A bright red fluorescent genetically encoded sensor for lactate imaging.

Lactate plays a crucial role in energy metabolism and greatly impacts protein activities, exerting diverse physiological and pathological effects. Therefore, convenient lactate assays for tracking spatiotemporal dynamics in living cells are desirable. In this paper, we engineered and optimized a red fluorescent protein sensor for l-lactate named FiLa-Red. This indicator exhibited a maximal fluorescence change of 730 % and an apparent dissociation constant (Kd) of approximately 460 µM. By utilizing FiLa-Red and other sensors, we monitored energy metabolism in a multiplex manner by simultaneously tracking lactate and NAD+/NADH abundance in the cytoplasm, nucleus, and mitochondria. The FiLa-Red sensor is expected to be a useful tool for performing metabolic analysis in vitro, in living cells and in vivo.

Detecting anteriorly displaced temporomandibular joint discs using super-resolution magnetic resonance imaging: a multi-center study.

Background: Magnetic resonance imaging (MRI) plays a crucial role in diagnosing anterior disc displacement (ADD) of the temporomandibular joint (TMJ). The primary objective of this study is to enhance diagnostic accuracy in two common disease subtypes of ADD of the TMJ on MRI, namely, ADD with reduction (ADDWR) and ADD without reduction (ADDWoR). To achieve this, we propose the development of transfer learning (TL) based on Convolutional Neural Network (CNN) models, which will aid in accurately identifying and distinguishing these subtypes. Methods: A total of 668 TMJ MRI scans were obtained from two medical centers. High-resolution (HR) MRI images were subjected to enhancement through a deep TL, generating super-resolution (SR) images. Naive Bayes (NB) and Logistic Regression (LR) models were applied, and performance was evaluated using receiver operating characteristic (ROC) curves. The model's outcomes in the test cohort were compared with diagnoses made by two clinicians. Results: The NB model utilizing SR reconstruction with 400 × 400 pixel images demonstrated superior performance in the validation cohort, exhibiting an area under the ROC curve (AUC) of 0.834 (95% CI: 0.763-0.904) and an accuracy rate of 0.768. Both LR and NB models, with 200 × 200 and 400 × 400 pixel images after SR reconstruction, outperformed the clinicians' diagnoses. Conclusion: The ResNet152 model's commendable AUC in detecting ADD highlights its potential application for pre-treatment assessment and improved diagnostic accuracy in clinical settings.

3D Ultrasensitive Polymers-Plasmonic Hybrid Flexible Platform for In-Situ Detection.

This paper introduces a three-dimensional (3D) pyramid to the polymers-plasmonic hybrid structure of polymethyl methacrylate (PMMA) composite silver nanoparticle (AgNPs) as a higher quality flexible surface-enhanced Raman scattering (SERS) substrate. Benefiting from the effective oscillation of light inside the pyramid valley could provide wide distributions of 3D "hot spots" in a large space. The inclined surface design of the pyramid structure could facilitate the aggregation of probe molecules, which achieves highly sensitive detection of rhodamine 6G (R6G) and crystal violet (CV). In addition, the AgNPs and PMMA composite structures provide uniform space distribution for analyte detection in a designated hot spot zone. The incident light can penetrate the external PMMA film to trigger the localized plasmon resonance of the encapsulated AgNPs, achieving enormous enhancement factor (~ 6.24 × 10 8 ). After undergoes mechanical deformation, the flexible SERS substrate still maintains high mechanical stability, which was proved by experiment and theory. For practical applications, the prepared flexible SERS substrate is adapted to the in-situ Raman detection of adenosine aqueous solution and the methylene-blue (MB) molecule detection of the skin of a fish, providing a direct and nondestructive active-platform for the detecting on the surfaces with any arbitrary morphology and aqueous solution.