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

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

BackgroundHypoxia is a potentially life-threatening condition that can be seen in pneumonia patients.ObjectiveWe 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 MethodsWe 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, HCO3-, K+, Na+, anion gap, C-reactive protein) served as ground truth.ResultsRadiologic 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.ConclusionThe 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

Systemic venous anomalies in the Middle East

INTRODUCTIONSystemic venous anomalies are quite rare and can be associated with congenital heart disease requiring surgery.MATERIALS AND METHODS All consecutive patients (pts) undergoing surgery for congenital heart defects were retrospectively analyzed for presence of systemic venous anomalies: a) persistent left superior vena cava (PLSVC)b) inferior vena cava (IVC) interruptionc) retro-aortic innominate vein. RESULTSFrom 9/2010 to 5/2012 155 pts, median age 7 months, mean age 1.3 years (3days-50years), median weight 4 kg, mean weight 7.2 kg (0.6-110kg) underwent congenital heart surgery. Twenty-nine systemic venous anomalies were identified in 28/155 patients (=18.1%). PLSVC was present in 21 pts (=13.5%), median age 4 months, mean age 2.7 years (3days-22years), median weight 6 kg, mean weight 10.1 kg (2.4-43.0 kg). IVC interruption was identified in 5 pts (=3.2%), median age 2 months, mean age 5.4 years (30days-26years), median weight 3.7kg, median weight 17 kg (2.3-68.0kg). Retro-aortic innominate vein was diagnosed in 3 pts (=1.9%), median age 5 years, mean age 3.7 years (10months-5years), median weight 12 kg, mean weight 10.1 kg (4.5-14kg). Complete pre-operative diagnosis was obtained in 14/28 (=50%) pts with echocardiography and in other 8/28 (=28.6%) only after computed tomography (CT) scan, for a total of 22/28 (=78.6%) correct pre-operative diagnosis. In 6/28 (=21.4%) patients the diagnosis was intra-operative.Total incidence of systemic venous anomalies was 18.1% (vs 4% in the literature, P=0.0009), with presence of PLSVC = 13.5% (vs 0.3-4.0%, respectively P=0.0004 and P=0.0012), IVC interruption = 3.2% (vs 0.1-1.3%,N.S.), and retro-aortic innominate vein = 1.9% (vs 0.2-1%,N.S.).CONCLUSIONSOur study showed an incidence of systemic venous anomalies in Middle Eastern pts with congenital heart defects higher than previously reported. In 78.6% of pts the diagnosis was correctly made before surgery (echocardiography or CT scan), with 21.4

MDCT evaluation of intramyocardialsinusoids-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

Significance of Cardiac Magnetic Resonance Feature Tracking of the Right Ventricle in Predicting Subclinical Dysfunction in Patients with Thalassemia Major

In patients with thalassemia major (TM), cardiac magnetic resonance feature-tracking (CMR-FT) has been shown to be an effective method for diagnosing subclinical left ventricular (LV) dysfunction. This study aimed to determine whether CMR-FT could detect abnormal RV dysfunction in patients with a normal right ventricular ejection fraction (RVEF). We performed a retrospective analysis of TM patients admitted to Dubai’s Rashid Hospital between July 2019 and March 2021. The inclusion criteria were TM patients with SSFP cine with T2* (T2*-weighted imaging), while exclusion criteria included any other cardiovascular disease. When there was no myocardial iron overload (MIO) (T2* ≥ 20 ms) and when there was significant MIO (T2* p = 0.014) and between left ventricular ejection fraction (LVEF) and T2* values (r = 0.256, p = 0.022). Using a multiple logistic regression model with predictors such as right ventricular longitudinal strain (RVGLS), LV ejection fraction (LV EF), and hemoglobin, abnormal myocardial iron overload can be predicted. This model demonstrates an AUC of 78.3%, a sensitivity of 72%, and a specificity of 76%. In the group with preserved RVEF > 53%, the left ventricular radial strain (LVGRS) (p = 0.001), right ventricular radial strain (RVGRS) (p = 0.000), and right ventricular basal circumferential strain (RVGCS-basal) (p = 0.000) CMR-FT strain values are significantly lower than those of the control group (p > 0.05). There was no significant correlation between the LVGLS and T2*. RVGLS was ranked among the most accurate predictors of abnormal myocardial iron overload. The LVGRS, RVGRS, and RVGCS-basal CMR-FT strain values were the best predictors of subclinical RV dysfunction in the group with preserved RVEF. The most accurate way to diagnose MIO is still T2*, but FT-strain can help us figure out how MIO affects the myocardium from a pathophysiological point of view

