Milk Density in Two Dairy Collection Routes for Pasteurizing Plant in Camagüey.

Milk density values were assessed in two  dairy collection routes (Route 6 and Purialito)  for Pasteurizing Plant in Camagüey, Cuba. The study was carried out in the months of March-April-May, 2013, where 179 samples were as-sessed, and a density values database was created. A descriptive statistical analysis for density was performed with SPSS 15.0. Using simple variance analysis through a general linear model, the effects of route, month, and their int e-ractions on milk  weight, were measured. The multiple comparison test (Tuckey) was performed to contrast density differences between months. Route and month significantly influenced on milk density (P ≤ 0.05); the same behavior was observed for  route-month interaction (P ≤ 0.05). The mean values and ranges of dairy density were within the normal limits, though in May they decreased to  1.027 g/cm3 . The multiple comparison test  (Tuckey) showed significant levels (P ≤ 0.001) between March and April; and March and May (P ≤ 0.05); no statistical significant dif-ferences were observed between April and May. The better route was Purialito in terms of dairy density

Evaluacion de campo de una vacuna cubana contra la leptospirosis

The present study was performed at a livestock center, where Zebu sires and heavy-breeding cows are raised, in Camagüey province. Results from a Cuban vaccine vs. Leptospira interrogans (var. pomona, icterohaemorrhagiae, and canicola) were evaluated from October 1997 to December 1998. A retrospective study on data from epizootiological records was carried out. These data comprised serological tests performed during the analyzed period of time and health index behavior related to leptospirosis disease. Data from economic records were also reviewed as sire commercialization was concerned. Protective antibodies against leptospirosis serotypes were detected in 90% of vaccined animals. Antibody values remained high over a 12-month period of time, in which neither disease signs and symptoms, nor nephropathies, or adverse reactions (local or general) due to vaccine were registered. Sire production increased, meaning $42 156,00 national currency and$1 800.00 USD gains regarding 1995Se evaluaron los resultados de una vacuna cubana contra los serovares de Leptospira interrogans (pomona, icterohahemorrhagiae y canicola ). El trabajo se desarrolló en una Empresa Pecuaria cuyo propósito productivo es la obtención de sementales y reproductoras de la raza Cebú, de alto valor genético. Para alcanzar esta intención, se realizó un estudio retrospectivo de los datos registrados en los expedientes epizootiológicos sobre las investigaciones serológicas practicadas durante octubre de 1997 a diciembre de 1998, y el comportamiento de los indicadores de salud correspondientes a la enfermedad. También se consultaron los registros económicos para extraer la información necesaria acerca de los resultados de la comercialización de sementales. Los resultados indican que en el 90% de la población inmunizada contra los referidos serovares, se detectaron títulos protectores de los anticuerpos correspondientes, que se mantuvieron con valores altos durante un período de 12 meses; que no hubo manifestaciones ni de la enfermedad, ni de nefropatías en la población investigada con posterioridad a la vacunación; no se detectaron reacciones adversas (locales o generales) a consecuencias de la aplicación de la vacuna y se aumentó la promoción de sementales, razón por la que se obtuvo beneficios de $42 156,00 en moneda nacional y$1 800,00 USD, con respecto al año 1995

Simulación de un brote de cólera porcino en una instalación de Camagüey.

Para mostrar el comportamiento de un brote epidémico de cólera porcino en una instalación de Camagüey se simuló la entrada de un animal enfermo sin tomar ninguna medida de contención ni preventiva. Se utilizó el programa R, y el paquete Odesolve para resolver el modelo SEIR (susceptibilidad, expuesto, infectado y recuperado). El estudio fue durante el mes de enero de 2001. Se tomó un índice de transmisibilidad de 0,35, una duración de la enfermedad de diez días y un período latente de diez días. El modelo de simulación mostró en el caso de no tomar ninguna medida, cómo se produciría un pico máximo de la enfermedad a los 12 días de iniciado: un total de 127 enfermos, lo que equivale al 63 % de la población expuesta. El número reproductivo básico (Ro) encontrado fue 3,26.Simulation of a Hog Cholera Outbreak on a Swine Breeding Farm ABSTRACT Admission of a swine infected by hog cholera on a swine breeding farm was simulated to demonstrate this disease outbreak performance when no restraining or preventive measures are affected. The SEIR model (susceptivity, exposure, infestation, and restoration) was applied by using the computer program R and the statistical package Odesolve. The study was conducted during January 2011. A transmissibility index of 0,35, a disease duration of ten days, and a latent period of ten days were set. The SEIR model showed how this disease peaked after a twelve-day onset with a total of 127 infected swines, i.e., 63 % of the exposed population, when no measures were affected. The basic reproductive number (Ro) was 3,26

Densidad láctea en dos rutas de recolección de leche destinada a la Planta Pasteurizadora Camagüey.

