Pregnancy outcomes in Benghazi, Libya, before and during the armed conflict in 2011

Stressful life events experienced by pregnant women may lead to adverse obstetric outcomes. This study in Benghazi compared the rates of preterm, low-birth-weight and caesarean-section births at Al-Jamhouria hospital in the months before and during the armed conflict in Libya in 2011. Data were collected on all women admitted to the delivery ward during February to May 2011 (the months of the most active fighting in the city) (n = 7096), and October to December 2010 (the months immediately before the war) (n = 5935). Compared with the preceding months there was a significant rise during the conflict in the rate of deliveries involving preterm (3.6% versus 2.5%) and low-birth-weight (10.1% versus 8.5%) infants and caesarean sections (26.9% versus 25.3%). Psychosocial stress may have been a factor (among others) in an increase in negative pregnancy outcomes, and obstetric hospitals should be aware of these issues in times of war

A Study of Risk Factors for Breast Cancer in a Primary Oncology Clinic in Benghazi-Libya

Introduction: Libya is a North African country classified under the Eastern Mediterranean Regional Office. In response to the general paucity of literature regarding cancer in Libya, this study aims to analyze various risk factors for breast cancer among patients in Benghazi, Libya. Material and Methods: Using records from a major primary oncology clinic, data was gathered from breast cancer patients. A total of 301 patients were diagnosed with breast cancer in the study period. For the purpose of risk factor determination, this hospital-based case control study consisted of 212 recently diagnosed cases of breast cancer attending the oncology clinic at Al-Jamhouria hospital in Benghazi. Age matched controls (n=219) were randomly enrolled from other medical departments of Al-Jamhouria hospital and the general population visiting the hospital. Chi square was used to assess significance of the risk factors and the corresponding odds ratio (O.R.) and 95% CI were calculated to assess the magnitude of associations. Results: A total of 1478 cases presented to the gynecological oncology clinic at Al-Jamhouria hospital during the period of 2007-2008. Of these cases, around 20% (n=301) were breast cancer patients. The average age of presentation was 49 years + S.D 13 years, with most of the cases (61%, n=184) being premenopausal. Over 90% (n=273) of breast cancer patients are diagnosed at stage II or later. More than 16% of cases seek medical attention when the malignancy has already reached stage IV. Diabetes, hypertension and family history of other malignancies were found to significantly increase the risk of developing breast cancer. Discussions: A range of socioeconomic risk factors were also analyzed (i.e. parity, breastfeeding etc...) and some were found to be protective. Libyan breast cancer cases are slightly older compared to the rest of the Arab world, but are younger than their counterparts in the West. The major issue in the Libyan scenario is delayed presentation which significantly worsens the prognosis. Hence, all the recommendations focus on increased awareness, the implementation of a national cancer control plan and a national screening program and training healthcare professions in palliative care

Thermo-Mechanical Modeling of High-Strength Concrete Column Subjected to Moderate Case Heating Scenario in a Fire

This paper presents a numerically developed computer model to simulatethe thermal behavior and evaluate the mechanical performance of a fixedend loaded loaded High Strength Concrete Column (HSCC), subjectedto Moderate Case Heating Scenario (MCHS), in a hydrocarbon fire. Thetemperature distribution within the mid-height cross-sectional area of thecolumn was obtained to determine the thermal and mechanical responsesas a function of temperature. The governing two-dimensional transient heattransfer partial differential equation (PDE), was converted into a set of ordinary algebraic equations, subsequently, integrated numerically by usingthe explicit finite difference method, (FDM). A computer program, VisualBasic for Applications (VBA), was then developed to solve the set of ordinary algebraic equations by implementing the boundary as well as initialconditions. The predictions of the model were validated against experimental data from previous studies. The general behavior of the model as wellas the effect of the key model parameters were investigated at length in thereview. Finally, the reduction in the column’s compression strength and themodulus of elasticity was estimated using correlations from existing literature. And the HSCC failure load under fire conditions was predicted usingthe Rankine formula. The results showed that the model predictions of thetemperature distribution within the concrete column are in good agreementwith the experimental data. Furthermore, the increase in temperature ofthe reinforced concrete column, (RCC), due to fire resulted in a significantreduction in the column compression strength and considerably acceleratesthe column fire failure load

Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association with Lung Function in Asbestos-exposed Patients

Purpose: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests.Materials and Methods: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient (r) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO).Results: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC (P = 0.001) and FVC (P = 0.04) values for the higher PPV patients, but not for DLCO (P = 0.19).Conclusion: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.</p

The burden of road traffic crashes, injuries and deaths in Africa:A systematic review and meta-analysis

Objective To estimate the burden of road traffic injuries and deaths for all road users and among different road user groups in Africa. Methods We searched MEDLINE, EMBASE, Global Health, Google Scholar, websites of African road safety agencies and organizations for registry- and population-based studies and reports on road traffic injury and death estimates in Africa, published between 1980 and 2015. Available data for all road users and by road user group were extracted and analysed. We conducted a random-effects meta-analysis and estimated pooled rates of road traffic injuries and deaths. Findings We identified 39 studies from 15 African countries. The estimated pooled rate for road traffic injury was 65.2 per 100000 population (95% confidence interval, CI: 60.8–69.5) and the death rate was 16.6 per 100 000 population (95% CI: 15.2–18.0). Road traffic injury rates increased from 40.7 per 100 000 population in the 1990s to 92.9 per 100 000 population between 2010 and 2015, while death rates decreased from 19.9 per 100 000 population in the 1990s to 9.3 per 100 000 population between 2010 and 2015. The highest road traffic death rate was among motorized four-wheeler occupants at 5.9 per 100 000 population (95% CI: 4.4–7.4), closely followed by pedestrians at 3.4 per 100 000 population (95% CI: 2.5–4.2). Conclusion The burden of road traffic injury and death is high in Africa. Since registry-based reports underestimate the burden, a systematic collation of road traffic injury and death data is needed to determine the true burden

Prognostic value of deep learning-mediated treatment monitoring in lung cancer patients receiving immunotherapy

BackgroundCheckpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria.MethodsA cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients' follow-ups. A classifier was employed to link imaging features learned by the network with overall survival.ResultsOur results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations.ConclusionsOur results demonstrate that deep learning can quantify tumor- and non-tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.Pathogenesis and treatment of chronic pulmonary disease

Estimating the incidence of breast cancer in Africa: a systematic review and meta-analysis

Background Breast cancer is estimated to be the most common cancer worldwide. We sought to assemble publicly available data from Africa to provide estimates of the incidence of breast cancer on the continent. Methods A systematic search of Medline, EMBASE, Global Health and African Journals Online (AJOL) was conducted. We included population- or hospital-based registry studies on breast cancer conducted in Africa, and providing estimates of the crude incidence of breast cancer among women. A random effects meta-analysis was employed to determine the pooled incidence of breast cancer across studies. Results The literature search returned 4648 records, with 41 studies conducted across 54 study sites in 22 African countries selected. We observed important variations in reported cancer incidence between population- and hospital-based cancer registries. The overall pooled crude incidence of breast cancer from population-based registries was 24.5 per 100 000 person years (95% confidence interval (CI) 20.1-28.9). The incidence in North Africa was higher at 29.3 per 100 000 (95% CI 20.0-38.7) than Sub-Saharan Africa (SSA) at 22.4 per 100 000 (95% CI 17.2-28.0). In hospital-based registries, the overall pooled crude incidence rate was estimated at 23.6 per 100 000 (95% CI 18.5-28.7). SSA and Northern Africa had relatively comparable rates at 24.0 per 100 000 (95% CI 17.5-30.4) and 23.2 per 100 000 (95% CI 6.6-39.7), respectively. Across both registries, incidence rates increased considerably between 2000 and 2015. Conclusions The available evidence suggests a growing incidence of breast cancer in Africa. The representativeness of these estimates is uncertain due to the paucity of data in several countries and calendar years, as well as inconsistency in data collation and quality across existing cancer registries