Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.

Introduction: This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats. Methods: Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP). Results: The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01. Conclusion: Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

Representation of International Medical Graduates Among Diagnostic Radiology Chairs, Neuroradiology Chiefs, and Program Directors.

RATIONALE AND OBJECTIVES: The number of international medical graduates (IMGs) entering radiology residencies and neuroradiology fellowships averaged 9.7% and 20.9% from 2021 to 2023, respectively. We aimed to determine whether IMG graduates are populating leadership roles at a proportionate rate in diagnostic radiology (DR) and neuroradiology. MATERIALS AND METHODS: We surveyed 191 DR program directors, 94 neuroradiology program directors (PDs), 192 chairs of radiology, and 91 directors of neuroradiology inquiring about their original citizenship and medical school (American Medical Graduates [AMG] vs IMG). We reviewed institutional websites to obtain missing data and recorded H indices for each person using Scopus. RESULTS: We confirmed the original citizenship and medical school location in 61-75% and 93-98% of each leadership group. We found that 16.2% of DR program directors, 43.7% of neuroradiology PDs, 28.5% of Chairs, and 40.6% of neuroradiology directors were not originally US citizens. The IMG rate was 18/188 (9.6%), 20/90 (22.2%), 26/186 (14.0%), and 19/85 (22.4%) for the same groups respectively. The most common country of origin and medical school cited was India for all leadership groups. IMGs had a median H index of 14 while AMG 10, significantly different (p = 0.021) CONCLUSION: Compared to the rate of diagnostic and neuroradiology trainees entering from 2021 to 2023, IMGs are proportionately represented at the leadership positions studied. The H index of the IMGs was higher than AMG. We conclude that IMGs have made substantial and proportionate inroads in radiology and neuroradiology leadership.

A Prospective Study of the Diagnostic Performance of Photon-Counting CT Compared With MRI in the Characterization of Renal Masses.

OBJECTIVES: The aim of this study was to assess the interreader reliability and per-RCC sensitivity of high-resolution photon-counting computed tomography (PCCT) in the detection and characterization of renal masses in comparison to MRI. MATERIALS AND METHODS: This prospective study included 24 adult patients (mean age, 52 ± 14 years; 14 females) who underwent PCCT (using an investigational whole-body CT scanner) and abdominal MRI within a 3-month time interval and underwent surgical resection (partial or radical nephrectomy) with histopathology (n = 70 lesions). Of the 24 patients, 17 had a germline mutation and the remainder were sporadic cases. Two radiologists (R1 and R2) assessed the PCCT and corresponding MRI studies with a 3-week washout period between reviews. Readers recorded the number of lesions in each patient and graded each targeted lesion's characteristic features, dimensions, and location. Data were analyzed using a 2-sample t test, Fisher exact test, and weighted kappa. RESULTS: In patients with von Hippel-Lindau mutation, R1 identified a similar number of lesions suspicious for neoplasm on both modalities (51 vs 50, P = 0.94), whereas R2 identified more suspicious lesions on PCCT scans as compared with MRI studies (80 vs 56, P = 0.12). R1 and R2 characterized more lesions as predominantly solid in MRIs (R1: 58/70 in MRI vs 52/70 in PCCT, P < 0.001; R2: 60/70 in MRI vs 55/70 in PCCT, P < 0.001). R1 and R2 performed similarly in detecting neoplastic lesions on PCCT and MRI studies (R1: 94% vs 90%, P = 0.5; R2: 73% vs 79%, P = 0.13). CONCLUSIONS: The interreader reliability and per-RCC sensitivity of PCCT scans acquired on an investigational whole-body PCCT were comparable to MRI scans in detecting and characterizing renal masses. CLINICAL RELEVANCE STATEMENT: PCCT scans have comparable performance to MRI studies while allowing for improved characterization of the internal composition of lesions due to material decomposition analysis. Future generations of this imaging modality may reveal additional advantages of PCCT over MRI.

Corrigendum to "Erdheim-Chester disease with tendon and muscle involvement: Reports of a rare presentation" [Radiology Case Reports 19 (2024) 1866-1871].

