Elements in trace amount with a significant role in human physiology: a tumor pathophysiological and diagnostic aspects

Cancer has a devastating impact globally regardless of gender, age, and community, which continues its severity to the population due to the lack of efficient strategy for the cancer diagnosis and treatment. According to the World Health Organisation report, one out of six people dies due to this deadly cancer and we need effective strategies to regulate it. In this context, trace element has a very hidden and unexplored role and require more attention from investigators. The variation in concentration of trace elements was observed during comparative studies on a cancer patient and a healthy person making them an effective target for cancer regulation. The percentage of trace elements present in the human body depends on environmental exposure, food habits, and habitats and could be instrumental in the early diagnosis of cancer. In this review, we have conducted inclusive analytics on trace elements associated with the various types of cancers and explored the several methods involved in their analysis. Further, intricacies in the correlation of trace elements with prominent cancers like prostate cancer, breast cancer, and leukaemia are represented in this review. This comprehensive information on trace elements proposes their role during cancer and as biomarkers in cancer diagnosis.

Multicenter prospective blinded melanoma detection study with a handheld elastic scattering spectroscopy device

Background

The elastic scattering spectroscopy (ESS) device (DermaSensor Inc., Miami, FL) is a noninvasive, painless, adjunctive tool for skin cancer detection.

Objectives

To investigate the performance of the ESS device in the detection of melanoma.

Methods

A prospective, investigator-blinded, multicenter study was conducted at 8 United States (US) and 2 Australian sites. All eligible skin lesions were clinically concerning for melanoma, examined with the ESS device, subsequently biopsied according to dermatologists' standard of care, and evaluated with histopathology. A total of 311 participants with 440 lesions were enrolled, including 44 melanomas (63.6% in situ and 36.4% invasive) and 44 severely dysplastic nevi.

Results

The observed sensitivity of the ESS device for melanoma detection was 95.5% (95% CI, 84.5% to 98.8%, 42 of 44 melanomas), and the observed specificity was 32.5% (95% CI, 27.2% to 38.3%). The positive and negative predictive values were 16.0% and 98.1%, respectively.

Limitations

The device was tested in a high-risk population with lesions selected for biopsy based on clinical and dermoscopic assessments of board-certified dermatologists. Most enrolled lesions were pigmented.

Conclusion

The ESS device's high sensitivity and NPV for the detection of melanoma suggest the device may be a useful adjunctive, point-of-care tool for melanoma detection.

Food fraud detection and reporting by food control officers in Finland

We studied food fraud detection and the reporting of suspected cases using a questionnaire survey and interviews with Finnish food control officers (FCOs). In total, 95 FCOs responded to the questionnaire, and 17 were interviewed. We found that even though many respondents had either suspected (69.2%) or detected (43.4%) food fraud or other food-related crime during the past five years, 46.8% thought they had no realistic chance of detecting food fraud during inspections. Challenges raised by the FCOs we interviewed included inadequate resources (8/17) and difficulties in inspecting documents or establishing their authenticity (14/17). Moreover, many interviewees highlighted difficulties in assessing whether to inform the police about a suspected case (7/17), and 62% (18/29) of respondents who had detected fraud had not reported it to the police. Training in food fraud detection, increased resources and guidelines on reporting suspected food fraud would improve food fraud detection and harmonize reporting.

Microbial contamination pathways in a poultry abattoir provided clues on the distribution and persistence of Arcobacter spp

