Spatial-temporal excess mortality patterns of the 1918-1919 influenza pandemic in Spain

Background: The impact of socio-demographic factors and baseline health on the mortality burden of seasonal and pandemic influenza remains debated. Here we analyzed the spatial-temporal mortality patterns of the 1918 influenza pandemic in Spain, one of the countries of Europe that experienced the highest mortality burden. Methods: We analyzed monthly death rates from respiratory diseases and all-causes across 49 provinces of Spain, including the Canary and Balearic Islands, during the period January-1915 to June-1919. We estimated the influenza-related excess death rates and risk of death relative to baseline mortality by pandemic wave and province. We then explored the association between pandemic excess mortality rates and health and socio-demographic factors, which included population size and age structure, population density, infant mortality rates, baseline death rates, and urbanization. Results: Our analysis revealed high geographic heterogeneity in pandemic mortality impact. We identified 3 pandemic waves of varying timing and intensity covering the period from Jan-1918 to Jun-1919, with the highest pandemic-related excess mortality rates occurring during the months of October-November 1918 across all Spanish provinces. Cumulative excess mortality rates followed a south-north gradient after controlling for demographic factors, with the North experiencing highest excess mortality rates. A model that included latitude, population density, and the proportion of children living in provinces explained about 40% of the geographic variability in cumulative excess death rates during 1918-19, but different factors explained mortality variation in each wave. Conclusions: A substantial fraction of the variability in excess mortality rates across Spanish provinces remained unexplained, which suggests that other unidentified factors such as comorbidities, climate and background immunity may have affected the 1918-19 pandemic mortality rates. Further archeo-epidemiological research should concentrate on identifying settings with combined availability of local historical mortality records and information on the prevalence of underlying risk factors, or patient-level clinical data, to further clarify the drivers of 1918 pandemic influenza mortality.

Altered expression of Alzheimer's disease-related genes in the cerebellum of autistic patients: a model for disrupted brain connectome and therapy

Autism and Alzheimer's disease (AD) are, respectively, neurodevelopmental and degenerative diseases with an increasing epidemiological burden. The AD-associated amyloid-beta precursor protein-alpha has been shown to be elevated in severe autism, leading to the 'anabolic hypothesis' of its etiology. Here we performed a focused microarray analysis of genes belonging to NOTCH and WNT signaling cascades, as well as genes related to AD and apoptosis pathways in cerebellar samples from autistic individuals, to provide further evidence for pathological relevance of these cascades for autism. By using the limma package from R and false discovery rate, we demonstrated that 31% (116 out of 374) of the genes belonging to these pathways displayed significant changes in expression (corrected P-values <0.05), with mitochondria- related genes being the most downregulated. We also found upregulation of GRIN1, the channel-forming subunit of NMDA glutamate receptors, and MAP3K1, known activator of the JNK and ERK pathways with anti-apoptotic effect. Expression of PSEN2 (presinilin 2) and APBB1 (or F65) were significantly lower when compared with control samples. Based on these results, we propose a model of NMDA glutamate receptor-mediated ERK activation of alpha-secretase activity and mitochondrial adaptation to apoptosis that may explain the early brain overgrowth and disruption of synaptic plasticity and connectome in autism. Finally, systems pharmacology analyses of the model that integrates all these genes together (NOWADA) highlighted magnesium (Mg2+) and rapamycin as most efficient drugs to target this network model in silico. Their potential therapeutic application, in the context of autism, is therefore discussed.

HPV infection, a described cause for human cancer: HPV detection and molecular mechanisms of HPV carcinogenesis (Review).

[en]Human papillomavirus (HPV) belongs to the Papillomaviridae virus family and it is one of the most common sexual transmission infections. HPV genome is composed of eight genes, including two early genes and six late genes. Among these late genes, E6 and E7 code for proteins that trigger cell-cycle re-entry in infected cells, which can lead to cervical cancer development. The IARC (International Agency for Research Cancer) proposed a guideline based on Hill’s criteria to determine whether the relation between HPV infection and cervical cancer is causal or not. Epidemiological studies have demonstrated that HPV infection is a necessary but non-sufficient cause for cervical cancer. Furthermore, HPV infection is considered the first necessary cause described of a human cancer, being HPV16 and 18 carcinogenic to humans and the most studied types. Cervical cancer is the second leading cause of cancer death among women worldwide. Different screening programs are carried out with the aim of preventing cervical cancer; such as cytologies and HPV tests. There are two main methods which are equally usable to detect HPV: the real-time PCR assays and the array assays. Regarding the molecular mechanisms of HPV mediated malignancies, E2, E6 and E7 proteins of HPV16 lead to immune response evasion, inducing IL-10 and TGF-β1 gene expression. Besides, E6 and E7 proteins allow cell-cycle reentry, phosphorylating RB and ubiquitinating p53 respectively. HPV genome integration in host genome leads to the alteration of host and viral genes expression, including oncogenes and tumor suppressor genes. However, the differences of E6 and E7 oncoproteins in different HPV types is poorly known due to the fact that almost the most studied HPV type has been HPV16.