Search

Addressing the pressing demand for rapid and inexpensive coagulation testing in cardiovascular care, this study introduces a novel application of repurposed COVID-19 rapid antigen tests (RATs) as paper-based lateral flow assays (LFAs) combined with machine learning for coagulation status evaluation. By further developing a mobile app prototype, we present a platform that enables clinicians to perform immediate and accurate anticoagulant dosing adjustments using existing post-pandemic resources. Our proof-of-concept employs a random forest machine learning classifier to interpret image feature variations on RAT NC membrane, correlating red blood cell (RBC) wicked diffusion distance in recalcified citrated whole blood with changes in coagulative viscosity, easily interpreted. Enhanced by confocal imaging studies of paper microfluidics, our approach provides insights into the mechanisms dissecting coagulation components, achieving high classification precision, recall, and F1-scores. The inverse relationship between RBC wicked diffusion distance and enoxaparin concentration paves the way for machine learning to inform real-time dose prescription adjustments, aligning with individual patient profiles to optimize therapeutic outcomes. This study not only demonstrates the potential of leveraging surplus RATs for coagulation management but also exemplifies a cost-effective, rapid, and smart strategy to enhance clinical decision-making in the post-pandemic era. Highlights: • Repurposed COVID-19 RATs provide an ideal platform for observing differences in blood coagulability. • Random Forest image classification algorithms can facilitate rapid coagulation status assessment on a paper-based LFA platform. [ABSTRACT FROM AUTHOR]

Diagnostics are critical on the path to commercial fusion reactors, since measurements and characterisation of the plasma is important for sustaining fusion reactions. Gamma spectroscopy is commonly used to provide information about the neutron energy spectrum from activation analysis, which can be used to calculate the neutron flux and fusion power. The detection limits for measuring nuclear dosimetry reactions used in such diagnostics are fundamentally related to Compton scattering events making up a background continuum in measured spectra. This background lies in the same energy region as peaks from low-energy gamma rays, leading to detection and characterisation limitations. This paper presents a digital machine learning Compton suppression algorithm (MLCSA), that uses state-of-the-art machine learning techniques to perform pulse shape discrimination for high purity germanium (HPGe) detectors. The MLCSA identifies key features of individual pulses to differentiate between those that are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background. This novel suppression algorithm improves gamma spectroscopy results by lowering minimum detectable activity (MDA) limits and thus reducing the measurement time required to reach the desired detection limit. In this paper, the performance of the MLCSA is demonstrated using an HPGe detector, with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60). The MDA of Am-241 improved by 51% and the signal to background ratio improved by 49%, while the Co-60 peaks were partially preserved (reduced by 78%). The MLCSA requires no modelling of the specific detector and so has the potential to be detector agnostic, meaning the technique could be applied to a variety of detector types and applications. [ABSTRACT FROM AUTHOR]

Earlier access to patients' biomarker status could transform disease management. However, gold-standard techniques such as enzyme-linked immunosorbent assays (ELISAs) are typically not deployed at the point-of-care due to their cumbersome instrumentation and complexity. Electrochemical immunosensors can be disruptive in this sector with their small size and lower cost but, without further modifications, the performance of these sensors in complex media (e.g., blood) has been limited. This paper presents a low-cost fluidic accessory fabricated using widely accessible materials and processes for boosting sensor sensitivity through confinement of the detection media next to the electrode surface. Liquid confinement first highlighted a spontaneous reaction between the pseudoreference electrode and ELISA detection substrate 3,3',5,5'-tetramethylbenzidine (TMB) that decreases the amount of oxTMB available for detection. Different strategies are investigated to limit this and maximize reliability. Next, flow cell integration during the signal amplification step of sensor preparation was shown to substantially enhance the detection of cytokine interleukin-6 (IL-6) with the best sensitivity boost recorded for fresh human plasma (x7 increase compared to x5.8 in purified serum and x5.5 in PBS). The flow cell requires no specialized equipment and can be seamlessly integrated with commercial sensors, making an ideal companion for electrochemical signal enhancement. [ABSTRACT FROM AUTHOR]

