The Islamic Republic of Iran reported its first COVID-19 cases by 19^thFebruary 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19^thto March 18^th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran’s I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.

Prolactin (PRL) is a peptidic hormone that displays pleiotropic functions in the organism including different actions in the brain. PRL exerts a neuroprotective effect against excitotoxicity produced by glutamate (Glu) or kainic acid in both in vitro and in vivo models. It is well known that Glu excitotoxicity causes cell death through apoptotic or necrotic pathways due to intracellular calcium ([Ca²⁺] i) overload. Therefore, the aim of the present study was to assess the molecular mechanisms by which PRL maintains cellular viability of primary cultures of rat hippocampal neurons exposed to Glu excitotoxicity. We determined cell viability by monitoring mitochondrial activity and using fluorescent markers for viable and dead cells. The intracellular calcium level was determined by a fluorometric assay and proteins involved in the apoptotic pathway were determined by immunoblot. Our results demonstrated that PRL afforded neuroprotection against Glu excitotoxicity, as evidenced by a decrease in propidium iodide staining and by the decrease of the LDH activity. In addition, the MTT assay shows that PRL maintains normal mitochondrial activity even in neurons exposed to Glu. Furthermore, the Glu-induced intracellular [Ca²⁺]i overload was attenuated by PRL. These data correlate with the reduction found in the level of active caspase-3 and the pro-apoptotic ratio (Bax/Bcl-2). Concomitantly, PRL elicited the nuclear translocation of the transcriptional factor NF-κB, which was detected by immunofluorescence and confocal microscopy. To our knowledge, this is the first report demonstrating that PRL prevents Glu excitotoxicity by a mechanism involving the restoration of the intracellular calcium homeostasis and mitochondrial activity, as well as an anti-apoptotic action possibly mediated by the activity of NF-κB. Overall, the current results suggest that PRL could be of potential therapeutic advantage in the treatment of neurodegenerative diseases.

The COVID-19 pandemic has already taken the lives of more than 2 million people worldwide, causing several political and socio-economic disturbances in our daily life. At the time of publication, there are non-effective pharmacological treatments, and vaccine distribution represents an important challenge for all countries. In this sense, research for novel molecules becomes essential to develop treatments against the SARS-CoV-2 virus. In this context, Mexican natural products have proven to be quite useful for drug development; therefore, in the present study, we perform an in silico screening of 100 compounds isolated from the most commonly used Mexican plants, against the SARS-CoV-2 virus. As results, we identify ten compounds that meet leadlikeness criteria (emodin anthrone, kaempferol, quercetin, aesculin, cichoriin, luteolin, matricin, riolozatrione, monocaffeoyl tartaric acid, aucubin). According to the docking analysis, only three compounds target the key proteins of SARS-CoV-2 (quercetin, riolozatrione and cichoriin), but only one appears to be safe (cichoriin). ADME (absorption, distribution, metabolism and excretion) properties and the physiologically based pharmacokinetic (PBPK) model show that cichoriin reaches higher lung levels (100 mg/Kg, IV); therefore, it may be considered in developing therapeutic tools.