Detection of a specific pattern of hyaluronan oligosaccharides and their binding proteins in human ovarian tumour

Tumour cells generate hyaluronan (HA) oligomers (O‐HA) by an autocrine mechanism to regulate their own behaviour through receptor interaction, necessitating analysis of HA sizes and its receptor expression in tumour progression. In this study for the first time, we identified specific size of HA in malignant ovarian tumour compared to benign tumour tissue. Therefore, we prepared the identified HA probes and conducted multiplex and monoplex ligand blot analysis and Immunohistochemistry to identify their receptor expression and distribution. Although, HA recognized CD44 as principle receptors despite of size, multiplex analysis showed multiple receptor expression with distribution at the tumour cell surface. Furthermore, the HA 6‐mer (major O‐HA of ovarian tumour) pull down of tumour tissue proteins showed 120 kDa protein along with CD44 with over expression in the malignant tumour. Upon depletion of CD44 protein HA 6‐mer showed a major 120 kDa protein with distribution at nuclear membrane, suggesting that this protein may play an important role in ovarian tumour progression. In summary, ovarian tumour cells of different grade showed heterogeneity in generation of HA oligomers and their interaction with specific receptors. Therefore, simultaneous analysis of O‐HA and their receptors expression could serve as a prognostic indicator during tumorigenesis. Copyright © 2016 John Wiley & Sons, Ltd

Post-RNA (mRNA) Vaccination Myocarditis: CMR Features

RNA (mRNA) vaccines used to prevent COVID-19 infection may cause myocarditis. We describe a case of acute myocarditis in a 27-year-old male after receiving the second dose of a Pfizer immunization. Three days after receiving the second dose of vaccine, he had acute chest pain. Electrocardiographic examination revealed non-specific ST-T changes in the inferior leads. Troponin levels in his laboratory tests were 733 ng/L. No abnormalities were detected on his echocardiography or coronary angiography. The basal inferoseptal segment was hypokinetic. The LV EF was 50%, whereas the RV EF was 46%. Epicardial and mesocardial LGE were shown in the left ventricle’s basal and mid anterolateral, posterolateral, and inferoseptal segments. The native T1 was 1265 ± 54 ms, and the native T2 was 57 ± 10 ms. Myocardial strain indicated that the baseline values for LV GLS (−14.55), RV GLS (−15.8), and RVCS (−6.88) were considerably lower. The diagnosis of acute myocarditis was determined based on the clinical presentation and cardiac magnetic resonance (CMR) findings

Risk of sexual dysfunctions in breastfeeding females: protocol for a systematic review and meta-analysis

Background Epidemiological studies do not provide accurate statistics on the percentage of breastfeeding women experiencing sexual dysfunctions and restraining from sexual activity. The data vary between 40% and 83% in the first group and 20–50% in the second one. Despite excessive studies on contributors to intimacy changes, breast feeding received little attention from researchers. The relationship between lactation and postpartum sexual dysfunctions remains unclear. This systematic review and meta-analysis will synthesise available data and establish the link between breast feeding and sexuality problems.Methods and analysis A comprehensive literature search will be performed in biomedical databases PubMed/Medline, Scopus, Web of Science, EMBASE and CINAHL. We will extract peer-reviewed original studies written in English, Arabic or Polish from 2000 to June 2023. We will also search for reports from international health organisations and local health authorities. The preliminary search was performed on 04 April 2023. The studies must provide data on dysfunction prevalence/incidence and the strength of the relationship between breast feeding and sexuality in generally healthy women. The Covidence software will be used to perform literature screening, data extraction and quality assessment of individual studies. We will use a random-effects model meta-analysis to calculate pooled weighted frequency measures and effect size. Between-study heterogeneity will be assessed with the I2 test.Ethics and dissemination This meta-analysis does not require ethical approval because it synthesises data from previously published original studies. The final work will be published in a peer-reviewed journal and presented at scientific conferences.PROSPERO registration number CRD42023411053