COPDVD: Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset.

Background: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systemic effects, e.

Using Artificial Intelligence for Assessment of Velopharyngeal Competence in Children Born With Cleft Palate With or Without Cleft Lip.

Objective: Development of an AI tool to assess velopharyngeal competence (VPC) in children with cleft palate, with/without cleft lip.

Design: Innovation of an AI tool using retrospective audio recordings and assessments of VPC.

Setting: Two datasets were used.

Fisher information for smart sampling in time-domain spectroscopy.

Time-domain spectroscopy encompasses a wide range of techniques, such as Fourier-transform infrared, pump-probe, Fourier-transform Raman, and two-dimensional electronic spectroscopies. These methods enable various applications, such as molecule characterization, excited state dynamics studies, or spectral classification. Typically, these techniques rarely use sampling schemes that exploit the prior knowledge scientists typically have before the actual experiment.

Adaptive sparse estimation of nonlinear chirp signals using Laplace priors.

The identification of nonlinear chirp signals has attracted notable attention in the recent literature, including estimators such as the variational mode decomposition and the nonlinear chirp mode estimator. However, most presented methods fail to process signals with close frequency intervals or depend on user-determined parameters that are often non-trivial to select optimally. In this work, we propose a fully adaptive method, termed the adaptive nonlinear chirp mode estimation.

BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance.

Post-marketing reports of suspected adverse drug reactions are important for establishing the safety profile of a medicinal product. However, a high influx of reports poses a challenge for regulatory authorities as a delay in identification of previously unknown adverse drug reactions can potentially be harmful to patients. In this study, we use natural language processing (NLP) to predict whether a report is of serious nature based solely on the free-text fields and adverse event terms in the report, potentially allowing reports mislabelled at time of reporting to be detected and prioritized for assessment.

[The Swecrit Biobank, associated clinical registries, and machine learning (artificial intelligence) improve critical care knowledge].

The unique Swecrit Biobank and its associated clinical registries for sepsis, ARDS, cardiac arrest, trauma, and COVID-19 include more than 150,000 blood samples and descriptions of critically ill patients. These assets provide a unique opportunity to research and improve the care of the most seriously ill patients through biomarker analyses, proteomic studies, and genetic and epigenetic studies using modern machine learning techniques (artificial intelligence). Interested researchers are invited to submit their proposals and participate.

Comparison of dyskinesia profiles after L-DOPA dose challenges with or without dopamine agonist coadministration.

Many patients with Parkinson's disease (PD) experiencing l-DOPA-induced dyskinesia (LID) receive adjunct treatment with dopamine agonists, whose functional impact on LID is unknown. We set out to compare temporal and topographic profiles of abnormal involuntary movements (AIMs) after l-DOPA dose challenges including or not the dopamine agonist ropinirole. Twenty-five patients with PD and a history of dyskinesias were sequentially administered either l-DOPA alone (150% of usual morning dose) or an equipotent combination of l-DOPA and ropinirole in random order.

Sparse optimization for nonlinear group delay mode estimation.

Nonlinear group delay signals with frequency-varying characteristics are common in a wide variety of fields, for instance, structural health monitoring and fault diagnosis. For such applications, the signal is composed of multiple modes, where each mode may overlap in the frequency-domain. The resulting decomposition and forming of time-frequency representations of the nonlinear group delay modes is a challenging task.

Tremor evaluation using smartphone accelerometry in standardized settings.

Tremor can be highly incapacitating in everyday life and typically fluctuates depending on motor state, medication status as well as external factors. For tremor patients being treated with deep-brain stimulation (DBS), adapting the intensity and pattern of stimulation according the current needs therefore has the potential to generate better symptomatic relief. We here describe a procedure for how patients independently could perform self-tests in their home to generate sensor data for on-line adjustments of DBS parameters.

Rapid measurement of heteronuclear transverse relaxation rates using non-uniformly sampled accordion experiments.

Multidimensional, heteronuclear NMR relaxation methods are used extensively to characterize the dynamics of biological macromolecules. Acquisition of relaxation datasets on proteins typically requires significant measurement time, often several days. Accordion spectroscopy offers a powerful means to shorten relaxation rate measurements by encoding the "relaxation dimension" into the indirect evolution period in multidimensional experiments.

Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow.

Identification of prostate gold fiducial markers in magnetic resonance imaging (MRI) images is challenging when CT images are not available, due to misclassifications from intra-prostatic calcifications. It is also a time consuming task and automated identification methods have been suggested as an improvement for both objectives. Multi-echo gradient echo (MEGRE) images have been utilized for manual fiducial identification with 100% detection accuracy.

Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care.

Background: Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM).

Compressed Sensing for Reconstructing Coherent Multidimensional Spectra.

We apply two sparse reconstruction techniques, the least absolute shrinkage and selection operator (LASSO) and the sparse exponential mode analysis (SEMA), to two-dimensional (2D) spectroscopy. The algorithms are first tested on model data, showing that both are able to reconstruct the spectra using only a fraction of the data required by the traditional Fourier-based estimator. Through the analysis of the sparsely sampled experimental fluorescence-detected 2D spectra of LH2 complexes, we conclude that both SEMA and LASSO can be used to significantly reduce the required data, still allowing one to reconstruct the multidimensional spectra.

Artificial neural networks improve and simplify intensive care mortality prognostication: a national cohort study of 217,289 first-time intensive care unit admissions.

Purpose: We investigated if early intensive care unit (ICU) scoring with the Simplified Acute Physiology Score (SAPS 3) could be improved using artificial neural networks (ANNs).

Methods: All first-time adult intensive care admissions in Sweden during 2009-2017 were included. A test set was set aside for validation.

Rapid NMR Relaxation Measurements Using Optimal Nonuniform Sampling of Multidimensional Accordion Data Analyzed by a Sparse Reconstruction Method.

Nonuniform sampling (NUS) of multidimensional NMR data offers significant time savings while improving spectral resolution or increasing sensitivity per unit time. However, NUS has not been widely used for quantitative analysis because of the nonlinearity of most methods used to model NUS data, which leads to problems in estimating signal intensities, relaxation rate constants, and their error bounds. Here, we present an approach that avoids these limitations by combining accordion spectroscopy and NUS in the indirect dimensions of multidimensional spectra and then applying sparse exponential mode analysis, which is well suited for analyzing accordion-type relaxation data in a NUS context.

Sparse estimation of backscattered echoes from underwater object using integrated dictionaries.

The problem of time-delays estimation of backscattered echoes from underwater targets is presented using a sparse reconstruction framework employing an integrated dictionary. To achieve high resolution, the used dictionary is usually defined over a finely spaced grid over the region of interest. Such a procedure may result in problems of being computational cumbersome or suffering from basis mismatch.

Batch-specific discrimination using nuclear quadrupole resonance spectroscopy.

In this paper, we report on the identification of batches of analgesic paracetamol (acetaminophen) tablets using nitrogen-14 nuclear quadrupole resonance spectroscopy ((14)N NQR). The high sensitivity of NQR to the electron charge distribution surrounding the quadrupolar nucleus enables the unique characterization of the crystal structure of the material. Two hypothesis were tested on batches of the same brand: the within the same batch variability and the difference between batches that varied in terms of their batch number and expiry date.

Spectral estimation of irregularly sampled exponentially decaying signals with applications to RF spectroscopy.

The problem of estimating the spectral content of exponentially decaying signals from a set of irregularly sampled data is of considerable interest in several applications, for example in various forms of radio frequency spectroscopy. In this paper, we propose a new nonparametric iterative adaptive approach that provides a solution to this estimation problem. As opposed to commonly used methods in the field, the damping coefficient, or linewidth, is explicitly modeled, which allows for an improved estimation performance.

Adaptive spectral doppler estimation.

In this paper, 2 adaptive spectral estimation techniques are analyzed for spectral Doppler ultrasound. The purpose is to minimize the observation window needed to estimate the spectrogram to provide a better temporal resolution and gain more flexibility when designing the data acquisition sequence. The methods can also provide better quality of the estimated power spectral density (PSD) of the blood signal.

Steroid hormones control circadian Elovl3 expression in mouse liver.

The Elovl3 gene belongs to the Elovl gene family, which encodes for enzymes involved in the elongation of very long chain fatty acids. The recognized role for the enzyme is to control the elongation of saturated and monounsaturated fatty acids up to 24 carbons in length. Elovl3 was originally identified as a highly expressed gene in brown adipose tissue on cold exposure.

On the efficient estimation of blood velocities.

Pulsed wave (PW) Doppler ultrasound systems are commonly used to examine blood flow dynamics and the technique plays a very important role in numerous diagnostic applications. Commonly, narrow-band PW systems estimate the blood velocity using an autocorrelation-based estimator. Herein, we examine a recently proposed hybrid frequency estimator, and via extensive numerical simulations using simulated blood scatterers show the achievable performance gain of this method as compared to the traditional approach.

Fatty acid elongases in mammals: their regulation and roles in metabolism.

A significant amount of the fatty acids synthesized by the cytosolic enzyme complex fatty acid synthase (FAS) or taken up by the diet are further elongated into very long chain fatty acids (VLCFA) in a four-step reaction cycle by membrane-bound enzymes predominantly located in the endoplasmic reticulum. Members of the Elovl (elongation-of-very-long-chain-fatty acids) gene family encode for enzymes (elongases), which are believed to perform the first, regulatory, step (condensation) in the elongation cycle in mammals. The family of enzymes consists of at least six members in mouse and human, believed to carry out substrate-specific elongation with fatty acids of different lengths and degrees of unsaturation.

Differential regulation of fatty acid elongation enzymes in brown adipocytes implies a unique role for Elovl3 during increased fatty acid oxidation.

The expression of the Elovl3 gene, which belongs to the Elovl gene family coding for microsomal enzymes involved in very long-chain fatty acid (VLCFA) elongation, is dramatically increased in mouse brown adipose tissue upon cold stimulation. In the present study, we show that the cold-induced Elovl3 expression is under the control of peroxisome proliferator-activated receptor-alpha (PPARalpha) and that this regulation is part of a fundamental divergence in the regulation of expression for the different members of the Elovl gene family. In cultured brown adipocytes, a mixture of norepinephrine, dexamethasone, and the PPARalpha ligand Wy-14643, which rendered the adipocytes a high oxidative state, was required for substantial induction of Elovl3 expression, whereas the same treatment suppressed Elovl1 mRNA levels.

Role for ELOVL3 and fatty acid chain length in development of hair and skin function.

Very little is known about the in vivo regulation of mammalian fatty acid chain elongation enzymes as well as the role of specific fatty acid chain length in cellular responses and developmental processes. Here, we report that the Elovl3 gene product, which belongs to a highly conserved family of microsomal enzymes involved in the formation of very long chain fatty acids, revealed a distinct expression in the skin that was restricted to the sebaceous glands and the epithelial cells of the hair follicles. By disruption of the Elovl3 gene by homologous recombination in mouse, we show that ELOVL3 participates in the formation of specific neutral lipids that are necessary for the function of the skin.