Paving initial forecasting COVID-19 spread capabilities by nonexperts: A case study.

Objective: The COVID-19 outbreak compelled countries to take swift actions across various domains amidst substantial uncertainties. In Israel, significant COVID-19-related efforts were assigned to the Israeli Home Front Command (HFC). HFC faced the challenge of anticipating adequate resources to efficiently and timely manage its numerous assignments despite the absence of a COVID-19 spread forecast. This paper describes the initiative of a group of motivated, though nonexpert, people to provide the needed COVID-19 rate of spread of the epidemic forecasts. Methods: To address this challenge, the Planning Chamber, reporting to the HFC Medical Commander, undertook the task of mapping HFC healthcare challenges and resource requirements. The nonexpert team continuously collected public COVID-19-related data published by the Israeli Ministry of Health (MoH) of verified cases, light cases, mild cases, serious condition cases, life-support cases, and deaths, and despite lacking expertise in statistics and healthcare and having no sophisticated statistical packages, generated forecasts using Microsoft® Excel. Results: The analysis methods and applications successfully demonstrated the desired outcome of the lockdown by showing a transition from exponential to polynomial growth in the spread of the virus. These forecasting activities enabled decision-makers to manage resources effectively, supporting the HFC's operations during the pandemic. Conclusions: Nonexpert forecasting may become a necessity and be beneficial, and similar analysis efforts can be easily replicated in future events. However, they are inherently short-lived and should persist only until knowledge centers can bridge the expertise gap. It is crucial to identify major events, such as lockdowns, during forecasting due to their potential impact on spread rates. Despite the expertise gap, the Planning Chamber's approach provided valuable resource management insights for HFC's COVID-19 response.

Prevalence and characteristics of osseous bridging between vertebral bodies in the cervical spine: A skeletal study.

Osseous bridging (OB) in three or more segments of motions (SOMs) of the mobile spine was initially defined as diffuse idiopathic skeletal hyperostosis (DISH), located particularly in the thoracic spine (T-spine). This pathological phenomenon is often characterized by calcification and ossification, which take place simultaneously or separately. The soft tissues, mainly ligaments and entheses, are calcified, with bone formation not originating from the anterior longitudinal ligament (ALL). DISH formation can involve osteophytes, which are created by the ossification process and can involve soft tissue such as the ALL. The ALL can also be calcified. Until recently, the prevalence of DISH in the general population was considered low (0%-5%) and rare in the cervical spine (C-spine). In a cross-sectional observational skeletal study, we investigated the prevalence and location of C-spine OB between vertebral bodies with fewer than three SOMs. We tested a large sample (n = 2779) of C-spines housed in the Cleveland Museum of Natural History (Ohio, USA). The human sources of the samples had died between the years 1912 and 1938 and represented both sexes and two different ethnic groups: Black Americans and White Americans. The process development can be seen on the ALLs as calcification, osteophytosis, and candle-shaped. Among all of the specimens, 139 (5%) were affected by OB, mostly in one SOM. Prevalence tended to be higher in women, White Americans, and the older age group. The levels most affected were C3-C4, followed by C2-C3 and subsequently, C5-C6. OB involving two consecutive SOMs was found only at C5-C7. We believe it is important to respond to the presence of a single SOM with a presumptive diagnosis of OB and to follow up, identify whether the diagnosis is correct, and take preventive action if possible. There is a need for updated diagnostic criteria and research approaches that reflect contemporary lifestyle factors and their impact on spine health.

Clinical correlation between disease progression and central vein sign in pediatric onset multiple sclerosis: A binational study.

BACKGROUND: The central vein sign (CVS) has been proposed as a novel MRI biomarker to improve diagnosis of pediatric-onset MS (POMS). However, the role of CVS in POMS progression has yet to be discovered. OBJECTIVES: To investigate the appearance of CVS and its correlation with POMS disease progression. METHODS: One hundred fifty-six POMS from two MS centers in Israel and Czech Republic MS centers were followed for five years. Patient assessment was performed by the Expanded Disability Status Scale (EDSS) and Annual Relapse Rate (ARR). Patients in whom at least 40 % of brain MRI lesions had CVS ("rule of 40") were determined as CVS-positive. RESULTS: The total group of POMS consisted of 96 CVS-negative (61.5 %), aged 14.6 ± 1.9 years, EDSS 2.0, 75 % Interquartile Range (IQR) 1.0-3.0, disease duration (DD) 6.28 ± 0.38 years, and 60 CVS-positive (38.5 %), aged 15.1 ± 0.3 years, EDSS 2.0, IQR 1.5-3.0, DD 5.62 ± 0.13 years, were analyzed. After a three and five-year follow-up, the CVS-positive patients had higher EDSS scores than those who were CVS-negative, 2.0, IQR 1.0-2.5, vs 1.0, IQR 1.0-2.0, (p = 0.009) and 2.0, IQR 1.0-3.25 vs 1.0, IQR 1.0-2.0, (p = 0.0003), respectively. Patients with CVS-positive POMS were characterized by a significantly higher ARR (0.78 ± 0.08 vs 0.57 ± 0.04, p = 0.002). These results were confirmed in subgroups of Disease Modifying Treatments (DMT) untreated and treated patients. CONCLUSION: CVS-positive POMS is characterized by higher disability progression than CVS-negative, indicating the importance of CVS in disease pathogenesis.

Normalization of Large-Scale Transcriptome Data Using Heuristic Methods.

In this study, we introduce an artificial intelligent method for addressing the batch effect of a transcriptome data. The method has several clear advantages in comparison with the alternative methods presently in use. Batch effect refers to the discrepancy in gene expression data series, measured under different conditions. While the data from the same batch (measurements performed under the same conditions) are compatible, combining various batches into 1 data set is problematic because of incompatible measurements. Therefore, it is necessary to perform correction of the combined data (normalization), before performing biological analysis. There are numerous methods attempting to correct data set for batch effect. These methods rely on various assumptions regarding the distribution of the measurements. Forcing the data elements into pre-supposed distribution can severely distort biological signals, thus leading to incorrect results and conclusions. As the discrepancy between the assumptions regarding the data distribution and the actual distribution is wider, the biases introduced by such "correction methods" are greater. We introduce a heuristic method to reduce batch effect. The method does not rely on any assumptions regarding the distribution and the behavior of data elements. Hence, it does not introduce any new biases in the process of correcting the batch effect. It strictly maintains the integrity of measurements within the original batches.