Excited-State Dynamics in Borylated Arylisoquinoline Complexes in Solution and in cellulo.

Two four-coordinate organoboron N,C-chelate complexes with different functional terminals on the PEG chains are studied with respect to their photophysical properties within human MCF-7 cells. Their excited-state properties are characterized by time-resolved pump-probe spectroscopy and fluorescence lifetime microscopy. The excited-state relaxation dynamics of the two complexes are similar when studied in DMSO. Aggregation of the complexes with the carboxylate terminal group is observed in water. When studying the light-driven excited-state dynamics of both complexes in cellulo, i. e., after being taken up into human MCF-7 cells, both complexes show different features depending on the nature of the anchoring PEG chains. The lifetime of a characteristic intramolecular charge-transfer state is significantly shorter when studied in cellulo (360±170 ps) as compared to in DMSO (∼960 ps) at 600 nm for the complexes with an amino group. However, the kinetics of the complexes with the carboxylate group are in line with those recorded in DMSO. On the other hand, the lifetimes of the fluorescent state are almost identical for both complexes in cellulo. These findings underline the importance to evaluate the excited-state properties of fluorophores in a complex biological environment in order to fully account for intra- and intermolecular effects governing the light-induced processes in functional dyes.

Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques.

Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.

Silicon-rhodamine isothiocyanate for fluorescent labelling.

An efficient synthesis for silicon-rhodamines was developed, enabling the preparation and evaluation of silicon-rhodamine isothiocyanate (SITC) as a novel tool for facile fluorescent labeling. Ease of use in conjugation to amino groups, high stability and excellent photophysical properties are demonstrated. SITC-actin was found to be neutral to F-actin polymerization induction and well suited for high resolution fluorescence microscopy.

Remote Arrhythmia Detection for Eldercare in Malaysia.

Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings.

A Hessian-Based Technique for Specular Reflection Detection and Inpainting in Colonoscopy Images.

In the field of Computer-Aided Detection (CADx), the use of AI-based algorithms for disease detection in endoscopy images, especially colonoscopy images, is on the rise. However, these algorithms often encounter performance issues due to obstructions like specular reflection, resulting in false positives. This paper presents a novel algorithm specifically designed to tackle the challenges posed by high specular reflection regions in colonoscopy images. The proposed algorithm identifies these regions and applies precise inpainting for restoration. The process entails converting the input image from RGB to HSV color space and focusing on the Saturation (S) component in convex regions detected using a Hessian-based method. This step creates a binary mask that pinpoints areas of specular reflection. The inpainting function then uses this mask to guide the restoration of these identified regions and their borders. To ensure a seamless blend of the restored regions with the background and adjacent pixels, a feathering process is applied to the repaired regions. This enhances both the accuracy and aesthetic coherence of the inpainted images. The performance of our algorithm was rigorously tested on five unique colonoscopy datasets and various endoscopy images from the Kvasir dataset, using an extensive set of evaluation metrics and a comparative analysis with existing methods consistently highlighted the superior performance of our algorithm.

PEGylation of Guanidinium and Indole Bearing Poly(methacrylamide)s - Biocompatible Terpolymers for pDNA Delivery.

This study describes the first example for shielding of a high performing terpolymer that consists of N-(2-hydroxypropyl)methacrylamide (HPMA), N-(3-guanidinopropyl)methacrylamide (GPMA), and N-(2-indolethyl)methacrylamide monomers (IEMA) by block copolymerization of a polyethylene glycol derivative - poly(nona(ethylene glycol)methyl ether methacrylate) (P(MEO9 MA)) via reversible addition-fragmentation chain transfer (RAFT) polymerization. The molecular weight of P(MEO9 MA) is varied from 3 to 40 kg mol-1 while the comonomer content of HPMA, GPMA, and IEMA is kept comparable. The influence of P(MEO9 MA) block with various molecular weights is investigated over cytotoxicity, plasmid DNA (pDNA) binding, and transfection efficiency of the resulting polyplexes. Overall, the increase in molecular weight of P(MEO9 MA) block demonstrates excellent biocompatibility with higher cell viability in L-929 cells and an efficient binding to pDNA at N/P ratio of 2. The significant transfection efficiency in CHO-K1 cells at N/P ratio 20 is obtained for block copolymers with molecular weight of P(MEO9 MA) up to 10 kg mol-1 . Moreover, a fluorescently labeled analogue of P(MEO9 MA), bearing perylene monoimide methacrylamide (PMIM), is introduced as a comonomer in RAFT polymerization. Polyplexes consisting of labeled block copolymer with 20 kg mol-1 of P(MEO9 MA) and pDNA are incubated in Hela cells and investigated through structured illumination microscopy (SIM).

Fast quantitative single-molecule detection at ultralow concentrations.

The applicability of single-molecule fluorescence assays in liquids is limited by diffusion to concentrations in the low picomolar range. Here, we demonstrate quantitative single-molecule detection at attomolar concentrations within 1 min by excitation and detection of fluorescence through a single-mode optical fiber in presence of turbulent flow. The combination of high detectability and short measurement times promises applications in ultrasensitive assays, sensors, and point-of-care medical diagnostics.

Economic-statistical design of synthetic np chart with estimated process parameter.

The economic-statistical design of the synthetic np chart with estimated process parameter is presented in this study. The effect of process parameter estimation on the expected cost of the synthetic np chart is investigated with the imposed statistical constraints. The minimum number of preliminary subgroups is determined where an almost similar expected cost to the known process parameter case is desired for the given cost model parameters. However, the available number of preliminary subgroups in practice is usually limited, especially when the number of preliminary subgroups is large. Consequently, the optimal chart parameters of the synthetic np chart are computed by considering the practical number of preliminary subgroups in which the cost function is minimized. This leads to a lower expected cost compared to that of adopting the optimal chart parameter corresponding to the known process parameter case.

cellSTORM-Cost-effective super-resolution on a cellphone using dSTORM.

High optical resolution in microscopy usually goes along with costly hardware components, such as lenses, mechanical setups and cameras. Several studies proved that Single Molecular Localization Microscopy can be made affordable, relying on off-the-shelf optical components and industry grade CMOS cameras. Recent technological advantages have yielded consumer-grade camera devices with surprisingly good performance. The camera sensors of smartphones have benefited of this development. Combined with computing power smartphones provide a fantastic opportunity for "imaging on a budget". Here we show that a consumer cellphone is capable of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we used a trained image-to-image generative adversarial network (GAN) to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance directly on the smartphone. We believe that "cellSTORM" paves the way to make super-resolution microscopy not only affordable but available due to the ubiquity of cellphone cameras.

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions.

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.