As an integral part of the European strategy for advancing fusion-generated electricity, IFMIF-DONES represents a high-intensity neutron irradiation plant with the main purpose of assessing the suitability of materials for fusion reactor applications. Its primary mission is to examine how materials respond to irradiation within a neutron flux that mimics the conditions expected in the first wall of the proposed DEMO reactor, which is intended to succeed ITER. Consequently, IFMIF-DONES, whose construction is slated to commence shortly, plays a pivotal role in aiding the development, approval, and safe operation of DEMO, as well as future fusion power plants. This paper provides a quick overview of the current development of the IFMIF-DONES neutron source with a particular snapshot of the present engineering design status for what concerns the instrumentation and control systems together with its complex diagnostics, that guarantees the safe monitoring, supervision and regulation of all operations. The current status of design, after the completion of the preliminary design phase is presented, as well as the existing and future plans for their integration also using some of the new capabilities offered by Artificial Intelligence tools. [ABSTRACT FROM AUTHOR]

Copyright of Die Dermatologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Molecular sampling with vacuum-assisted tissue electroporation is a novel, minimally invasive method for molecular profiling of solid lesions. In this paper, we report on the design of the battery-powered pulsed electric field generator and electrode configuration for an electroporation-based molecular sampling device for skin cancer diagnostics. Using numerical models of skin electroporation corroborated by the potato tissue phantom model, we show that the electroporated tissue volume, which is the maximum volume for biomarker sampling, strongly depends on the electrode's geometry, needle electrode skin penetration depths, and the applied pulsed electric field protocol. In addition, using excised human basal cell carcinoma (BCC) tissues, we show that diffusion of proteins out of human BCC tissues into water strongly depends on the strength of the applied electric field and on the time after the field application. The developed numerical simulations, confirmed by experiments in potato tissue phantoms and excised human cancer lesions, provide essential tools for the development of electroporation-based molecular markers sampling devices for personalized skin cancer diagnostics. [ABSTRACT FROM AUTHOR]

Evaluation of fish stock status is a key step for fisheries management. Tuna Regional Fisheries Management Organizations (t-RFMOs) are moving towards management strategy evaluation (MSE), a process that combines science and policy and depends on technical aspects, developed by scientists, designed to meet management objectives established by managers and other stakeholders. In the Indian Ocean, the current management advice for swordfish (Xiphias gladius) is based on an ensemble of 24 models considering four areas of uncertainty about the stock dynamics. There is an ongoing MSE process for swordfish, and this paper describes the methodology being applied for the conditioning of the operating model (OM), including model selection and validation. In the MSE, nine sources of uncertainty were considered, each being characterized by 2–3 levels. A partial factorial design was employed to reduce the number of models from a full factorial design to those needed to encompass the overall uncertainty. A selection and validation process was carried out, filtering models that converged, showed good predictive skills, and provided plausible estimates. Overall, the estimated spawning stock biomass (SSB) relative to SSB at maximum sustainable yield (MSY), and fishing mortality (F) relative to FMSY encompasses the estimates of the stock assessment ensemble at the most optimist area of the distribution. The MSE for swordfish is an ongoing process that is expected to provide more robust management advice in the future. Further developments to the OM can still occur, but the methods presented herein can be applied to this, or other species, MSE processes. [ABSTRACT FROM AUTHOR]

This article continues and supplements the article with the same title published in Russian Journal of Nondestructive Testing, no. 1, 2022, which provides six ways to assess the quality and reliability of semiconductor products (SCPs) using low-frequency (LF) noise parameters. This paper presents a generalization of the results previously obtained by the authors and describes four more methods for diagnosing and evaluating the reliability of SCPs by varying the parameters of low- frequency noise under additional external influences such as electrostatic discharge and (or) thermal annealing. It is shown that additional impacts make it possible to increase the reliability of the assessment of the SCP reliability. [ABSTRACT FROM AUTHOR]

Copyright of Herzschrittmachertherapie und Elektrophysiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Preliminary evidence from the Childhood Joint Attention Rating Scale (C-JARS; Mundy et al., 2017) suggests symptoms related to diminished joint attention and the spontaneous sharing of experience with others can be assessed with a parent-report measure in children and adolescents with autism. This study was designed to expand on the previous study by examining the validity of both a Social Symptom (SS) and a Prosocial (PS) scale of the C-JARS in a study of school-aged autistic children (n = 89) with and without co-occurring intellectual disability (ID), as well as an age matched neurotypical sample (n = 62). Results indicated that both C-JARS scales were sensitive and specific with respect to identifying the diagnostic status of the children. In addition, the PS scale was sensitive to differences in cognitive abilities (IQ) and sex differences in the autism group. These results are consistent with the hypothesis that joint attention and spontaneous sharing of experience symptoms are not only characteristic of preschool children with autism but may also constitute a developmentally continuous dimension of the social phenotype of autism that can be measured in school-aged children. [ABSTRACT FROM AUTHOR]

