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Effect of Er substitution on ultrasonic anomaly in Dy(0.5-x)Er(x)Ba(0.5)CoO3 cobaltates

Shamsuddin S, Ibrahim AB, Yahya AK

Ultrasonics, 2013 Aug;53(6):1084-8.
PMID: 23497912 DOI: 10.1016/j.ultras.2013.02.002

Rare-earth cobaltates Dy0.5-xErxBa0.5CoO3 (x=0, 0.03 and 0.05) have been systematically investigated to elucidate the effect of Er substitution on elastic as well as magnetic and transport properties. DC electrical resistance and AC susceptibility measurements showed that the x=0 sample exhibited an insulating behavior and an anti-ferromagnetic (AFM) transition, TN at 198 K as well as ferromagnetic (FM) transition, TC at 260 K. Increasing of Er content suppressed the FM and AFM state suggestively due to the increase in size disorder arising from the size mismatch between A-site cations as shown from our calculation of variance σ(2). On the other hand, both absolute longitudinal and shear velocities and related elastic moduli measured at 210 K decreased with Er content in conjunction with the declining in the FM domain indicating a weakening in elastic properties. A longitudinal velocity anomaly characterized by a drop in velocity upon cooling before hardening with further cooling was observed for all samples. This abnormal elastic anomaly can be attributed due to the Jahn-Teller (JT) distortion of intermediate-spin Co(3+) ions. Analysis of the elastic anomaly using the mean-field theory suggested that it is related to the JT effect which transformed from dynamic to static type with decreasing temperature. The elastic anomaly shifted to lower temperature from 129 K (x=0) to 124 K (x=0.05) with Er substitution indicating a weakening of the static JT effect.
Medical ecology and epidemiology

Muul I, Liat LB, Ibrahim AB

Med J Malaya, 1972 Dec;27(2):125-8.
PMID: 4268038
Medical ecology and epidemiology

Muul I, Lim BL, Ibrahim AB

Med J Malaysia, 1972 Dec;27(2):125-128.
PMID: 35158490

No abstract available.
Fulltext Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects

Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, et al.

J Infect Public Health, 2020 Oct;13(10):1381-1396.
PMID: 32646771 DOI: 10.1016/j.jiph.2020.06.028

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.
Status of vancomycin-resistant Enterococcus in species of wild birds: A systematic review and meta-analysis

Wada Y, Ibrahim AB, Umar YA, Afolabi HA, Wada M, Alissa M, et al.

J Infect Public Health, 2024 Apr 10;17(6):1023-1036.
PMID: 38657438 DOI: 10.1016/j.jiph.2024.04.004

Wild birds could be a reservoir of medically relevant microorganisms, particularly multidrug-resistant Enterococcus spp. Resistant bacteria's epidemiology and transmission between animals and humans has grown, and their zoonotic potential cannot be ignored. This is the first study to evaluate the status of vancomycin resistant enterococci (VRE) in various wild bird species using meta-analysis and a systematic review. In this study, the pooled prevalence was obtained by analyzing data from published articles on the occurrence of VRE in wild bird species. It's unclear how the antibiotic resistance gene transfer cycle affects wild birds. Google Scholar and PubMed were used to conduct the research. The data and study methodology was assessed and extracted by two reviewers independently, with a third reviewing the results. Heterogeneity between study and publication bias were analyzed using the random effect model. Thirty-eight studies were included in the meta-analysis. 382 out of the 4144 isolates tested, were VRE. The pooled prevalence of VRE among wild birds was estimated at 11.0% (95% CI; 6.9 -17.2%; I2 = 93.204%; P
Fulltext Global Impact of COVID-19 on Stroke Care and IV Thrombolysis

Nogueira RG, Qureshi MM, Abdalkader M, Martins SO, Yamagami H, Qiu Z, et al.

Neurology, 2021 Jun 08;96(23):e2824-e2838.
PMID: 33766997 DOI: 10.1212/WNL.0000000000011885

OBJECTIVE: To measure the global impact of COVID-19 pandemic on volumes of IV thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with 2 control 4-month periods.
METHODS: We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases.
RESULTS: There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95% confidence interval [CI] -11.7 to -11.3, p < 0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95% CI -13.8 to -12.7, p < 0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95% CI -13.7 to -10.3, p = 0.001). Recovery of stroke hospitalization volume (9.5%, 95% CI 9.2-9.8, p < 0.0001) was noted over the 2 later (May, June) vs the 2 earlier (March, April) pandemic months. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was noted in 3.3% (1,722/52,026) of all stroke admissions.
CONCLUSIONS: The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID-19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months.