[This corrects the article DOI: 10.1016/j.radcr.2024.02.009.].

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

INTRODUCTION: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. MATERIAL AND METHODS: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). RESULTS: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. CONCLUSION: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.

Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses.

Background: The von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal volume loss and subsequent renal function decline. Recent studies have utilized parenchyma volume as an estimate of renal function prior to surgery for renal cell carcinoma; however, it is not yet validated for surgically altered kidneys with multifocal masses and complex cysts such as are present in VHL. Objective: We sought to validate a magnetic resonance imaging (MRI)-based volumetric analysis with mercaptoacetyltriglycine (MAG-3) renogram and postoperative renal function. Design setting and participants: We identified patients undergoing renal surgery at the National Cancer Institute from 2015 to 2020 with preoperative MRI. Renal tumors, cysts, and parenchyma of the operated kidney were segmented manually using ITK-SNAP software. Outcome measurements and statistical analysis: Serum creatinine and urinalysis were assessed preoperatively, and at 3- and 12-mo follow-up time points. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine-based CKD-EPI 2021 equation. A statistical analysis was conducted on R Studio version 4.1.1. Results and limitations: Preoperative MRI scans of 113 VHL patients (56% male, median age 48 yr) were evaluated between 2015 and 2021. Twelve (10.6%) patients had a solitary kidney at the time of surgery; 59 (52%) patients had at least one previous partial nephrectomy on the renal unit. Patients had a median of three (interquartile range [IQR]: 2-5) tumors and five (IQR: 0-13) cysts per kidney on imaging. The median preoperative GFR was 70 ml/min/1.73 m2 (IQR: 58-89). Preoperative split renal function derived from MAG-3 studies and MRI split renal volume were significantly correlated (r = 0.848, p < 0.001). On the multivariable analysis, total preoperative parenchymal volume, solitary kidney, and preoperative eGFR were significant independent predictors of 12-mo eGFR. When only considering patients with two kidneys undergoing partial nephrectomy, preoperative parenchymal volume and eGFR remained significant predictors of 12-mo eGFR. Conclusions: A parenchyma volume analysis on preoperative MRI correlates well with renogram split function and can predict long-term renal function with added benefit of anatomic detail and ease of application. Patient summary: Prior to kidney surgery, it is important to understand the contribution of each kidney to overall kidney function. Nuclear medicine scans are currently used to measure split kidney function. We demonstrated that kidney volumes on preoperative magnetic resonance imaging can also be used to estimate split kidney function before surgery, while also providing essential details of tumor and kidney anatomy.

The interaction between CRY1 Polymorphism and Alternative Healthy Eating Index (AHEI) on cardiovascular risk factors in overweight women and women with obesity: a cross-sectional study.

BACKGROUND: According to some studies, diet can be interaction with CRY1 polymorphism and may be related to obesity and the risk of cardiovascular diseases (CVD). So, this study examined the interaction between CRY1 polymorphism and AHEI on cardiovascular risk factors in overweight women and women with obesity. METHODS: This cross-sectional study was performed on 377 Iranian women with overweight and obesity aged 18-48(BMI ≥ 25 kg/m2). Dietary intake was evaluated by the use of a food frequency questionnaire (FFQ) with 147 items. The AHEI was calculated based on previous studies. Anthropometric and biochemical measurements were assessed and the bioelectrical impedance analysis method was used for body analysis. The rs2287161 was genotyped by the restriction fragment length polymorphism (PCR-RFLP) method. Objects were divided into three groups based on rs2287161 genotypes. RESULTS: Our findings determined that the prevalence of the C allele was 51.9% and the G allele was 48.0%. The mean age and BMI were 36.6 ± 9.1years and 31 ± 4 kg/m2 respectively. After controlling for confounders (BMI, age, total energy intake, and physical activity), this study demonstrated that there was a significant interaction between CC genotype and adherence to AHEI on odds of hyper LDL (OR = 1.94, 95% CI = 1.24-3.05, P for interaction = 0.004), hypertension (OR = 1.80, 95% CI = 1.11-2.93, P for interaction = 0.01) and hyperglycemia (OR = 1.56, 95% CI = 0.98-2.47, P for interaction = 0.05). CONCLUSIONS: This study indicated that adherence to AHEI can reduce the odds of hyper LDL, hypertension, and hyperglycemia in the CC genotype of rs2287161.

