Improved pan-specific MHC class I peptide binding predictions using a novel representation of the MHC binding cleft environment

Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. One of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC–peptide-binding data sets are constantly being made available, and also new structures of MHC class I molecules with a bound peptide have been published. In order to test if the NetMHCpan method can be improved by integrating this novel information, we created new pseudo-sequence definitions for the MHC-binding cleft environment from sequence and structural analyses of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train the method, the NetMHCpan method achieved a significant increase in the predictive performance, in particular, of non-human MHCs. This study hence showed that an improved performance of MHC-binding methods can be achieved not only by the accumulation of more MHC–peptide-binding data but also by a refined definition of the MHC-binding environment including information from non-human species.Fil: Carrasco Pro, Sebastián. Universidad Peruana Cayetano Heredia; PerúFil: Zimic, Mirko. Universidad Peruana Cayetano Heredia; PerúFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentin

An algorithm for characterizing skin moles using image processing and machine learning

Melanoma, the most serious type of skin cancer, forms in cells (melanocytes) that produce melanin, the pigment that gives color to the skin. There are low-income regions that lack specialized dermatologists, causing skin cancer to be diagnosed in advanced stages. In Peru, in high Andean communities with low resources, the problem is aggravated by the high incidence of ultraviolet radiation and lack of medical resources to make the diagnosis. Normally, mole images are obtained from dermatoscopes. The present work seeks to use mole images obtained from smartphones to make the classification of them as suspected or not suspected of being melanoma, by means of a feature extraction algorithm. The first step is to make color and lighting corrections. After this, the image is segmented using the K-Means algorithm, and we obtain the areas of the mole and skin. With the segmented mole we proceed to extract the main visual characteristics and then use classification algorithms such as support vector machine (SVM), random forest and naïve bayes, which obtained an accuracy of 0.9473, 0.7368 and 0.6842, respectively. These results show that it is possible to use images obtained from smartphones to develop a classification algorithm with 94.73% accuracy to detect melanoma in skin moles

Identifying RO9021 as a Potential Inhibitor of PknG from Mycobacterium tuberculosis: Combinative Computational and in Vitro Studies

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb). Despite being considered curable and preventable, the increase of antibiotic resistance is becoming a serious public health problem. Mtb is a pathogen capable of surviving in macrophages, causing long-Term latent infection where the mycobacterial serine/threonine protein kinase G (PknG) plays a protective role. Therefore, PknG is an important inhibitory target to prevent Mtb from entering the latency stage. In this study, we use a pharmacophore-based virtual screening and biochemical assays to identify the compound RO9021 (CHEMBL3237561) as a PknG inhibitor. In detail, 1.5 million molecules were screened using a scalable cloud-based setup, identifying 689 candidates, which were further subjected to additional screening employing molecular docking. Molecular docking spotted 62 compounds with estimated binding affinities of-7.54 kcal/mol (s.d. = 0.77 kcal/mol). Finally, 14 compounds were selected for in vitro experiments considering previously reported biological activities and commercial availability. In vitro assays of PknG activity showed that RO9021 inhibits the kinase activity similarly to AX20017, a known inhibitor. The inhibitory effect was found to be dose dependent with a relative IC50value of 4.4 ± 1.1 μM. Molecular dynamics simulations predicted that the PknG-RO9021 complex is stable along the tested timescale. Altogether, our study indicates that RO9021 is a noteworthy drug candidate for further developing new anti-TB drugs that hold excellent reported pharmacokinetic parameters.Revisión por pare

Prediction Score for Antimony Treatment Failure in Patients with Ulcerative Leishmaniasis Lesions

The manuscript is relevant because of the finding of a new risk factor for chemotherapy failure and the development of a prognosis score for cutaneous leishmaniasis. The proportion of patients that have multiple lesions in American Tegumentary Leishmaniasis (ATL) is considerable. Publications and our experience permit to estimate that they represent around 20% of the affected population from the Amazon basin with cutaneous lesions. In addition, about 1/3 of them would correspond to the concomitant distant lesions category, the novel risk factor identified with a very high odds ratio (20–30) associated. Such numbers merit study of concomitant distant ulcers category on its own, not only because of clinical management implications, but also to search for factors that are contributing to chemotherapy failure. Finally, the simple equation proposed in the manuscript can be easily adapted to smart phone technologies. Similar prognosis equations are scarce for other pathologies and do not exist for Cutaneous Leishmaniasis at all. The simplicity of this tool should be followed by subsequent epidemiologic studies in other ATL endemic regions

An algorithm for detection of tuberculosis bacilli in Ziehl-Neelsen sputum smear images