The term long COVID (LC) effectively describes the broad long-term disease burden of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infections, encompassing individual suffering and significant socioeconomic impacts. However, its general use hampers precise epidemiological research, diagnostics and therapeutic strategies. Misinterpretations occur, for example, when population surveys are compared to studies using health record data, because both refer to these data as LC. This also emphasizes the need for different terminology. The National Institute for Health and Care Excellence (NICE) rapid guideline differentiates ongoing symptomatic COVID-19 from post-COVID conditions, yet real-world observations challenge these two subgroup definitions. We propose refining the term LC into three subgroups: ongoing symptomatic COVID-19, SARS-CoV-2 induced or exacerbated diseases and post-acute COVID condition. This stratification aids targeted diagnostics, treatment and epidemiological research. Subgroup-specific documentation using the International Classification of Diseases, Tenth Revision (ICD-10) codes ensures accurate tracking and understanding of long-term effects. The subgroup of post-acute COVID condition again includes various symptoms, syndromes and diseases like post-exertional malaise (PEM), dysautonomia or cognitive dysfunctions. In this regard, differentiation, especially considering PEM, is crucial for effective diagnostics and treatment. [ABSTRACT FROM AUTHOR]

Rapid identification of bacterial species in patient samples is essential for the treatment of infectious diseases and the economics of health care. In this study, we investigated an algorithm to improve the accuracy of bacterial species identification with fluorescence spectroscopy based on autofluorescence from bacteria, and excitation wavelengths suitable for identification. The diagnostic accuracy of each algorithm for ten bacterial species was verified in a machine learning classifier algorithm. The three machine learning algorithms with the highest diagnostic accuracy, extra tree (ET), logistic regression (LR), and multilayer perceptron (MLP), were used to determine the number and wavelength of excitation wavelengths suitable for the diagnosis of bacterial species. The key excitation wavelengths for the diagnosis of bacterial species were 280 nm, 300 nm, 380 nm, and 480 nm, with 280 nm being the most important. The median diagnostic accuracy was equivalent to that of 200 excitation wavelengths when two excitation wavelengths were used for ET and LR, and three excitation wavelengths for MLP. These results demonstrate that there is an optimum wavelength range of excitation wavelengths required for spectroscopic measurement of bacterial autofluorescence for bacterial species identification, and that measurement of only a few wavelengths in this range is sufficient to achieve sufficient accuracy for diagnosis of bacterial species. [ABSTRACT FROM AUTHOR]

Cancer is one of the major noncommunicable diseases, responsible for millions of deaths every year worldwide. Though various cancer detection and treatment modalities are available today, many deaths occur owing to its late-stage detection and metastatic nature. Noninvasive detection using luminescence-based imaging tools is considered one of the promising techniques owing to its low cost, high sensitivity, and brightness. Moreover, these tools are unique and valuable as they can detect even the slightest changes in the cellular microenvironment. To achieve this, a fluorescent probe with strong tumor uptake and high spatial and temporal resolution, especially with high water solubility, is highly demanded. Recently, several water-soluble molecules with emission windows in the visible (400–700 nm), first near-infrared (NIR-I, 700–1000 nm), and second near-infrared (NIR-II, 1000–1700 nm) windows have been reported in literature. This review highlights recently reported water-soluble small organic fluorophores/dyes with applications in cancer diagnosis and therapeutics. We systematically highlight and describe the key concepts, structural classes of fluorophores, strategies for imparting water solubility, and applications in cancer therapy and diagnosis, i.e., theragnostics. We discuss examples of water-soluble fluorescent probes based on coumarin, xanthene, boron–dipyrromethene (BODIPY), and cyanine cores. Some other emerging classes of dyes based on carbocyclic and heterocyclic cores are also discussed. Besides, emerging molecular engineering methods to obtain such fluorophores are discussed. Finally, the opportunities and challenges in this research area are also delineated. [ABSTRACT FROM AUTHOR]

Smart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor. [ABSTRACT FROM AUTHOR]

