Publications by authors named "M Irfan Mahmud"

290 Publications

Does 'COVID-19 phobia' stimulate career anxiety?: Experience from a developing country.

Heliyon 2021 Mar 8;7(3):e06346. Epub 2021 Mar 8.

Department of Textile Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.

Due to the outbreak of COVID-19 different forms of anxiety disorder have been seen in the mindset of people all around the world. This study aims to examine a structural relationship between 'COVID-19 phobia' and career anxiety among the workforce from the perspective of a developing country. This study collected survey data using a structured questionnaire by applying the scales of 'COVID-19 Phobia' and career anxiety. Study results reveal that the factors of the 'COVID-19 phobia' have a substantial influence on generating career-related anxiety among the workforce. Study results can play a vital role for the policymakers to formulate long-term policies to retrieve the world's economy. This study combined the concept of specific phobia and general anxiety disorder (GAD) to figure out how the global pandemic impacted peoples' mindsets and create career anxiety. The study results have theoretical and practical implications in many folds.
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http://dx.doi.org/10.1016/j.heliyon.2021.e06346DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8035488PMC
March 2021

Antibiotics at birth and later antibiotic courses: effects on gut microbiota.

Pediatr Res 2021 Apr 6. Epub 2021 Apr 6.

PEDEGO Research Unit and Medical Research Centre Oulu, University of Oulu, Oulu, Finland.

Background: Intrapartum antibiotic prophylaxis (IAP) is widely used, but the evidence of the long-term effects on the gut microbiota and subsequent health of children is limited. Here, we compared the impacts of perinatal antibiotic exposure and later courses of antibiotic courses on gut microbiota.

Methods: This was a prospective, controlled cohort study among 100 vaginally delivered infants with different perinatal antibiotic exposures: control (27), IAP (27), postnatal antibiotics (24), and IAP and postnatal antibiotics (22). At 1 year of age, we performed next-generation sequencing of the bacterial 16S ribosomal RNA gene of fecal samples.

Results: Exposure to the perinatal antibiotics had a clear impact on the gut microbiota. The abundance of the Bacteroidetes phylum was significantly higher in the control group, whereas the relative abundance of Escherichia coli was significantly lower in the control group. The impact of the perinatal antibiotics on the gut microbiota composition was greater than exposure to later courses of antibiotics (28% of participants).

Conclusions: Perinatal antibiotic exposure had a marked impact on the gut microbiota at the age of 1 year. The timing of the antibiotic exposure appears to be the critical factor for the changes observed in the gut microbiota.

Impact: Infants are commonly exposed to IAP and postnatal antibiotics, and later to courses of antibiotics during the first year of life. Perinatal antibiotics have been associated with an altered gut microbiota during the first months of life, whereas the evidence regarding the long-term impact is more limited. Perinatal antibiotic exposure had a marked impact on the infant's gut microbiota at 1 year of age. Impact of the perinatal antibiotics on the gut microbiota composition was greater than that of the later courses of antibiotics at the age of 1 year.
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http://dx.doi.org/10.1038/s41390-021-01494-7DOI Listing
April 2021

Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap.

Pers Ubiquitous Comput 2021 Mar 26:1-24. Epub 2021 Mar 26.

Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka India.

The pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global health calamity that has a profound impact on the way of perceiving the world and everyday lives. This has appeared as the greatest threat of the time for the entire world in terms of its impact on human mortality rate and many other societal fronts or driving forces whose estimations are yet to be known. Therefore, this study focuses on the most crucial sectors that are severely impacted due to the COVID-19 pandemic, in particular reference to India. Considered based on their direct link to a country's overall economy, these sectors include economic and financial, educational, healthcare, industrial, power and energy, oil market, employment, and environment. Based on available data about the pandemic and the above-mentioned sectors, as well as forecasted data about COVID-19 spreading, four inclusive mathematical models, namely-exponential smoothing, linear regression, Holt, and Winters, are used to analyse the gravity of the impacts due to this COVID-19 outbreak which is also graphically visualized. All the models are tested using data such as COVID-19 infection rate, number of daily cases and deaths, GDP of India, and unemployment. Comparing the obtained results, the best prediction model is presented. This study aims to evaluate the impact of this pandemic on country-driven sectors and recommends some strategies to lessen these impacts on a country's economy.
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http://dx.doi.org/10.1007/s00779-021-01530-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996129PMC
March 2021

Speech categorization is better described by induced rather than evoked neural activity.

