Aberrant E-cadherin Expression in Lobular Carcinoma In Situ: A Comprehensive Immunohistochemical Evaluation of N-terminal, Extracellular, and C-terminal E-cadherin Domains.

E-cadherin (E-cad) immunohistochemistry is commonly used to distinguish lobular carcinoma in situ (LCIS) from ductal carcinoma in situ in histologically uncertain or ambiguous cases. Although most LCIS cases show an absence of E-cad expression on the neoplastic cell membranes, some show aberrant E-cad expression which can lead to diagnostic confusion. Awareness and understanding of the frequency, patterns, and distribution of aberrant E-cad staining in LCIS is crucial to achieving a correct diagnosis.

Recovery of Nd and Dy from E-Waste Using Adsorbents from Spent Tyre Rubbers: Batch and Column Dynamic Assays.

This paper investigates the use of spent tyre rubber as a precursor for synthesising adsorbents to recover rare earth elements. Through pyrolysis and CO activation, tyre rubber is converted into porous carbonaceous materials with surface properties suited for rare earth element adsorption. The study also examines the efficiency of leaching rare earth elements from NdFeB magnets using optimised acid leaching methods, providing insights into recovery processes.

Detection of Leishmania infantum and Leishmania amazonensis in Bats From Endemic and Non-endemic Areas of São Paulo State, Brazil.

Aims: Leishmaniasis is an endemic disease in several regions of Brazil, a tropical country that presents specific environmental conditions that contribute to the development of phlebotomine vectors. This study aimed to detect Leishmania species in naturally infected bats from 17 municipalities in the São Paulo state.

Methods And Results: Spleen and liver samples from 203 bats were analysed by real-time PCR and confirmed by conventional PCR followed by gene sequencing.

Dyadic Partition-Based Training Schemes for TV/TGV Denoising.

Due to their ability to handle discontinuous images while having a well-understood behavior, regularizations with total variation (TV) and total generalized variation (TGV) are some of the best-known methods in image denoising. However, like other variational models including a fidelity term, they crucially depend on the choice of their tuning parameters. A remedy is to choose these automatically through multilevel approaches, for example by optimizing performance on noisy/clean image pairs.