The reliable conjugation of antibodies to a solid matrix is crucial for robust immunoassays in microfluidic devices. Various magnetic particles (MPs) have been employed due to their high surface-to-volume ratios and ease of magnetic manipulation, providing a reliable surface for antibody immobilization. However, achieving uniform positioning of MPs across the channel surface has been challenging due to inadequate magnetic forces or magnetic field uniformity. Here, we present the utilization of Halbach magnetic arrays to enable consistent deployment of MPs on the microfluidic surface, thereby facilitating robust immunoassay capabilities. Using finite element method magnetics (FEMM) simulations, we predicted that incorporating Halbach magnetic arrays beneath the microfluidic channels would create more uniform and augmented magnetic flux density gradients over the surface. Subsequently, we applied this platform to assess cancer biomarkers in patients' blood plasma and achieved statistically reliable results, comparable to those obtained using an FDA-approved device. We detected three cancer biomarkers, including prostate-specific antigen (PSA), alpha-fetoprotein (AFP), and carcinoembryonic antigen (CEA). The limit of detection (LOD) of three biomarkers were < 1 ng/mL, ranging from 0.38–0.95 ng mL−1. This platform provided within-run, between-run, and between-day precisions for the three cancer biomarkers, ranging from 0.37–9.87%. This advancement holds significant promise for improving the accuracy and performance of immunoassays in various microfluidic diagnostic applications. [ABSTRACT FROM AUTHOR]

Background: With the increasingly serious environmental pollution and natural environment damage, renewable energy such as solar cells have gradually become the key to change this situation. Therefore, the local abnormal diagnosis of the charge and discharge of solar cell capacitors is particularly important. Objective: To extend the life of ultracapacitors by resolving the issue of their low detection rate and enhancing the capacity to recognize fault diagnosis factors. A novel approach to charging and discharging, as well as the diagnosis of local anomalies, is put forth, utilizing switching networks. Methods: By controlling the capacitors of multiple solar cells and supercapacitors to work together, it is possible to compensate for the shortage of photovoltaic power. The performance of fault diagnosis is optimized by combining principal component analysis and binary K-means clustering, which completes the fault diagnosis of capacitors. Results: The experimental results show that the research method can increase the maximum output power of photovoltaic by 32.9% under multi-layer shadows. In the charging state of the training set, the number of abnormal capacitors is 6, and the number of normal capacitors is 12, and both of them are in accordance with the preset value. The number of abnormal capacitors and normal capacitors in the discharge state is the same as that in the charging state, which is also 6 and 12. Conclusion: The research method can effectively address the issue of unbalanced energy storage battery packs and minimize the impact of local shadows on photovoltaic systems. In comparison to fuzzy C-means clustering, this method requires fewer iterations, enables faster fault diagnosis, and produces more accurate clustering results. It can provide technical support for diagnosing local abnormalities in the charging and discharging of solar cell capacitors. [ABSTRACT FROM AUTHOR]

Nanomedicine has been extensively explored for therapeutic and diagnostic applications in recent years, owing to its numerous advantages such as controlled release, targeted delivery, and efficient protection of encapsulated agents. Integration of microneedle technologies with nanomedicine has the potential to address current limitations in nanomedicine for drug delivery including relatively low therapeutic efficacy and poor patient compliance and enable theragnostic uses. In this Review, we first summarize representative types of nanomedicine and describe their broad applications. We then outline the current challenges faced by nanomedicine, with a focus on issues related to physical barriers, biological barriers, and patient compliance. Next, we provide an overview of microneedle systems, including their definition, manufacturing strategies, drug release mechanisms, and current advantages and challenges. We also discuss the use of microneedle-mediated nanomedicine systems for therapeutic and diagnostic applications. Finally, we provide a perspective on the current status and future prospects for microneedle-mediated nanomedicine for biomedical applications. [ABSTRACT FROM AUTHOR]

In Brazil, the cultivation of macadamia (Macadamia integrifolia) has expanded within the nut industry. However, there is a lack of information concerning the primary macadamia diseases under Brazilian conditions, which is crucial to defining management strategies. This study aims to identify the causal agents responsible for macadamia diseases and determine their prevalence based on a 2-year survey in a representative production area in Brazil. Samples were collected from leaves, trunk sections, racemes, and macadamia fruit from November 2019 to September 2021 in Brazil. All associated microorganisms were isolated, and pathogenicity tests were conducted to fulfill Koch's postulates. For isolates confirmed as pathogenic, morphological characterisation and molecular identification were performed. The diseases observed, along with their respective prevalence, were as follows: leaf spot caused by Neopestalotiopsis (7%), stem canker caused by Lasiodiplodia (28%), Cladosporium blight caused by Cladosporium (72%), and fruit rot caused by Colletotrichum (18%). This study marks the first reporting of the fungi species L. pseudotheobromae, C. xanthochromaticum, and Co. siamense as phytopathogens of macadamia in Brazil. Additionally, it reports C. xanthochromaticum and Co. siamense for the first time as causal agents of Cladosporium blight and fruit rot in macadamia worldwide, respectively. [ABSTRACT FROM AUTHOR]

