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Nonalcoholic fatty liver disease (NAFLD), as the most common liver disease in the world, can range from simple steatosis to steatohepatitis. We evaluated the association between meat consumption and risk of NAFLD in the Golestan Cohort Study (GCS). The GCS enrolled 50,045 participants, aged 40-75 years in Iran. Dietary information was collected using a 116-item semiquantitative food frequency questionnaire at baseline (2004-2008). A random sample of 1,612 cohort members participated in a liver-focused study in 2011. NAFLD was ascertained through ultrasound. Total red meat consumption and total white meat consumption were categorized into quartiles based on the GCS population, with the first quartile as the referent group. Multivariable logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). The median intake of total red meat was 17 and total white meat was 53 g/d. During follow-up, 505 individuals (37.7%) were diagnosed with NAFLD, and 124 of them (9.2%) had elevated alanine transaminase. High total red meat consumption (ORQ4 vs Q1 = 1.59, 95% CI = 1.06-2.38, P trend = 0.03) and organ meat consumption (ORQ4 vs Q1 = 1.70, 95% CI = 1.19-2.44, P trend = 0.003) were associated with NAFLD. Total white meat, chicken, or fish consumption did not show significant associations with NAFLD. In this population with low consumption of red meat, individuals in the highest group of red meat intake were at increased odds of NAFLD. Furthermore, this is the first study to show an association between organ meat consumption and NAFLD (see Visual Abstract, http://links.lww.com/AJG/B944).

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.

We investigated whether using demographic characteristics and alarm symptoms can accurately predict cancer in patients with dyspepsia in Iran, where upper GI cancers and H. pylori infection are common. All consecutive patients referred to a tertiary gastroenterology clinic in Tehran, Iran, from 2002 to 2009 were invited to participate in this study. Each patient completed a standard questionnaire and underwent upper gastrointestinal endoscopy. Alarm symptoms included in the questionnaire were weight loss, dysphagia, GI bleeding, and persistent vomiting. We used logistic regression models to estimate the diagnostic value of each variable in combination with other ones, and to develop a risk-prediction model. A total of 2,847 patients with dyspepsia participated in this study, of whom 87 (3.1%) had upper GI malignancy. Patients reporting at least one of the alarm symptoms constituted 66.7% of cancer patients compared to 38.9% in patients without cancer (p<0.001). Esophageal or gastric cancers in patients with dyspepsia was associated with older age, being male, and symptoms of weight loss and vomiting. Each single predictor had low sensitivity and specificity. Using a combination of age, alarm symptoms, and smoking, we built a risk-prediction model that distinguished between high-risk and low-risk individuals with an area under the ROC curve of 0.85 and acceptable calibration. None of the predictors demonstrated high diagnostic accuracy. While our risk-prediction model had reasonable accuracy, some cancer cases would have remained undiagnosed. Therefore, where available, low cost endoscopy may be preferable for dyspeptic older patient or those with history of weight loss.

Non-alcoholic fatty liver disease (NAFLD) is associated with an increased risk of cardiovascular events. HDL exerts various protective functions on the cardiovascular system including anti-inflammatory activity by suppressing adhesion molecules expression in inflammation-induced endothelial cells. This study was designed to search if the anti-inflammatory capacity of apolipoprotein B-depleted plasma (apoB-depleted plasma) is altered in NAFLD patients. A total of 83 subjects including 42 NAFLD and 41 control subjects were included in this cross-sectional study. Anti-inflammatory function of HDL was determined as the ability of apoB-depleted plasma to inhibit tumor necrosis factor-α (TNF-α)-induced expression of adhesion molecules in human umbilical vein endothelial cells (HUVECs). Incubation of inflammation-stimulated HUVECs with the NAFLD patients' apo-B depleted plasma led to higher levels of expression of adhesion molecules compared to the control subjects' plasma samples, reflecting an impaired anti-inflammatory capacity of apoB-depleted plasma in the NAFLD patients. Impaired anti-inflammatory capacity of apoB-depleted plasma was correlated with fatty liver and obesity indices. After adjustment with obesity indices, the association of anti-inflammatory capacity of apoB-depleted plasma with NAFLD remained significant. Impaired anti-inflammatory activity of apoB-depleted plasma was independently associated with NAFLD.

The rapid outbreak of coronavirus threatens humans’ life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus.

