Relationship between admission selection tools and student attrition in the early years of medical school.

Objectives: Placement in medical schools is highly sought after worldwide with fierce competition among applicants. However, some of the best students withdraw after being accepted to medical school. The aim of this study was to investigate early student attrition within the first 2 years of medical school and determine its relationship to admission selection tools. Methods: Quantitative research was conducted at the College of Medicine and Health Sciences from 2016 until 2020, during which time routine admission data and students' examination results for the first 2 years were collected and analyzed. Results: The attrition rate during the study period was 31.7%. High school and college written examination scores were significantly related to completing the premedical program (p = 0.001 and p = 0.002, respectively). Female students scored significantly higher in multiple mini interviews (MMIs) compared with male counterparts (p < 0.001). However, the difference in MMI score was not related to student attrition (p = 0.148). Conclusion: The cause of early attrition is complex and cannot be attributed to a single factor.Undergraduate high school score and written admission examination results were statistically significant factors in relation to student attrition rate and low academic performance. The results of this study showed that the female students scored significantly higher in the multiple MMI tests compared to male students. However, MMI score alone was not significantly related to student attrition.

Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates.

BACKGROUND: The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models. METHODS: We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index. RESULTS: The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia. CONCLUSION: Analyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings.

MRI Markers of Degenerative Disc Disease in Young Patients With Multiple Sclerosis.

Background and Purpose: Evidence has emerged for an association between degenerative disc disease (DDD) and multiple sclerosis (MS). The purpose of the current study is to determine the presence and extent of cervical DDD in young patients (age <35) with MS, an age cohort that is less well studied for these changes. Methods: Retrospective chart review of consecutive patients aged <35 referred from the local MS clinic who were MRI scanned between May 2005 and November 2014. 80 patients (51 female and 29 male) with MS of any type ranging between 16 and 32 years of age (average 26) were included. Images were reviewed by 3 raters and assessed for presence and extent of DDD, as well as cord signal abnormalities. Interrater agreement was assessed using Kendall's W and Fleiss' Kappa statistics. Results: Substantial to very good interrater agreement was observed using our novel DDD grading scale. At least some degree of DDD was found in over 91% of patients. The majority scored mild (grade 1, 30-49%) to moderate (grade 2, 39-51%) degenerative changes. Cord signal abnormality was seen in 56-63%. Cord signal abnormality, when present, occurred exclusively at degenerative disc levels in only 10-15%, significantly lower than other distributions (P < .001 for all pairwise comparisons). Conclusions: MS patients demonstrate unexpected cervical DDD even at a young age. Future study is warranted to investigate the underlying etiology, such as altered biomechanics. Furthermore, cord lesions were found to occur independently of DDD.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Case report: Birk-Landau-Perez syndrome linked to the SLC30A9 gene-identification of additional cases and expansion of the phenotypic spectrum.

Birk-Landau-Perez syndrome (BILAPES) is an autosomal recessive cerebro-renal syndrome associated with genetic defects in the SLC30A9 gene, initially reported in 2017 in six individuals belonging to a large Bedouin kindred. The SLC30A9 gene encodes a putative mitochondrial zinc transporter with ubiquitous expression, the highest found in the brain, kidney, and skeletal muscle. Since the first report, only one additional affected patient has been described, but there were some inconsistencies, such as hearing loss, failure to thrive, and neuroimaging findings between the clinical presentation of the disease in the Bedouin family and the second patient. Here, we present two more patients from a consanguineous Middle Eastern family with features of chronic kidney disease, neurodevelopmental regression, ataxia, hearing loss, and eye abnormalities, which were largely consistent with BILAPES. Whole-exome sequencing detected a homozygous in-frame deletion c.1049_1051delCAG (p.Ala350del) in the SLC30A9 gene, which was the same variant detected in the patients from the primary literature report and the variant segregated with disease in the family. However, in the patients described here, brain MRI showed cerebellar atrophy, which was not a cardinal feature of the syndrome from the primary report. Our findings provide further evidence for SLC30A9-associated BILAPES and contribute to defining the clinical spectrum.

Multimodal diagnostics in multiple sclerosis: predicting disability and conversion from relapsing-remitting to secondary progressive disease course - protocol for systematic review and meta-analysis.

BACKGROUND: The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS. METHODS AND ANALYSIS: We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS. ETHICS AND DISSEMINATION: The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42022354179.

Unraveling Lifelong Brain Morphometric Dynamics: A Protocol for Systematic Review and Meta-Analysis in Healthy Neurodevelopment and Ageing.

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a 'Summary of findings' table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

Novel compound heterozygous variants (c.971delA/c.542C > T) in SLC1A4 causes spastic tetraplegia, thin corpus callosum, and progressive microcephaly: a case report and mutational analysis.

