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To utilize the UK Biobank to identify genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease (CeVD). Cerebrovascular disease occurs because of a complex interplay between vascular, environmental, and genetic factors. It is the second leading cause of disability worldwide. Understanding who may be genetically predisposed to cerebrovascular disease can help guide preventative efforts. Moreover, there is considerable interest in the use of real-world data, such as EHR (electronic health records) to better understand disease mechanisms and to discover new treatment strategies, but whether ICD10-based diagnosis can be used to study CeVD genetics is unknown. Using the UK Biobank, we conducted a genome-wide association study (GWAS) where we analyzed the genomes of 11,155 cases and 122,705 controls who were sex, age and ancestry-matched in a 1:11 case: control design. Genetic variants were identified by Plink's firth logistic regression and assessed for association with the ICD10 codes corresponding to CeVD. We identified two groups of SNPs closely linked to PITX2 and LRRTM4 that were significantly associated with CeVD in this study (p < 5 x 10-8) and had a minor allele frequency of > 0.5%. Disease assignment based on ICD10 codes may underestimate prevalence; however, for CeVD, this does not appear to be the case. Compared to the age- and sex-matched control population, individuals with CeVD were more frequently diagnosed with comorbid conditions, such as hypertension, hyperlipidemia & atrial fibrillation or flutter, confirming their contribution to CeVD. The UK Biobank based ICD10 study identified 2 groups of variants that were associated with CeVD. The association between PITX2 and CeVD is likely explained by the increased rates of atrial fibrillation and flutter. While the mechanism explaining the relationship between LRRTM4 and CeVD is unclear, this has been documented in previous studies.

Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin's lymphoma (NHL) and accounts for approximately 25% of all NHLs in developed countries. The patients usually present with constitutional symptoms and rapidly enlarging lymphadenopathy and symptomatic mass typically located in the neck or abdomen, along with an aggressive disease course. Most of the patients present with advanced disease with 60% presenting with stage 3 or 4, and those who present with extranodal involvement are usually seen at an earlier stage. Different conditions are associated with non-Hodgkin’s lymphoma ranging from hereditary immunodeficiency disorders, autoimmune disorders, infections such as HIV, Epstein-Barr virus (EBV), hepatitis C virus (HCV), Helicobacter pylori, and drugs such as immunosuppressants and chemotherapeutic agents. Epstein-Barr virus (EBV) is the main etiology of DLBCLs with an identified cause and it accounts for 10% of all DLBCLs. We report a case of a 51-year-old woman who came with a non-traumatic left femur fracture and was subsequently found to have EBV-positive DLBCL. Lymphoma commonly presents as a lymph node swelling and it’s uncommon to present as primary bone disease.

To utilize the UK Biobank to identify genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease (CeVD). Cerebrovascular disease occurs because of a complex interplay between vascular, environmental, and genetic factors. It is the second leading cause of disability worldwide. Understanding who may be genetically predisposed to cerebrovascular disease can help guide preventative efforts. Moreover, there is considerable interest in the use of real-world data, such as EHR (electronic health records) to better understand disease mechanisms and to discover new treatment strategies, but whether ICD10-based diagnosis can be used to study CeVD genetics is unknown. Using the UK Biobank, we conducted a genome-wide association study (GWAS) where we analyzed the genomes of 11,155 cases and 122,705 controls who were sex, age and ancestry-matched in a 1:11 case: control design. Genetic variants were identified by Plink's firth logistic regression and assessed for association with the ICD10 codes corresponding to CeVD. We identified two groups of SNPs closely linked to PITX2 and LRRTM4 that were significantly associated with CeVD in this study (p 0.5%. Disease assignment based on ICD10 codes may underestimate prevalence; however, for CeVD, this does not appear to be the case. Compared to the age- and sex-matched control population, individuals with CeVD were more frequently diagnosed with comorbid conditions, such as hypertension, hyperlipidemia & atrial fibrillation or flutter, confirming their contribution to CeVD. The UK Biobank based ICD10 study identified 2 groups of variants that were associated with CeVD. The association between PITX2 and CeVD is likely explained by the increased rates of atrial fibrillation and flutter. While the mechanism explaining the relationship between LRRTM4 and CeVD is unclear, this has been documented in previous studies.

