Citations

Abstract Objective After a new electronic health record (EHR) was implemented at Mayo Clinic, a training program called reBoot Camp was created to enhance ongoing education in response to needs identified by physician leaders. Materials and Methods A reBoot camp focused on EHR topics pertinent to ambulatory care was offered from April 2018 through June 2020. There were 37 2-day sessions and 43 1-day sessions, with 673 unique participants. To evaluate outcomes of the reBoot camp, we used survey data to study baseline, immediate, and long-term perceptions of program satisfaction and self-assessed skills with the EHR. The study was conducted among practitioners at a large ambulatory practice network based in several states. Data were collected from April 2018 through January 2021. We analyzed automatically collected metadata and scores that evaluated the amount of personalization and proficiency of use. Results Confidence in skills increased by 13.5 points for general EHR use and was significant in 5 subdomains of use (13–18 point improvement). This degree of user confidence was maintained at the 6-month reassessment. The outcomes of configuration and proficiency scores also improved significantly. Discussion Ongoing education regarding EHR tools is necessary to support continued use of technology. This study was novel because of the amount and breadth of data collected, diversity of user participation, and validation that improvements were maintained over time. Conclusions Participating in a reBoot camp significantly improved user confidence in each domain of the EHR and demonstrated use of best-practice tools. Users maintained gains at the 6-month evaluation phase.

Abstract Glial fibrillary acidic protein (GFAP) is a promising candidate blood-based biomarker for Alzheimer’s disease (AD) diagnosis and prognostication. The timing of its disease-associated changes, its clinical correlates, and biofluid-type dependency will influence its clinical utility. We evaluated plasma, serum, and CSF GFAP in families with autosomal dominant AD (ADAD), leveraging the predictable age at symptom onset to determine changes by stage of disease. Plasma GFAP elevations appear a decade before expected symptom onset, after β-amyloid accumulation and prior to neurodegeneration and cognitive decline. Plasma GFAP distinguished β-amyloid-positive from β-amyloid-negative ADAD participants and showed a stronger relationship with β-amyloid load in asymptomatic than symptomatic ADAD. Higher plasma GFAP was associated with the degree and rate of neurodegeneration and cognitive impairment. Serum GFAP showed similar relationships, but these were less pronounced for CSF GFAP. Our findings support a role for plasma GFAP as a clinical biomarker for β-amyloid-associated cognitive deterioration in AD.

A BSTRACT In the present study, a particular technique of artificial intelligence (AI) is applied for diagnosis and classifying medical images of patients with coronavirus disease (COVID-19). Chest radiography and laboratory-based tests are two of the most important diagnostic approaches for the detection of people with the coronavirus. Recently, a lot of studies have been carried out on using AI techniques for achieving appropriate diagnosis of COVID-19 patients using computed tomography (CT) of the chest. The present study is reviewing all available literature that have investigated the role of chest CT toward AI in the detection of COVID-19. As a novel field of computer science, AI focuses on teaching computers to be capable of learning complex tasks and decide about their solution methods. In this study, we used Matlab, Payton, and Fortran software as well as other software which are suitable for this research. In this regard, the present review study is aimed to collect the information from all the studies conducted on the role of AI as a decisive and comprehensive technology for the detection of coronavirus in patients to have a more accurate diagnosis and investigate its epidemiology.

Suicidality is increased in autism spectrum disorder (ASD), yet effective interventions are lacking. Developing biologically based approaches for preventing and treating suicidality in ASD hinges on the identification of biomarkers of suicidal ideation (SI). Here, we assessed magnetic resonance spectroscopy (MRS) markers of glutamatergic neurotransmission in ASD youth and young adults. Twenty-eight ASD participants (16–33 years) underwent 1H-MRS, and metabolites were quantified using LCModel. N-acetylaspartate (NAA), glutamate (Glu), and the NAA/Glu ratio from the left dorsolateral prefrontal cortex were compared between ASD SI+ (n = 13) and ASD SI− (n = 15) participants. We found that ASD SI+ participants had a higher NAA/Glu ratio compared ASD SI- participants. The NAA/Glu ratio also predicted SI and significantly discriminated between ASD SI+/SI− participants. All analyses including NAA and Glu alone were non-significant. Here, we provide preliminary evidence for the importance of NAA/Glu in ASD with SI, with implications for biomarker discovery. Further mechanistic research into risk and interventional approaches to address SI in ASD are needed.

