The feasibility of using acoustic measures for predicting the Total Opacity Scores of chest computed tomography scans in patients with COVID-19.

To study the possibility of using acoustic parameters, i.e., Acoustic Voice Quality Index (AVQI) and Maximum Phonation Time (MPT) for predicting the degree of lung involvement in COVID-19 patients. This cross-sectional case-control study was conducted on the voice samples collected from 163 healthy individuals and 181 patients with COVID-19. Each participant produced a sustained vowel/a/, and a phonetically balanced Persian text containing 36 syllables. AVQI and MPT were measured using Praat scripts. Each patient underwent a non-enhanced chest computed tomographic scan and the Total Opacity score was rated to assess the degree of lung involvement. The results revealed significant differences between patients with COVID-19 and healthy individuals in terms of AVQI and MPT. A significant difference was also observed between male and female participants in AVQI and MPT. The results from the receiver operating characteristic curve analysis and area under the curve indicated that MPT (0.909) had higher diagnostic accuracy than AVQI (0.771). A significant relationship was observed between AVQI and TO scores. In the case of MPT, however, no such relationship was observed. The findings indicated that MPT was a better classifier in differentiating patients from healthy individuals, in comparison with AVQI. The results also showed that AVQI can be used as a predictor of the degree of patients' and recovered individuals' lung involvement. A formula is suggested for calculating the degree of lung involvement using AVQI.

A Scoping Review of Adopted Information Extraction Methods for RCTs.

Background: Randomized controlled trials (RCTs) provide the strongest evidence for therapeutic interventions and their effects on groups of subjects. However, the large amount of unstructured information in these trials makes it challenging and time-consuming to make decisions and identify important concepts and valid evidence. This study aims to explore methods for automating or semi-automating information extraction from reports of RCT studies. Methods: We conducted a systematic search of PubMed, ACM Digital Library, and Web of Science to identify relevant articles published between January 1, 2010, and 2022. We focused on published Natural Language Processing (NLP), machine learning, and deep learning methods that automate or semi-automate key elements of information extraction in the context of RCTs. Results: A total of 26 publications were included, which discussed the automatic extraction of key characteristics of RCTs using various PICO frameworks (PIBOSO and PECODR). Among these publications, 14 (53.8%) extracted key characteristics based on PICO, PIBOSO, and PECODR, while 12 (46.1%) discussed information extraction methods in RCT studies. Common approaches mentioned included word/phrase matching, machine learning algorithms such as binary classification using the Naïve Bayes algorithm and powerful BERT network for feature extraction, support vector machine for data classification, conditional random field, non-machine-dependent automation, and machine learning or deep learning approaches. Conclusion: The lack of publicly available software and limited access to existing software makes it difficult to determine the most powerful information extraction system. However, deep learning models like Transformers and BERT language models have shown better performance in natural language processing.

Voice Quality Evaluation in Patients With COVID-19: An Acoustic Analysis.

OBJECTIVES: With the COVID-19 outbreak around the globe and its potential effect on infected patients' voice, this study set out to evaluate and compare the acoustic parameters of voice between healthy and infected people in an objective manner. METHODS: Voice samples of 64 COVID-19 patients and 70 healthy Persian speakers who produced a sustained vowel /a/ were evaluated. Between-group comparisons of the data were performed using the two-way ANOVA and Wilcoxon's rank-sum test. RESULTS: The results revealed significant differences in CPP, HNR, H1H2, F0SD, jitter, shimmer, and MPT values between COVID-19 patients and the healthy participants. There were also significant differences between the male and female participants in all the acoustic parameters, except jitter, shimmer and MPT. No interaction was observed between gender and health status in any of the acoustic parameters. CONCLUSION: The statistical analysis of the data revealed significant differences between the experimental and control groups in this study. Changes in the acoustic parameters of voice are caused by the insufficient airflow, and increased aperiodicity, irregularity, signal perturbation and level of noise, which are the consequences of pulmonary and laryngological involvements in patients with COVID-19.