The consumption of contaminated poultry meat is a significant threat for public health, as it implicates in foodborne pathogen infections, such as those caused by Arcobacter. The mitigation of clinical cases requires the understanding of contamination pathways in each food process and the characterization of resident microbiota in the productive environments, so that targeted sanitizing procedures can be effectively implemented. Nowadays these investigations can benefit from the complementary and thoughtful use of culture- and omics-based analyses, although their application in situ is still limited. Therefore, the 16S-rRNA gene-based sequencing of total DNA and the targeted isolation of Arcobacter spp. through enrichment were performed to reconstruct the environmental contamination pathways within a poultry abattoir, as well as the dynamics and distribution of this emerging pathogen. To that scope, broiler's neck skin and caeca have been sampled during processing, while environmental swabs were collected from surfaces after cleaning and sanitizing. Metataxonomic survey highlighted a negligible impact of fecal contamination and a major role of broiler's skin in determining the composition of the resident abattoir microbiota. The introduction of Arcobacter spp. in the environment was mainly conveyed by this source rather than the intestinal content. Arcobacter butzleri represented one of the most abundant species and was extensively detected in the abattoir by both metataxonomic and enrichment methods, showing higher prevalence than other more thermophilic Campylobacterota. In particular, Arcobacter spp. was recovered viable in the plucking sector with high frequency, despite the adequacy of the sanitizing procedure.IMPORTANCEOur findings have emphasized the persistence of Arcobacter spp. in a modern poultry abattoir and its establishment as part of the resident microbiota in specific environmental niches. Although the responses provided here are not conclusive for the identification of the primary source of contamination, this biogeographic assessment underscores the importance of monitoring Arcobacter spp. from the early stages of the production chain with the integrative support of metataxonomic analysis. Through such combined detection approaches, the presence of this pathogen could be soon regarded as hallmark indicator of food safety and quality in poultry slaughtering.

Primary Health Care Interventions for Potentially Preventable Ear, Nose, and Throat Conditions in Rural and Remote Areas: A Systematic Review

Background:Primary and secondary level preventive primary health care programs providing early detection and timely management of ear, nose, and throat (ENT) conditions in rural and remote regions are fundamental to preventing downstream impacts on health, social, and educational outcomes. However, the range and quality of evidence is yet to be reviewed. Objectives: The study objectives were to identify and synthesize the evidence of primary health care interventions for detection and management of ENT conditions in rural and remote areas, and evaluate the quality of the research and effectiveness of interventions. Methods: A systematic literature search of 6 databases (February 2023). The review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, and the quality appraisal of studies was evaluated using the Mixed Methods Appraisal Tool (initial screening questions: Are there clear research questions? Do the collected data allow to address the research questions?). Results: Ten studies met the inclusion criteria. The results describe interventions for detection and management of respiratory tract infections, otitis media, and ear disease in primary health care settings. No studies met the inclusion criteria for tonsillitis. The role of community-based programs and allied health workers in the detection and management of ENT conditions was found to be effective in rural and remote regions. Only 2 of the studies met the screening criteria for quality appraisal. Conclusions: The study findings may inform future programs and policy development to address detection and management of ENT conditions in rural and remote primary care settings, and supports the need for further research on innovative models of care targeting potentially preventable hospitalizations through primary and secondary level prevention.

Headpulse measurement can reliably identify large-vessel occlusion stroke in prehospital suspected stroke patients: Results from the EPISODE-PS-COVID study

BACKGROUND

Large-vessel occlusion (LVO) stroke represents one-third of acute ischemic stroke (AIS) in the United States but causes two-thirds of poststroke dependence and >90% of poststroke mortality. Prehospital LVO stroke detection permits efficient emergency medical systems (EMS) transport to an endovascular thrombectomy (EVT)-capable center. Our primary objective was to determine the feasibility of using a cranial accelerometry (CA) headset device for prehospital LVO stroke detection. Our secondary objective was development of an algorithm capable of distinguishing LVO stroke from other conditions.

METHODS

We prospectively enrolled consecutive adult patients suspected of acute stroke from 11 study hospitals in four different U.S. geographical regions over a 21-month period. Patients received device placement by prehospital EMS personnel. Headset data were matched with clinical data following informed consent. LVO stroke diagnosis was determined by medical chart review. The device was trained using device data and Los Angeles Motor Scale (LAMS) examination components. A binary threshold was selected for comparison of device performance to LAMS scores.

RESULTS

A total of 594 subjects were enrolled, including 183 subjects who received the second-generation device. Usable data were captured in 158 patients (86.3%). Study subjects were 53% female and 56% Black/African American, with median age 69 years. Twenty-six (16.4%) patients had LVO and 132 (83.6%) were not LVO (not-LVO AIS, 33; intracerebral hemorrhage, nine; stroke mimics, 90). COVID-19 testing and positivity rates (10.6%) were not different between groups. We found a sensitivity of 38.5% and specificity of 82.7% for LAMS ≥ 4 in detecting LVO stroke versus a sensitivity of 84.6% (p < 0.0015 for superiority) and specificity of 82.6% (p = 0.81 for superiority) for the device algorithm (CA + LAMS).

