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The synthesis of stimulus-responsive poly(N-isopropylacrylamide-co-N-isopropylmethacrylamide)/chitosan core/shell nanohydrogels made by batch emulsion polymerization in the presence of chitosan (CS) micelles is reported. The ratio of monomers required to obtain copolymers with a volume phase transition temperature (TVPT) in the range of the temperatures observed in the human body in response to an infection (38 to 40 °C) was estimated with the Fox equation. The conversion was determined by gravimetry; mean particle size, size distribution, and thermal response were measured by quasi-elastic light scattering (QLS). The core/shell structure was confirmed by TEM, and FTIR showed the presence of N-isopropyl acrilamide (NIPA), N-isopropyl methacrylamide (NIPMA), and CS in the nanohydrogels. The nanohydrogels were loaded with the drug doxycycline hyclate, and their release kinetic profile was determined at pH = 2.0 and 7.4 at their volume phase transition temperatures (TVPT). A higher amount of drug was released at acidic pH. Some mathematical models described in the literature were used to fit the experimental drug release data. [ABSTRACT FROM AUTHOR]

Several studies have addressed the benefits of mentoring from the mentor's perspective, especially those related to soft skills. However, to the best of our knowledge, there are no studies that either relate the mentoring in STEM areas with female role models or that analyze them from a data-mining perspective. In this work, a questionnaire was elaborated to address the mentor's benefits related to soft skills and technical knowledge; afterward, a data-mining methodology was used to analyze the mentor's perceptions related to female role models and STEM reinforcement. In addition, sentiment analysis was performed in order to determine the emotional polarity in the text used by the mentors to describe their mentoring experience. The results show that soft and technical skills are acquired by the mentors, and participating in mentoring programs allows them to perceive themselves as female role models. Additionally, by using decision trees, it was possible to determine the mentors' characteristics that perceive a STEM reinforcement or that produce attraction. In addition, the results show that the general perception of the mentors' experience was positive. Finally, the use of machine learning techniques, specifically data mining and sentiment analysis, allowed us to both confirm the results obtained in a qualitative way and to obtain new interesting results. [ABSTRACT FROM AUTHOR]