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Fulltext Monkeypox genome mutation analysis using a timeseries model based on long short-term memory

Pathan RK, Uddin MA, Paul AM, Uddin MI, Hamd ZY, Aljuaid H, et al.

PLoS One, 2023;18(8):e0290045.
PMID: 37611023 DOI: 10.1371/journal.pone.0290045

Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family's Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows "Root Mean Square Error" (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained.
Fulltext Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi Y, Patel N, Kaka N, Desai A, Kaiwan O, Sheth M, et al.

J Clin Med, 2022 Nov 29;11(23).
PMID: 36498651 DOI: 10.3390/jcm11237072

The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.
Fulltext How Different Preparation Techniques Affect MRI-Induced Anxiety of MRI Patients: A Preliminary Study

Hamd ZY, Alorainy AI, Alrujaee LA, Alshdayed MY, Wdaani AM, Alsubaie AS, et al.

Brain Sci, 2023 Feb 27;13(3).
PMID: 36979226 DOI: 10.3390/brainsci13030416

Magnetic resonance imaging (MRI) exams may cause patients to feel anxious before or during the scan, which affects the scanning outcome and leads to motion artifacts. Adequate preparation can effectively alleviate patients' anxiety before the scan. We aimed to assess the effect of different preparation methods on MRI-induced anxiety: We conducted a prospective randomized study on MRI patients between March and May 2022. We divided 30 patients into two groups: the control group, which received routine preparation (RP), and the experimental group, which received video preparation (VP). We used the State-Trait Anxiety Inventory (STAI) to measure anxiety levels before and after the interventions. We assessed patients' self-satisfaction after the scan: After preparation, VP (STAI mean = 10.7500) and RP (STAI mean = 12.7857), we observed a significant association between the pre- and post-STAI results in VP (p = 0.025). The effects of both methods in decreasing anxiety were more significant for first-timers (p = 0.009 in RP/0.014 in VP). We noted high satisfaction levels for both forms of preparation. The VP technique was superior in reducing patient anxiety, especially in first-time MRI patients. Hence, VP techniques can be used in different clinical settings to reduce anxiety and facilitate patients' understanding of the instructions given.
Fulltext The impact of health volunteering of radiology students on improving their self-skills and practical capabilities in the Kingdom of Saudi Arabia

Alzain AF, Elhussein N, Hamd ZY, Fadulelmulla IA, Omer AM, Alotaibi A, et al.

Front Med (Lausanne), 2023;10:1243014.
PMID: 38486825 DOI: 10.3389/fmed.2023.1243014

BACKGROUND: Volunteering is a beneficial activity with a wide range of positive outcomes, from the individual to the communal level. In many ways, volunteering has a positive impact on the development of a volunteer's personality and experience. This study aimed to evaluate the impact of health volunteering on improving the self-skills and practical capacities of students in the western region of the Kingdom of Saudi Arabia.
MATERIALS AND METHODS: The study was a descriptive cross-sectional electronic web-based survey that was submitted on a web-based questionnaire; 183 students answered the survey, and then, the data were analyzed using SPSS.
RESULTS: This study shows that 95.6% of participants agree and strongly agree that the health volunteering experience was useful, 2.7% of the participants neither agree nor disagree, and 1.6% disagree and strongly disagree. Regarding the distribution of the participants on skills learned from volunteering experience, the largest proportion of student (36.1%) volunteers in the health sector acquired communication skills and the smallest proportion of student (14.8%) volunteers in the acquired time management skills. Regarding the disadvantages, 81.4% of the participants do not think there were any disadvantages to their previous health volunteering experience, while only 18.6% of them think there were any disadvantages to their previous health volunteering experience. Additionally, the study found that the type of the sector affects the skills acquired from health volunteering.
CONCLUSION: Research revealed that the majority considered volunteering a great experience. Volunteering increased the self-skills and practical capacities of radiology students, which proved the hypothesis.
Fulltext The diagnostic performance of International Ovarian Tumor Analysis: Simple Rules for diagnosing ovarian tumors-a systematic review and meta-analysis

Gareeballah A, Gameraddin M, Alshoabi SA, Alsaedi A, Elzaki M, Alsharif W, et al.

Front Oncol, 2024;14:1474930.
PMID: 39902128 DOI: 10.3389/fonc.2024.1474930

INTRODUCTION: Adnexal masses are a common health issue in gynecology; the challenge lies in the differential diagnosis of these masses. The International Ovarian Tumor Analysis Simple Rules (IOTA-SR) offers the first scoring system to aid in diagnosis. It is based on a set of five ultrasound imaging features indicative of a malignant ovarian tumor and five features indicative of a benign tumor. This review aims to assess the diagnostic performance of IOTA-SR for classifying ovarian tumors as benign or malignant.
METHODS: A systematic review was conducted on MEDLINE, Embase, Google Scholar, Scopus, and Web of Science. The terminologies "IOTA-SR", "adnexal, mass", and "ovarian tumors scoring" were employed. Twenty-seven research articles conducted from 2008 to 2022 were included in the meta-analysis; the publication outcome indicates that performance quality tests were extracted directly or indirectly, including true positive (TP), false positive (FP), true negative (TN), and false negative (FN). The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the study quality and estimate the risk of bias. After estimating the pooled effect of the sensitivity, specificity, and diagnostic odds ratio (DOR), the summary receiver operating characteristic (SROC) curve was estimated using the bivariate random effects model. Utilizing Cochran's Q statistics and Higgins's inconsistency test through the I2 index for pooled analysis, the heterogeneity of studies was quantitatively evaluated. The funnel plot and Egger's test were utilized to visually and quantitatively evaluate potential publication bias.
RESULTS: Among 27 studies, including 7,841 adnexal masses, the results of this meta-analysis showed excellent diagnostic performance with a pooled sensitivity of 92% [95% confidence interval (CI), 0.89-0.94] and a pooled specificity of 92% (95% CI, 0.89-0.94). The IOTA-SR was applicable in 85.7% of adnexal masses.
CONCLUSION: The IOTA-SR is highly effective in the presurgical differentiation of malignant versus benign adnexal masses when applied by an expert ultrasonography operator.