Citations

AbstractIntroductionA few copy number variations (CNVs) have been reported for Alzheimer's disease (AD). However, there is a lack of a systematic investigation of CNVs in AD based on whole genome sequencing (WGS) data.MethodsWe used four methods to identify consensus CNVs from the WGS data of 1,411 individuals and further investigated their functional roles in AD using the matched transcriptomic and clinicopathological data.ResultsWe identified 3,012 rare AD‐specific CNVs whose residing genes are enriched for cellular glucuronidation and neuron projection pathways. Genes whose mRNA expressions are significantly correlated with common CNVs are involved in major histocompatibility complex class II receptor activity. Integration of CNVs, gene expression, and clinical and pathological traits further pinpoints a key CNV that potentially regulates immune response in AD.DiscussionWe identify CNVs as potential genetic regulators of immune response in AD. The identified CNVs and their downstream gene networks reveal novel pathways and targets for AD.

349527 Background: Symptoms are common during cancer care but often go undetected. Digital systems that elicit patient-reported outcomes (PRO) surveys may detect symptoms early and prompt clinicians to intervene, thereby alleviating suffering and averting complications. Methods: In a cluster-randomized trial, U.S.-based community oncology practices were randomized 1:1 to digital symptom monitoring with PRO surveys, or to usual care control. Patients receiving systemic treatment for metastatic cancer were eligible. At PRO practices, participants were invited to complete a weekly survey via web or automated telephone system for up to one year, including questions about nine common symptoms, performance status, and falls. Severe or worsening symptoms triggered electronic alerts to care team nurses, and reports showing longitudinal symptom data were available to oncologists at visits. Pre-specified secondary outcomes included impact on physical function, symptom control, and health-related quality of life (HRQL). The primary outcome of survival is not yet mature. Results: At 52 practices, 1,191 patients were eligible and enrolled (593 PRO; 598 control). Clinically meaningful benefits were experienced in physical function by 13.8% more patients with PRO versus control (P=0.009); symptom control by 16.1% (P=0.003); and HRQL by 13.4% (P=0.006). Mean changes from baseline were superior with PRO versus control for physical function (mean difference 2.47, 95% CI 0.41-4.53; P=0.02), symptom control (2.56, 0.95-4.17; P=0.002), and HRQL (2.43, 0.90-3.96; P=0.002). Patients completed 20,565/22,486 (91.5%) of expected weekly PRO surveys. Conclusions: Digital symptom monitoring during cancer treatment confers clinical benefits. Clinical trial information: NCT03249090.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Background. Teratomas in the pediatric population are most commonly found in the sacrococcygeal region. Pediatric intraspinal teratomas, however, are an exceedingly rare central nervous system (CNS) neoplasm. The clinical presentation of these intraspinal neoplasms can vary significantly and thus can be difficult to identify in infants less than one year of age where verbal expression and motor development are still lacking. Case Description. A 7-month-old, previously healthy male presented with a thoracic scoliosis and an asymptomatic right midupper thoracic spinal prominence present since birth. MRI revealed an extensive heterogenous mass in the right epidural space from T5-T6 and the right paravertebral space, resulting in severe spinal stenosis. Outcome. Complete resection of the tumor, including a three-level neurotomy, was achieved by posterior decompression/laminectomy. The final tumor was consistent with a mature teratoma. The surgical resection was performed without any immediate complications. Conclusions. Extramedullary epidural teratomas are exceptionally rare tumors in the pediatric population. Clinical presentation can be ambiguous, particularly in an infant. MRI was useful in suggesting a teratoma as a potential diagnosis and for postoperative surveillance for recurrence. However, histopathological analysis remains the gold standard for definitive diagnosis. Surgical resection is the mainstay of treatment, especially in the setting of cord compression and progressive loss of motor function. Close follow-up is crucial to monitor for progressive spinal deformity or recurrence.