Purpose Available evidence suggests functional differences in visceral and subcutaneous fat. We investigated the association between quantitative measures of central adiposity with indicators of carotid atherosclerosis including intima-media thickness (IMT) and plaque in a general population using a semi-automated method on magnetic resonance imaging (MRI) data. Methods In this cross-sectional study 200 subjects (52 % female), aged 50–77 years, were randomly selected from Golestan Cohort Study. Participants underwent ultrasound examination of carotid arteries and abdominal MRI. Segmentation and calculation of visceral (VFA) and subcutaneous fat area (SFA) were performed on three levels using semi-automated software. Various conventional anthropometric indices were also measured. Results Among 191 enrolled subjects, 77 (40 %) participants had IMT ≥0.8 mm. Carotid plaques were detected in 86 (44 %) subjects. In separate multivariate analysis models, unlike SFA and other anthropometric indices, the last tertile of VFA values was associated with at least threefold excess risk for IMT ≥0.8 mm (OR 3.8, 95 % CI 1.36–6.94, p  = 0.02). There was no significant difference between mean values of categorized obesity indices in subjects with and without plaque, while participants in the highest tertile of VFA values were demonstrated to have higher risk of more than one plaque (OR 4.57, 95 % CI 1.03–20.11, p  = 0.034). Conclusions A higher amount of visceral fat, measured by a semi-automated technique using MRI, is associated with increased IMT and having more than one carotid plaque in a general population, while subcutaneous fat measures are poor indicators for identifying carotid atherosclerosis.

Background Non-alcoholic fatty liver disease (NAFLD) patients are at a substantial risk for developing cardiovascular disease (CVD). High-density lipoprotein (HDL) is well known to have protective effects against the development of atherosclerotic CVD. One of the major antiatherogenic effects of HDL is its anti-oxidative function. Objectives This study investigated the association of anti-oxidative capacity of HDL with subclinical atherosclerosis in NAFLD and non-NAFLD subjects. Methods A total of 143 subjects including 51 NAFLD and 92 control subjects were included in this case–control study. HDL oxidative index (HOI) was determined spectrophotometrically using a cell-free method in the presence of a fluorescent substrate dichlorofluorescein diacetate (DCFDA). Paraoxonase 1 (PON1) activity, superoxide dismutase (SOD) activity, and malondialdehyde (MDA) plasma levels were assessed in both groups. Results The NAFLD patients with impaired HDL anti-oxidative function (HOI  ≥ 1) had higher MDA levels, aspartate amino transferase (AST), liver stiffness (LS), and carotid intima-media thickness (cIMT) values compared to the controls. HDL oxidative index (HOI) was positively correlated with MDA levels and cIMT and negatively correlated with SOD activity. Conclusions Higher circulating levels of MDA were associated with the impaired anti-oxidative function of HDL in NAFLD. The impaired anti-oxidative capacity of HDL might be related to NAFLD severity and subclinical atherosclerosis in NAFLD patients.

Acute bowel ischemia (ABI) can be life threatening with high mortality rate. In spite of the advances made in diagnosis and treatment of ABI, no significant change has occurred in the mortality over the past decade. ABI is potentially reversible with prompt diagnosis. The radiologist plays a central role in the initial diagnosis and preventing progression to irreversible intestinal ischemic injury or bowel necrosis. The most single imaging findings described in the literature are either non-specific or only present in the late stages of ABI, urging the use of a constellation of features to reach a more confident diagnosis. While ABI has been traditionally categorized based on the etiology with a wide spectrum of imaging findings overlapped with each other, the final decision for patient’s management is usually made on the stage of the ABI with respect to the underlying pathophysiology. In this review, we first discuss the pathologic stages of ischemia and then summarize the various imaging signs and causes of ABI. We also emphasize on the correlation of imaging findings and pathological staging of the disease. Finally, a management approach is proposed using combined clinical and radiological findings to determine whether the patient may benefit from surgery or not.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70–75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80–98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.

Diagnosis of intestinal vasculitis is often challenging due to the non-specific clinical and imaging findings. Vasculitides with gastrointestinal (GI) manifestations are rare, but their diagnosis holds immense significance as late or missed recognition can result in high mortality rates. Given the resemblance of radiologic findings with some other entities, GI vasculitis is often overlooked on small bowel studies done using computed tomography/magnetic resonance enterography (CTE/MRE). Hereon, we reviewed radiologic findings of vasculitis with gastrointestinal involvement on CTE and MRE. The variety of findings on MRE/CTE depend upon the size of the involved vessels. Signs of intestinal ischemia, e.g., mural thickening, submucosal edema, mural hyperenhancement, and restricted diffusion on diffusion-weighted imaging, are common in intestinal vasculitis. Involvement of the abdominal aorta and the major visceral arteries is presented as concentric mural thickening, transmural calcification, luminal stenosis, occlusion, aneurysmal changes, and collateral vessels. Such findings can be observed particularly in large- and medium-vessel vasculitis. The presence of extra-intestinal findings, including within the liver, kidneys, or spleen in the form of focal areas of infarction or heterogeneous enhancement due to microvascular involvement, can be another radiologic clue in diagnosis of vasculitis. The link between the clinical/laboratory findings and MRE/CTE abnormalities needs to be corresponded when it comes to the diagnosis of intestinal vasculitis.