This work proposes an algorithm oriented to the detection of tuberculosis bacilli in digital images of sputum samples, inked with the Ziehl Neelsen method and prepared with the direct, pellet and diluted pellet methods. The algorithm aims at automating the optical analysis of bacilli count and the calculation of the concentration level. Several algorithms have been proposed in the literature with the same objective, however, in no case is the performance in sensitivity and specificity evaluated for the 3 preparation methods. The proposed algorithm improves the contrast of the colors of interest, then thresholds the image and segments by labeling the objects of interest (bacilli). Each object then has its geometrical descriptors and photometric descriptors. With all this, a characteristic vector is formed, which are used in the training and classification process of an SVM. For the training 225 images obtained by the 3 preparation methods were used. The proposed algorithm reached, for the direct method, a sensitivity level of 93.67% and a specificity level of 89.23%. In the case of the Pellet method, a sensitivity of 92.13% and a specificity of 82.58% was obtained, while for diluted Pellet the sensitivity was 92.81% and the specificity 83.61%

A novel algorithm for detection of tuberculosis bacilli in sputum smear fluorescence images

This work proposes an algorithm aimed at recognizing and accounting Koch bacilli in digital images of microbiological sputum samples stained with auramine, in order to determine the degree of concentration and the state of the disease (tuberculosis). The algorithm was developed with the main objective of maximizing the sensitivity and specificity of the analysis of microbiological samples (recognition and counting of bacilli) according to each preparation method (direct and diluted pellets) in order to reduce the subjectivity of the visual inspection applied by the specialist at the time of analyzing the samples. The proposed algorithm consists of a background removal, an image improvement stage based on consecutive morphological closing operations, a segmentation stage of objects of interest based on thresholdization and a classification stage based on SVM. Each algorithmic stage was developed taking into account the method of preparation of the sample to be processed, being this aspect the main contribution of the proposed work, since it was possible to achieve very satisfactory results in terms of specificity and sensitivity. In this context, sensitivity levels of 91.24% and 93.79% were obtained. Specificity levels of 90.33% and 94.85% were also achieved for direct and diluted pellet methods respectively

Diseño y evaluación de un ventilador mecánico

This paper describes the design and prototyping of a low cost (under 2500 USD), high precision (error percentage lower than 5%) mechanical ventilator in response to the global demand for this equipment. The ventilator is designed to deliver continuous mandatory ventilation (CMV) in two forms: volumen controlled (V-CMV) and pressure controlled (P-CMV), and pressure support ventilation (PSV). CMV inspiration triggering can be assisted or controlled, which in combination results in five different ventilation modes. It's construction is based on industrial devices, high precision machined parts and standard clinical ventilation elements.The prototype’s mechanism consists of a piston-cylinder system driven by a stepper motor and connected by a lead screw and nut. The distance and velocity of the piston displacement is defined by the quantity and frequency of electronic signal pulses from the programmed control system. The piston movement displaces a mix of air and medical oxygen to the patient.The prototype's V-CMV mode has been tested on an electronic lung to simulate the response of a real organ in typical conditions. As a result an average error of 3% was obtained. Further upgrades are suggested for performance optimization, pre-clinical tests and clinical validation.En respuesta al actual déficit a escala mundial de ventiladores mecánicos causado por el COVID-19, se ha desarrollado un prototipo de ventilador mecánico de bajo costo (aproximadamente 2500 USD) y alta precisión (error menor a 5%). Este equipo permite realizar ventilación mandatoria continua (CMV) controlada por volumen (V-CMV), por presión (P-CMV) y ventilación con presión soporte (PSV). Bajo la CMV se puede trabajar con disparo asistido y controlado; lo que, en combinación, suma un total de cinco modos de ventilación. Su construcción se basa en el uso de componentes comerciales de gama industrial, piezas mecanizadas con alta precisión y elementos de circuitos de ventilación clínica estándar.El mecanismo del prototipo consta de un cilindro dentro del cual se desplaza un émbolo accionado por un motor paso a paso. Esto se logra a través de un sistema de transmisión compuesto por un tornillo sin fin y una tuerca. Dependiendo de la cantidad y frecuencia de los pulsos eléctricos emitidos por el sistema de control, se define el avance y velocidad del émbolo. De este modo, el émbolo desplaza la mezcla de aire y oxígeno hacia el paciente.El prototipo fue evaluado en modo V-CMV mediante pruebas de laboratorio con un pulmón electrónico, que simula las condiciones de operación típicas. Como resultado se obtuvo un error promedio del 3% de las variables de funcionamiento del equipo