Search

COVID-19 does not show signs of having disappeared, being a very contagious disease, the WHO recommended limiting the free movement of people, since from its appearance until May 1st, 2021, it caused the death of more than 3.2 million of people around the world. In Peru, it economically affected those people who generated income every day to survive, for this reason some activities were reactivated complying with the biosafety measures that are the use of mandatory mask and social distancing (more than 1 meter). Taking body temperature with an infrared thermometer is an optional measure, generating rejection by specialists, indicating that there is little evidence of its sensitivity and specificity and of its doubtful ability to detect fever. In view of this problem, this article will implement a thermal image processing system to detect possible cases of patients with COVID-19, in such a way that the system performs a more accurate measurement of body temperature, and it can be implemented in any place where such measurement is intended, helping to combat the spread of the virus that currently continues to affect many people. The system has a more accurate measurement of body temperature with an efficiency of 95% at 1 meter between the drone and the person, in such a way that if it presents a body temperature higher than 40°C it could be infected with COVID-19

Chagas disease is caused by the parasite Trypanosoma Cruzi and transmitted by the Vinchuca. Bolivia is the country with the highest prevalence in the South American region; for example, in 2015, there was a prevalence of 33.4%. This disease causes severe intestinal and cardiac problems in the long term, 30% of the cases register cardiac symptoms, and 10% have alterations in the esophagus or colon. This research aims to analyze the relationship between environmental factors and Chagas outbreaks in an area of Bolivia to identify the environmental conditions in which the disease is transmitted, using epidemiological, meteorological data and also environmental indexes extracted from Landsat 8 satellite images. Through a Principal Components Analysis (PCA) of the environmental indexes extracted from the satellite images and the meteorological information, has been found that the environmental conditions that have a correlation with the occurrence of cases are: temperature, relative humidity, visibility, Normalized Difference Soil Index (NDSI) and Modified Normalized Difference Water Index (MNDWI).

This exploratory observational study analyzes the variation of the amount of vertical electrons (vTEC) in the ionosphere, days before to the telluric events with a degree greater than M7.0 during the year 2016. Sixteen events were analyzed with these characteristics. The data was obtained from 55 satellites and 300 GPS receivers. This data was downloaded from the Center for Orbit Determination in Europe (CODE). The measurement of the vTEC is done at a height of 450km, and from these values, ionospheric anomalies are calculated. These disturbances are detected assuming that in a time gap window of 8 days the vTEC data has a normal distribution. The vTEC data used for the analysis consists in the variations out of the ranges considered normal. The data is downloaded with software developed by our laboratory and generates maps of the vTEC present in the ionosphere with a periodicity of two hours, delivering data from 17 days before to 4 days after. The analysis area is a circle with 1000 km radius centered at the epicenter of every earthquake. Afterwards, these areas were analyzed and the vTEC, registering the information in this study. It was found that in 100% of the earthquakes there is a significant and positive variation in the vTEC, a negative variation in the 81.25% of the events. It was also found a vTEC variation 6 days before every telluric event in 100% of the cases. This study is a preliminary analysis that shows us the feasibility to analyze this information to obtain evidence of causality, which is the next step of our study, calculating the risk of telluric events in beforehand that could benefit worldwide population.

Conventional methods to diagnose anemia require a blood draw. This generates a great problem in patients due to the fear of contracting a disease through syringes, or sensitivity to this element. The palpebral conjunctiva is an indicator of diseases such as the hordeolum, chalazion, marginal blepharitis, bacterial conjunctivitis, trachoma, and anemia. The palpebral conjunctiva pallor is an indicator of anemia and if we wanted to develop an automatic system, for the non-invasive diagnosis of anemia based on the analysis of photographs of the palpebral conjunctiva, we would need algorithms for the segmentation and analysis of this membrane. In this sense, this research proposes and develops a method for the automatic segmentation of the palpebral conjunctiva using an Android application and image processing techniques. As a result, the success of this segmentation method is 92.2%.

