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Background/Objective: Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies. Methods: Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease. Results: The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical–demographics models significantly increased discriminatory ability in both diagnostic and prognostic models. Conclusions: This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC. [ABSTRACT FROM AUTHOR]

Con el propósito de evaluar el comportamiento poblacional de insectos y ácaros fitófagos en tres variedades de tomate (Amalia, Mariela y Mara) se realizó un experimento en la Finca «Las Papas», perteneciente al Instituto Nacional de Ciencias Agrícolas (INCA), ubicada en el Municipio San José de las Lajas, Provincia Mayabeque, durante los meses de octubre a diciembre de 2008. Las evaluaciones se realizaron semanalmente y comenzaron a los quince días después del trasplante. Los insectos y los ácaros fitófagos se cuantificaron e identificaron en el Laboratorio de Entomología perteneciente a la Facultad de Agronomía de la Universidad Agraria de la Habana ¨Fructuoso Rodríguez Pérez¨. Los organismos nocivos detectados fueron Bemisia tabaci Gennadius, Liriomyza trifolii Burgess, Myzus persicae Sulzer, Spodoptera spp., Frankliniella schultzei Trybom y Polyphagotarsonemus latus Banks. La mayor cantidad de especies de fitófagos se asociaron a la variedad Amalia, mientras que en Mariela y Mara se detectó igual número de especies. La variedad Mara mostró la mayor susceptibilidad a B. tabaci durante los diferentes momentos de evaluación y Mariela fue la más susceptible para el minador. M. persicae manifestó un comportamiento similar sobre las variedades Amalia y Mariela. La mayor densidad poblacional total de B. tabaci se alcanzó para las variedades Amalia y Mariela; en el caso de L. trifolii y M. persicae, la densidad poblacional total no difirió para las diferentes variedades. La mayor riqueza de especies y de diversidad biológica se manifestó en la variedad de tomate Amalia. [ABSTRACT FROM AUTHOR]

An illustrated key for the species of Thysanoptera in ornamental crops in Central America is presented. The key includes a total of 21 species, distributed in 16 in the suborder Terebrantia and five in the suborder Tubulifera. In all cases the location of collections is indicated and when if possible also the ornamental crop which was found. [ABSTRACT FROM AUTHOR]

In order to make an inventory of phytophagous insects of the bean crop on two planting dates and to determine their population density in the Bat-304 bean variety, an investigation was carried out at "San Miguel" farm on two planting dates, the first on September 5th, 2015 and the second on February 18th, 2016. Sampling was carried out 15 days after sowing the crop, performing 6 double diagonal samplings. Samples were taken from 15 plants at random in each sampling, taking one leaf from each level. The samples taken were taken to the Entomology Laboratory for quantification and identification of the different species. The phytophagous insects detected in the bean crop on the two planting dates were: Empoasca kraemeri Ross and Moore; Bemisia tabaci Gennadius; Liriomyza trifolii Burgess; Thrips palmi Karny; Diabrotica balteata Leconte and Cerotoma ruficornis Olivier. The mean population density of B. tabaci was higher at the lower level of the plant, while the remaining species (E. kraemeri; L. trifolii and T. palmi) did not show a preference for a particular level. The intensity of chrysomelid damage was higher at the early planting date and at the middle and lower levels for both planting dates. E. kraemeri showed a higher mean population density at the late sowing date; while T. palmi showed a tendency towards a higher mean population density at the early sowing date. [ABSTRACT FROM AUTHOR]

Common beans (Phaseolus vulgaris L.) shelter a varied entomofauna of thysanurans, among which Thrips palmi Karny stands out. In late 2019, the crop was seriously affected in the western region with severe damages associated with the presence of great populations of an unknown species of thrips. The objective of the present work was to identify the species associated with this picture of damages in the bean crop and describe its symptoms. The specimens were collected cleared for mounting on slides, and identified by using the taxonomic keys. The results showed the presence of Megalurothrips usitatus (Bagnall) (Thysanoptera: Thripidae) in the evaluated plant material, where female and male adults and nymphs of the first and second instars were verified. The symptoms observed on the bean plants agreed with the form of feeding of the thrips. The symptoms observed in the affected fields were diverse according to the plant age and phenological state and the crop development. [ABSTRACT FROM AUTHOR]