CONCLUSIONS

Obtaining adequate recordings with a CA headset is highly feasible in the prehospital environment. Use of the device algorithm incorporating both CA and LAMS data for LVO detection resulted in significantly higher sensitivity without reduced specificity when compared to the use of LAMS alone.

Pyrococcus furiosus Argonaute Based Detection Assays for Porcine Deltacoronavirus

Porcine deltacoronavirus (PDCoV) is a major cause of diarrhea and diarrhea-related deaths among piglets and results in massive losses to the overall porcine industry. The clinical manifestations of porcine diarrhea brought on by the porcine epidemic diarrhea virus (PEDV), porcine transmissible gastroenteritis virus (TGEV), and PDCoV are oddly similar to each other. Hence, the identification of different pathogens through molecular diagnosis and serological techniques is crucial. Three novel detection methods for identifying PDCoV have been developed utilizing recombinase-aided amplification (RAA) or reverse transcription recombinase-aided amplification (RT-RAA) in conjunction with Pyrococcus furiosus Argonaute (PfAgo): RAA-PfAgo, one-pot RT-RAA-PfAgo, and one-pot RT-RAA-PfAgo-LFD. The indicated approaches have a detection limit of around 60 copies/μL of PDCoV and do not cross-react with other viruses including PEDV, TGEV, RVA, PRV, PCV2, or PCV3. The applicability of one-pot RT-RAA-PfAgo and one-pot RT-RAA-PfAgo-LFD were examined using clinical samples and showed a positive rate comparable to the qPCR method. These techniques offer cutting-edge technical assistance for identifying, stopping, and managing PDCoV.

Tunable Q-factor wavelet transform based identification of diabetic patients using ECG signals

Diabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3 rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods.

Development of a Large-Scale Soil DNA Extraction Method for Molecular Quantification of Fusarium oxysporum f. sp. fragariae in Soil

The most common soilborne diseases affecting the strawberry industry in California include Verticillium wilt due to Verticillium dahliae, charcoal root rot due to Macrophomina phaseolina, and Fusarium wilt due to Fusarium oxysporum f. sp. fragariae. Detection of these pathogens in soil is an important facet of disease management and fumigation recommendations. Whereas the soil populations of both M. phaseolina and V. dahliae can be readily quantified with quantitative PCR (qPCR) assays using DNA extractions with 500 mg of soil, the single-cell nature of the F. oxysporum chlamydospore does not provide enough pathogen DNA from 500-mg extractions to be reliably quantified. Here, we describe an improved DNA extraction protocol from 10 to 15 g of soil that allows for the quantification of F. oxysporum f. sp. fragariae populations below 10 CFU/g. The relationship between results from the TaqMan qPCR assay and pathogen population density in soil was determined by using this extraction method in pathogen-free soils artificially infested with a hygromycin-resistant strain of F. oxysporum f. sp. fragariae to facilitate accurate colony counts when plated on a selective medium. Although the protocol was developed for F. oxysporum f. sp. fragariae, it is applicable for detection and quantification of other soilborne pathogens.

Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study

BACKGROUND

Respiratory syncytial virus (RSV) affects children, causing serious infections, particularly in high-risk groups. Given the seasonality of RSV and the importance of rapid isolation of infected individuals, there is an urgent need for more efficient diagnostic methods to expedite this process.

OBJECTIVE

This study aimed to investigate the performance of a machine learning model that leverages the temporal diversity of symptom onset for detecting RSV infections and elucidate its discriminatory ability.