Se evaluaron los valores de densidad láctea en dos rutas de recolección de leche (ruta 6 y Purialito) destinadas a la Planta Pasteurizadora Camagüey, Cuba. El estudio abarcó el trimestre marzo-abril-mayo de 2013. Se evaluaron 179 muestras de leche y se creó una base de datos con sus valores de densidad. Con el programa SPSS versión 15.0 se realizó análisis estadístico descriptivo para la densidad. Con el análisis de varianza simple a través de un modelo lineal general se midió el efecto de la ruta y el mes, y de sus interacciones sobre el peso de la leche. Se realizó prueba de comparaciones múltiples (Tuckey) para confrontar las diferencias de densidad entre los meses. La ruta y el mes influyeron significativamente sobre la densidad de la leche (P ≤0,05) al igual que la interacción ruta-mes (P ≤0,05). Los valores medios y los rangos de la densidad láctea están dentro de los límites normales, aunque en mayo descendió hasta 1,027 g/cm3. La prueba de comparación múltiple (Tuckey) muestra niveles de significación (P ≤0,001) entre los meses de marzo y abril y entre marzo y mayo (P ≤0,05), abril y mayo no difieren estadísticamente. La me-jor ruta en relación con la densidad láctea resultó ser Purialito

Valoración in vitro del efecto ixodicida de macerados de pseudotallo de plátano sobre larvas de Amblyomma cajennense

In vitro evaluation of ixodicide effect of four banana pseudo-stem macerated solutions with different aging times on Amblyomma cajennense tick larvae was performed. Five solutions with different concentration levels were prepared from the above mentioned ones, and four assays –A,B,C, and D- were carried out taking into account macerated solutions aging time (25; 40; 70, and 130 days, respectively). Each assay comprised 6 treatments: 1 to 5 using different concentration level solutions, and 6 using distilled water as a control treatment. Five replicas per assay were performed. Solution effectiveness approval depended on larvae mortality rate during the first 24-hour post-treatment period. It was detected that mortality rate values were lower than 67,0% in every assay; however, treatment 3 from assay C reached an 89,2% larvicide value, significantly different from the other assay results. Bacteria and fungi accidentally growing in culture broth, but not tanin activity and pH decrease, can be the cause of such a larvicide effect. Therefore, the ixodicide effect of banana pseudo-stem macerated solutions is moderate though interesting. Besides, macerated solution aging time did not enhance ixodicide effect, while concentration level increase did.Con el objetivo de evaluar in vitro, sobre larvas de garrapatas de la especie Amblyomma cajennense, el efecto ixodicida de cuatro macerados de pseudotallo de plátano (Musa sapientum), con diferentes tiempos de envejecimiento, se prepararon cinco soluciones con distintas concentraciones, procediéndose a la realización de cuatro ensayos: A; B, C y D, en función del tiempo de envejecimiento de los macerados (25; 40; 70 y 130 días, respectivamente). Cada experimento constó de seis tratamientos (del 1 al 5, a diferentes concentraciones y el tratamiento 6 fue de control, con agua destilada). Para cada experimento se realizaron cinco réplicas. El criterio de efectividad de las soluciones, se emitió según la mortalidad manifestada por las larvas durante las primeras 24 horas post-tratamiento, donde se observó que en todos los experimentos la mortalidad está por debajo del 67,0%, excepto para el tratamiento número 3, del experimento C, que manifestó un efecto larvicida del 89,2%, valor que difiere de forma altamente significativa con respecto a los demás resultados alcanzados para la totalidad de los experimentos. Estos resultados se atribuyen más a la presencia de bacterias y hongos que crecieron fortuitamente en el caldo, que a la acción de los taninos y a la disminución del pH. Se concluye que el efecto ixodicida de los macerados de pseudotallos de plátano, son moderados, pero no carentes de interés. Además se evidenció que el envejecimiento de los macerados no mejora su efecto ixodicida; mientras que el aumento de la concentración sí lo favorece

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI