Tracking Multidrug Resistance in Gram-Negative Bacteria in Alexandria, Egypt (2020-2023): An Integrated Analysis of Patient Data and Diagnostic Tools.

Background: The rise in carbapenem-resistant (CRE) in Egypt, particularly in hospital settings, poses a significant public health challenge. This study aims to develop a combined epidemiological surveillance tool utilizing the Microreact online platform (version 269) and molecular microarray technology to track and analyze carbapenem-resistant strains in Egypt. The objective is to integrate molecular diagnostics and real-time data visualization to better understand the spread and evolution of multidrug-resistant (MDR) bacteria.

Design of a Duplex-to-Complex Structure-Switching Approach for the Homogeneous Determination of Marine Biotoxins in Water.

In this report, we describe a fluorescent assay for the detection of six marine toxins in water. The mechanism of detection is based on a duplex-to-complex structure-switching approach. The six aptamers specific to the targeted cyanotoxins were conjugated to a fluorescent dye, carboxyfluorescein (FAM).

Design of an innovative aptasensor for the detection of chemotherapeutic drug Fludarabine phosphate.

Monitoring the concentration of Fludarabine phosphate, a standard chemotherapeutic drug widely used in cancer treatment, is vital for ensuring the drug's safety and effectiveness, tailoring treatments to individual needs, and consequently improving overall patient outcomes. Regarding the limitations of conventional techniques in terms of complexity, large time measurements, and a high cost, there is an urgent need to develop simple, rapid, and cost-effective devices. In this paper, we report the design of an aptasensor for the specific and selective detection of Fludarabine.

Covalent Self-Assembly of Bio-HCP Nanoparticles for Shell-Programmed Encapsulation of Microbial Cells.

Modifying the bacterial surface through grafting functional nanoparticles is a common strategy for programing bacteria. At this moment, the targeted nanoparticles face a dilemma of no multifunctional structure, high toxicity, and weak chemical driving forces, which restrict the broad practical applications. Like a multistage booster of a rocket, we propose a multistage covalent self-assembly strategy to protect, expand, and control the encapsulated shells of microbial cells via biocompatible hyper-cross-linked polymer nanoparticles (Bio-HCP NPs) with internal porosity and surface functional groups.

Artifacts and Anomalies in Raman Spectroscopy: A Review on Origins and Correction Procedures.

Raman spectroscopy, renowned for its unique ability to provide a molecular fingerprint, is an invaluable tool in industry and academic research. However, various constraints often hinder the measurement process, leading to artifacts and anomalies that can significantly affect spectral measurements. This review begins by thoroughly discussing the origins and impacts of these artifacts and anomalies stemming from instrumental, sampling, and sample-related factors.

Evaluating the potential of vancomycin-modified magnetic beads as a tool for sample preparation in diagnostic assays.

Vancomycin-functionalized micro- or nanoparticles are frequently used for isolation and enrichment of bacteria from various samples. Theoretically, only Gram-positive organisms should adhere to the functionalized surfaces as vancomycin is an antibiotic targeting a peptidoglycan precursor in the cell wall, which in Gram-negative bacteria is shielded by the outer cell membrane. In the literature, however, it is often reported that Gram-negative bacteria also bind efficiently to the vancomycin-modified particles.

Spectral Zones-Based SHAP/LIME: Enhancing Interpretability in Spectral Deep Learning Models Through Grouped Feature Analysis.

Interpretability is just as important as accuracy when it comes to complex models, especially in the context of deep learning models. Explainable artificial intelligence (XAI) approaches have been developed to address this problem. The literature on XAI for spectroscopy mainly emphasizes independent feature analysis with limited application of zone analysis.

Cellular Output and Physicochemical Properties of the Membrane-Derived Vesicles Depend on Chemical Stimulants.

Synthetic liposomes are widely used as drug delivery vehicles in biomedical treatments, such as for mRNA-based antiviral vaccines like those recently developed against SARS-CoV-2. Extracellular vesicles (EVs), which are naturally produced by cells, have emerged as a next-generation delivery system. However, key questions regarding their origin within cells remain unresolved.

Towards an understanding of physical activity-induced post-exertional malaise: Insights into microvascular alterations and immunometabolic interactions in post-COVID condition and myalgic encephalomyelitis/chronic fatigue syndrome.

