Publications by authors named "J Porras"

Husks of rice (RH), coffee (CH), and cholupa (CLH) were used to produce natural adsorbents. The natural adsorbents were used to remove pharmaceuticals such as diclofenac, ciprofloxacin, and acetaminophen in a mixture of distilled water. However, CH stood out for its efficiency in removing ciprofloxacin (74%) due to the higher concentration of acidic groups, as indicated by the Boehm method.

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Objective: Segmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)-based segmentation methods can streamline the process. The objective of this study was to evaluate a label-efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans.

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Purpose: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging. However, surgery elicits anatomical changes not represented in the preoperative model, generating an inaccurate basis for navigation during surgery progression.

Methods: We propose a first vision-based approach to update the preoperative 3D anatomical model leveraging intraoperative endoscopic video for navigated sinus surgery where relative camera poses are known.

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This study focused on synthesizing and characterizing PEGylated amphiphilic block copolymers with pendant linoleic acid (Lin) moieties as an alternative to enhance their potential in drug delivery applications. The synthesis involved a two-step process, starting with ring-opening polymerization of ε-caprolactone (CL) and propargylated cyclic carbonate (MCP) to obtain PEG-b-P(CL-co-MCP) copolymers, which were subsequently modified via click chemistry. Various reaction conditions were explored to improve the yield and efficiency of the click chemistry step.

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To overcome the limitations of current methods for diagnosing paracoccidioidomycosis (PCM), it is critical to develop novel diagnostic strategies that can be implemented in low-resource settings and dramatically improve turnaround times. This study focused on the development of a portable molecular test to screen for Paracoccidioides spp. The proposed approach integrated double-tagging polymerase chain reaction (PCR) and a paper-based lateral flow assay (LFA) for readout, using carbon nanoparticles as a signal generation system.

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