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Background: The global outbreak of the 2019 novel Coronavirus Disease (COVID-19) caused by the infection with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which appeared in China at the end of 2019, signifies a major public health issue at the current time. Objective: The objective of the present study is to characterize the physicochemical properties of the SARS-CoV-2 proteins at a residues level, and to generate a “bioinformatics fingerprint” in the form of a “PIM® profile” created for each sequence utilizing the Polarity Index Method® (PIM®), suitable for the identification of these proteins. Methods: Two different bioinformatics approaches were used to analyze sequence characteristics of these proteins at the residues level, an in-house bioinformatics system PIM®, and a set of the commonly used algorithms for the predic-tion of protein intrinsic disorder predisposition, such as PONDR® VLXT, PONDR® VL3, PONDR® VSL2, PONDR® FIT, IUPred_short and IUPred_long. The PIM® profile was generated for four SARS-CoV-2 structural proteins and compared with the corresponding profiles of the SARS-CoV-2 non-structural proteins, SARS-CoV-2 putative proteins, SARS-CoV proteins, MERS-CoV proteins, sets of bacterial, fungal, and viral proteins, cell-penetrating peptides, and a set of intrinsically disordered proteins. We also searched for the UniProt proteins with PIM® profiles similar to those of SARS-CoV-2 structural, non-structural, and putative proteins. Results: We show that SARS-CoV-2 structural, non-structural, and putative proteins are characterized by a unique PIM® profile. A total of 1736 proteins were identified from the 562,253 “reviewed” proteins from the UniProt database, whose PIM® profile was similar to that of the SARS-CoV-2 structural, non-structural, and putative proteins. Conclusion: The PIM® profile represents an important characteristic that might be useful for the identification of proteins similar to SARS-CoV-2 proteins.

Background: Selective Cationic Amphipathic Antibacterial Peptides (SCAAPs) occupy a prominent place in the production of new drugs on account of their high toxicity towards bacteria and low toxicity towards mammalian cells, low hemolytic activity, and contribution to the protection of the human immune system. Aim: their number in nature is very low, and experimental tests are very protracted and costly. Therefore, it would be useful to have bioinformatics tools that would identify them in the existing databases and also propose new synthetic SCAAPs. Methods: In order to reduce the costs of identification and/or chemical synthesis and to know the physicochemical characteristics of SCAAPs at a residues level and to obtain a “bioiformatics fingerprint” suitable for their selection, we have modified the Polarity Index Method® (PIM®) and the α-helical configuration of each sequence is included in determining their individual “PIM® profile”. We have also used a set of the computer program to determine their “Intrinsic Disorder Predisposition”. This information was then compared with other protein groups, such as bacteria, fungi, virus and Cell-Penetrating Peptides (CPP) from the UniProt database and a set of intrinsically disordered proteins. Once the “fingerprint” of SCAAPs was obtained, it was used for searching among the 559228 “reviewed” proteins from the UniProt database and a set of synthetic SCAAPs characterized by the predefined “PIM® profile” selected. Results: Our results showed that the metric named “PIM® profile” can identify, with a high level of accuracy, a group of bacterial SCAAPs. This bioinformatics study was supported at residues level, using the in-house bioinformatics system Polarity Index Method the commonly used algorithm for the prediction of intrinsic disorder predisposition, PONDR® FIT. Conclusions: The Polarity Index Method seems highly efficient identifying SCAAP candidates.

Introducción: la ventilación mecánica es una práctica común en las unidades de cuidados intensivos y anestesiología. Tiene implicaciones terapéuticas, pero también es potencialmente nociva para el sistema respiratorio y los órganos distantes, por lo cual es imprescindible monitorizar los parámetros ventilatorios de manera continua. Objetivo: describir la ecuación del movimiento del sistema respiratorio y sus aplicaciones clínicas en el paciente bajo ventilación mecánica. Desarrollo: la ecuación del movimiento del sistema respiratorio integra las fuerzas dinámicas generadas por el ventilador y las propiedades intrínsecas del pulmón y la caja torácica. Expresa la presión en el sistema respiratorio en relación con el volumen, la elastancia, la resistencia, el flujo de aire y las presiones generadas por el ventilador y el paciente. Las presiones elevadas en el sistema respiratorio se asocian a una mayor mortalidad en pacientes con ventilación mecánica, por lo que la identificación de los componentes que condicionan la elevación de las presiones mediante la ecuación del movimiento del sistema respiratorio permite realizar modificaciones a los parámetros programados del ventilador para mantener una ventilación protectora. Conclusión: la toma de decisiones basada en la ecuación del movimiento del sistema respiratorio permite realizar ajustes en los parámetros ventilatorios según las características y enfermedades del paciente bajo ventilación mecánica.

Background The dynamic interactions of severe infectious diseases with epidemic potential and their hosts are complex. Therefore, it remains uncertain if a sporadic zoonosis restricted to a certain area will become a global pandemic or something in between. Objective The objective of the study was to present a surveillance system for acute severe infections with epidemic potential based on a deterministic-stochastic model, the StochCum Method. Design The StochCum Method is founded on clinical, administrative, and sociodemographic variables that provide a space/time map as a preventive warning of possible outbreaks of severe infections that can be complemented based on the sum of all the first accumulated cases. If the outbreak is happening in high-risk areas, an early warning can be elicited to activate the health response system and save time while waiting for the confirmation of symptomatic cases. Results The surveillance system was tested virtually for 1 month on admissions to the emergency room of a public hospital located in Mexico City, Mexico. It promptly identified simulated cases of acute respiratory infections with epidemic potential. Conclusions The StochCum Method proved to be a practical and useful system for conducting epidemic surveillance on a hospital network.