Background: A considerable number of patients who contracted SARS-CoV-2 are affected by persistent multi-systemic symptoms, referred to as Post-COVID Condition (PCC). Post-exertional malaise (PEM) has been recognized as one of the most frequent manifestations of PCC and is a diagnostic criterion of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Yet, its underlying pathomechanisms remain poorly elucidated.

Imaging Diffractometric Biosensors for Label-Free, Multi-Molecular Interaction Analysis.

Biosensors are used for the specific and sensitive detection of biomolecules. In conventional approaches, the suspected target molecules are bound to selected capture molecules and successful binding is indicated by additional labelling to enable optical readout. This labelling requires additional processing steps tailored to the application.

Biomedical SERS - the current state and future trends.

Surface enhanced Raman spectroscopy (SERS) is meeting the requirements in biomedical science being a highly sensitive and specific analytical tool. By employing portable Raman systems in combination with customized sample pre-treatment, point-of-care-testing (POCT) becomes feasible. Powerful SERS-active sensing surfaces with high stability and modification layers if required are available for testing and application in complex biological matrices such as body fluids, cells or tissues.

Standoff Identification of Plastic Waste Using a Low-Cost Compact Laser-Induced Breakdown Spectroscopy (LIBS) Detection System.

We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m.

Explainable artificial intelligence for spectroscopy data: a review.

Explainable artificial intelligence (XAI) has gained significant attention in various domains, including natural and medical image analysis. However, its application in spectroscopy remains relatively unexplored. This systematic review aims to fill this gap by providing a comprehensive overview of the current landscape of XAI in spectroscopy and identifying potential benefits and challenges associated with its implementation.

Estimation of biological variance in coherent Raman microscopy data of two cell lines using chemometrics.

Broadband Coherent Anti-Stokes Raman Scattering (BCARS) is a valuable spectroscopic imaging tool for visualizing cellular structures and lipid distributions in biomedical applications. However, the inevitable biological changes in the samples (cells/tissues/lipids) introduce spectral variations in BCARS data and make analysis challenging. In this work, we conducted a systematic study to estimate the biological variance in BCARS data of two commonly used cell lines (HEK293 and HepG2) in biomedical research.

Effects and avoidance of photoconversion-induced artifacts in confocal and STED microscopy.

Fluorescence microscopy is limited by photoconversion due to continuous illumination, which results in not only photobleaching but also conversion of fluorescent molecules into species of different spectral properties through photoblueing. Here, we determined different fluorescence parameters of photoconverted products for various fluorophores under standard confocal and stimulated emission depletion (STED) microscopy conditions. We observed changes in both fluorescence spectra and lifetimes that can cause artifacts in quantitative measurements, which can be avoided by using exchangeable dyes.

Parallel detection of multiple biomarkers in a point-of-care-competent device for the prediction of exacerbations in chronic inflammatory lung disease.

Sudden aggravations of chronic inflammatory airway diseases are difficult-to-foresee life-threatening episodes for which advanced prognosis-systems are highly desirable. Here we present an experimental chip-based fluidic system designed for the rapid and sensitive measurement of biomarkers prognostic for potentially imminent asthma or COPD exacerbations. As model biomarkers we chose three cytokines (interleukin-6, interleukin-8, tumor necrosis factor alpha), the bacterial infection marker C-reactive protein and the bacterial pathogen Streptococcus pneumoniae-all relevant factors in exacerbation episodes.

A Proof-of-Concept Protein Microarray-Based Approach for Serotyping of Strains.

, a bacterium causing foodborne illnesses like salmonellosis, is prevalent in Europe and globally. It is found in food, water, and soil, leading to symptoms like diarrhea and fever. Annually, it results in about 95 million cases worldwide, with increasing antibiotic resistance posing a public health challenge.

Selection of ssDNA aptamers and construction of aptameric electrochemical biosensor for the detection of Giardia intestinalis trophozoite protein.

Giardia intestinalis is one of the most widespread intestinal parasites and is considered a major cause of epidemic or sporadic diarrhea worldwide. In this study, we aimed to develop a rapid aptameric diagnostic technique for G. intestinalis infection.

Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy.

Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming.