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The recent advancements in breast cancer precision medicine have highlighted the urgency for the precise and reproducible characterization of clinically actionable biomarkers. Despite numerous standardization efforts, biomarker testing by conventional methodologies is challenged by several issues such as high inter-observer variabilities, the spatial heterogeneity of biomarkers expression, and technological heterogeneity. In this respect, artificial intelligence-based digital pathology approaches are being increasingly recognized as promising methods for biomarker testing and subsequently improved clinical management. Here, we provide an overview on the most recent advances for artificial intelligence-assisted biomarkers testing in breast cancer, with a particular focus on tumor-infiltrating lymphocytes, programmed death-ligand 1, phosphatidylinositol-3 kinase catalytic alpha, and estrogen receptor 1. Challenges and solutions for this integrative analysis in pathology laboratories are also provided.

The diagnostic assessment of thyroid nodules is hampered by the persistence of uncertainty in borderline cases, and further complicated by the inclusion of non-invasive follicular tumor with papillary-like nuclear features (NIFTP) as a less aggressive alternative to papillary thyroid carcinoma (PTC). In this setting, computational methods might facilitate the diagnostic process by unmasking key nuclear characteristics of NIFTPs. The main aims of this work were to (1) identify morphometric features of NIFTP and PTC that are interpretable for the human eye, and (2) develop a deep learning model for multi-class segmentation as a support tool to reduce diagnostic variability. Our findings confirmed that nuclei in NIFTP and PTC share multiple characteristics, setting them apart from hyperplastic nodules (HP). The morphometric analysis identified 15 features that can be translated into nuclear alterations readily understandable by pathologists, such as a remarkable inter-nuclear homogeneity for HP in contrast to a major complexity in the chromatin texture of NIFTP, and to the peculiar pattern of nuclear texture variability of PTC. A few NIFTP cases with available NGS data were also analyzed to initially explore the impact of RAS-related mutations on nuclear morphometry. Finally, a pixel-based deep learning model was trained and tested on whole slide images (WSIs) of NIFTP, PTC, and HP cases. The model, named NUTSHELL (NUclei from Thyroid tumors Segmentation to Highlight Encapsulated Low-malignant Lesions), successfully detected and classified the majority of nuclei in all WSIs’ tiles, showing comparable results with already well-established pathology nuclear scores. NUTSHELL provides an immediate overview of NIFTP areas and can be used to detect microfoci of PTC within extensive glandular samples or identify lymph node metastases. NUTSHELL can be run inside WSInfer with an easy rendering in QuPath, thus facilitating the democratization of digital pathology.

Nowadays, the development of new effective photocatalytic materials for the purification of real wastewaters and model systems containing organic molecules constitutes an important challenge. Here we present a preparation strategy for composite materials based on hexamolybdenum cluster complexes and exfoliated hexagonal boron nitride (h-BN) nanosheets. Cluster deposition on the nanosheet surface was achieved by impregnation of the matrix by a (Bu4N)2[{Mo6I8}(NO3)6]/acetone solution. Successful cluster immobilization and chemical composition of the samples were verified by inductively coupled plasma atomic emission spectroscopy, transmission electron microscopy with elemental mapping (TEM/EDS), X-ray photoelectron spectroscopy (XPS), and optical diffuse-reflectance spectroscopy. A small amount of water in acetone initiates the hydrolysis of a molybdenum cluster precursor with labile NO3–ligands, which are absent in the final composite, according to the XPS data. Intermediate hydrolyzed cluster forms anchor to the surface of h-BN nanosheets and promote growth of the insoluble compound [{Mo6I8}(H2O)2(OH)4]·yH2O as the final hydrolysis product. TEM/EDS proves that the cluster exists at the nanosheet surface in the form of an X-ray diffraction amorphous thin film. The samples obtained show high photocatalytic activity in the degradation of a model pollutant rhodamine B under UV- and visible-light irradiation. The materials retain their initial photocatalytic efficacy during at least six cycles without the need for recovery.

Over the past decade, nanosized metal oxides, metals, and bimetallic particles have been actively researched as enzyme mimetic nanomaterials. However, the common issues with individual nanoparticles (NPs) are stabilization, reproducibility, and blocking of active sites by surfactants. These problems promote further studies of composite materials, where NPs are spread on supports, such as graphene derivatives or dichalcogenide nanosheets. Another promising type of support for NPs is the few-layered hexagonal boron nitride (hBN). In this study, we develop surfactant-free nanocomposites containing Pt NPs dispersed on chemically modified hydrophilic hBN nanosheets (hBNNSs). Ascorbic acid was used as a reducing agent for the chemical reduction of the Pt salt in the presence of hBNNS aqueous colloid, resulting in Pt/hBNNS nanocomposites, which were thoroughly characterized with X-ray diffraction, transmission electron microscopy, dynamic light scattering, and X-ray photoelectron and infrared spectroscopies. Similar to graphene oxide binding the metal NPs more efficiently than pure graphene, hydrophilic hBNNSs well stabilize Pt NPs, with particle size down to around 8 nm. We further demonstrate for the first time that Pt/hBNNS nanocomposites exhibit peroxidase-like catalytic activity, accelerating the oxidation of the classical colorless peroxidase substrate 3,3′,5,5′-tetramethylbenzidine (TMB) to its corresponding blue-colored oxidized product in the presence of H2O2. Kinetic and mechanism studies involving terephthalic acid and isopropanol as a fluorescent probe and an •OH radical scavenger, respectively, proved that Pt/hBNNSs assist H2O2decomposition to active oxygen species (•OH), which are responsible for TMB oxidation. The Pt/hBNNS nanocomposite-assisted oxidation of TMB provides an effective platform for the colorimetric detection of dopamine, an important biomolecule. The presence of increased amounts of dopamine gradually inhibits the catalytic activity of Pt/hBNNSs for the oxidation of TMB by H2O2, thus enabling selective sensing of dopamine down to 0.76 μM, even in the presence of common interfering molecules and on real blood serum samples. The present investigation on Pt/hBNNSs contributes to the knowledge of hBN-based nanocomposites and discovers their new usage as nanomaterials with good enzyme-mimicking activity and dopamine-sensing properties.

We have obtained three new rhenium(IV) chalcobromides belonging to the homologous series {Re4S4}Br8(TeBr2)n(n= 0, 3, 4): a molecular complex {Re4S4}Br8(TeBr2)4(1), a dimeric complex [{Re4S4}(TeBr2)3Br7(μ-Br)]2(2), and a two-dimensional (2D) polymeric compound {Re4S4}Br8(3). Compound 1is isotypic to the already known {Re4Te4}(TeBr2)4Br8, while 2and 3exhibit a new type of binding of tetrahedral clusters via μ-Br bridges. Compounds were characterized by X-ray single-crystal diffraction, X-ray powder diffraction, and thermal and elemental analyses. In compound 2, two tetrahedral cluster cores {Re4S4}8+are linked together forming a dimer through two Re−μ-Br–Re bridges. Calculations of the electron localization function (ELF) showed that there is no covalent interaction between rhenium atoms of neighboring clusters. In compound 3, each rhenium atom of the {Re4S4}8+core is coordinated by three Br ligands: one terminal Br and two bridging μ-Br ligands. As a result, eight bridging bromine atoms link {Re4S4}8+cluster cores into goffered layers. {Re4S4}Br8is the new stable rhenium(IV) thiobromide, the first discovered in the Re–S–Br system, along with the already known octahedral rhenium(III) thiobromides Re6S4+xBr10–2x(x= 0–4).