A water-soluble, cell-permeable Mn(ii) sensor enables visualization of manganese dynamics in live mammalian cells.

Central roles of Mn2+ ions in immunity, brain function, and photosynthesis necessitate probes for tracking this essential metal ion in living systems. However, developing a cell-permeable, fluorescent sensor for selective imaging of Mn2+ ions in the aqueous cellular milieu has remained a challenge. This is because Mn2+ is a weak binder to ligand-scaffolds and Mn2+ ions quench fluorescent dyes leading to turn-off sensors that are not applicable for in vivo imaging. Sensors with a unique combination of Mn2+ selectivity, µM sensitivity, and response in aqueous media are necessary for not only visualizing labile cellular Mn2+ ions live, but also for measuring Mn2+ concentrations in living cells. No sensor has achieved this combination thus far. Here we report a novel, completely water-soluble, reversible, fluorescent turn-on, Mn2+ selective sensor, M4, with a K d of 1.4 µM for Mn2+ ions. M4 entered cells within 15 min of direct incubation and was applied to image Mn2+ ions in living mammalian cells in both confocal fluorescence intensity and lifetime-based set-ups. The probe was able to visualize Mn2+ dynamics in live cells revealing differential Mn2+ localization and uptake dynamics under pathophysiological versus physiological conditions. In a key experiment, we generated an in-cell Mn2+ response curve for the sensor which allowed the measurement of the endogenous labile Mn2+ concentration in HeLa cells as 1.14 ± 0.15 µM. Thus, our computationally designed, selective, sensitive, and cell-permeable sensor with a 620 nM limit of detection for Mn2+ in water provides the first estimate of endogenous labile Mn2+ levels in mammalian cells.

Diabetic Foot Ulcer Identification: A Review.

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient's lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal.

Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach.

Coronavirus Disease (COVID19) is a fast-spreading infectious disease that is currently causing a healthcare crisis around the world. Due to the current limitations of the reverse transcription-polymerase chain reaction (RT-PCR) based tests for detecting COVID19, recently radiology imaging based ideas have been proposed by various works. In this work, various Deep CNN based approaches are explored for detecting the presence of COVID19 from chest CT images. A decision fusion based approach is also proposed, which combines predictions from multiple individual models, to produce a final prediction. Experimental results show that the proposed decision fusion based approach is able to achieve above 86% results across all the performance metrics under consideration, with average AUROC and F1-Score being 0.883 and 0.867, respectively. The experimental observations suggest the potential applicability of such Deep CNN based approach in real diagnostic scenarios, which could be of very high utility in terms of achieving fast testing for COVID19.