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Over the last few decades, field-effect transistor (FET)-based biosensors have demonstrated great potential across various industries, including medical, food, agriculture, environmental, and military sectors. These biosensors leverage the electrical properties of transistors to detect a wide range of biomolecules, such as proteins, DNA, and antibodies. This article presents a comprehensive review of advancements in the architectures of FET-based biosensors aiming to enhance device performance in terms of sensitivity, detection time, and selectivity. The review encompasses an overview of emerging FET-based biosensors and useful guidelines to reach the best device dimensions, favorable design, and realization of FET-based biosensors. Consequently, it furnishes researchers with a detailed perspective on design considerations and applications for future generations of FET-based biosensors. Finally, this article proposes intriguing avenues for further research on the topology of FET-based biosensors., (Copyright © 2024, Smaani et al.)

This paper presents a novel framework for breast cancer detection using mammogram images. The proposed solution aims to output an explainable classification from a mammogram image. The classification approach uses a Case-Based Reasoning system (CBR). CBR accuracy strongly depends on the quality of the extracted features. To achieve relevant classification, we propose a pipeline that includes image enhancement and data augmentation to improve the quality of extracted features and provide a final diagnosis. An efficient segmentation method based on a U-Net architecture is used to extract Regions of interest (RoI) from mammograms. The purpose is to combine deep learning (DL) with CBR to improve classification accuracy. DL provides accurate mammogram segmentation, while CBR gives an explainable and accurate classification. The proposed approach was tested on the CBIS-DDSM dataset and achieved high performance with an accuracy (Acc) of 86.71 % and a recall of 91.34 %, outperforming some well-known machine learning (ML) and DL approaches., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Lacking disease-modifying osteoarthritis drugs (DMOADs) for knee osteoarthritis (KOA), Total Knee Arthroplasty (TKA) is often considered an important clinical outcome. Thus, it is important to determine the most relevant factors that are associated with the risk of TKA. The present study aims to develop a model based on a combination of X-ray trabecular bone texture (TBT) analysis, and clinical and radiological information to predict TKA risk in patients with or at risk of developing KOA. This study involved 4382 radiographs, obtained from the OsteoArthritis Initiative (OAI) cohort. Cases were defined as patients with TKA on at least one knee prior to the 108-month follow-up time point and controls were defined as patients who had never undergone TKA. The proposed TKA-risk prediction model, combining TBT parameters and Kellgren-Lawrence (KL) grades, was performed using logistic regression. The proposed model achieved an AUC of 0.92 (95% Confidence Interval [CI] 0.90, 0.93), while the KL model achieved an AUC of 0.86 (95% CI 0.84, 0.86; p < 0.001). This study presents a new TKA prediction model with a good performance permitting the identification of at risk patient with a good sensitivy and specificity, with a 60% increase in TKA case prediction as reflected by the recall values., (© 2022. The Author(s).)

Food resources contaminated with spoilage or pathogenic microorganisms pose severe problems to all higher organisms. Here, we describe a food-hygienic strategy of the emerald cockroach wasp Ampulex compressa. The wasp larvae develop on and inside the American cockroach Periplaneta americana, a host that can harbor various putrefactive microbes, as well as human and insect pathogens. From P. americana, we isolated the Gram-negative bacterium Serratia marcescens, which is a potent entomopathogen that can rapidly kill insect larvae. It is also known as a food contaminant and as an opportunistic human pathogen. Using behavioral observations and chemical analyses, we demonstrated that A. compressa larvae impregnate their cockroach hosts from inside with large amounts of an oral secretion containing a blend of γ-lactones and isocoumarins with (R)-(-)-mellein [(R)-(-)-3,4-diydro-8-hydroxy-3-methylisocoumarin] and micromolide [(4R,9Z)-octadec-9-en-4-olide] as dominant components. We fractionated hexane extracts of the secretion and investigated the antimicrobial properties of the fraction containing the lactones and isocoumarins, as well as of synthetic (R)-(-)-mellein and micromolide, against S. marcescens and a Gram-positive bacterium, Staphylococcus hyicus, in broth microdilution assays. The test fraction inhibited growth of both tested bacteria. The activity of the fraction against S. marcescens was explained by (R)-(-)-mellein alone, and the activity against S. hyicus was explained by the combined action of (R)-(-)-mellein and micromolide. Our data suggest that the specific combination of antimicrobials in the larval secretion provides an effective frontline defense against the unpredictable spectrum of microbes that A. compressa larvae may encounter during their development inside their cockroach hosts.

The first enantioselective synthesis of (-)-paeonilide is reported. Starting from inexpensive furan-3-carboxylic acid the targeted monoterpene was obtained in 12 steps via an asymmetric cyclopropanation-lactonization cascade and a stereoselective side chain insertion at an acetal-like position., (This journal is © The Royal Society of Chemistry 2012)