Nuclear extraction from endometrial tumors for single nuclei sequencing v1

Modified from: Slyper, M. et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 2020 26526, 792–802 (2020). A optimized protocol for nuclear extraction from endometrial tumors. Performed with endometrial adenocarcinoma, endometrioid type, FIGO grade 1.

Laser-Induced Thermal Treatment of Superficial Human Tumors: An Advanced Heating Strategy and Non-Arrhenius Law for Living Tissues

The most interesting, but insufficiently known results obtained by the author in modeling laser-induced hyperthermia of human tumors are discussed. It is important that the traditional equation for the local bio-heat transfer does not work in superficial layers of the body. It is shown also that the classical Arrhenius law is not applicable to living tissues because of the tissue regeneration due to oxygen supplied by the arterial blood. The latter is one of the main reasons of the suggested strategy of laser heating of tumors in the therapeutic window of semitransparency when the tumor asphyxiation is considered as one of important weapons against the cancer. The other advantages of this advanced strategy of a soft thermal treatment (in few of sessions), which is painless for patients, are discussed as well. Some features of modeling various heat transfer modes are also considered. The best choice between the simplest differential models for the radiative transfer calculations is dependent of the particular problem statement. The known finite-difference or finite element algorithms can be preferable in solving transient heat transfer problems. As a rule, it depends on the shape of the computational region. It is expected that this paper will help the colleagues to overcome some typical weaknesses of computational modeling of infrared photothermal treatment of superficial tumors.

Plasmonic Metasurfaces for Medical Diagnosis Applications: A Review

Plasmonic metasurfaces have been widely used in biosensing to improve the interaction between light and biomolecules through the effects of near-field confinement. When paired with biofunctionalization, plasmonic metasurface sensing is considered as a viable strategy for improving biomarker detection technologies. In this review, we enumerate the fundamental mechanism of plasmonic metasurfaces sensing and present their detection in human tumors and COVID-19. The advantages of rapid sampling, streamlined processes, high sensitivity, and easy accessibility are highlighted compared with traditional detection techniques. This review is looking forward to assisting scientists in advancing research and developing a new generation of multifunctional biosensors.

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.