Abstract In an effort to enhance the trustworthiness of its clinical practice guidelines, the Endocrine Society has recently adopted new policies and more rigorous methodologies for its guideline program. In this Clinical Practice Guideline Communication, we describe these recent enhancements—many of which reflect greater adherence to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach to guideline development—in addition to the rationale for such changes. Improvements to the Society’s guideline development practices include, but are not limited to, enhanced inclusion of nonendocrinologist experts, including patient representatives, on guideline development panels; implementation of a more rigorous conflict/duality of interest policy; a requirement that all formal recommendations must be demonstrably underpinned by systematic evidence review; the explicit use of GRADE Evidence-to-Decision frameworks; greater use and explanation of standardized guideline language; and a more intentional approach to guideline updating. Lastly, we describe some of the experiential differences our guideline readers are most likely to notice.

Abstract Background: An elevated risk of developing non-Hodgkin's lymphoma (NHL) has been associated with a family history of NHL and several other malignancies, but the magnitude of risks and mechanisms are uncertain. Methods: We used self-reported family history data from a recent multicenter U.S.-based case-control studies of NHL to evaluate familial aggregation of NHL with various hematolymphoproliferative and other cancers. Estimates of familial aggregation were obtained as hazard ratios (HR) that compare incidence of different cancers in first-degree relatives of NHL cases with that in the first-degree relatives of NHL controls. Limitations of the study included low participation rates (76% for cases and 52% for controls) and potential differential accuracy of recall. Results: Risk of NHL was elevated in relatives of NHL cases [HR, 2.9; 95% confidence interval (95% CI), 0.95–8.53]; the aggregation seems to be stronger for siblings (HR, 7.6; 95% CI, 0.98–58.8) and for male relatives (HR, 6.2; 95% CI, 0.77–50.0). Risk of Hodgkin's lymphoma seems to be also elevated among relatives of early-onset (<50 years) NHL cases (HR, 3.2; 95% CI, 0.88–11.6). Evaluation of family history of other cancers provided modest evidence for an increased risk of melanoma of the skin (HR, 2.9; 95% CI, 1.08–7.75), pancreatic cancer (HR, 2.1; 95% CI, 0.96–4.43), stomach cancer (HR, 1.8; 95% CI, 0.91–3.63), and prostate cancer (HR, 1.3; 95% CI, 0.87–1.99). Conclusions: These results are consistent with previous findings of familial aggregation of NHL, Hodgkin's lymphoma, and a few other cancers. The pattern of male-specific and sibling-specific familial aggregation of NHL we observed, if confirmed, may shed new light on the possible mechanisms that underlie familial aggregation of the disease.

AbstractIntroductionAs knowledge about neurological examination findings in autosomal dominant Alzheimer disease (ADAD) is incomplete, we aimed to determine the frequency and significance of neurological examination findings in ADAD.MethodsFrequencies of neurological examination findings were compared between symptomatic mutation carriers and non mutation carriers from the Dominantly Inherited Alzheimer Network (DIAN) to define AD neurological examination findings. AD neurological examination findings were analyzed regarding frequency, association with and predictive value regarding cognitive decline, and association with brain atrophy in symptomatic mutation carriers.ResultsAD neurological examination findings included abnormal deep tendon reflexes, gait disturbance, pathological cranial nerve examination findings, tremor, abnormal finger to nose and heel to shin testing, and compromised motor strength. The frequency of AD neurological examination findings was 65.1%. Cross‐sectionally, mutation carriers with AD neurological examination findings showed a more than two‐fold faster cognitive decline and had greater parieto‐temporal atrophy, including hippocampal atrophy. Longitudinally, AD neurological examination findings predicted a significantly greater decline over time.DiscussionADAD features a distinct pattern of neurological examination findings that is useful to estimate prognosis and may inform clinical care and therapeutic trial designs.

AbstractThe extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of ‘sporadic’ late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease.We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer’s Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset.Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants.These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.