ICAR: endoscopic skull‐base surgery

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Effects of Conflict Zones on Two Western Healthcare Systems: Italy and Israel

In the last decade, and especially since recent events in the Middle East and ensuing wars, armed and military conflicts have risen to levels not witnessed in the past. Now, in nearly 50 geographic areas, civil wars, sectarian violence,famine, religious persecutions and genocide have caused significant population migration across borders, indirectly impacting medical care in neighboring recipient countries and continents. The United Nations, Council of European Union, and the World Health Organization have treaties, policies and articles addressing and outlining health delivery for migrants and refugees around the world. These policies serve as a legal framework for recipient countries to address this issue as part of their obligation to administer medical care. How these policies are translated into practice and the effects these policies have on healthcare delivery vary from one country to another, from one municipality to another, and from one hospital to another. Yet, overall, delivery of care is upheld and in line with the above mentioned policies. This qualitative study addresses how conflicts in North Africa and Syria impact hospitals and refugee sites in Florence, Italy and Nahariyya, Israel. It is based on direct participant observation, literature review and interviews with physicians, nurses and hospital administrators. The paper aims to outline how hospitals in these particular locations apply international laws, such as United Nations High Commission for Refugees (UNHCR), International Covenant on Economic, Social, and Cultural Rights (ICESCR) and UN Resolution 18/2816 and describes the challenges for staff, institutions and organizations providing care for patients affected by conflicts who are unable to access medical care in their native country

Effects of Conflict Zones on Two Western Healthcare Systems: Italy and Israel

In the last decade, and especially since recent events in the Middle East and ensuing wars, armed and military conflicts have risen to levels not witnessed in the past. Now, in nearly 50 geographic areas, civil wars, sectarian violence,famine, religious persecutions and genocide have caused significant population migration across borders, indirectly impacting medical care in neighboring recipient countries and continents. The United Nations, Council of European Union, and the World Health Organization have treaties, policies and articles addressing and outlining health delivery for migrants and refugees around the world. These policies serve as a legal framework for recipient countries to address this issue as part of their obligation to administer medical care. How these policies are translated into practice and the effects these policies have on healthcare delivery vary from one country to another, from one municipality to another, and from one hospital to another. Yet, overall, delivery of care is upheld and in line with the above mentioned policies. This qualitative study addresses how conflicts in North Africa and Syria impact hospitals and refugee sites in Florence, Italy and Nahariyya, Israel. It is based on direct participant observation, literature review and interviews with physicians, nurses and hospital administrators. The paper aims to outline how hospitals in these particular locations apply international laws, such as United Nations High Commission for Refugees (UNHCR), International Covenant on Economic, Social, and Cultural Rights (ICESCR) and UN Resolution 18/2816 and describes the challenges for staff, institutions and organizations providing care for patients affected by conflicts who are unable to access medical care in their native country

HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression

Graph analytical approaches permit identifying novel genes involved in complex diseases, but are limited by (i) inferring structural network similarity of connected gene nodes, ignoring potentially relevant unconnected nodes; (ii) using homogeneous graphs, missing gene-disease associations’ complexity; (iii) relying on disease/gene-phenotype associations’ similarities, involving highly incomplete data; (iv) using binary classification, with gene-disease edges as positive training samples, and non-associated gene and disease nodes as negative samples that may include currently unknown disease genes; or (v) reporting predicted novel associations without systematically evaluating their accuracy. Addressing these limitations, we develop the Heterogeneous Integrated Graph for Predicting Disease Genes (HetIG-PreDiG) model that includes gene-gene, gene-disease, and gene-tissue associations. We predict novel disease genes using low-dimensional representation of nodes accounting for network structure, and extending beyond network structure using the developed Gene-Disease Prioritization Score (GDPS) reflecting the degree of gene-disease association via gene co-expression data. For negative training samples, we select non-associated gene and disease nodes with lower GDPS that are less likely to be affiliated. We evaluate the developed model’s success in predicting novel disease genes by analyzing the prediction probabilities of gene-disease associations. HetIG-PreDiG successfully predicts (Micro-F1 = 0.95) gene-disease associations, outperforming baseline models, and is validated using published literature, thus advancing our understanding of complex genetic diseases.</jats:p

Comparison of the developed model (HetIG-PreDiG) with the baseline models using 10-fold cross-validation for prediction of the top 30% of predicted disease genes.

The source code of baseline models is listed. The Micro-F1 score column indicates average and standard deviation model performance of predicting gene-disease associations.</p

An ablation study.

Demonstrating the effectiveness of using the developed Gene-Disease Prioritization Score (GDPS) in the developed HetIG-PreDiG model that incorporates a logistic regression (LR) classifier. (A) Receiver operating characteristic (ROC) curves of the developed model with (blue), and without (red) GDPS. The area under each ROC curve (AUC) is indicated. (B) A zoomed view of the top left corner of Fig 4A.</p

A summary of literature evidence for the Top 10 predicted genes that are not associating with a disease for each of the selected 30 diseases.

The Success Rate column is the ratio between the number of predicted genes with PubMed supporting evidence divided by 10.</p

An illustrated example of Algorithm 1 (Fig 1).

Z is a gene-gene co-expression similarity matrix. Line 1 in Algorithm 1: given disease d that is known to associate with genes g2 and g3, columns 2 and 3 are selected in Z to create matrix Z′. Lines 4 to 6 in Algorithm 1: each row in Z′ is averaged into a Gene-Disease Prioritization Score. The prioritization score of g1 for d is (0.1+0.4)/2 = 0.25, reflecting the average similarity of a non-associated gene g1 to genes associated with d.</p