Spastic tetraplegia, thin corpus callosum, and progressive microcephaly (SPATCCM) are linked to SLC1A4 genetic variants since the first reported case in 2015. SLC1A4 encodes for the neutral amino acid transporter ASCT1 which is involved in the transportation of serine between astrocytes and neurons. Although most of the reported cases are of Ashkenazi Jewish ancestry, SPATCCM has also been reported in Irish, Italian, Czech, Palestinian, and Pakistani ethnicities. Herein, we report two Pakistani male siblings from a non-consanguineous marriage presented with global developmental delay associated with spastic quadriplegia, microcephaly, and infantile spasm. Since infancy, both siblings suffered from microcephaly with brain MRI demonstrating generalized atrophy of the frontal, temporal, and parietal lobes with a prominence of the subarachnoid spaces, widening of the Sylvian fissures, and enlargement of the ventricular system not compatible with the chronological age of both patients associated with thinning of the corpus callosum. Whole-exome sequencing of both affected brothers revealed novel compound heterozygous variants in the SLC1A4 gene (NM_003038) segregating from their parents. The maternal c.971delA (p.N324Tfs*29) deletion variant disturbs the transcript reading frame leading to the generation of a premature stop codon and its subsequent degradation by nonsense-mediated mRNA decay as detected through expression analysis. The paternal c.542C > T (p.S181F) missense variant was predicted deleterious via multiple in silico prediction tools as the amino acid substitution is speculated to affect the overall ASCT1 structural confirmation due to the loss of an H-bond at the core of the protein at this position which might affect its function as concluded from the simulation analysis. The presented cases expand the genetic and clinical spectrum of ASCT1 deficiency and support the importance of including SLC1A4 gene screening in infants with unexplained global neurodevelopmental delay regardless of ethnicity.

Management of Blunt Sternal Fractures in a Community-Based Hospital.

Background: Sternal fractures are not commonly observed in patients with blunt trauma. The routine use of computed tomography (CT) in the evaluation of chest trauma helps identify these fractures. We studied the incidence, injury mechanism, management, and outcome of sternal fractures in patients with blunt trauma treated at our community-based hospital. Methods: We retrospectively reviewed the chest CT scans of all patients with blunt trauma who were presented to our community-based hospital from October 2010 to March 2019. The study variables included age at the time of injury, sex, mechanism of injury, type, and site of fracture, associated injuries, Glasgow Coma Scale, Injury Severity Score, need for intensive care unit admission, hospital stay, and long-term outcome. Results: In total, 5632 patients with blunt trauma presented to our hospital during the study period, and chest CT scan was performed for 2578 patients. Sternal fractures were diagnosed in 63 patients. The primary mechanism of injury was a motor vehicle collision. The most common site of fracture was the body of the sternum (47 patients; 74.6%). Twenty (31.7%) patients had an isolated sternal fracture with no other injuries. Seven (11.1%) patients were discharged directly from the emergency department. Two patients died (overall mortality rate, 3.2%) and two experienced long-term disability. Conclusions: The incidence of sternal fractures in our patient population was similar to that reported by tertiary hospitals. Patients with a sternal fracture and normal cardiac enzyme levels and electrocardiogram may be safely discharged from the emergency department, provided there are no other major injuries.

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision.

Background: Hypoxia is a potentially life-threatening condition that can be seen in pneumonia patients. Objective: We aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT. Materials and Methods: We enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO2, H C O 3 - , K +, Na +, anion gap, C-reactive protein) served as ground truth. Results: Radiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant. Conclusion: The constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.

Background: The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques. Objective: To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia. Materials and methods: We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia. Results: In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases. Conclusion: The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

Proportional Changes in Cognitive Subdomains During Normal Brain Aging.

Background: Neuroscience lacks a reliable method of screening the early stages of dementia. Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains. Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes. Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age. Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline.

Background: Neuronal reactions and cognitive processes slow down during aging. The onset, rate, and extent of changes vary considerably from individual to individual. Assessing the changes throughout the lifespan is a challenging task. No existing test covers all domains, and batteries of tests are administered. The best strategy is to study each functional domain separately by applying different behavioral tasks whereby the tests reflect the conceptual structure of cognition. Such an approach has limitations that are described in the article. Objective: Our aim was to improve the diagnosis of early cognitive decline. We estimated the onset of cognitive decline in a healthy population, using behavioral tests, and predicted the age group of an individual. The comparison between the predicted ("cognitive") and chronological age will contribute to the early diagnosis of accelerated aging. Materials and Methods: We used publicly available datasets (POBA, SSCT) and Pearson correlation coefficients to assess the relationship between age and tests results, Kruskal-Wallis test to compare distribution, clustering methods to find an onset of cognitive decline, feature selection to enhance performance of the clustering algorithms, and classification methods to predict an age group from cognitive tests results. Results: The major results of the psychophysiological tests followed a U-shape function across the lifespan, which reflected the known inverted function of white matter volume changes. Optimal values were observed in those aged over 35 years, with a period of stability and accelerated decline after 55-60 years of age. The shape of the age-related variance of the performance of major cognitive tests was linear, which followed the trend of lifespan gray matter volume changes starting from adolescence. There was no significant sex difference in lifelong dynamics of major tests estimates. The performance of the classification model for identifying subject age groups was high. Conclusions: ML models can be designed and utilized as computer-aided detectors of neurocognitive decline. Our study demonstrated great promise for the utility of classification models to predict age-related changes. These findings encourage further explorations combining several tests from the cognitive and psychophysiological test battery to derive the most reliable set of tests toward the development of a highly-accurate ML model.

