Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aβ pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice

Influence of Feeding Enzymatically Hydrolyzed Yeast Cell Wall on Growth Performance and Digestive Function of Feedlot Cattle during Periods of Elevated Ambient Temperature.

In experiment 1, eighty crossbred steers (239±15 kg) were used in a 229-d experiment to evaluate the effects of increasing levels of enzymatically hydrolyzed yeast (EHY) cell wall in diets on growth performance feedlot cattle during periods of elevated ambient temperature. Treatments consisted of steam-flaked corn-based diets supplemented to provide 0, 1, 2, or 3 g EHY/hd/d. There were no effects on growth performance during the initial 139-d period. However, from d 139 to harvest, when 24-h temperature humidity index averaged 80, EHY increased dry matter intake (DMI) (linear effect, p<0.01) and average daily gain (ADG) (linear effect, p = 0.01). There were no treatment effects (p>0.10) on carcass characteristics. In experiment 2, four Holstein steers (292±5 kg) with cannulas in the rumen and proximal duodenum were used in a 4×4 Latin Square design experiment to evaluate treatments effects on characteristics of ruminal and total tract digestion in steers. There were no treatment effects (p>0.10) on ruminal pH, total volatile fatty acid, molar proportions of acetate, butyrate, or estimated methane production. Supplemental EHY decreased ruminal molar proportion of acetate (p = 0.08), increased molar proportion of propionate (p = 0.09), and decreased acetate:propionate molar ratio (p = 0.07) and estimated ruminal methane production (p = 0.09). It is concluded that supplemental EHY may enhance DMI and ADG of feedlot steers during periods of high ambient temperature. Supplemental EHY may also enhance ruminal fiber digestion and decrease ruminal acetate:propionate molar ratios in feedlot steers fed steam-flaked corn-based finishing diets

COVID-19 and Guillain-Barre Syndrome: a systematic review of case reports

Background: Guillain-Barre Syndrome (GBS) is a neurological autoimmune disease that can lead to respiratory failure and death. Whether COVID-19 patients are at high risk of GBS is unknown. Through a systematic review of case reports, we aimed to summarize the main features of patients with GBS and COVID-19. Methods: Without any restrictions, we searched MEDLINE, Embase, Global Health, Scopus, Web of Science and MedXriv (April 23 rd , 2020). Two reviewers screened and studied titles, abstracts and reports. We extracted information to characterize sociodemographic variables, clinical presentation, laboratory results, treatments and outcomes. Results: Eight reports (n=12 patients) of GBS and COVID-19 were identified; one was a Miller Fisher case. Overall, the median age was 62.5 (interquartile range (IQR)=54.5-70.5) years, and there were more men (9/102). GBS symptoms started between 5 and 24 days after those of COVID-19. The median protein levels in cerebrospinal fluid samples was 101.5 mg/dl (IQR=51-145). None of the cerebrospinal fluid samples tested positive for COVID-19. Six patients debuted with ascendant weakness and three with facial weakness. Five patients had favourable evolution, four remained with relevant symptoms or required critical care and one died; the Miller Fisher case had successful resolution. Conclusions: GBS is emerging as a disease that may appear in COVID-19 patients. Although limited, preliminary evidence appears to suggest that GBS occurs after COVID-19 onset. Practitioners and investigators should have GBS in mind as they look after COVID-19 patients and conduct research on novel aspects of COVID-19. Comparison with GBS patients in the context of another viral outbreak (Zika), revealed similarities and differences that deserves further scrutiny and epidemiological studies

Dopaminergic Modulation of Corticostriatal Responses in Medium Spiny Projection Neurons from Direct and Indirect Pathways

Suprathreshold corticostriatal responses recorded from medium spiny neurons (MSNs) from the direct and indirect pathways of the basal ganglia are different. Their differences readily distinguish D1- and D2-type receptor expressing MSNs in both bacterial artificial chromosome-transgenic mice and their control littermates as well as in rats: indirect pathway neurons are more excitable than direct pathway neurons revealing autoregenerative spikes underlying their spike trains, whereas direct pathway neurons exhibit more prolonged plateau potentials and spike trains. SFK 81297, a selective agonist for D1-class receptors enhanced corticostriatal responses in direct pathway neurons, while quinelorane, a selective agonist for D2-class receptors reduced orthodromic and autoregenerative responses in indirect pathway neurons thus making both neuron classes similarly excitable. Because dopaminergic postsynaptic actions target CaV1 (L) class voltage-gated calcium channels in MSNs, we hypothesized that these channels are involved and can explain a part of the dopaminergic actions on corticostriatal integration. Both 2.5 μM nicardipine and 400 nM calciseptine, selective CaV1 channel blockers, reduced corticostriatal responses in both D1- and D2-receptor expressing neurons, respectively. A previous blockade of CaV1 channels occluded the actions of dopamine agonists in both neuronal classes. In contrast, a CaV1 (L) channel activator, 2.5 μM Bay K 8644, enhanced corticostriatal responses in neurons from both pathways. It is concluded that CaV1 intrinsic currents mediate a part of the dopaminergic modulation during orthodromic synaptic integration of cortical inputs in both classes of MSNs

Different Corticostriatal Integration in Spiny Projection Neurons from Direct and Indirect Pathways

