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IntroductionAdolescent motherhood (childbearing below 18 years of age) is a major global health and social problem. Understanding the impact of early motherhood on maternal and child health indices is important to community and population health promotion in developing countries. This study examined the association between adolescent motherhood and maternal and child health indices in Maiduguri, Nigeria.MethodsA cross-sectional design method was used to recruit 220 mothers (age=14–25 years) from four communities in the city of Maiduguri, Northeastern Nigeria. Participants were surveyed using a self-developed interviewer-administered questionnaire that assesses maternal and child health indices and sociodemographic characteristics. Logistic regression analysis was used to compute adjusted OR and 95% CI of the associations between motherhood in adolescence (mothers below 18 years old) and maternal and child health indices.ResultsCompared to adult mothers, adolescent mothers were more likely to experience fistula (OR=5.01, 95% CI=3.01 to 14.27), to have postpartum haemorrhage (OR=6.83, 95% CI=2.93 to 15.92), to have sexually transmitted infections (OR=6.29, 95% CI=2.26 to 17.51) and to lose a child within 5 years of birth (OR=3.52, 95% CI=1.07 to 11.60). Children born to adolescent mothers were less likely to have normal weight at birth (OR=0.34, CI=0.15 to 0.73) than those born to adult mothers.ConclusionAdolescent motherhood was associated with negative maternal and child health indices. The findings can be used by public health professionals including physiotherapists in this role to inform effective primary healthcare practice and community health advocacy to improve maternal and child health indices among adolescent mothers in Maiduguri. Future studies are needed to confirm the evidence at the regional or national level including the rural population in Nigeria.

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Background: Stroke has a great adverse effect not only on the lives of stroke survivors but also on the informal caregivers, as they provide care ranging from assistance in activities of daily living, leading to a high risk of depression and other psychological morbidities. Objectives: This study explored the prevalence of depression and associated factors among primary caregivers of stroke survivors in Nigeria. Methods: A cross sectional survey on the prevalence of depression among primary caregivers of stroke survivors was conducted at selected hospitals in Maiduguri, Nigeria. A sample of convenience was used to recruit participants in this study. The Beck depression inventory was used to screen for depression. Data form and patients folders were used to obtain socio-demographics and clinical characteristics of participants and stroke survivors. Result: A total of 115 primary caregivers participated, out of which 53% were male and 47% were female. The mean age and overall beck depression inventory score were 30.78 ± 9.66 and 15.42 ± 7.84, respectively. The overall prevalence of depression was found to be 46.1%. Type of stroke (P = 0.03) and post stroke duration (P = 0.02) of stroke survivors cared for was found to be associated with depression among caregivers of stroke survivors. Conclusions: Caring for the patients with stroke presents increased in the psychological impact of caregiving for stroke survivors, it is therefore recommended that information, education, and support should be provided to primary caregivers as they are key in sustaining rehabilitation gains and long term overall well-being of stroke survivors.

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children. Outcome of patients treated on standard protocols, in a multidisciplinary cancer center setting outside of clinical trials, is not well reported. We reviewed characteristics and outcome of 23 pediatric patients treated at a single, multidisciplinary cancer center in Lebanon, between April 2002 and December 2010. Median follow-up was 41 months. The most commonly affected primary site was the head and neck (48%, n = 11). Nineteen tumors (82.6%) were of embryonal histology. Tumor size was ≥5 cm in eight (34.8%) patients. Sixteen patients (69.6%) had localized disease, and one (4.4%) had metastatic disease. Fifteen (65.2%) had Group III tumors. All patients received chemotherapy, for a duration ranging 21-51 weeks. Upfront surgical resection was performed in 10 patients (43.5%). Eighteen patients (78.3%) received radiation therapy. The 5-year overall and disease-free survival rates were 83% and 64%, respectively. Relapse correlated with absence of surgery. Treatment of childhood RMS in a multidisciplinary cancer center in Lebanon results in similar survival to that in developed countries when similar protocols are applied. There was a higher incidence of local relapse, but those were salvageable with further therapy and surgical local control.

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniae and healthy subjects using Chest X-Ray images. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable models.

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, five different deep learning models (ResNet18, ResNet34, InceptionV3, Incep-tionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniae and healthy subjects using Chest X-Ray images. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable models.

The perfusion imaging using C-arm CT could be used intraoperatively for liver cancer treatment planning and evaluation. To deal with undersampled data due to slow C-arm CT rotation and pause between the rotations, we applied model-based reconstruction methods. Recent works using the time separation technique with an analytical basis function set have led to a significant improvement in the quality of C-arm CT perfusion maps. In this work we apply the time separation technique with a prior knowledge basis function set extracted using singular value decomposition from CT perfusion reconstructions. On C-arm CT liver perfusion scan simulated based on the real CT liver perfusion scan we show that the bases extracted from only two CT perfusion scans are capable of modeling the C-arm CT data correctly.