Partially avulsed anus in blunt traumatic perineal laceration: Case report.

Blunt civilian perineal laceration with anorectal avulsion is rare and usually associated with severe pelvic trauma. The principles of management of these injuries consist of repair of the laceration (primarily or secondary), diversion of fecal stream, and presacral drainage of the wound. Unnecessary diversion of fecal stream may add complications and increases patient's morbidity. We report a case of severe blunt traumatic perineal laceration associated with partially avulsed anus which was managed without colostomy. The wound healed completely with preserved anal sphincter function. To our knowledge, no similar cases of anal avulsion were treated without diversion of the fecal stream in the English literature.

Brain Morphometry and Cognitive Performance in Normal Brain Aging: Age- and Sex-Related Structural and Functional Changes.

BACKGROUND: The human brain structure undergoes considerable changes throughout life. Cognitive function can be affected either negatively or positively. It is challenging to segregate normal brain aging from the accelerated one. OBJECTIVE: To work out a descriptive model of brain structural and functional changes in normal aging. MATERIALS AND METHODS: By using voxel-based morphometry and lesion segmentation along with linear statistics and machine learning (ML), we analyzed the structural changes in the major brain compartments and modeled the dynamics of neurofunctional performance throughout life. We studied sex differences in lifelong dynamics of brain volumetric data with Mann-Whitney U-test. We tested the hypothesis that performance in some cognitive domains might decline as a linear function of age while other domains might have a non-linear dependence on it. We compared the volumetric changes in the major brain compartments with the dynamics of psychophysiological performance in 4 age groups. Then, we tested linear models of structural and functional decline for significant differences between the slopes in age groups with the T-test. RESULTS: White matter hyperintensities (WMH) are not the major structural determinant of the brain normal aging. They should be viewed as signs of a disease. There is a sex difference in the speed and/or in the onset of the gray matter atrophy. It either starts earlier or goes faster in males. Marked sex difference in the proportion of total cerebrospinal fluid (CSF) and intraventricular CSF (iCSF) justifies that elderly men are more prone to age-related brain atrophy than women of the same age. CONCLUSION: The article gives an overview and description of the conceptual structural changes in the brain compartments. The obtained data justify distinct patterns of age-related changes in the cognitive functions. Cross-life slowing of decision-making may follow the linear tendency of enlargement of the interhemispheric fissure because the center of task switching and inhibitory control is allocated within the medial wall of the frontal cortex, and its atrophy accounts for the expansion of the fissure. Free online tool at https://med-predict.com illustrates the tests and study results.

Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings.

Background: Data on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing studies have mainly focused on the pediatric and elderly population. Objective: Assess whether age and sex interact with other risk factors to influence the severity of SARS-CoV-2 infection. Material and Methods: The study sample included all consecutive patients who satisfied the inclusion criteria and who were treated from 24 February to 1 July 2020 in Dubai Mediclinic Parkview (560 cases) and Al Ain Hospital (605 cases), United Arab Emirates. We compared disease severity estimated from the radiological findings among patients of different age groups and sex. To analyze factors associated with an increased risk of severe disease, we conducted uni- and multivariate regression analyses. Specifically, age, sex, laboratory findings, and personal risk factors were used to predict moderate and severe COVID-19 with conventional machine learning methods. Results: Need for O2 supplementation was positively correlated with age. Intensive care was required more often for men of all ages (p < 0.01). Males were more likely to have at least moderate disease severity (p = 0.0083). These findings were aligned with the results of biochemical findings and suggest a direct correlation between older age and male sex with a severe course of the disease. In young males (18-39 years), the percentage of the lung parenchyma covered with consolidation and the density characteristics of lesions were higher than those of other age groups; however, there was no marked sex difference in middle-aged (40-64 years) and older adults (≥65 years). From the univariate analysis, the risk of the non-mild COVID-19 was significantly higher (p < 0.05) in midlife adults and older adults compared to young adults. The multivariate analysis provided similar findings. Conclusion: Age and sex were important predictors of disease severity in the set of data typically collected on admission. Sexual dissimilarities reduced with age. Age disparities were more pronounced if studied with the clinical markers of disease severity than with the radiological markers. The impact of sex on the clinical markers was more evident than that of age in our study.