Background The USA Medical Licensing Examination Step 1 is a computerised multiple-choice examination that tests the basic biomedical sciences. It is administered after the second year in a traditional four-year MD programme. Most Step 1 scores fall between 140 and 260, with a mean (SD) of 227 (22). Step 1 scores are an important selection criterion for residency choice. Little is known about which study habits are associated with a higher score. Objective To identify which self-reported study habits correlate with a higher Step 1 score. Methods A survey regarding Step 1 study habits was sent to third year medical students at Tulane University School of Medicine every year between 2009 and 2011. The survey was sent approximately 3 months after the examination. Results 256 out of 475 students (54%) responded. The mean (SD) Step 1 score was 229.5 (22.1). Students who estimated studying more than 8–11 h per day had higher scores (p<0.05), but there was no added benefit with additional study time. Those who reported studying <40 days achieved higher scores (p<0.05). Those who estimated completing >2000 practice questions also obtained higher scores (p<0.01). Students who reported studying in a group, spending the majority of study time on practice questions or taking >40 preparation days did not achieve higher scores. Conclusions Certain self-reported study habits may correlate with a higher Step 1 score compared with others. Given the importance of achieving a high Step 1 score on residency choice, it is important to further identify which characteristics may lead to a higher score.

Objectives To utilize the UK Biobank to identify genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease (CeVD). Background Cerebrovascular disease occurs because of a complex interplay between vascular, environmental, and genetic factors. It is the second leading cause of disability worldwide. Understanding who may be genetically predisposed to cerebrovascular disease can help guide preventative efforts. Moreover, there is considerable interest in the use of real-world data, such as EHR (electronic health records) to better understand disease mechanisms and to discover new treatment strategies, but whether ICD10-based diagnosis can be used to study CeVD genetics is unknown. Methods Using the UK Biobank, we conducted a genome-wide association study (GWAS) where we analyzed the genomes of 11,155 cases and 122,705 controls who were sex, age and ancestry-matched in a 1:11 case: control design. Genetic variants were identified by Plink’s firth logistic regression and assessed for association with the ICD10 codes corresponding to CeVD. Results We identified two groups of SNPs closely linked to PITX2 and LRRTM4 that were significantly associated with CeVD in this study (p < 5 x 10−8) and had a minor allele frequency of > 0.5%. Discussion Disease assignment based on ICD10 codes may underestimate prevalence; however, for CeVD, this does not appear to be the case. Compared to the age- and sex-matched control population, individuals with CeVD were more frequently diagnosed with comorbid conditions, such as hypertension, hyperlipidemia & atrial fibrillation or flutter, confirming their contribution to CeVD. The UK Biobank based ICD10 study identified 2 groups of variants that were associated with CeVD. The association between PITX2 and CeVD is likely explained by the increased rates of atrial fibrillation and flutter. While the mechanism explaining the relationship between LRRTM4 and CeVD is unclear, this has been documented in previous studies.

Cardiac neoplasms are a rare finding of which a cardiac myxoma is the most commonly encountered. Therefore, a density identified in the left atrium commonly leads to the presumptive diagnosis of an atrial myxoma. However, other pathologies, such as atrial thrombi, can mimic in clinical presentation and appearance to a myxoma. Clinically, these pathologies may lead to obstructive symptoms such as syncope, palpitations, or sudden cardiac death. At present, echocardiography, magnetic resonance imaging, or computed tomography can be used to identify such masses, but fall short of identifying the primary cause. The management of atrial thrombi is not yet fully understood and definite recommendations have not been established. We present a case of an 87-year-old man complaining of syncopal episodes found to be secondary to an incidental intracardiac density resulting from age-related amyloidosis.

PurposeTo compare diagnostic performance of a 5-min knee MRI protocol to that of a standard knee MRI.Materials and methodsOne hundred 3 T (100 patients, mean 38.8 years) and 50 1.5 T (46 patients, mean 46.4 years) MRIs, consisting of 5 fast, 2D multi-planar fast-spin-echo (FSE) sequences and five standard multiplanar FSE sequences, from two academic centers (1/2015–1/2016), were retrospectively reviewed by four musculoskeletal radiologists. Agreement between fast and standard (interprotocol agreement) and between standard (intraprotocol agreement) readings for meniscal, ligamentous, chondral, and bone pathology was compared for interchangeability. Frequency of major findings, sensitivity, and specificity was also tested for each protocol.ResultsInterprotocol agreement using fast MRI was similar to intraprotocol agreement with standard MRI (83.0–99.5%), with no excess disagreement (≤ 1.2; 95% CI, −4.2 to 3.8%), across all structures. Frequency of major findings (1.1–22.4% across structures) on fast and standard MRI was not significantly different (p ≥ 0.215), except more ACL tears on fast MRI (p = 0.021) and more cartilage defects on standard MRI (p < 0.001). Sensitivities (59–100%) and specificities (73–99%) of fast and standard MRI were not significantly different for meniscal and ligament tears (95% CI for difference, −0.08–0.08). For cartilage defects, fast MRI was slightly less sensitive (95% CI for difference, −0.125 to −0.01) but slightly more specific (95% CI for difference, 0.01–0.5) than standard MRI.ConclusionA fast 5-min MRI protocol is interchangeable with and has similar accuracy to a standard knee MRI for evaluating internal derangement of the knee.