The limited availability of standardized datasets has hindered the implementation of artificial intelligence (AI) models in serous fluid cytology, particularly in pleural fluid analysis. In this paper, we present the construction of a dataset of pleural fluid cytology images. The objective is to generate a dataset of pleural fluid cytologic images validated by two pathologists and classified into five categories for cell diagnosis, which will be used to train AI models. As a methodology, the images represent pleural fluid cytology samples that have been prepared through medical procedures and transferred to slides, providing valuable information when evaluated under the microscope by medical specialists through cytological examination. We documented the entire process for building the pleural fluid cytological image dataset, from image capture, labeling, preprocessing, standardization, and uploading to public platforms. As a result, we obtained a pleural fluid cytology dataset based on the International System (TIS) criteria for reporting serous fluids, classifying samples into AUS, MAL, ND, NFM, and SFM. This dataset is intended to support medical research, deep learning applications in medical image analysis, and improved diagnostic methodologies.

Conventional selfie sticks, typically limited to a maximum reach of 1.5 m, are unable to effectively capture large group photographs or wide-background scenes, frequently resulting in restricted framing and facial cropping. This work presents the design, implementation, and experimental validation of a Bluetooth-controlled mobile robot with five degrees of freedom capable of capturing photographs at distances of up to 10 m using a dual-smartphone master–slave architecture. The proposed system integrates an Arduino Nano microcontroller, an L298N motor driver, three SG90 servo motors, an HC-05 Bluetooth module, and a rechargeable Li-Ion battery mounted on a four-wheel chassis. A master smartphone remotely controls both the mobile platform and the articulated camera arm, while a slave smartphone mounted on the arm captures images through its high-resolution rear camera with real-time video streaming. Experimental evaluations demonstrated 100% command transmission success at distances up to 10 m, automatic emergency stopping within 100–110 ms after Bluetooth disconnection, accurate face centering with minimal servo adjustments, and approximately 45 min of battery autonomy. The proposed open-source, low-cost teleoperated platform extends the effective photographic range from 1.5 m to 10 m while providing potentially improved photographic quality due to rear-camera imaging characteristics and full remote framing control compared with conventional selfie accessories.

Machine learning is used in medicine to diagnose diseases quickly and accurately, the results of which support the physician in making correct decisions. Pleural effusion, a common disease in which 50% of patients are diagnosed with cancer. The objective was to describe machine learning techniques that are used for the diagnosis of cancer cells in cytological images of pleural fluid. For the systematic review, the PICO strategy and the PRISMA methodology were used. For the research questions, selection criteria were established, identifying 142 articles, selecting 18 articles after filtering. The techniques used were U_Net with 8 articles, Transfer Learning with 4 articles, Support vector machine with 3 articles, CNN with 3 articles, ANN with 3 articles, X-Boost with one article, K-Means with one article and other ML techniques with 4 articles. Regarding the data set, the most used were cytological images in 10 investigations, CT images in 4 investigations, X-ray images in 3 investigations and one investigation with 1 ultrasound. This literature review will support future research to apply the models and techniques, since there are not many studies on the prediction of cancer cells in pleural fluid.

Las formas de evaluación del desempeño laboral empleadas durante años atrás carecían de indicadores específicos acerca de actitudes o conductas de los colaboradores y se realizaba cada fin de año; para una mejor evaluación, la empresa inmobiliaria OPTIMA S. A., planteó una evaluación consistente en un seguimiento continuo del desempeño que permitiera la intervención oportuna para regular los procesos, capacitar al personal y brindar los estímulos apropiados para lograr una mayor concientización y compromiso de los colaboradores. Para lograr este propósito se empleó un módulo informático organizado en base a los diez comportamientos de la Escala de observación del comportamiento agrupados en habilidades laborales, personales y organizacionales. La información proporcionada por los coordinadores de los equipos arrojó en primer lugar una eficiencia del 96,88% de los colaboradores, la cual no guardó relación con los resultados del análisis de los aspectos considerados en los que solo un 68,7% eran competentes, un 62,6% realizaban un trabajo de calidad quizá por el trabajo en equipo pero que no era realizado por todos, quedando registrado que el 68,8% de ellos se daba un compromiso parcial con la empresa. Se concluye que la evaluación con el módulo diseñado resultó más eficaz al proporcionar una mayor y más precisa información de las habilidades laborales, personales y organizacionales que poseen los trabajadores, en un menor tiempo y con menor demanda de recursos.