J Acoust Soc Am 2021 Mar;149(3):1644

School of Communication Sciences and Disorders, University of Memphis, 4055 North Park Loop, Memphis, Tennessee 38152, USA.

Categorical perception (CP) describes how the human brain categorizes speech despite inherent acoustic variability. We examined neural correlates of CP in both evoked and induced electroencephalogram (EEG) activity to evaluate which mode best describes the process of speech categorization. Listeners labeled sounds from a vowel gradient while we recorded their EEGs. Using a source reconstructed EEG, we used band-specific evoked and induced neural activity to build parameter optimized support vector machine models to assess how well listeners' speech categorization could be decoded via whole-brain and hemisphere-specific responses. We found whole-brain evoked β-band activity decoded prototypical from ambiguous speech sounds with ∼70% accuracy. However, induced γ-band oscillations showed better decoding of speech categories with ∼95% accuracy compared to evoked β-band activity (∼70% accuracy). Induced high frequency (γ-band) oscillations dominated CP decoding in the left hemisphere, whereas lower frequencies (θ-band) dominated the decoding in the right hemisphere. Moreover, feature selection identified 14 brain regions carrying induced activity and 22 regions of evoked activity that were most salient in describing category-level speech representations. Among the areas and neural regimes explored, induced γ-band modulations were most strongly associated with listeners' behavioral CP. The data suggest that the category-level organization of speech is dominated by relatively high frequency induced brain rhythms.
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http://dx.doi.org/10.1121/10.0003572DOI Listing
March 2021

Data-driven machine learning models for decoding speech categorization from evoked brain responses.

J Neural Eng 2021 Mar 9. Epub 2021 Mar 9.

School of Communication Sciences & Disorders, University of Memphis, 4055 North Park Loop, Memphis, Tennessee, 38152, UNITED STATES.

Categorical perception (CP) of audio is critical to understand how the human brain perceives speech sounds despite widespread variability in acoustic properties. Here, we investigated the spatiotemporal characteristics of auditory neural activity that reflects CP for speech (i.e., differentiates phonetic prototypes from ambiguous speech sounds). We recorded high density EEGs as listeners rapidly classified vowel sounds along an acoustic-phonetic continuum. We used support vector machine (SVM) classifiers and stability selection to determine when and where in the brain CP was best decoded across space and time via source-level analysis of the event related potentials (ERPs). We found that early (120 ms) whole-brain data decoded speech categories (i.e., prototypical vs. ambiguous speech tokens) with 95.16% accuracy [area under the curve (AUC) 95.14%; F1-score 95.00%]. Separate analyses on left hemisphere (LH) and right hemisphere (RH) responses showed that LH decoding was more robust and earlier than RH (89.03% vs. 86.45% accuracy; 140 ms vs. 200 ms). Stability (feature) selection identified 13 regions of interest (ROIs) out of 68 brain regions (including auditory cortex, supramarginal gyrus, and Brocas area) that showed categorical representation during stimulus encoding (0-260 ms). In contrast, 15 ROIs (including fronto-parietal regions, Broca's area, motor cortex) were necessary to describe later decision stages (later 300 ms) of categorization but these areas were highly associated with the strength of listeners' categorical hearing (i.e., slope of behavioral identification functions). Our data-driven multivariate models demonstrate that abstract categories emerge surprisingly early (~120 ms) in the time course of speech processing and are dominated by engagement of a relatively compact fronto-temporal-parietal brain network.
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http://dx.doi.org/10.1088/1741-2552/abecf0DOI Listing
March 2021