The striatum is the principal input structure of the basal ganglia. Major glutamatergic afferents to the striatum come from the cerebral cortex and make monosynaptic contacts with medium spiny projection neurons (MSNs) and interneurons. Also: glutamatergic afferents to the striatum come from the thalamus. Despite differences in axonal projections, dopamine (DA) receptors expression and differences in excitability between MSNs from “direct” and “indirect” basal ganglia pathways, these neuronal classes have been thought as electrophysiologically very similar. Based on work with bacterial artificial chromosome (BAC) transgenic mice, here it is shown that corticostriatal responses in D1- and D2-receptor expressing MSNs (D1- and D2-MSNs) are radically different so as to establish an electrophysiological footprint that readily differentiates between them. Experiments in BAC mice allowed us to predict, with high probability (P > 0.9), in rats or non-BAC mice, whether a recorded neuron, from rat or mouse, was going to be substance P or enkephalin (ENK) immunoreactive. Responses are more prolonged and evoke more action potentials in D1-MSNs, while they are briefer and exhibit intrinsic autoregenerative responses in D2-MSNs. A main cause for these differences was the interaction of intrinsic properties with the inhibitory contribution in each response. Inhibition always depressed corticostriatal depolarization in D2-MSNs, while it helped in sustaining prolonged depolarizations in D1-MSNs, in spite of depressing early discharge. Corticostriatal responses changed dramatically after striatal DA depletion in 6-hydroxy-dopamine (6-OHDA) lesioned animals: a response reduction was seen in substance P (SP)+ MSNs whereas an enhanced response was seen in ENK+ MSNs. The end result was that differences in the responses were greatly diminished after DA depletion

An app based on cooperative learning for the detection of danger points and the prevention of risk areas in a city

This article gives a general review of the presence of crime in today’s society, its impact in the daily life of the citizens and proposes the use of Safe Paths, a mobile application focused on risk prevention based on social collaboration and cooperative learning to identify dangerous areas and give alerts based on their users’ location and the risks around to them. It also describes some technical aspects of Safe Paths such as its architecture from the MVC approach; the use cases and actors involved in said application and finally shows its graphical user interface

IPAD: Stable Interpretable Forecasting with Knockoffs Inference

Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features which can enhance greatly the interpretability is still largely underdeveloped in the econometric settings. To this end, in this paper we exploit the general framework of model-X knockoffs introduced recently in Cand\`{e}s, Fan, Janson and Lv (2018), which is nonconventional for reproducible large-scale inference in that the framework is completely free of the use of p-values for significance testing, and suggest a new method of intertwined probabilistic factors decoupling (IPAD) for stable interpretable forecasting with knockoffs inference in high-dimensional models. The recipe of the method is constructing the knockoff variables by assuming a latent factor model that is exploited widely in economics and finance for the association structure of covariates. Our method and work are distinct from the existing literature in that we estimate the covariate distribution from data instead of assuming that it is known when constructing the knockoff variables, our procedure does not require any sample splitting, we provide theoretical justifications on the asymptotic false discovery rate control, and the theory for the power analysis is also established. Several simulation examples and the real data analysis further demonstrate that the newly suggested method has appealing finite-sample performance with desired interpretability and stability compared to some popularly used forecasting methods

Measuring Health Related Quality of Life for Dengue Patients in Iquitos, Peru

Previous studies measuring the health-related quality of life (HRQoL) of individuals with dengue focused on treatment seeking populations. However, the vast majority of global dengue cases are unlikely to be detected by health systems. Representative measurements of HRQoL should therefore include patients with disease not likely to trigger treatment-seeking behavior. This study based in Iquitos, Peru used the Quality of Wellbeing Scale-Self Administered, a survey that enquires about not only physical health, but also psychological health, self-care, mobility, and usual social activities, and rates HRQoL between 0 (death) and 1 (optimum function), to evaluate the impact of dengue on HRQoL. In order to enroll treatment and non treatment-seeking participants, three modalities of participant recruitment were used. In addition to clinic and community-based febrile surveillance, a contact-cluster methodology was also employed to identify infected individuals less likely to seek treatment. We measured changes in HRQoL and identified common areas of health impairment in 73 virologically confirmed dengue cases at 3 time points during the participant\u27s illness; the early-acute (days 0-6 post symptom onset), late-acute (days 7-20), and convalescent illness phases (days 21 +). Participants reported HRQoL related impairments at significantly higher frequency during the early-acute versus convalescent illness phase (Fisher\u27s exact: P\u3c0.01). There was substantial heterogeneity in scores during each illness phase with median scores in the early-acute, late-acute and convalescent phases of 0.56 (IQR: 0.41-0.64), 0.70 (IQR: 0.57-0.94), and 1 (IQR: 0.80-1.00), respectively. In all illness phases participants recruited in clinics had on average the lowest HRQoL scores where as those recruited in the contact clusters had the highest. Only 1 individual who was recruited in the contact-clusters had no reduction in HRQoL score during their illness. These data illustrate that dengue should be considered as a disease that may have significant implications for not only physical health but also psychological health and social functioning. The impact of dengue on the HRQoL of non-treatment-seeking individuals, although lower than the impact among treatment-seeking individuals, is not necessarily trivial

Tumor markers in breast cancer - European Group on Tumor Markers recommendations

Recommendations are presented for the routine clinical use of serum and tissue-based markers in the diagnosis and management of patients with breast cancer. Their low sensitivity and specificity preclude the use of serum markers such as the MUC-1 mucin glycoproteins ( CA 15.3, BR 27.29) and carcinoembryonic antigen in the diagnosis of early breast cancer. However, serial measurement of these markers can result in the early detection of recurrent disease as well as indicate the efficacy of therapy. Of the tissue-based markers, measurement of estrogen and progesterone receptors is mandatory in the selection of patients for treatment with hormone therapy, while HER-2 is essential in selecting patients with advanced breast cancer for treatment with Herceptin ( trastuzumab). Urokinase plasminogen activator and plasminogen activator inhibitor 1 are recently validated prognostic markers for lymph node-negative breast cancer patients and thus may be of value in selecting node-negative patients that do not require adjuvant chemotherapy. Copyright (C) 2005 S. Karger AG, Basel