Applying the Inverse Efficiency Score to Visual-Motor Task for Studying Speed-Accuracy Performance While Aging.

Background: The current study examines the relationship between speed and accuracy of performance in a reaction time setting and explores the informative value of the inverse efficiency score (IES) regarding the possibility to reflect age-related cognitive changes. Objectives: To study the characteristics of speed and accuracy while performing psychophysiological tests throughout the lifespan; to examine the speed-accuracy ratio in age groups and to apply IES to discriminative visual-motor reaction task; and to figure out the predictive potential of psychophysiological tests to identify IES values. Methods: We utilize nonparametric statistical tests, regression analysis, and supervised machine learning methods. Results and Conclusion: The examinees under 20 and over 60 years of age share one tendency regarding the speed-accuracy ratio without speed-accuracy trade-off. Both at the time of active developmental changes in adolescence and during ongoing atrophic changes in elderly there is a tendency toward a rise of the number of mistakes while slowing the reaction. In the age range from 20 to 60 the relationship between the speed and accuracy is opposite and speed-accuracy trade-off is present. In our battery, complex visual-motor reaction is the only test with the significant negative association between reaction time and error rate in the subcohort of young and midlife adults taken together. On average, women perform more slowly and accurately than men in the speed-accuracy task, however most of the gender-related differences are insignificant. Using results of other psychophysiological tests, we predicted IES values for the visual-motor reaction with high accuracy (R 2 = 0.77 ± 0.08; mean absolute error / IES range = 3.37%). The regression model shows the best performance in the cognitively preserved population groups of young and middle-aged adults (20-60 years). Because of the individual rate of neurodevelopment in youth and cognitive decline in the elderly, the regression model for these subcohorts has a low predictive performance. IES accounts for different cognitive subdomains and may reflect their disproportional changes throughout the lifespan. This encourages us to proceed to explore the combination of executive functioning and psychophysiological test results utilizing machine learning models. The latter can be designed as a reliable computer-aided detector of cognitive changes at early stages.

Challenges for cancer patients returning home during SARS-COV-19 pandemic after medical tourism - a consensus report by the emirates oncology task force.

BACKGROUND: The COVID-19 pandemic has caused a global health crisis. Numerous cancer patients from non-Western countries, including the United Arab Emirates (UAE), seek cancer care outside their home countries and many are sponsored by their governments for treatment. Many patients interrupted their cancer treatment abruptly and so returned to their home countries with unique challenges. In this review we will discuss practical challenges and recommendations for all cancer patients returning to their home countries from treatment abroad. METHOD: Experts from medical, surgical and other cancer subspecialties in the UAE were invited to form a taskforce to address challenges and propose recommendations for patients returning home from abroad after medical tourism during the SARS-COV-19 Pandemic. RESULTS: The taskforce which consisted of experts from medical oncology, hematology, surgical oncology, radiation oncology, pathology, radiology and palliative care summarized the current challenges and suggested a practical approaches to address these specific challenges to improve the returning cancer patients care. Lack of medical documentation, pathology specimens and radiology images are one of the major limitations on the continuation of the cancer care for returning patients. Difference in approaches and treatment recommendations between the existing treating oncologists abroad and receiving oncologists in the UAE regarding the optimal management which can be addressed by early and empathic communications with patients and by engaging the previous treating oncologists in treatment planning based on the available resources and expertise in the UAE. Interruption of curative radiotherapy (RT) schedules which can potentially increase risk of treatment failure has been a major challenge, RT dose-compensation calculation should be considered in these circumstances. CONCLUSION: The importance of a thorough clinical handover cannot be overstated and regulatory bodies are needed to prevent what can be considered unethical procedure towards returning cancer patients with lack of an effective handover. Clear communication is paramount to gain the trust of returning patients and their families. This pandemic may also serve as an opportunity to encourage patients to receive treatment locally in their home country. Future studies will be needed to address the steps to retain cancer patients in the UAE rather than seeking cancer treatment abroad.

MDCT evaluation of intramyocardial-sinusoids-coronary artery communications in a neonate with pulmonary atresia and intact ventricular septum.

A patient of tetrology of Fallot with complete atresia of the pulmonary outflow tract with ventriculocoronary connections is presented. MDCT imaging revealed left coronary sinus, with a large fistula draining into the free wall of hypoplastic right ventricular cavity with tortuous channel arising from right ventricular outflow, and communicating with proximal limb of the fistula forming a complete loop suggesting a right ventricle-to - left coronary sinus sinusoid.