Machine learning toward high-performance electrochemical sensors.

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.

Nanoparticle-Protein Interaction: Demystifying the Correlation between Protein Corona and Aggregation Phenomena.

Protein corona formation and nanoparticles' aggregation have been heavily discussed over the past years since the lack of fine-mapping of these two combined effects has hindered the targeted delivery evolution and the personalized nanomedicine development. We present a multitechnique approach that combines dynamic light and small-angle X-ray scattering techniques with cryotransmission electron microscopy in a given fashion that efficiently distinguishes protein corona from aggregates formation. This methodology was tested using ∼25 nm model silica nanoparticles incubated with either model proteins or biologically relevant proteomes (such as fetal bovine serum and human plasma) in low and high ionic strength buffers to precisely tune particle-to-protein interactions.

Real-Time and Monitoring of the Synthesis of Silica Nanoparticles.

The real-time and monitoring of the synthesis of nanomaterials (NMs) remains a challenging task, which is of pivotal importance by assisting fundamental studies (e.g., synthesis kinetics and colloidal phenomena) and providing optimized quality control.

Potential displacement of the native by the invasive in South America suggested by differences in climate suitability.

Tenuisvalvae notata (Mulsant) (Coccinellidae) is a predatory ladybird beetle native to South America. It specializes in mealybugs prey (Pseudococcidae), but relatively little is known about its ecology. In contrast, the ladybird beetle Cryptolaemus montrouzieri Mulsant (Coccinellidae) is indigenous to Australia and has been introduced to many countries worldwide including Brazil for biological control of mealybugs.

Protein corona meets freeze-drying: overcoming the challenges of colloidal stability, toxicity, and opsonin adsorption.

Freeze-drying of nanoparticle suspensions is capable of generating stable nanoformulations with improved storage times and easier transportation. Nonetheless, nanoparticle aggregation is likely induced during freeze-drying, which reduces its redispersibility upon reconstitution and leads to undesirable effects such as non-specific toxicity and impaired efficacy. In this work, bovine serum albumin (BSA) is described as a suitable protectant for silica nanoparticles (SNPs), which result in solid structures with excellent redispersibility and negligible signs of aggregation even when longer storage times are considered.

Colloidal Stability and Redispersibility of Mesoporous Silica Nanoparticles in Biological Media.

The outreach of nanoparticle-based medical treatments has been severely hampered due to the imbalance between the efforts in designing extremely complex materials and the general lack of studies devoted to understanding their colloidal stability in biological environments. Over the years, the scientific community has neglected the relevance related to the nanoparticles' colloidal state, which consequently resulted in very poor bench-to-clinic translation. In this work, we show how mesoporous silica nanoparticles (MSNs, one of the most promising and tested drug delivery platforms) can be efficiently synthesized and prepared, resulting in a colloidally stable system.

Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers.

Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs () themselves and carried proteins (). However, simple, high-throughput, and accurate determination of these targets remains a key challenge.

Freeze-drying of silica nanoparticles: redispersibility toward nanomedicine applications.

Aim: To study freeze-drying of silica nanoparticles (SiONPs) in order to find suitable conditions to produce lyophilized powders with no aggregation after resuspension and storage.

Methods: SiONPs were synthesized using a Stöber-based procedure, and characterized by scanning electron microscopy, dynamic light scattering and nitrogen adsorption/desorption isotherms. SiONPs hydrodynamic diameters were compared prior and after freeze-drying in the presence/absence of carbohydrate protectants.

Are PTH levels related to oxidative stress and inflammation in chronic kidney disease patients on hemodialysis?

Introduction:: Patients at end stage renal disease have higher levels of inflammation and oxidative stress than the general population. Many factors contribute to these issues, and the parathyroid hormone (PTH) is also implicated.

Objective:: The study was conducted in order to assess the relationship between PTH levels and inflammation and oxidative stress in hemodialysis patients.