METHODS

The study was conducted in pediatric and emergency outpatient settings in Japan. We developed a detection model that remotely confirms RSV infection based on patient-reported symptom information obtained using a structured electronic template incorporating the differential points of skilled pediatricians. An extreme gradient boosting-based machine learning model was developed using the data of 4174 patients aged ≤24 months who underwent RSV rapid antigen testing. These patients visited either the pediatric or emergency department of Yokohama City Municipal Hospital between January 1, 2009, and December 31, 2015. The primary outcome was the diagnostic accuracy of the machine learning model for RSV infection, as determined by rapid antigen testing, measured using the area under the receiver operating characteristic curve. The clinical efficacy was evaluated by calculating the discriminative performance based on the number of days elapsed since the onset of the first symptom and exclusion rates based on thresholds of reasonable sensitivity and specificity.

RESULTS

Our model demonstrated an area under the receiver operating characteristic curve of 0.811 (95% CI 0.784-0.833) with good calibration and 0.746 (95% CI 0.694-0.794) for patients within 3 days of onset. It accurately captured the temporal evolution of symptoms; based on adjusted thresholds equivalent to those of a rapid antigen test, our model predicted that 6.9% (95% CI 5.4%-8.5%) of patients in the entire cohort would be positive and 68.7% (95% CI 65.4%-71.9%) would be negative. Our model could eliminate the need for additional testing in approximately three-quarters of all patients.

CONCLUSIONS

Our model may facilitate the immediate detection of RSV infection in outpatient settings and, potentially, in home environments. This approach could streamline the diagnostic process, reduce discomfort caused by invasive tests in children, and allow rapid implementation of appropriate treatments and isolation at home. The findings underscore the potential of machine learning in augmenting clinical decision-making in the early detection of RSV infection.

A Panorama of Extracellular Vesicle Applications: From Biomarker Detection to Therapeutics

Extracellular vesicles (EVs) secreted by all cell types are involved in the cell-to-cell transfer of regulatory factors that influence cell and tissue phenotypes in normal and diseased tissues. EVs are thus a rich source of biomarker targets for assays that analyze blood and urinary EVs for disease diagnosis. Sensitive biomarker detection in EVs derived from specific cell populations is a key major hurdle when analyzing complex biological samples, but innovative approaches surveyed in this Perspective can streamline EV isolation and enhance the sensitivity of EV detection procedures required for clinical application of EV-based diagnostics and therapeutics, including nanotechnology and microfluidics, to achieve EV characterizations. Finally, this Perspective also outlines opportunities and challenges remaining for clinical translation of EV-based assays.

Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review

BACKGROUND

Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM.

METHODS

A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases.

RESULTS

A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%.

CONCLUSION

Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study

BACKGROUND

Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records.

OBJECTIVE

This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.

METHODS

Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras.

RESULTS

There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras.

CONCLUSIONS

This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Detection of Salmonella enterica and Listeria monocytogenes in alternative irrigation water by culture and qPCR-based methods in the Mid-Atlantic U.S

Alternative irrigation waters (rivers, ponds, and reclaimed water) can harbor bacterial foodborne pathogens like Salmonella enterica and Listeria monocytogenes, potentially contaminating fruit and vegetable commodities. Detecting foodborne pathogens using qPCR-based methods may accelerate testing methods and procedures compared to culture-based methods. This study compared detection of S. enterica and L. monocytogenes by qPCR (real-time PCR) and culture methods in irrigation waters to determine the influence of water type (river, pond, and reclaimed water), season (winter, spring, summer, and fall), or volume (0.1, 1, and 10 L) on sensitivity, accuracy, specificity, and positive (PPV), and negative (NPV) predictive values of these methods. Water samples were collected by filtration through modified Moore swabs (MMS) over a 2-year period at 11 sites in the Mid-Atlantic U.S. on a bi-weekly or monthly schedule. For qPCR, bacterial DNA from culture-enriched samples (n = 1,990) was analyzed by multiplex qPCR specific for S. enterica and L. monocytogenes. For culture detection, enriched samples were selectively enriched, isolated, and PCR confirmed. PPVs for qPCR detection of S. enterica and L. monocytogenes were 68% and 67%, respectively. The NPV were 87% (S. enterica) and 85% (L. monocytogenes). Higher levels of qPCR/culture agreement were observed in spring and summer compared to fall and winter for S. enterica; for L. monocytogenes, lower levels of agreement were observed in winter compared to spring, summer, and fall. Reclaimed and pond water supported higher levels of qPCR/culture agreement compared to river water for both S. enterica and L. monocytogenes, indicating that water type may influence the agreement of these results.