Insulinaemic potential of diet and lifestyle and risk of type 2 diabetes in the Iranian adults: result from Yazd health study.

BACKGROUND: Previous studies have shown that insulin directly affects the risk of type 2 diabetes mellitus (T2DM) but the relationship between insulinaemic potential of diet and lifestyle and the T2DM risk is still unknown. Accordingly, we aimed to investigate the relationship between the insulinaemic potential of diet and lifestyle based on indices including empirical dietary index for hyperinsulinaemia (EDIH), empirical lifestyle index for hyperinsulinaemia (ELIH), empirical dietary index for insulin resistance (EDIR) and empirical lifestyle index for insulin resistance (ELIR) and the T2DM risk in the Iranian adults. METHODS: This study was performed on data of enrollment phase of the Yazd Health Study (YaHS) and TAghzieh Mardom-e-Yazd (Yazd Nutrition Study) (TaMYZ) on 5714 adults aged 20-70 years (mean: 36.29 years). A validated food frequency questionnaire and clinical tests were used to assess food intake and T2DM ascertainment, respectively. We used the Cox regression analysis for determining the relationship between the indices and T2DM risk. RESULTS: After adjusting for confounding variables, our findings showed that diet with higher ELIH score is 2.28 times more likely for T2DM risk (RR 2.28 [95% CI 1.69-2.56]), but there was no significant relationship between the EDIH, ELIR and EDIR scores and T2DM risk in adults, in the entire study population. CONCLUSIONS: Our findings suggest that diets with higher ELIH score increases the T2DM risk, but there was no significant relationship between the EDIH, ELIR and EDIR scores and T2DM risk. Further epidemiological studies are needed to confirm our findings.

Deep learning-based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI.

BACKGROUND: von Hippel-Lindau syndrome (VHL) is an autosomal dominant hereditary syndrome with an increased predisposition of developing numerous cysts and tumors, almost exclusively clear cell renal cell carcinoma (ccRCC). Considering the lifelong surveillance in such patients to monitor the disease, patients with VHL are preferentially imaged using MRI to eliminate radiation exposure. PURPOSE: Segmentation of kidney and tumor structures on MRI in VHL patients is useful in lesion characterization (e.g., cyst vs. tumor), volumetric lesion analysis, and tumor growth prediction. However, automated tasks such as ccRCC segmentation on MRI is sparsely studied. We develop segmentation methodology for ccRCC on T1 weighted precontrast, corticomedullary, nephrogenic, and excretory contrast phase MRI. METHODS: We applied a new neural network approache using a novel differentiable decision forest, called hinge forest (HF), to segment kidney parenchyma, cyst, and ccRCC tumors in 117 images from 115 patients. This data set represented an unprecedented 504 ccRCCs with 1171 cystic lesions obtained at five different MRI scanners. The HF architecture was compared with U-Net on 10 randomized splits with 75% used for training and 25% used for testing. Both methods were trained with Adam using default parameters ( α = 0.001 , ß 1 = 0.9 , ß 2 = 0.999 $\alpha = 0.001,\ \beta _1 = 0.9,\ \beta _2 = 0.999$ ) over 1000 epochs. We further demonstrated some interpretability of our HF method by exploiting decision tree structure. RESULTS: The HF achieved an average kidney, cyst, and tumor Dice similarity coefficient (DSC) of 0.75 ± 0.03, 0.44 ± 0.05, 0.53 ± 0.04, respectively, while U-Net achieved an average kidney, cyst, and tumor DSC of 0.78 ± 0.02, 0.41 ± 0.04, 0.46 ± 0.05, respectively. The HF significantly outperformed U-Net on tumors while U-Net significantly outperformed HF when segmenting kidney parenchymas ( α < 0.01 $\alpha < 0.01$ ). CONCLUSIONS: For the task of ccRCC segmentation, the HF can offer better segmentation performance compared to the traditional U-Net architecture. The leaf maps can glean hints about deep learning features that might prove to be useful in other automated tasks such as tumor characterization.

Adherence to Mediterranean dietary quality index and risk of breast cancer in adult women: a case-control study.

BACKGROUND: Breast cancer (BC) is the fifth most prevalent cause of cancer-related deaths in Iran. Given that the role of whole-diet on cancer risk is important, this study aimed to assess the association of MedDQI and breast cancer risk. METHODS: This hospital-based case-control study was performed on 150 women with pathologically confirmed breast cancer within the period of less than 3 months. Controls were 150 apparently healthy that were matched by age. Dietary data was collected using a validated questionnaire. To examine participants' adherence to MedDQI, the MedDQI was created according to foods and nutrients highlighted or minimized in the MedDQI construction. RESULTS: After adjusting for possible confounders, participants in the highest quartile of the MedDQI score had 55% lower odds of breast cancer than women in the bottom quartile (OR: 0.45, 95% CI: 0.21, 0.94, P trend: 0.02). Stratified analysis by menopausal status showed such association in postmenopausal women (OR: 0.24, 95% CI: 0.07, 0.8, P trend: 0.055) after controlling for age and energy intake. CONCLUSION: The results showed an inverse association between adherence to the MedDQI and risk of breast cancer among Iranian women. More prospective studies are needed to confirm our results.

Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images.

We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.

An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome.

PURPOSE: Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors. MATERIALS AND METHODS: A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT < = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs). RESULTS: The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56). CONCLUSION: Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.

Obesity and Diabetic Complications: A Study from the Nationwide Diabetes Report of the National Program for Prevention and Control of Diabetes (NPPCD-2021) Implications for Action on Multiple Scales.

BACKGROUND: Obesity plays a major role in the pathogenesis and development of macro- and microvascular complications of type 2 diabetes (T2D) and type 1 diabetes (T1D). We aimed to assess the association between obesity and macrovascular and microvascular complications of diabetes. METHODS: This study consisted of 111,830 patients (age range: 1-106) with diabetes including 10,641 T1D (3187 obese [38.2% men] and 7454 non-obese [45.5% men]) and 101,189 T2D (51,873 obese [27.5% men] and 49,316 non-obese [33.4% men]) from the National Program for Prevention and Control of Diabetes (NPPCD-2021) in Iran, who attended academic tertiary care outpatient clinics from February 2016 to April 2021. A pooled logistic regression model was used to examine the association between obesity and diabetic complications. RESULTS: Among patients with T1D, a significant association was found between obesity and cardiovascular disease (CVD), neuropathy, nephropathy and retinopathy (OR= 1.75, 1.56, 1.80 and 1.92, P-value= 0.001, 0.004, 0.001 and <0.001, respectively). In T2D, a statistically significant association was found between obesity and CVD, neuropathy and nephropathy (OR= 1.63, 1.98, 1.21, respectively, P-values <0.001). CONCLUSION: Obesity was independently associated with CVD, neuropathy and nephropathy in patients with T1D and T2D and with retinopathy only in T1D, to different degrees. The association between obesity and retinopathy and neuropathy was the strongest among T1D and T2D, respectively. Findings from this study suggest that obesity affects diabetic complications differently among the two types of diabetes, in terms of epidemiology and pathophysiology. This signifies the importance of different preventive and therapeutic approaches to obesity in T1D compared to T2D, on a national and global scale.

The effects of omega-3 polyunsaturated fatty acids supplementation in pregnancy, lactation, and infancy: An umbrella review of meta-analyses of randomized trials.

