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The elucidation of protein interaction networks is critical to understanding fundamental biology as well as developing new therapeutics. Proximity labeling platforms (PLPs) are state-of-the-art technologies that enable the discovery and delineation of biomolecular networks through the identification of protein-protein interactions. These platforms work via catalytic generation of reactive probes at a biological region of interest; these probes then diffuse through solution and covalently “tag” proximal biomolecules. The physical distance that the probes diffuse determines the effective labeling radius of the PLP and is a critical parameter that influences the scale and resolution of interactome mapping. As such, by expanding the degrees of labeling resolution offered by PLPs, it is possible to better capture the various size scales of interactomes. At present, however, there is little quantitative understanding of the labeling radii of different PLPs. Here, we report the development of a superresolution microscopy-based assay for the direct quantification of PLP labeling radii. Using this assay, we provide direct extracellular measurements of the labeling radii of state-of-the-art antibody-targeted PLPs, including the peroxidase-based phenoxy radical platform (269 ± 41 nm) and the high-resolution iridium-catalyzed µMap technology (54 ± 12 nm). Last, we apply these insights to the development of a molecular diffusion-based approach to tuning PLP resolution and introduce a new aryl-azide-based µMap platform with an intermediate labeling radius (80 ± 28 nm).

The merger of photoredox catalysis with transition metal catalysis, termed metallaphotoredox catalysis, has become a mainstay in synthetic methodology over the past decade. Metallaphotoredox catalysis has combined the unparalleled capacity of transition metal catalysis for bond formation with the broad utility of photoinduced electron- and energy-transfer processes. Photocatalytic substrate activation has allowed the engagement of simple starting materials in metal-mediated bond-forming processes. Moreover, electron or energy transfer directly with key organometallic intermediates has provided novel activation modes entirely complementary to traditional catalytic platforms. This Review details and contextualizes the advancements in molecule construction brought forth by metallaphotocatalysis.

Modern proximity labeling techniques have enabled significant advances in understanding biomolecular interactions. However, current tools primarily utilize activation modes that are incompatible with complex biological environments, limiting our ability to interrogate cell- and tissue-level microenvironments in animal models. Here, we report µMap-Red, a proximity labeling platform that uses a red light-excited Sn(IV) chlorin e6 catalyst to activate a phenyl azide biotin probe. We validate µMap-Red by demonstrating photonically-controlled protein labeling in vitro through several layers of tissue, and we then apply our platform in cellulo to label EGFR microenvironments and validate performance with STED microscopy and quantitative proteomics. Finally, to demonstrate labeling in a complex biological sample, we deploy µMap-Red in whole mouse blood to profile erythrocyte cell-surface proteins. This work represents a significant methodological advance toward light-based proximity labeling in complex tissue environments and animal models.

BACKGROUND AND OBJECTIVE: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. METHODS: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. RESULTS: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. CONCLUSIONS: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.

INTRODUCTION AND OBJECTIVES: Cardiac rehabilitation programs (CRP) are a set of interventions to improve the prognosis of cardiovascular disease by influencing patients' physical, mental, and social conditions. However, there are no studies evaluating the optimal duration of these programs. We aimed to compare the results of a standard vs a brief intensive CRP in patients after ST-segment elevation and non-ST-segment elevation acute coronary syndrome through the Más por Menos study (More Intensive Cardiac Rehabilitation Programs in Less Time). METHODS: In this prospective, randomized, open, evaluator-blind for end-point, and multicenter trial (PROBE design), patients were randomly allocated to either standard 8-week CRP or intensive 2-week CRP with booster sessions. A final visit was performed 12 months later, after completion of the program. We assessed adherence to the Mediterranean diet, psychological status, smoking, drug therapy, functional capacity, quality of life, cardiometabolic and anthropometric parameters, cardiovascular events, and all-cause mortality during follow-up. RESULTS: A total of 497 patients (mean age, 57.8±10.0 years; 87.3% men) were finally assessed (intensive: n=262; standard: n=235). Baseline characteristics were similar between the 2 groups. At 12 months, the results of treadmill ergometry improved by = 1 MET in = 93% of the patients. In addition, adherence to the Mediterranean diet and quality of life were significantly improved by CRP, with no significant differences between the groups. The occurrence of cardiovascular events was similar in the 2 groups. CONCLUSIONS: Intensive CRP could be as effective as standard CRP in achieving adherence to recommended secondary prevention measures after acute coronary syndrome and could be an alternative for some patients and centers. Registered at ClinicalTrials.gov (Identifier: NCT02619422).

The work described herein is a continuation of a previous study centered on the bioprospect of cardoon (Cynara cardunculus) leaf extracts through the isolation of secondary metabolites with phytotoxic activity. Chromatographic fractionations of the ethyl acetate extract and spectroscopic analysis showed that the majority of the components were sesquiterpene lactones. Of these compounds, aguerin B, grosheimin, and cynaropicrin were very active on etiolated wheat coleoptile, standard target species, and weed growth. The joint action of binary mixtures of these three active sesquiterpene lactones and one nonactive compound (11,13-dihydroxy-8-desoxygrosheimin) was studied. The activities of fixed-ratio mixtures were assessed on wheat coleoptile. The results can be interpreted with respect to a reference model by considering dose−response analyses and isobolograms with linear regression analyses. A total of 17 binary mixtures at different levels of inhibition (ED25, ED50, and ED75) were studied, and predominantly they responded additively (25). Deviations from additivity included seven synergistic responses and two antagonistic responses. The joint action of major sesquiterpene lactones isolated from C. cardunculus can explain the activities observed in extracts and fractions. The results reported here reiterate the utility of the wheat coleoptile bioassay as a quick tool to detect potential synergistic effects in binary mixtures.

In this chapter, we present a discussion about the practical application of the fractal properties of the medium in the mathematical model through the use of fractional partial derivatives. We present one of the known models for the flow in saturated media and its generalization in fractional order derivatives. In the middle section, we present one of the main arguments that motivate the use of fractional derivatives in the porous media models, this is the Professor Nigmatullin’s work. The final part describes the process for obtaining the coupled system of three equations for the monophase flow with triple porosity and triple permeability, briefly mentioning the method used for the first solutions of the system.