Establishing a Libyan Medical Research Council is Urgently Needed

To the Editor: I read with interest the study by Bakoush et al addressing the issue of medical publication in Libya [1]. The number of published reports from Libya was compared with another three Arabic countries (Morocco, Tunisia and Yemen). I suggest to the authors the extention of their study to include all twenty-three Arabic countries. This would enhance our knowledge of the scientific productivity of the Arab world

Alien marine species of Libya: first inventory and new records in El-Kouf National Park (Cyrenaica) and the neighbouring areas

The presence of marine alien species in El-Kouf National Park and the neighbouring areas was assessed using a compilation of available information and observations, a field survey conducted on October 2010 in the framework of the MedMPAnet project and results of further monitoring during June and September 2012. A total of 9 alien species were reported: the Rhodophyta Asparagopsis taxiformis (Delile) Trevisan de Saint-Léon, the Chlorophyta Caulerpa racemosa var. cylindracea (Sonder) Verlaque, Huisman & Boudouresque, the crab Percnon gibbesi (H. Milne-Edwards, 1853) and the fishes Fistularia commersonii Rüppell, 1838, Siganus luridus (Rüppell, 1829), Siganus rivulatus Forsskål, 1775, Pempheris vanicolensis Cuvier, 1831, Lagocephalus sceleratus (Gmelin, 1789) and Sphyraena flavicauda Rüppell, 1838. Several of them were until now unknown for the National Park. The list of alien marine species of Libya is updated and discussed. Until now 63 marine aliens species were recorded along the Libyan coasts. These include 3 Foraminifera, 3 Ochrophyta, 5 Rhodophyta, 5 Chlorophyta, 1 Magnoliophyta, 11 Arthropoda, 13 Mollusca, 1 Echinodermata and 21 Chordata. Among these Non Indigenous Species, 43 are known as established along the Libyan coast including 8 invasive, 11 casual, 6 questionable, 3 cryptogenic and 1 unknown. An in-depth study of the marine organisms would substantially increase the number of alien species occurring in Libya. Monitoring of marine assemblages of MPAs is a valuable opportunity to go further into the knowledge of native and introduced species

Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries

Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with limited access to high-end ultrasound equipment and data. This work investigates different strategies to reduce the domain-shift effect for a fetal plane classification model trained on a high-resource clinical centre and transferred to a new low-resource centre. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to $0.92 \pm 0.04$ and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Frequency and clinical patterns of stroke in Iran - Systematic and critical review

<p>Abstract</p> <p>Background</p> <p>Cerebrovascular disease is the second commonest cause of death, and over a third of stroke deaths occur in developing countries. To fulfil the current gap on data, this systematic review is focused on the frequency of stroke, risk factors, stroke types and mortality in Iran.</p> <p>Methods</p> <p>Thirteen relevant articles were identified by keyword searching of PubMed, Iranmedex, Iranian University index Libraries and the official national data on burden of diseases.</p> <p>Results</p> <p>The publication dates ranged from 1990 to 2008. The annual stroke incidence of various ages ranged from 23 to 103 per 100,000 population. This is comparable to the figures from Arab Countries, higher than sub-Saharan Africa, but lower than developed countries, India, the Caribbean, Latin America, and China. Similarly to other countries, ischaemic stroke was the commonest subtype. Likewise, the most common related risk factor is hypertension in adults, but cardiac causes in young stroke. The 28-day case fatality rate is reported at 19-31%.</p> <p>Conclusions</p> <p>Data on the epidemiology of stroke, its pattern and risk factors from Iran is scarce, but the available data highlights relatively low incidence of stroke. This may reflect a similarity towards the neighbouring nations, and a contrast with the West.</p

The Arab world's contribution to solid waste literature: a bibliometric analysis

BACKGROUND: Environmental and health-related effects of solid waste material are considered worldwide problems. The aim of this study was to assess the volume and impact of Arab scientific output published in journals indexed in the Science Citation Index (SCI) on solid waste. METHODS: We included all the documents within the SCI whose topic was solid waste from all previous years up to 31 December 2012. In this bibliometric analysis we sought to evaluate research that originated from Arab countries in the field of solid waste, as well as its relative growth rate, collaborative measures, productivity at the institutional level, and the most prolific journals. RESULTS: A total of 382 (2.35 % of the overall global research output in the field of solid waste) documents were retrieved from the Arab countries. The annual number of documents published in the past three decades (1982–2012) indicated that research productivity demonstrated a noticeable rise during the last decade. The highest number of articles associated with solid waste was that of Egypt (22.8 %), followed by Tunisia (19.6), and Jordan (13.4 %). the total number of citations over the analysed years at the date of data collection was 4,097, with an average of 10.7 citations per document. The h-index of the citing articles was 31. Environmental science was the most researched topic, represented by 175 (45.8 %) articles. Waste Management was the top active journal. The study recognized 139 (36.4 %) documents from collaborations with 25 non-Arab countries. Arab authors mainly collaborated with countries in Europe (22.5 %), especially France, followed by countries in the Americas (9.4 %), especially the USA. The most productive institution was the American University of Beirut, Lebanon, with 6.3 % of total publications. CONCLUSIONS: Despite the expected increase in solid waste production from Arab world, research activity about solid waste is still low. Governments must invest more in solid waste research to avoid future unexpected problems. Finally, since solid waste is a multidisciplinary science, research teams in engineering, health, toxicology, environment, geology and others must be formulated to produce research in solid waste from different scientific aspects

The Combination of Homocysteine and C-Reactive Protein Predicts the Outcomes of Chinese Patients with Parkinson's Disease and Vascular Parkinsonism

BACKGROUND: The elevation of plasma homocysteine (Hcy) and C-reactive protein (CRP) has been correlated to an increased risk of Parkinson's disease (PD) or vascular diseases. The association and clinical relevance of a combined assessment of Hcy and CRP levels in patients with PD and vascular parkinsonism (VP) are unknown. METHODOLOGY/PRINCIPAL FINDINGS: We performed a cross-sectional study of 88 Chinese patients with PD and VP using a clinical interview and the measurement of plasma Hcy and CRP to determine if Hcy and CRP levels in patients may predict the outcomes of the motor status, non-motor symptoms (NMS), disease severity, and cognitive declines. Each patient's NMS, cognitive deficit, disease severity, and motor status were assessed by the Nonmotor Symptoms Scale (NMSS), Mini-Mental State Examination (MMSE), the modified Hoehn and Yahr staging scale (H&Y), and the unified Parkinson's disease rating scale part III (UPDRS III), respectively. We found that 100% of patients with PD and VP presented with NMS. The UPDRS III significantly correlated with CRP (P = 0.011) and NMSS (P = 0.042) in PD patients. The H&Y was also correlated with Hcy (P = 0.002), CRP (P = 0.000), and NMSS (P = 0.023) in PD patients. In VP patients, the UPDRS III and H&Y were not significantly associated with NMSS, Hcy, CRP, or MMSE. Strong correlations were observed between Hcy and NMSS as well as between CRP and NMSS in PD and VP. CONCLUSIONS/SIGNIFICANCE: Our findings support the hypothesis that Hcy and CRP play important roles in the pathogenesis of PD. The combination of Hcy and CRP may be used to assess the progression of PD and VP. Whether or not anti-inflammatory medication could be used in the management of PD and VP will produce an interesting topic for further research