Bibliometric study of traumatic brain injury rehabilitation.

Purpose: This paper aims to present a bibliometric analysis of scientific documents in the field of traumatic brain injury rehabilitation.Methods: Web of Science was used to collect bibliographic data of traumatic brain injury rehabilitation documents from 1983 until the end of 2017.Results: Of a total of 6069 documents retrieved, 78.2% were journal articles. The average annual growth of the documents as of the year 2000 was 9.4%. The most frequent subject categories in this field were Rehabilitation, Neurosciences and Neurology, Sport Sciences, Psychology, and General and Internal Medicine. The most active journal was Brain Injury. More than 50% of the documents were published in 10 journals. The most prolific and impactful institutions were from the USA, Australia and Canada. Traumatic brain injury, rehabilitation, brain injury, stroke and outcome were the most commonly used keywords. Mild traumatic brain injury and concussion were the topics receiving attention in recent years.Conclusion: Traumatic brain injury rehabilitation is a young and constantly growing field. Since the late 1990s, traumatic brain injury rehabilitation documents published yearly comprised about 3-4% of all rehabilitation documents. It was shown that review papers and proceedings have more impact than journal articles, and collaborative papers receive more citations. It was also revealed that knowledge does not become obsolete rapidly in this field.

Do you have COVID-19? An artificial intelligence-based screening tool for COVID-19 using acoustic parameters.

This study aimed to develop an artificial intelligence (AI)-based tool for screening COVID-19 patients based on the acoustic parameters of their voices. Twenty-five acoustic parameters were extracted from voice samples of 203 COVID-19 patients and 171 healthy individuals who produced a sustained vowel, i.e., /a/, as long as they could after a deep breath. The selected acoustic parameters were from different categories including fundamental frequency and its perturbation, harmonicity, vocal tract function, airflow sufficiency, and periodicity. After the feature extraction, different machine learning methods were tested. A leave-one-subject-out validation scheme was used to tune the hyper-parameters and record the test set results. Then the models were compared based on their accuracy, precision, recall, and F1-score. Based on accuracy (89.71%), recall (91.63%), and F1-score (90.62%), the best model was the feedforward neural network (FFNN). Its precision function (89.63%) was a bit lower than the logistic regression (90.17%). Based on these results and confusion matrices, the FFNN model was employed in the software. This screening tool could be practically used at home and public places to ensure the health of each individual's respiratory system. If there are any related abnormalities in the test taker's voice, the tool recommends that they seek a medical consultant.

Impact of United States political sanctions on international collaborations and research in Iran.

International research collaborations improve individual, institutional and governmental capacities to respond to health crises and inequalities but may be greatly affected by political environments. Iran ranks highly in tertiary education, productivity growth, knowledge impact and successful patent applications. In many countries, economic hardship has correlated with increased international research collaborations. Some have hypothesised that financial constraint drives scholars to seek outside collaborations for cost and risk sharing, and to access funding, materials and patient populations otherwise unavailable. This paper explores the history and importance of US political sanctions on the health of Iran's academic sector. Although Iran's international research collaborations increased during periods of increased sanctions, the Pearson correlation coefficient between gross domestic product and international research collaborations was not significant (r=0.183, p=0.417). This indicates that other factors are at least in part responsible. Additionally, we found Iran's quantitative (eg, publication number) and qualitative (eg, visibility indices) publishing metrics to be discordant (two-tailed Mann-Kendall trend; p<0.0002 for both). Reasons for this are multifactorial, including increased indexing of Iranian journals, willingness of lower visibility journals to handle manuscripts with Iranian authors, widespread linkage of career advancement to science visibility indices, and others. During periods of increased sanctions, Iranian scholars were increasingly denied opportunities to publish scientific findings, attend scientific meetings, access to essential medical and laboratory supplies and information resources. We conclude that academic boycotts violate researchers' freedom and curtail progress. Free exchange of ideas irrespective of creed is needed to optimize global scientific progress.