We aimed to perform an umbrella review of systematic reviews and meta-analyses (SRMAs) of randomized clinical trials (RCT) of the effects of long-chain omega-3 fatty acid supplementation in pregnancy, lactation, and infancy. We searched PubMed, Scopus, and Web of Science to November 2020. Two independent investigators extracted the information, evaluated the methodological quality of SRMAs using A Measurement Tool to Assess Systematic Reviews-2 (AMSTAR2), and rated the certainty of evidence using the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach. Either a fixed-effects or a random-effects model was used to recalculate the effect sizes and 95%CIs, depending on the number of trials. Overall, 28 SRMAs of RCTs, reporting 124 outcomes from 672 RCTs with 273,523 participants were considered eligible for the present umbrella review. Our results demonstrated evidence of moderate to high certainty that omega-3 supplementation reduced the risk of pre-eclampsia and low-birth weight and improved head circumference when used in pregnant women, and reduced severe retinopathy of prematurity and cholestasis when used in infancy. There were also favorable effects on preterm delivery, pre-natal and post-partum depression, glycemic control and inflammation markers in pregnant women, and sensitization to peanuts, positive skin prick tests, anthropometric measures, language development, visual acuity, and duration of ventilation in infants (GRADE = low). Our findings suggested that omega-3 supplementation during pregnancy can exert favorable effects against pre-eclampsia, low-birth weight, pre-term delivery, and post-partum depression, and can improve anthropometric measures, immune system, and visual activity in infants and cardiometabolic risk factors in pregnant mothers.

Neuroimaging in Attention-Deficit/Hyperactivity Disorder: Recent Advances.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition that leads to impaired attention and impulsive behaviors diagnosed in, but not limited to, children. ADHD can cause symptoms throughout life. This article summarizes the structural (conventional, volumetric, and diffusion tensor imaging) and functional (task-based functional MRI [fMRI], resting-state fMRI, PET, and MR spectroscopy) brain findings in patients with ADHD. Consensus is lacking regarding altered anatomic or functional imaging findings of the brain in children with ADHD, likely because of the heterogeneity of the disorder. Most anatomic studies report abnormalities in the frontal lobes, basal ganglia, and corpus callosum; decreased surface area in the left ventral frontal and right prefrontal cortex; thinner medial temporal lobes; and smaller caudate nuclei. Using fMRI, researchers have focused on the prefrontal and temporal regions, reflecting perception-action mapping alterations. Artificial intelligence models evaluating brain anatomy have highlighted changes in cortical thickness and the shape of the inferior frontal cortex, bilateral sensorimotor cortex, left temporal lobe, and insula. Early intervention and/or normal brain maturation can alter imaging patterns and convert functional imaging studies to a normal pattern. Although imaging findings provide insight into the neuropathophysiology of the disease, no definitive structural or functional pattern defines the disorder from a neuroradiologic perspective.

Effects of a Mediterranean diet on the development of diabetic complications: A longitudinal study from the nationwide diabetes report of the National Program for Prevention and Control of Diabetes (NPPCD 2016-2020).

OBJECTIVE: To evaluate the effectiveness of a Mediterranean dietary pattern on the incidence of macrovascular and microvascular complications of diabetes, namely cardiovascular disease (CVD), diabetic foot disorders, diabetic retinopathy, nephropathy, and neuropathy. METHODS: This longitudinal study was conducted among 71392 adults with diabetes who attended academic tertiary-care outpatient clinics from February 2016 to March 2020 across Iran using the National Program for Prevention and Control of Diabetes database. Among them, 22187 patients with diabetes (type 1 and type 2) completed 2-11 follow-up visits after baseline registration. The association between adherence to a Mediterranean diet and diabetic complications was assessed using pooled logistic regression models. This association was adjusted for potential confounders. The effect of time was assessed using fractional polynomials. RESULTS: A total of 22187 participants were included in the analysis (30.22% men and 69.78% women) with either type 1 (mean age 50.7 years) or type 2 (mean age 59.9 years) diabetes. After adjustment for confounding variables, there was a negative correlation between adherence to a Mediterranean diet and the incidence of CVD among patients with type 1 diabetes (T1D) and 2 diabetes (T2D) (OR= 0.53, 95% CI: 0.37 - 0.75, p-value <0.001 and OR= 0.61, 95% CI: 0.57 - 0.89, p-value <0.001, respectively). Also, the diet had a statistically significant protective effect against incident symptomatic neuropathy (OR= 0.32, 95% CI: 0.23 - 0.43, p-value <0.001, and OR= 0.68, 95% CI: 0.64 - 0.72, p-value <0.001, respectively), nephropathy (OR= 0.42, 95% CI: 0.30 - 0.58, p-value <0.001, and OR= 0.88, 95% CI: 0.80 - 0.96, p-value= 0.007, respectively), and retinopathy (OR= 0.32, 95% CI: 0.24 - 0.44, p-value <0.001, and OR= 0.68, 95% CI: 0.61 - 0.71, p-value <0.001, respectively) in T1D and T2D. CONCLUSION: The Mediterranean dietary pattern is associated with a lower incidence of CVD and microvascular complications (i.e. diabetic retinopathy, nephropathy, and neuropathy) among a cohort of patients with T1D and T2D in Iran.

