RESUMO
Activatable photosensitizers (PSs) generating 1O2 only under specific conditions can minimize concomitant injury to normal tissues. Heavy-atom-free PSs hold the merits of low dark toxicity, long triplet-state lifetimes, good photostability, and relatively low cost. PSs with emission in the second near-infrared (NIR-II) window are highly valuable for deep-tissue, high-contrast imaging. Herein, we have designed and synthesized a series of heavy-atom-free PSs by a one-step reaction between an easily accessible rhodamine derivative and commercially available thiophene aldehydes. One of the as-prepared PSs, 2b-3T, exhibits emission maxima at 810 nm and tails to the NIR-II region at 1140 nm, together with large Stokes shift (178 nm). Importantly, the newly developed PSs, featuring functional carboxylic acid groups, present promising opportunities as versatile platforms for creating activatable PSs. To validate our concept, we developed Cu2+/pH-activatable PSs using the spirocyclization mechanism of rhodamine. Ultimately, we showcased the effectiveness of these innovative PSs in photodynamic therapy through in vitro experiments.
RESUMO
Purpose: Accurate segmentation of the endometrium in ultrasound images is essential for gynecological diagnostics and treatment planning. Manual segmentation methods are time-consuming and subjective, prompting the exploration of automated solutions. We introduce "segment anything with inception module" (SAIM), a specialized adaptation of the segment anything model, tailored specifically for the segmentation of endometrium structures in ultrasound images. Approach: SAIM incorporates enhancements to the image encoder structure and integrates point prompts to guide the segmentation process. We utilized ultrasound images from patients undergoing hysteroscopic surgery in the gynecological department to train and evaluate the model. Results: Our study demonstrates SAIM's superior segmentation performance through quantitative and qualitative evaluations, surpassing existing automated methods. SAIM achieves a dice similarity coefficient of 76.31% and an intersection over union score of 63.71%, outperforming traditional task-specific deep learning models and other SAM-based foundation models. Conclusions: The proposed SAIM achieves high segmentation accuracy, providing high diagnostic precision and efficiency. Furthermore, it is potentially an efficient tool for junior medical professionals in education and diagnosis.
RESUMO
A study is presented of the thermal-mechanical noise and response to sound of microphones that are designed to be driven by the viscous forces in air rather than by sound pressure. Virtually all existing microphone designs are intended to respond to sound pressure. The structures examined here consist of thin, micro-scale, cantilever beams. The viscous forces that drive the beams are proportional to the relative velocity between the beams and fluid medium. The beams' movement in response to sound is similar to that of the air in a plane acoustic wave. The thermal-mechanical noise of these beams is found to be a very weak function of their width and length; the size of the sensing structure does not appear to significantly affect the performance. This differs from the well-known importance of the size of a pressure-sensing microphone in determining the pressure-referred noise floor. Creating microphones that sense fluid motion rather than pressure could enable a significant reduction in the size of the sensing element. Calculated results are revealed to be in excellent agreement with the measured pressure-referred thermal noise.