A biologically detailed computational model is described of how categorization judgments become automatic in tasks that depend on procedural learning. The model assumes 2 neural pathways from sensory ...association cortex to the premotor area that mediates response selection. A longer and slower path projects to the premotor area via the striatum, globus pallidus, and thalamus. A faster, purely cortical path projects directly to the premotor area. The model assumes that the subcortical path has greater neural plasticity because of a dopamine-mediated learning signal from the substantia nigra. In contrast, the cortical-cortical path learns more slowly via (dopamine independent) Hebbian learning. Because of its greater plasticity, early performance is dominated by the subcortical path, but the development of automaticity is characterized by a transfer of control to the faster cortical-cortical projection. The model, called SPEED (Subcortical Pathways Enable Expertise Development), includes differential equations that describe activation in the relevant brain areas and difference equations that describe the 2- and 3-factor learning. A variety of simulations are described, showing that the model accounts for some classic single-cell recording and behavioral results.
Homology Directed Repair (HDR) enables precise genome editing, but the implementation of HDR-based therapies is hindered by limited efficiency in comparison to methods that exploit alternative DNA ...repair routes, such as Non-Homologous End Joining (NHEJ). In this study, we develop a functional, pooled screening platform to identify protein-based reagents that improve HDR in human hematopoietic stem and progenitor cells (HSPCs). We leverage this screening platform to explore sequence diversity at the binding interface of the NHEJ inhibitor i53 and its target, 53BP1, identifying optimized variants that enable new intermolecular bonds and robustly increase HDR. We show that these variants specifically reduce insertion-deletion outcomes without increasing off-target editing, synergize with a DNAPK inhibitor molecule, and can be applied at manufacturing scale to increase the fraction of cells bearing repaired alleles. This screening platform can enable the discovery of future gene editing reagents that improve HDR outcomes.
Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is ...to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. institution. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.
The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. ...Mitochondrial DNA variants and in particular, heteroplasmic variants, are critical for determining human disease severity. While there are approaches for obtaining mitochondrial DNA variants from NGS data, these software do not account for the unique characteristics of mitochondrial genetics and can be inaccurate even for homoplasmic variants. We introduce MitoScape, a novel, big-data, software for extracting mitochondrial DNA sequences from NGS. MitoScape adopts a novel departure from other algorithms by using machine learning to model the unique characteristics of mitochondrial genetics. We also employ a novel approach of using rho-zero (mitochondrial DNA-depleted) data to model nuclear-encoded mitochondrial sequences. We showed that MitoScape produces accurate heteroplasmy estimates using gold-standard mitochondrial DNA data. We provide a comprehensive comparison of the most common tools for obtaining mtDNA variants from NGS and showed that MitoScape had superior performance to compared tools in every statistically category we compared, including false positives and false negatives. By applying MitoScape to common disease examples, we illustrate how MitoScape facilitates important heteroplasmy-disease association discoveries by expanding upon a reported association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men (adjusted p-value = 0.003). The improved accuracy of mitochondrial DNA variants produced by MitoScape will be instrumental in diagnosing disease in the context of personalized medicine and clinical diagnostics.
Cardiac diffusion tensor imaging (DTI) is an emerging technique for the in vivo characterisation of myocardial microstructure, and there is a growing need for its validation and standardisation. We ...sought to establish the accuracy, precision, repeatability and reproducibility of state‐of‐the‐art pulse sequences for cardiac DTI among 10 centres internationally. Phantoms comprising 0%–20% polyvinylpyrrolidone (PVP) were scanned with DTI using a product pulsed gradient spin echo (PGSE; N = 10 sites) sequence, and a custom motion‐compensated spin echo (SE; N = 5) or stimulated echo acquisition mode (STEAM; N = 5) sequence suitable for cardiac DTI in vivo. A second identical scan was performed 1–9 days later, and the data were analysed centrally. The average mean diffusivities (MDs) in 0% PVP were (1.124, 1.130, 1.113) x 10−3 mm2/s for PGSE, SE and STEAM, respectively, and accurate to within 1.5% of reference data from the literature. The coefficients of variation in MDs across sites were 2.6%, 3.1% and 2.1% for PGSE, SE and STEAM, respectively, and were similar to previous studies using only PGSE. Reproducibility in MD was excellent, with mean differences in PGSE, SE and STEAM of (0.3 ± 2.3, 0.24 ± 0.95, 0.52 ± 0.58) x 10−5 mm2/s (mean ± 1.96 SD). We show that custom sequences for cardiac DTI provide accurate, precise, repeatable and reproducible measurements. Further work in anisotropic and/or deforming phantoms is warranted.
There is a growing need for validation of pulse sequences for cardiac diffusion tensor imaging (DTI). We show, in a multicentre study involving 10 sites, that custom sequences for cardiac DTI based on motion‐compensated spin echo and stimulated echo acquisition mode provide accurate, precise and reproducible measurements.