In this study, three generations of polymerase chain reaction (PCR) assays: (i) conventional PCR, (ii) qPCR and (iii) droplet digital PCR (ddPCR), were systematically tested for their abilities to detect non-pathogenic and pathogenic populations of Vibrio parahaemolyticus. The limit of detection (LOD) for the ddPCR was 1.1 pg/µL of purified DNA, followed by the qPCR (5.6 pg/µL) and the conventional PCR (8.8 pg/µL). Regarding the LOD for V. parahaemolyticus cells, the ddPCR assay was able to detect 29 cells, followed by the conventional PCR assay (58 cells) and the qPCR assay (115 cells). Regarding the sensitivities to detect this pathogen from PCR inhibition prone samples (naturally contaminated mussels), the ddPCR assay significantly outperformed the conventional PCR and qPCR. The ddPCR assay was able to consistently detect non-pathogenic and pathogenic populations of V. parahaemolyticus from naturally contaminated mussels, indicating its tolerance to various PCR inhibitors. This study also revealed the significant difference between conventional PCR and qPCR. The conventional PCR assay showed significantly greater sensitivity than that of the qPCR assay in detecting V. parahaemolyticus in crude samples, whereas the qPCR assay showed better sensitivity in detecting the presence of V. parahaemolyticus in purified DNA samples. [ABSTRACT FROM AUTHOR]

The electrophysical parameters of the plasma and the kinetics of plasma-chemical processes in a CF4 + H2 + Ar mixture while varying the CF4/H2 ratio are studied. When using diagnostic methods and plasma modeling together, it is found that replacing tetrafluoromethane with hydrogen (a) leads to a decrease in the plasma density and an increase in electronegativity; and (b) it causes a disproportionately sharp drop in the concentration of fluorine atoms. The reason for the latter effect is the increase in the frequency of the death of atoms in reactions of the CHFx + F → CFx + HF type initiated by heterogeneous recombination via the CFx + H → CHFx mechanism. The simultaneous increase in the concentration of polymer-forming CHxFy (x + y < 3) radicals indicates an increase in the polymerization load of the plasma on the surfaces in contact with it. [ABSTRACT FROM AUTHOR]

Background: Amidst growing concern about the safety of sport-related repetitive subconcussive head impacts (RSHI), biofluid markers may provide sensitive, informative, and practical assessment of the effects of RSHI exposure. Objective: This scoping review aimed to systematically examine the extent, nature, and quality of available evidence from studies investigating the effects of RSHI on biofluid markers, to identify gaps and to formulate guidelines to inform future research. Methods: PRISMA extension for Scoping Reviews guidelines were adhered to. The protocol was pre-registered through publication. MEDLINE, Scopus, SPORTDiscus, CINAHL, PsycINFO, Cochrane Library, OpenGrey, and two clinical trial registries were searched (until March 30, 2022) using descriptors for subconcussive head impacts, biomarkers, and contact sports. Included studies were assessed for risk of bias and quality. Results: Seventy-nine research publications were included in the review. Forty-nine studies assessed the acute effects, 23 semi-acute and 26 long-term effects of RSHI exposure. The most studied sports were American football, boxing, and soccer, and the most investigated markers were (in descending order): S100 calcium-binding protein beta (S100B), tau, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), neuron-specific enolase (NSE), brain-derived neurotrophic factor (BDNF), phosphorylated tau (p-tau), ubiquitin C-terminal hydrolase L1 (UCH-L1), and hormones. High or moderate bias was found in most studies, and marker-specific conclusions were subject to heterogeneous and limited evidence. Although the evidence is weak, some biofluid markers—such as NfL—appeared to show promise. More markedly, S100B was found to be problematic when evaluating the effects of RSHI in sport. Conclusion: Considering the limitations of the evidence base revealed by this first review dedicated to systematically scoping the evidence of biofluid marker levels following RSHI exposure, the field is evidently still in its infancy. As a result, any recommendation and application is premature. Although some markers show promise for the assessment of brain health following RSHI exposure, future large standardized and better-controlled studies are needed to determine biofluid markers' utility. Key Points: This is the first systematically conducted review focused on scoping biofluid markers in sport-related repetitive subconcussive head impact (RSHI) research, identifying a significant body of evidence not previously featured in relevant systematic reviews. The scoping review identified critical limitations of the current research in the field, including lack of impact monitoring and failure to sufficiently control for confounding variables such as concussion history and the effect of exercise. Findings reveal that the current evidence base is largely heterogeneous, limiting any firm conclusions at this stage. Despite limited and heterogeneous evidence, some markers appeared to show promise in detecting the effects of subconcussive head impacts. [ABSTRACT FROM AUTHOR]