of bronchiectasis, as well as treatment and risk factors for bronchiectasis were analysed in the Indian bronchiectasis registry. Comparisons of demographics were made with published European and US registries, and quality of care was benchmarked against the 2017 European Respiratory Society guidelines. FINDINGS: From June 1, 2015, to Sept 1, 2017, 2195 patients were enrolled. Marked differences were observed between India, Europe, and the USA. Patients in India were younger (median age 56 years [IQR 41-66] vs the European and US registries; p<0\ub70001]) and more likely to be men (1249 [56\ub79%] of 2195). Previous tuberculosis (780 [35\ub75%] of 2195) was the most frequent underlying cause of bronchiectasis and Pseudomonas aeruginosa was the most common organism in sputum culture (301 [13\ub77%]) in India. Risk factors for exacerbations included being of the male sex (adjusted incidence rate ratio 1\ub717, 95% CI 1\ub703-1\ub732; p=0\ub7015), P aeruginosa infection (1\ub729, 1\ub710-1\ub750; p=0\ub7001), a history of pulmonary tuberculosis (1\ub720, 1\ub707-1\ub734; p=0\ub7002), modified Medical Research Council Dyspnoea score (1\ub732, 1\ub725-1\ub739; p<0\ub70001), daily sputum production (1\ub716, 1\ub703-1\ub730; p=0\ub7013), and radiological severity of disease (1\ub703, 1\ub701-1\ub704; p<0\ub70001). Low adherence to guideline-recommended care was observed; only 388 patients were tested for allergic bronchopulmonary aspergillosis and 82 patients had been tested for immunoglobulins. INTERPRETATION: Patients with bronchiectasis in India have more severe disease and have distinct characteristics from those reported in other countries. This study provides a benchmark to improve quality of care for patients with bronchiectasis in India. FUNDING: EU/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative inhaled Antibiotics in Bronchiectasis and Cystic Fibrosis Consortium, European Respiratory Society, and the British Lung Foundation

Large bandgap reduced graphene oxide (rGO) based n-p(+) heterojunction photodetector with improved NIR performance

Integration of two-dimensional reduced graphene oxide (rGO) with conventional Si semiconductor offers novel strategies for realizing broadband photodiode with enhanced device performance. In this quest, we have synthesized large bandgap rGO and fabricated metal-free broadband (300-1100 nm) back-to-back connected np-pn hybrid photodetector utilizing drop casted n-rGO/p(+)-Si heterojunctions with high performance in NIR region (830 nm). With controlled illumination, the device exhibited a peak responsivity of 16.7 AW(-1) and peak detectivity of 2.56 x 10(12) Jones under 830 nm illumination (11 mu Wcm(-2)) at 1 V applied bias with fast response (similar to 460 mu s) and recovery time (similar to 446 mu s). The fabricated device demonstrated excellent repeatability, durability and photoswitching behavior with high external quantum efficiency (similar to 2.5 x 10(3)%), along with ultrasensitive behavior at low light conditions

Ultrasensitive self-powered large area planar GaN UV-photodetector using reduced graphene oxide electrodes

A simplistic design of a self-powered UV-photodetector device based on hybrid reduced-graphene-oxide (r-GO)/gallium nitride (GaN) is demonstrated. Under zero bias, the fabricated hybrid photodetector shows a photosensivity of similar to 85% while the ohmic contact GaN photodetector with an identical device structure exhibits only similar to 5.3% photosensivity at 350 nm illumination (18 mu W/cm(2)). The responsivity and detectivity of the hybrid device were found to be 1.54mA/W and 1.45 x 10(10) Jones (cm Hz(1/2) W-1), respectively, at zero bias with fast response (60 ms), recovery time (267 ms), and excellent repeatability. Power density-dependent responsivity and detectivity revealed ultrasensitive behaviour under low light conditions. The source of the observed self-powered effect in the hybrid photodetector is attributed to the depletion region formed at the r-GO and GaN quasi-ohmic interface

Ultrasensitive self-powered large area planar GaN UV-photodetector using reduced graphene oxide electrodes

A simplistic design of a self-powered UV-photodetector device based on hybrid reduced-graphene-oxide (r-GO)/gallium nitride (GaN) is demonstrated. Under zero bias, the fabricated hybrid photodetector shows a photosensivity of similar to 85% while the ohmic contact GaN photodetector with an identical device structure exhibits only similar to 5.3% photosensivity at 350 nm illumination (18 mu W/cm(2)). The responsivity and detectivity of the hybrid device were found to be 1.54mA/W and 1.45 x 10(10) Jones (cm Hz(1/2) W-1), respectively, at zero bias with fast response (60 ms), recovery time (267 ms), and excellent repeatability. Power density-dependent responsivity and detectivity revealed ultrasensitive behaviour under low light conditions. The source of the observed self-powered effect in the hybrid photodetector is attributed to the depletion region formed at the r-GO and GaN quasi-ohmic interface

Binary Multifunctional Ultrabroadband Self-Powered g-C3N4/Si Heterojunction High-Performance Photodetector

Compact optical detectors with fast binary photoswitching over a broad range of wavelength are essential as an interconnect for any light-based parallel, real-time computing. Despite of the tremendous technological advancements yet there is no such single device available that meets the specifications. Here a multifunctional self-powered high-speed ultrabroadband (250-1650 nm) photodetector is reported based on graphitic carbon-nitride (g-C3N4)/Si hybrid 2D/3D structure. The device shows a novel binary photoswitching (change in current from positive to negative) in response to OFF/ON light illumination at small forward bias (<= 0.1 V) covering 250-1350 nm. At zero bias, the device displays an extremely high ON/OFF ratio of approximate to 1.2 x 10(5) under 680 nm (49 mu W cm(-2)) illumination. The device also shows an ultrasensitive behavior over the entire operating range at low light illuminations, with highest responsivity (1.2 A W-1), detectivity (2.8 x 10(14) Jones), and external quantum efficiency (213%) at 680 nm. The response and recovery speeds are typically 0.23 and 0.60 ms, respectively, under 288 Hz light switching frequency. Dramatically improved performance of the device is attributed to the heterojunctions formed by the ultrathin g-C3N4 nanosheets embedded in the Si surface

Small bowel hemangiomas: Diagnostic role of capsule endoscopy

Vascular anomalies involving the small bowel are an uncommon cause of gastrointestinal bleeding in childhood. We present here an 11-year-old boy who presented with severe anemia and malena. The routine investigations did not reveal any pathology. A capsule endoscopy study was performed, which clinched the diagnosis and identified two intestinal hemangiomas. The hemangiomas were resected and the child recovered