AbstractBackgroundAppropriate use criteria (AUC) provide patient‐centered physician guidance in test selection. An initial set of AUC was reported by the American Society of Dermatopathology (ASDP) in 2018. AUC reflect evidence collected at single timepoints and may be affected by evolving evidence and experience. The objective of this study was to update and expand AUC for selected tests.MethodsRAND/UCLA (RAND Corporation [Santa Monica, CA]/University of California Los Angeles) methodology used includes the following: (a) literature review; (b) review of previously rated tests and previously employed clinical scenarios; (c) selection of previously rated tests for new ratings; (d) development of new clinical scenarios; (e) selection of additional tests; (f) three rating rounds with feedback and group discussion after rounds 1 and 2.ResultsFor 220 clinical scenarios comprising lymphoproliferative (light chain clonality), melanocytic (comparative genomic hybridization, fluorescence in situ hybridization, reverse transcription polymerase chain reaction, telomerase reverse transcriptase promoter), vascular disorders (MYC), and inflammatory dermatoses (periodic acid‐Schiff, Gömöri methenamine silver), consensus by panel raters was reached in 172 of 220 (78%) scenarios, with 103 of 148 (70%) rated “usually appropriate” or “rarely appropriate” and 45 of 148 (30%), “appropriateness uncertain.” Limitations: The study design only measures appropriateness. Cost, availability, test comparison, and additional clinical considerations are not measured. The possibility that the findings of this study may be influenced by the inherent biases of the dermatopathologists involved in the study cannot be excluded.ConclusionsAUC are reported for selected diagnostic tests in clinical scenarios that occur in dermatopathology practice. Adhering to AUC may reduce inappropriate test utilization and improve healthcare delivery.

Abstract Aims We sought to determine whether the Diamond–Forrester classification using non-invasive haemodynamic measurements by 2-D and Doppler echocardiography would predict hospital mortality in cardiac intensive care unit (CICU) patients. Methods and results We retrospectively analysed unique patients admitted to the CICU at Mayo Clinic Rochester from 2007 to 2018. Doppler-derived cardiac index (CI) and ratio of mitral valve E velocity to medial mitral annulus e′ velocity (E/e′ ratio) were used to classify patients into four profiles: Profile I (warm/dry), Profile II (warm/wet), Profile III (cold/dry), and Profile IV (cold/wet). Logistic regression was used to determine predictors of hospital mortality, and Cox proportional-hazards analysis was used to determine predictors of mortality during one year of follow-up. We included 4563 patients with a mean age of 68.3 ± 14.3 years, including 36.2% female patients. The distribution of each profile was as follows: I, 47.4%; II, 36.2%; III, 7.9%; IV, 8.5%. A total of 5.8% patients died during hospitalization, and 18.1% died by 1 year. Patients with either low CI or elevated E/e′ ratio had higher in-hospital and 1 year mortality. Patients with elevated E/e′ ratio (i.e. Profiles II and IV) had an increased risk of death during hospitalization and at 1 year after multivariate adjustment (adjusted hazard ratio 1.72 and 2.17 for 1 year mortality, respectively, compared with Profile I, P &lt; 0.01). Conclusions Simple Doppler echocardiographic assessment can be used to identify haemodynamic profiles defined by the Diamond–Forester classification in patients admitted in CICU. These profiles predict outcomes and may be used to guide therapy in critically ill patients.

Abstract Not all apolipoprotein E ( APOE ) ε4 carriers who survive to advanced age develop Alzheimer's disease (AD); factors attenuating the risk of ε4 on AD may exist. Guided by the top ε4‐attenuating signals from methylome‐wide association analyses (N = 572, ε4+ and ε4–) of neurofibrillary tangles and neuritic plaques, we conducted a meta‐analysis for pathological AD within the ε4+ subgroups (N = 235) across four independent collections of brains. Cortical RNA‐seq and microglial morphology measurements were used in functional analyses. Three out of the four significant CpG dinucleotides were captured by one principal component (PC1), which interacts with ε4 on AD, and is associated with expression of innate immune genes and activated microglia. In ε4 carriers, reduction in each unit of PC1 attenuated the odds of AD by 58% (odds ratio = 2.39, 95% confidence interval = [1.64,3.46], P = 7.08 × 10 –6 ). An epigenomic factor associated with a reduced proportion of activated microglia (epigenomic factor of activated microglia, EFAM) appears to attenuate the risk of ε4 on AD.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Nuclear accumulation of CHMP7 leads to multiple cellular pathologies that can be mitigated via CHMP7 ASOs in familial and sporadic ALS iPSNs.