Molecular imaging modalities show valuable non-invasive techniques capable of precisely and selectively addressing molecular markers associated with prostate cancer (PCa). This systematic review provides an overview of imaging markers utilized in positron emission tomography (PET) methods, specifically focusing on the pathways and mediators involved in PCa. This systematic review aims to evaluate and analyse existing literature on the diagnostic accuracy of molecular imaging techniques for detecting PCa. The PubMed, EBSCO, ScienceDirect, and Web of Science databases were searched, identifying 32 studies that reported molecular imaging modalities for detecting PCa. Numerous imaging modalities and radiotracers were used to detect PCa, including 68Ga-prostate-specific membrane antigen (PSMA) PET/computed tomography (CT), 68Ga-PSMA-11 PET/magnetic resonance imaging (MRI), 18F-PSMA-1007 PET/CT, 18F-DCFPyL PET/MRI, 18F-choline PET/MRI, and 18F-fluoroethylcholine PET/MRI. Across 11 studies, radiolabelled 68Ga-PSMA PET/CT imaging had a pooled sensitivity of 80 (95% confidence interval [CI]: 35-93), specificity of 90 (95% CI: 71-98), and accuracy of 86 (95% CI: 64-96). The PSMA-ligand 68Ga-PET/CT showed good diagnostic performance and appears promising for detecting and staging PCa.

Polycyclic aromatic hydrocarbons (PAHs) represent a category of persistent organic pollutants that pose a global concern in the realm of food safety due to their recognized carcinogenic properties in humans. Food can be contaminated with PAHs that are present in water, air, or soil, or during food processing and cooking. The wide and varied sources of PAHs contribute to their persistent contamination of food, leading to their accumulation within these products. As a result, monitoring of the levels of PAHs in food is necessary to guarantee the safety of food products as well as the public health. This review paper attempts to give its readers an overview of the impact of PAHs on crops, their occurrence and sources, and the methodologies employed for the sample preparation and detection of PAHs in food. In addition, possible directions for future research are proposed. The objective is to provide references for the monitoring, prevention, and in-depth exploration of PAHs in food.

BACKGROUND: Although the present diagnosis of acute kidney injury (AKI) involves measurement of acute increases in serum creatinine (SC) and reduced urine output (UO), measurement of UO is underutilized for diagnosis of AKI in clinical practice. The purpose of this investigation was to conduct a systematic literature review of published studies that evaluate both UO and SC in the detection of AKI to better understand incidence, healthcare resource use, and mortality in relation to these diagnostic measures and how these outcomes may vary by population subtype.
METHODS: The systematic literature review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Data were extracted from comparative studies focused on the diagnostic accuracy of UO and SC, relevant clinical outcomes, and resource usage. Quality and validity were assessed using the National Institute for Health and Care Excellence (NICE) single technology appraisal quality checklist for randomized controlled trials and the Newcastle-Ottawa Quality Assessment Scale for observational studies.
RESULTS: A total of 1729 publications were screened, with 50 studies eligible for inclusion. A majority of studies (76%) used the Kidney Disease: Improving Global Outcomes (KDIGO) criteria to classify AKI and focused on the comparison of UO alone versus SC alone, while few studies analyzed a diagnosis of AKI based on the presence of both UO and SC, or the presence of at least one of UO or SC indicators. Of the included studies, 33% analyzed patients treated for cardiovascular diseases and 30% analyzed patients treated in a general intensive care unit. The use of UO criteria was more often associated with increased incidence of AKI (36%), than was the application of SC criteria (21%), which was consistent across the subgroup analyses performed. Furthermore, the use of UO criteria was associated with an earlier diagnosis of AKI (2.4-46.0 h). Both diagnostic modalities accurately predicted risk of AKI-related mortality.
CONCLUSIONS: Evidence suggests that the inclusion of UO criteria provides substantial diagnostic and prognostic value to the detection of AKI.

This review examines the utilization of nanotechnology-based chemosensors for identifying environmental toxic ions. Over recent decades, the creation of nanoscale materials for applications in chemical sensing, biomedical, and biological analyses has emerged as a promising avenue. Nanomaterials play a vital role in improving the sensitivity and selectivity of chemosensors, thereby making them effective tools for monitoring and evaluating environmental contamination. This is due to their highly adjustable size- and shape-dependent chemical and physical properties. Nanomaterials possess distinct surface chemistry, thermal stability, high surface area, and large pore volume per unit mass, which can be harnessed for sensor development. The discussion encompasses different types of nanomaterials utilized in chemosensor design, LOD, their sensing mechanisms, and their efficacy in detecting specific toxic ions. Furthermore, the review explores the progress made, obstacles faced, and future prospects in this rapidly evolving field, highlighting the potential contributions of nanotechnology to the creation of robust sensing platforms for environmental monitoring.

