Trends in using deep learning algorithms in biomedical prediction systems.

In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

Mesoporous silica nanoparticles with chiral pattern topological structure function as "antiskid tires" on the intestinal mucosa to facilitate oral drugs delivery.

The weak adhesion between nanocarriers and the intestinal mucosa was one of the main reasons caused the failure in oral delivery. Inspired by the "antiskid tires" with complex chiral patterns, mesoporous silica nanoparticles AT-R@CMSN exhibiting geometrical chiral structure were designed to improve the surface/interface roughness in nanoscale, and employed as the hosting system for insoluble drugs nimesulide (NMS) and ibuprofen (IBU). Once performing the delivery tasks, AT-R@CMSN with rigid skeleton protected the loaded drug and reduced the irritation of drug on gastrointestinal tract (GIT), while their porous structure deprived drug crystal and improved drug release. More importantly, AT-R@CMSN functioned as "antiskid tire" to produce higher friction on intestinal mucosa and substantively influenced multiple biological processes, including "contact", "adhesion", "retention", "permeation" and "uptake", compared to the achiral S@MSN, thereby improving the oral adsorption effectiveness of such drug delivery systems. By engineering AT-R@CMSN to overcome the stability, solubility and permeability bottlenecks of drugs, orally administered NMS or IBU loaded AT-R@CMSN could achieve higher relative bioavailability (705.95% and 444.42%, respectively) and stronger anti-inflammation effect. In addition, AT-R@CMSN displayed favorable biocompatibility and biodegradability. Undoubtedly, the present finding helped to understand the oral adsorption process of nanocarriers, and provided novel insights into the rational design of nanocarriers.

Chiral mesoporous silica nano-screws as an efficient biomimetic oral drug delivery platform through multiple topological mechanisms.

In the microscale, bacteria with helical body shapes have been reported to yield advantages in many bio-processes. In the human society, there are also wisdoms in knowing how to recognize and make use of helical shapes with multi-functionality. Herein, we designed atypical chiral mesoporous silica nano-screws (CMSWs) with ideal topological structures (e.g., small section area, relative rough surface, screw-like body with three-dimension chirality) and demonstrated that CMSWs displayed enhanced bio-adhesion, mucus-penetration and cellular uptake (contributed by the macropinocytosis and caveolae-mediated endocytosis pathways) abilities compared to the chiral mesoporous silica nanospheres (CMSSs) and chiral mesoporous silica nanorods (CMSRs), achieving extended retention duration in the gastrointestinal (GI) tract and superior adsorption in the blood circulation (up to 2.61- and 5.65-times in AUC). After doxorubicin (DOX) loading into CMSs, DOX@CMSWs exhibited controlled drug release manners with pH responsiveness in vitro. Orally administered DOX@CMSWs could efficiently overcome the intestinal epithelium barrier (IEB), and resulted in satisfactory oral bioavailability of DOX (up to 348%). CMSWs were also proved to exhibit good biocompatibility and unique biodegradability. These findings displayed superior ability of CMSWs in crossing IEB through multiple topological mechanisms and would provide useful information on the rational design of nano-drug delivery systems.