This is a descriptive study aimed to know the knowledge that the university students have about blood donation. A study from the US shows that people with tertiary education have more probability to donate blood [1]. The sampled population consisted in students of two universities studying Science, Engineering and Biology. A total of 114 individuals were approached of which 19 did not wish to proceed or did not complete the questionnaire leaving a total of 95 study participants. The questionnaire was divided into three sections. The first section refers to the potential benefits of blood donation and asks participants about their views on 23 benefit statements. Each statement has been assessed by an average of 43 individuals with approval scores of 75%. Of the study population it was found that 80% did not have prior knowledge of the benefits of blood donation. The second part contained 15 questions designed to identify if participants' are eligible or not for blood donation. It was found that 47%, 35%, and 18% of participants were eligible, not eligible, or undetermined for donating blood respectively. The third section of the questionnaire identifies the participants' willingness to donate blood following exposure to information on its potential benefits. It was found that 52.63%, 38.95%, and 8.42 % of participants had the same, more, or less willingness after exposure to information than before the questionnaire respectively. The results show us that it is possible to influence the target population's will to donate blood after their exposure to acknowledge the blood donation benefits. The statements, about blood donation benefits, with higher score were chosen. The ultimate goal of this study is to raise awareness of blood donation and to increase participation during campaigns on university campuses. The research group keeps working on preventive health and changing the picture of voluntary blood donation.

Remote sensing is the technology that has enabled us to obtain information about the Earth's surface without directly contacting it. For this reason, currently, the Bolivian state has considered a list of interesting applications of remote sensing in the country, including the following: biodiversity and environment monitoring, mining and geology, epidemiology, agriculture, water resources and land use planning. The use of satellite images has become a great tool for epidemiology because with this technological advance we can determine the environment in which transmission occurs, the distribution of the disease and its evolution over time. In that context, one of the important diseases related to public health in Bolivia is Chagas disease, also known as South American Trypanosomiasis. Chagas is caused by a blood-sucking bug or Vinchuca, which causes serious intestinal and heart long term problems and affects 33.4% of the Bolivian population. This disease affects mostly humble people, so the Bolivian state invests millions of dollars to acquire medicine and distribute it for free. Due to the above reasons, the present research aims to analyze some areas of Bolivia using satellite images for developing an epidemiology study. The primary objective is to understand the environment in which the transmission of the disease happens, and the climatic conditions under which occurs, observe the behavior of the blood-sucking bug, identify in which months occur higher outbreaks, in which months the bug leaves its eggs, and under which weather conditions this happens. All this information would be contrasted with information extracted from the satellite images and data from the Ministry of Health, and the Institute of Meteorology in Bolivia. All this data will allow us to have a more integrated understanding of this disease and promote new possibilities to prevent and control it.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.

Background and rationale. While experimental psychology has focused on the study of processes that determine the individual’s behaviour in specific experimental situations, the studies concerning individual differences have analyzed the stability of observed differences on performance between individuals, in particular those which can be generalized through diverse situations or tasks (Williams et al., J Exp Anal Behav 90:219–231, 2008). Several studies have shown that processing speed is a useful variable to study how age and ability in a specific task affect the performance in more general cognitive tasks such as language (Zheng et al., Psyc Bulletin & Rev 7:113–120, 2000). In studies with infants for example, it is known that younger children are slower to process linguistic stimuli compared with older children while they respond to the same task (Fernald et al. 1998); and that there is a positive relation between vocabulary development and processing speed during the second year of life (Fernald et al., Child Dev 72(4):1003–1015, 2001; Fernald et al., Dev Psychol 42(1):98–116, 2006; Zangl et al., J Cogn Dev 6(2):179–208, 2005).

Pneumonia is one of the major causes of child mortality, but it is curable if one can achieves early diagnostics. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. The use of ultrasound to detect pneumonia is limited by the image analysis for interpretation, which is carried by human experts. Pattern recognition and image analysis is a potential tool to facilitate recognition of pneumonia consolidates in absence of medical experts for automatic diagnostics. To perform an automatic analysis of lung ultrasound images for pneumonia detection, the noise introduced by the image portion of the skin, notably complicates the processing and interpretation. This paper presents a methodology to recognize and eliminate the portion of the skin in lung ultrasound images.

Human sapovirus has been shown to be one of the most important etiologies in pediatric patients with acute diarrhea. However, very limited data are available about the causative roles and epidemiology of sapovirus in community settings. A nested matched case-control study within a birth cohort study of acute diarrhea in a peri-urban community in Peru from 2007 to 2010 was conducted to investigate the attributable fraction (AF) and genetic diversity of sapovirus. By quantitative reverse transcription–real-time PCR (qPCR) sapovirus was detected in 12.4% (37/299) of diarrheal and 5.7% (17/300) of nondiarrheal stools ( P = 0.004). The sapovirus AF (7.1%) was higher in the second year (13.2%) than in the first year (1.4%) of life of children. Ten known genotypes and one novel cluster ( n = 5) within four genogroups (GI, GII, GIV, and GV) were identified by phylogenetic analysis of a partial VP1 gene. Further sequence analysis of the full VP1 gene revealed a possible novel genotype, tentatively named GII.8. Notably, symptomatic reinfections with different genotypes within the same ( n = 3) or different ( n = 5) genogroups were observed in eight children. Sapovirus exhibited a high attributable burden for acute gastroenteritis, especially in the second year of life, of children in a Peruvian community. Further large-scale studies are needed to understand better the global burden, genetic diversity, and repeated infections of sapovirus.

Objetivos. Implementar un sistema para el diagnóstico remoto de tuberculosis y multidrogorresistencia (MDR) usando el método Microscopic-Observation Drug Susceptibility Assay (MODS) en el Laboratorio de Micobacterias del Centro de Excelencia en Tuberculosis de Trujillo (CENEX-Trujillo). El sistema incluyó una variante de un algoritmo de reconocimiento de Mycobacterium tuberculosis recientemente reportado a partir de imágenes digitales de cultivos MODS de muestras de esputo. Materiales y métodos. Se optimizó un algoritmo de reconocimiento por medio de un reentrenamiento del modelo estadístico basado en imágenes digitales de cultivos MODS provenientes del Laboratorio de Micobacterias del CENEX-Trujillo. Se obtuvieron imágenes de 50 cultivos MODS positivos de pacientes con sospecha de tuberculosis multidrogorresistente entre enero y octubre de 2012 en el CENEX-Trujillo. Resultados. La sensibilidad y la especificidad en objetos, para reconocer cordones de tuberculosis fueron de 92,04% y de 94,93% respectivamente. La sensibilidad y la especificidad en foto, para determinar un campo positivo a tuberculoisis fueron 95,4% y de 98,07% respectivamente. Conclusiones. Los resultados demostraron la factibilidad de la implementación de telediagnóstico en lugares remotos, el cual puede contribuir con la detección temprana de tuberculosis multidrogorresistente mediante el método MODS Objectives. To implement a system for remote diagnosis of tuberculosis and multidrug resistance (MDR) using the Microscopic-Observation Drug Susceptibility Assay (MODS) method in the Mycobacteria Laboratory, Trujillo Center of Excellence in Tuberculosis (CENEX-Trujillo). The system included a variant of an algorithm for recognition of Mycobacterium tuberculosis recently reported from digital images of MODS cultures of sputum samples. Materials and methods. The recognition algorithm was optimized using a retraining statistical model based on digital images of MODS cultures from CENEX-Trujillo. Images of 50 positive MODS cultures of patients with suspected multidrug-resistant tuberculosis were obtained between January and October 2012 in the CENEX-Trujillo. Results. The sensitivity and specificity to recognize strings of tuberculosis were 92.04% and 94.93% respectively using objects. The sensitivity and specificity to determine a positive tuberculosis field were 95.4% and 98.07% respectively using pictures. Conclusions. The results demonstrated the feasibility of the implementation of telediagnostics in remote locations, which may contribute to the early detection of multidrug-resistant tuberculosis by MODS method

Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS) assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB) to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7–10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity) and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii). The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.

"El lenguaje permite explorar el mundo y expresar lo que se conoce de él. En el caso del número gramatical, poder asociar claves verbales de singular o plural con uno o más objetos permite a los niños tanto inferir significado como discriminar, anticipar y rastrear referentes. La comprensión temprana del plural está supeditada a las claves verbales proporcionadas; mientras que la producción, al núm e ro de ejemplares de un conjunto. No obstante, se desconoce la interrelación que guardan ambos factores en la comprens ión temprana del plural en una lengua morfológicamente rica como el español. Así, el objetivo del presente trabajo es explorar si el número de claves verbales influye en la habilidad de niños de 24 meses de edad, aprendices del español, para distinguir el plural compuesto por un conjunto pequeño de objetos (2 versus 1). Se realizaron dos experimentos utilizando el Paradigma Intermodal de Atención Preferencial . Se evaluó si los niños asociaban claves verbales múltiples (E1) y claves en la terminación del sus tantivo (E2) indicativas de plural a un conjunto de dos objetos. Los niños asociaron las claves múltiples de pluralidad, pero no la terminación del sustantivo, al conjunto de dos objetos. Los resultados sugieren cierto nivel de dificultad en la comprensión temprana del plural cuando un conjunto está integrado por dos objetos; no obstante, se confirma la capacidad de los niños para extraer información de marcos sintácticos enriquecidos."