•In a recent randomized controlled trial, high-intensity interval exercise training (HIIT) was more effective than moderate intensity steady-state (MISS) exercise training for improving ...cardiorespiratory fitness in people with coronary artery disease attending cardiac rehabilitation. HIIT was also safe and well tolerated. We conducted a secondary health economic analysis to find out if HIIT or MISS exercise training was more cost-effective.•HIIT participants reported slightly higher health service use costs than MISS participants at 12 months.•HIIT participants reported greater gains in quality of life at 12 months.•HIIT was cost effective compared with MISS.
To perform a cost-effectiveness analysis of high-intensity interval training (HIIT) compared with moderate intensity steady-state (MISS) training in people with coronary artery disease (CAD) attending cardiac rehabilitation (CR).
Secondary cost-effectiveness analysis of a prospective, assessor-blind, parallel group, multi-center RCT.
Six outpatient National Health Service cardiac rehabilitation centers in England and Wales, UK.
382 participants with CAD (N=382).
Participants were randomized to twice-weekly usual care (n=195) or HIIT (n=187) for 8 weeks. Usual care was moderate intensity continuous exercise (60%-80% maximum capacity, MISS), while HIIT consisted of 10 × 1-minute intervals of vigorous exercise (>85% maximum capacity) interspersed with 1-minute periods of recovery.
We conducted a cost-effectiveness analysis of the HIIT or MISS UK trial. Health related quality of life was measured with the EQ-5D-5L to estimate quality-adjusted life years (QALYs). Costs were estimated with health service resource use and intervention delivery costs. Cost-utility analysis measured the incremental cost-effectiveness ratio (ICER). Bootstrapping assessed the probability of HIIT being cost-effective according to the UK National Institute for Health and Care Excellence (NICE) threshold value (£20,000 per QALY). Missing data were imputed. Uncertainty was estimated using probabilistic sensitivity analysis. Assumptions were tested using univariate/1-way sensitivity analysis.
124 (HIIT, n=59; MISS, n=65) participants completed questionnaires at baseline, 8 weeks, and 12 months. Mean combined health care use and delivery cost was £676 per participant for HIIT, and £653 for MISS. QALY changes were 0.003 and -0.013, respectively. For complete cases, the ICER was £1448 per QALY for HIIT compared with MISS. At a willingness-to-pay threshold of £20,000 per QALY, the probability of HIIT being cost-effective was 96% (95% CI, 0.90 to 0.95).
For people with CAD attending CR, HIIT was cost-effective compared with MISS. These findings are important to policy makers, commissioners, and service providers across the health care sector.
Identifying the factors that are associated with the magnitude of treatment benefits from anti-vascular endothelial growth factor (anti-VEGF) therapy for diabetic macular edema (DME) may help refine ...treatment expectations.
To identify the baseline factors that are associated with vision and anatomic outcomes when managing DME with anti-VEGF and determine if there are interactions between factors and the agent administered.
This post hoc analysis of data from the Diabetic Retinopathy Clinical Research Network multicenter randomized clinical trial , Protocol T, was conducted between December 2016 and December 2017. Between August 22, 2012, and August 28, 2013, 660 participants were enrolled with central-involved DME and vision impairment (approximate Snellen equivalent, 20/32-20/320).
Repeated 0.05-mL intravitreous injections of 2.0-mg aflibercept (201 eyes), 1.25-mg bevacizumab (185 eyes), or 0.3-mg ranibizumab (192 eyes) per protocol.
Change in visual acuity (VA) and optical coherence tomography (OCT) central subfield thickness at 2 years and change in VA over 2 years (area under the curve AUC).
Among 578 participants, the median age (interquartile range) was 61 (54-67) years. Across anti-VEGF treatment groups, each baseline factor was associated with mean improvement in VA and a reduction in central DME compared with the baseline. For every decade of participant age, the mean VA improvement was reduced by 2.1 letters (95% CI, -3.0 to -1.2; P < .001) in the VA and 1.9 letters (95% CI, -2.4 to -1.3; P < .001) in the VA AUC analyses. For each 1% increase in hemoglobin A1c levels, VA improvement was reduced by 1 letter in the VA (95% CI, -1.5 to -0.5; P < .001) and 0.5 letters (95% CI, -0.9 to -0.2; P < .001) in the VA AUC analyses. Eyes with no prior panretinal photocoagulation (PRP) and less than severe nonproliferative diabetic retinopathy had an approximately 3-letter improvement in the VA (95% CI, 0.9-5.4; P = .007) and VA AUC (95% CI, 1.3-4.2; P < .001) analyses compared with eyes with prior PRP. On average, African American participants had greater reductions in central subfield thickness compared with eyes of white participants (-27.3 μm, P = .01), as did eyes with central subretinal fluid compared with eyes without this OCT feature (-22.9 μm, P = .01). There were no interactions between the predictive factors and the specific anti-VEGF agent that was administered for any VA or OCT outcome.
Lower hemoglobin A1c levels were associated with the magnitude of vision improvement following anti-VEGF therapy, providing further evidence to encourage glycemic control among persons with diabetes. Younger patients and those without prior PRP might expect greater improvement in VA than older patients or those with prior PRP.
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