Leukemia is a rare but fatal cancer of the blood. This cancer arises from abnormal bone marrow cells and requires prompt diagnosis for effective treatment and positive patient prognosis. Traditional diagnostic methods (e.g., microscopy, flow cytometry, and biopsy) pose challenges in both accuracy and time, demanding an inquisition on the development and use of deep learning (DL) models, such as convolutional neural networks (CNN), which could allow for a faster and more exact diagnosis. Using specific, objective criteria, DL might hold promise as a tool for physicians to diagnose leukemia. The purpose of this review was to report the relevant available published literature on using DL to diagnose leukemia. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, articles published between 2010 and 2023 were searched using Embase, Ovid MEDLINE, and Web of Science, searching the terms \"leukemia\" AND \"deep learning\" or \"artificial neural network\" OR \"neural network\" AND \"diagnosis\" OR \"detection.\" After screening retrieved articles using pre-determined eligibility criteria, 20 articles were included in the final review and reported chronologically due to the nascent nature of the phenomenon. The initial studies laid the groundwork for subsequent innovations, illustrating the transition from specialized methods to more generalized approaches capitalizing on DL technologies for leukemia detection. This summary of recent DL models revealed a paradigm shift toward integrated architectures, resulting in notable enhancements in accuracy and efficiency. The continuous refinement of models and techniques, coupled with an emphasis on simplicity and efficiency, positions DL as a promising tool for leukemia detection. With the help of these neural networks, leukemia detection could be hastened, allowing for an improved long-term outlook and prognosis. Further research is warranted using real-life scenarios to confirm the suggested transformative effects DL models could have on leukemia diagnosis.

Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI\'s role in CME diagnosis and its performance in \"detection\", \"classification\" and \"segmentation\" of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96% in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.

Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size, and computational power limitations. Since these IoT devices are connected to the Internet and have low security levels, one of the main risks of these devices is being compromised by malicious malware and becoming part of IoT botnets. IoT botnets are used for launching different types of large-scale attacks including Distributed Denial-of-Service (DDoS) attacks. These attacks are continuously evolving, and researchers have conducted numerous analyses and studies in this area to narrow security vulnerabilities. This paper systematically reviews the prominent literature on IoT botnet DDoS attacks and detection techniques. Architecture IoT botnet DDoS attacks, evaluations of those attacks, and systematically categorized detection techniques are discussed in detail. The paper presents current threats and detection techniques, and some open research questions are recommended for future studies in this field.

In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.

The ability to analyze blunt-force trauma is crucial for deciphering valuable clues concerning mechanisms of injury and as evidence for medico-legal investigations. The use of alternate light sources (ALS) has been studied over the past decade, and is proposed to outperform conventional white light (CWL) during bruise assessments. In response to the growing interest of the technology worldwide, a systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) to address the ability of ALS to detect and visualize bruising. From an initial 4055 records identified, ten studies met the eligibly criteria and were selected for this review. Evaluation also included a novel framework, referred to as SPICOT, to further systematically assess both scientific evidence and risk of bias in forensic literature. Analysis reveals that narrowband wavelengths within in the infrared or ultraviolet spectral ranges do not significantly outperform CWL in visualizing or detecting bruising. However, wavelengths within the visible spectrum, particularly 415 nm combined with longpass or bandpass yellow filters, are more effective. However, the majority of selected studies only address the sensitivity of ALS, and therefore, results may only be considered valid when the location of a bruise is known. Further investigation is required to understand the specificity of ALS, in particular how the use of topical cosmetic products, previous wounds/scar-tissue, tattoos, moles and freckles may affect detection. The ethical concern regarding the interpretation of enhanced visualized trauma should also be considered in prospect discussions prior to implementing ALS into routine practice. Nevertheless, this review finds that narrowband ALS within the visible spectrum demonstrates potential for improved injury documentation, outperforming CWL in the detection and visualization of bruising.

Rice (Oryza sativa L.) is one of the most consumed cereals that along with several important nutritional constituents typically provide more than 21% of the caloric requirements of human beings. Aflatoxins (AFs) are toxic secondary metabolites of several Aspergillus species that are prevalent in cereals, including rice. This review provides a comprehensive overview on production factors, prevalence, regulations, detection methods, and decontamination strategies for AFs in the rice production chain. The prevalence of AFs in rice is more prominent in African and Asian than in European countries. Developed nations have more stringent regulations for AFs in rice than in the developing world. The contamination level of AFs in the rice varied at different stages of rice production chain and is affected by production practices, environmental conditions comprising temperature, humidity, moisture, and water activity as well as milling operations such as de-husking, parboiling, and polishing. A range of methods including chromatographic techniques, immunochemical methods, and spectrophotometric methods have been developed, and used for monitoring AFs in rice. Chromatographic methods are the most used methods of AFs detection followed by immunochemical techniques. AFs decontamination strategies adopted worldwide involve various physical, chemical, and biological strategies, and even using plant materials. In conclusion, adopting good agricultural practices, implementing efficient AFs detection methods, and developing innovative aflatoxin decontamination strategies are imperative to ensure the safety and quality of rice for consumers.