Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features.

The term "bulbar involvement" is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the quality of life and lifespan of ALS patients suffering from bulbar involvement. The main objective, and the principal contribution, of this research, was to design a new methodology, based on the phonatory-subsystem and time-frequency characteristics for detecting bulbar involvement automatically. This study focused on providing a set of 50 phonatory-subsystem and time-frequency features to detect this deficiency in males and females through the utterance of the five Spanish vowels. Multivariant Analysis of Variance was then used to select the statistically significant features, and the most common supervised classifications models were analyzed. A set of statistically significant features was obtained for males and females to capture this dysfunction. To date, the accuracy obtained (98.01% for females and 96.10% for males employing a random forest) outperformed the models in the literature. Adding time-frequency features to more classical phonatory-subsystem features increases the prediction capabilities of the machine-learning models for detecting bulbar involvement. Studying men and women separately gives greater success. The proposed method can be deployed in any kind of recording device (i.e., smartphone).

A Machine-Learning Model for Lung Age Forecasting by Analyzing Exhalations.

Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person's lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users' expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.

Simple iodoalkyne-based organocatalysts for the activation of carbonyl compounds.

A novel approach for the formation of bisindolylmethane derivatives (BIMs) is described as a proof of concept to evaluate the catalytic capacity of iodoalkynes. The use of these derivatives is reported as an example of simple halogen bond-based organocatalyst. This kind of activation has not been used before for the synthesis of bisindolylmethane derivatives 3. Interestingly, the preparation of 3-(1H-indol-3-yl)-1-phenylbutan-1-one (8) has been also achieved for the first time with an iodoalkyne derivative. We prove the efficiency of this family of new catalysts by developing a simple and easy operational methodology, opening the door to the development of alternative catalysts in the area of halogen bond-based organocatalysts.

Enhancing Permeability: Unraveling the Potential of Microporous Organic Polymers in Mixed Matrix Membranes.

Mixed matrix membranes (MMMs) were formed by using seven polymeric matrices with a wide range of permeabilities. All of the polymeric matrices have been polyimides, namely: P84, Pi-DAPOH, Pi-DAROH, Matrimid, Pi-HABAc, PI-DAM, and PIM-1 in the order of increasing O2 permeability. A fixed (10%) concentration of a microporous organic polymer (TFAP-Trp), formed by the combination of trifluoroacetophenone and triptycene, was added as a porous filler. The material properties as well as their separation performances for multiple pure gases, specifically the permeabilities of He, N2, O2, CH4, and CO2, were measured. The correlation between the relative increase in permeability in MMMs and that of the matrix polymeric membrane has been quantitatively analyzed. This study proves that the increased permeability of MMMs is largely linked to the contribution of the high permeability of the filler. The addition of the TFAP-Trp porous filler proves to be especially beneficial for matrices with low to moderate permeabilities, significantly enhancing the matrix permeability overall. The fitted relationship is approximately linear in accordance with the existing models to predict permeability in dual-phase systems for low proportions of the dispersed phase. An extrapolation allows the evaluation of the permeability of the pure microporous organic polymer, which agrees with the previous values described by the group for different filler contents and in other polymeric matrices. In all cases, the selectivity remains approximately constant while the permeability increases. The addition of TFAP-Trp to all the polymeric matrices led to a moderate improvement of the MMM separation performances, mainly centered on their permeabilities.

Voiceprint and machine learning models for early detection of bulbar dysfunction in ALS.

BACKGROUND AND OBJECTIVE: Bulbar dysfunction is a term used in amyotrophic lateral sclerosis (ALS). It refers to motor neuron disability in the corticobulbar area of the brainstem which leads to a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar dysfunction is voice deterioration characterized by grossly defective articulation, extremely slow laborious speech, marked hypernasality and severe harshness. Recently, research efforts have focused on voice analysis to capture this dysfunction. The main aim of this paper is to provide a new methodology to diagnose this dysfunction automatically at early stages of the disease, earlier than clinicians can do. METHODS: The study focused on the creation of a voiceprint consisting of a pattern generated from the quasi-periodic components of a steady portion of the five Spanish vowels and the computation of the five principal and independent components of this pattern. Then, a set of statistically significant features was obtained using multivariate analysis of variance and the outcomes of the most common supervised classification models were obtained. RESULTS: The best model (random forest) obtained an accuracy, sensitivity and specificity of 88.3%, 85.0% and 95.0% respectively when classifying bulbar vs. control participants but the results worsened when classifying bulbar vs. no-bulbar patients (accuracy, sensitivity and specificity of 78.7%, 80.0% and 77.5% respectively for support vector machines). Due to the great uncertainty found in the annotated corpus of the ALS patients without bulbar involvement, we used a safe semi-supervised support vector machine to relabel the ALS participants diagnosed without bulbar involvement as bulbar and no-bulbar. The performance of the results obtained increased, especially when classifying bulbar and no-bulbar patients obtaining an accuracy, sensitivity and specificity of 91.0%, 83.3% and 100.0% respectively for support vector machines. This demonstrates that our model can improve the diagnosis of bulbar dysfunction compared not only with clinicians, but also the methods published to date. CONCLUSIONS: The results obtained demonstrate the efficiency and applicability of the methodology presented in this paper. It may lead to the development of a cheap and easy-to-use tool to identify this dysfunction in early stages of the disease and monitor progress.

Nonaqueous Interfacial Polymerization-Derived Polyphosphazene Films for Sieving or Blocking Hydrogen Gas.

A series of cyclomatrix polyphosphazene films have been prepared by nonaqueous interfacial polymerization (IP) of small aromatic hydroxyl compounds in a potassium hydroxide dimethylsulfoxide solution and hexachlorocyclotriphosphazene in cyclohexane on top of ceramic supports. Via the amount of dissolved potassium hydroxide, the extent of deprotonation of the aromatic hydroxyl compounds can be changed, in turn affecting the molecular structure and permselective properties of the thin polymer networks ranging from hydrogen/oxygen barriers to membranes with persisting hydrogen permselectivities at high temperatures. Barrier films are obtained with a high potassium hydroxide concentration, revealing permeabilities as low as 9.4 × 10-17 cm3 cm cm-2 s-1 Pa-1 for hydrogen and 1.1 × 10-16 cm3 cm cm-2 s-1 Pa-1 for oxygen. For films obtained with a lower concentration of potassium hydroxide, single gas permeation experiments reveal a molecular sieving behavior, with a hydrogen permeance of around 10-8 mol m-2 s-1 Pa-1 and permselectivities of H2/N2 (52.8), H2/CH4 (100), and H2/CO2 (10.1) at 200 °C.

Gas Permeability through Polyimides: Unraveling the Influence of Free Volume, Intersegmental Distance and Glass Transition Temperature.

The relationships between gas permeability and free volume fraction, intersegmental distance, and glass transition temperature, are investigated. They are analyzed for He, CO2, O2, CH4, and N2 gases and for five similar polyimides with a wide range of permeabilities, from very low to extremely high ones. It has been established here that there is an exponential relationship between permeability and the free volume fraction, and between permeability and the most probable intersegmental distance as measured by WAXS; in both cases, with an exponential coefficient that depends on the kinetic gas diameter as a quadratic polynomial and with a preexponential positive constant. Moreover, it has been proven that the intersegmental distance increases linearly with the free volume fraction. Finally, it has been established that the free volume fraction increases with the glass transition temperature for the polymers tested, and that they depend on each other in an approximate linear way.

Automated detection of COVID-19 cough.

Easy detection of COVID-19 is a challenge. Quick biological tests do not give enough accuracy. Success in the fight against new outbreaks depends not only on the efficiency of the tests used, but also on the cost, time elapsed and the number of tests that can be done massively. Our proposal provides a solution to this challenge. The main objective is to design a freely available, quick and efficient methodology for the automatic detection of COVID-19 in raw audio files. Our proposal is based on automated extraction of time-frequency cough features and selection of the more significant ones to be used to diagnose COVID-19 using a supervised machine-learning algorithm. Random Forest has performed better than the other models analysed in this study. An accuracy close to 90% was obtained. This study demonstrates the feasibility of the automatic diagnose of COVID-19 from coughs, and its applicability to detecting new outbreaks.

Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study.

BACKGROUND: Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement. OBJECTIVE: The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis. METHODS: The study focused on the extraction of features from the phonatory subsystem-jitter, shimmer, harmonics-to-noise ratio, and pitch-from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained. RESULTS: To date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can improve human diagnosis, which can often misdiagnose bulbar involvement. CONCLUSIONS: The results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement.

Novel Polymeric Thin-Film Composite Membranes for High-Temperature Gas Separations.

Novel selective polymeric thin-film composite membranes (TFCMs) for applications at elevated temperatures were developed. Thin selective layers of the polyimides Matrimid 5218® and 6FDA-6FpDA were cast on a developed polybenzimidazole (PBI) porous support prepared by a phase inversion process. The TFCM properties were investigated with different gases in a wide temperature range, including temperatures up to 270 °C. The membranes showed very high thermal stability and performed well at the elevated temperatures. The development of highly thermally resistant polymeric membranes such as these TFCMs opens opportunities for application in high-temperature integrated processes, such as catalytic membrane reactors for the water-gas shift reaction in order to maximize H2 yield.

Gas Separation Properties of Polyimide Thin Films on Ceramic Supports for High Temperature Applications.

Novel selective ceramic-supported thin polyimide films produced in a single dip coating step are proposed for membrane applications at elevated temperatures. Layers of the polyimides P84®, Matrimid 5218®, and 6FDA-6FpDA were successfully deposited onto porous alumina supports. In order to tackle the poor compatibility between ceramic support and polymer, and to get defect-free thin films, the effect of the viscosity of the polymer solution was studied, giving the entanglement concentration (C*) for each polymer. The C* values were 3.09 wt. % for the 6FDA-6FpDA, 3.52 wt. % for Matrimid®, and 4.30 wt. % for P84®. A minimum polymer solution concentration necessary for defect-free film formation was found for each polymer, with the inverse order to the intrinsic viscosities (P84® ≥ Matrimid® >> 6FDA-6FpDA). The effect of the temperature on the permeance of prepared membranes was studied for H2, CH4, N2, O2, and CO2. As expected, activation energy of permeance for hydrogen was higher than for CO2, resulting in H2/CO2 selectivity increase with temperature. More densely packed polymers lead to materials that are more selective at elevated temperatures.

Study of the Effect of Inorganic Particles on the Gas Transport Properties of Glassy Polyimides for Selective CO2 and H2O Separation.

Three polyimides and six inorganic fillers in a form of nanometer-sized particles were studied as thick film solution cast mixed matrix membranes (MMMs) for the transport of CO2, CH4, and H2O. Gas transport properties and electron microscopy images indicate good polymer-filler compatibility for all membranes. The only filler type thatdemonstrated good distribution throughout the membrane thickness at 10 wt. % loading was BaCe0.2Zr0.7Y0.1O3 (BCZY). The influence of this filler on MMM gas transport properties was studied in detail for 6FDA-6FpDA in a filler content range from one to 20 wt. % and for Matrimid® and P84® at 10 wt. % loading. The most promising result was obtained for Matrimid®-10wt. % BCZY MMM, which showed improvement in CO2 and H2O permeabilities accompanied by increased CO2/CH4 selectivity and high water selective membrane at elevated temperatures without H2O/permanent gas selectivity loss.

Claisen thermally rearranged (CTR) polymers.

Thermally rearranged (TR) polymers, which are considered the next-generation of membrane materials because of their excellent transport properties and high thermal and chemical stability, are proven to have significant drawbacks because of the high temperature required for the rearrangement and low degree of conversion during this process. We demonstrate that using a [3,3]-sigmatropic rearrangement, the temperature required for the rearrangement of a solid glassy polymer was reduced by 200°C. Conversions of functionalized polyimide to polybenzoxazole of more than 97% were achieved. These highly mechanically stable polymers were almost five times more permeable and had more than two times higher degrees of conversion than the reference polymer treated under the same conditions. Properties of these second-generation TR polymers provide the possibility of preparing efficient polymer membranes in a form of, for example, thin-film composite membranes for various gas and liquid membrane separation applications.