Pulmonary embolism in pregnancy with COVID-19 infection: A case report.

Pregnant women in the third trimester are at the highest risk. Contracting COVID-19 increases the complications. Hence, it is critical for pregnant women, especially during the third trimester, with slightest COVID-19 symptoms to visit as soon as possible. Early diagnosis considerably contributes to saving both the mother and the fetus.

Prevalence of diabetes-associated autoantibodies among patients presenting with type 2 diabetes and related metabolic differences.

BACKGROUND: To explores the prevalence of autoantibodies (zinc transporter 8 autoantibodies (ZnT8A), antibodies to insulin (IAAs), glutamic acid decarboxylase autoantibody (GAD65)), the relation of the type of positive autoantibody and the number of positive autoantibodies with the glycemic and lipid profile of the patients with LADA (Latent Autoimmune Diabetes in Adults) and compares it to the metabolic profile of patients presenting with type 2 diabetes (T2DM). METHODS: 263 patients with T2DM were recruited for this cross-sectional study in Tehran, Iran. Data from patients included complete medical history, GAD65, ZnT8A, IAA and routine metabolic laboratory workup. Assay for autoantibodies were conducted using ELISA kits. The association between autoantibodies and glycemic and lipid profile of patients with diabetes was assessed using univariate and multivariate regression analysis. RESULTS: Our study revealed that among 263 patients with T2DM, 29 (11%) cases were positive for IAAs, 9 (3.4%) for ZnT8A, and 12 (4.6%) for GAD65. Six (2.3%) of the patients had triple positive antibodies. Patients with positive results were younger, had lower body mass index (BMI), c-peptide, triglyceride, low-density lipoprotein (LDL), and higher high-density lipoprotein (HDL), HbA1c and fasting blood glucose (FBG) levels. Triple antibody positivity was significantly associated with lower levels of C-Peptide, Triglycerides, FBG, and HbA1c compared to triple negative antibodies. CONCLUSION: Patients with LADA positive for either of the autoantibodies (GAD65, ZnT8 and IAA) presented with worse glycemic control. Measurement of these autoantibodies can assist in discrimination of these patients and help with earlier control of glycemic profile.

COVID-19: A double threat to takotsubo cardiomyopathy and spontaneous coronary artery dissection?

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic that has affected millions of individuals worldwide. Prior studies suggest that COVID-19 may be associated with an increased risk for various cardiovascular disorders, such as myocardial injury, arrhythmia, acute coronary syndrome, and venous thromboembolism. Early reports of non-COVID-19 patients have described the concurrence of takotsubo cardiomyopathy (TTC) and spontaneous coronary artery dissection (SCAD). However, the interplay between COVID-19, TTC and SCAD has not been well established. We herein propose two sets of two-hit hypotheses for the development of SCAD and TTC in the context of COVID-19. The first two-hit hypothesis explains the development of SCAD, in which TTC-associated formation of vulnerable coronary substrate serves as the first hit (predisposing factor), and COVID-19-associated inflammation and vascular disruption serves as the second hit (precipitating factor). The second two-hit hypothesis is proposed to explain the development of TTC, in which SCAD-associated formation of vulnerable myocardial substrate serves as the first hit, and COVID-19-associated sympathetic overactivity serves as the second hit. Under this conceptual framework, COVID-19 poses a double threat for the development of SCAD (among patients with underlying TTC) as well as TTC (among patients with underlying SCAD), thereby forming a reciprocal causation. This hypothesis provides a rationale for the joint assessment of TTC and SCAD in COVID-19 patients with pertinent cardiovascular manifestations.