IMPORTANCE

Detecting foodborne pathogens in irrigation water can inform interventions and management strategies to reduce risk of contamination and illness associated with fresh and fresh-cut fruits and vegetables. The use of non-culture methods like qPCR has the potential to accelerate the testing process. Results indicated that pond and reclaimed water showed higher levels of agreement between culture and qPCR methods than river water, perhaps due to specific physiochemical characteristics of the water. These findings also show that season and sample volume affect the agreement of qPCR and culture results. Overall, qPCR methods could be more confidently utilized to determine the absence of Salmonella enterica and Listeria monocytogenes in irrigation water samples examined in this study.

Liquid Biopsy for Detection of Pancreaticobiliary Cancers by Functional Enrichment and Immunofluorescent Profiling of Circulating Tumor Cells and Their Clusters

Circulating tumor cells (CTCs) have historically been used for prognostication in oncology. We evaluate the performance of liquid biopsy CTC assay as a diagnostic tool in suspected pancreaticobiliary cancers (PBC). The assay utilizes functional enrichment of CTCs followed by immunofluorescent profiling of organ-specific markers. The performance of the assay was first evaluated in a multicentric case-control study of blood samples from 360 participants, including 188 PBC cases (pre-biopsy samples) and 172 healthy individuals. A subsequent prospective observational study included pre-biopsy blood samples from 88 individuals with suspicion of PBC and no prior diagnosis of cancer. CTCs were harvested using a unique functional enrichment method and used for immunofluorescent profiling for CA19.9, Maspin, EpCAM, CK, and CD45, blinded to the tissue histopathological diagnosis. TruBlood® malignant or non-malignant predictions were compared with tissue diagnoses to establish sensitivity and specificity. The test had 95.9% overall sensitivity (95% CI: 86.0-99.5%) and 92.3% specificity (95% CI: 79.13% to 98.38%) to differentiate PBC (n = 49) from benign conditions (n = 39). The high accuracy of the CTC-based TruBlood test demonstrates its potential clinical application as a diagnostic tool to assist the effective detection of PBC when tissue sampling is unviable or inconclusive.

Nanozyme-Enhanced Electrochemical Biosensors: Mechanisms and Applications

Nanozymes, as innovative materials, have demonstrated remarkable potential in the field of electrochemical biosensors. This article provides an overview of the mechanisms and extensive practical applications of nanozymes in electrochemical biosensors. First, the definition and characteristics of nanozymes are introduced, emphasizing their significant role in constructing efficient sensors. Subsequently, several common categories of nanozyme materials are delved into, including metal-based, carbon-based, metal-organic framework, and layered double hydroxide nanostructures, discussing their applications in electrochemical biosensors. Regarding their mechanisms, two key roles of nanozymes are particularly focused in electrochemical biosensors: selective enhancement and signal amplification, which crucially support the enhancement of sensor performance. In terms of practical applications, the widespread use of nanozyme-based electrochemical biosensors are showcased in various domains. From detecting biomolecules, pollutants, nucleic acids, proteins, to cells, providing robust means for high-sensitivity detection. Furthermore, insights into the future development of nanozyme-based electrochemical biosensors is provided, encompassing improvements and optimizations of nanozyme materials, innovative sensor design and integration, and the expansion of application fields through interdisciplinary collaboration. In conclusion, this article systematically presents the mechanisms and applications of nanozymes in electrochemical biosensors, offering valuable references and prospects for research and development in this field.

Rapid Dot-Blot Immunoassay for Detecting Multiple Salmonella enterica Serotypes

Salmonella, a major contributor to foodborne infections, typically causes self-limiting gastroenteritis. However, it is frequently invasive and disseminates across the intestinal epithelium, leading to deadly bacteremia. Although the genus is subdivided into >2,600 serotypes based on their antigenic determinants, only few serotypes are responsible for most human infections. In this study, a rapid dot-blot immunoassay was developed to diagnose multiple Salmonella enterica serotypes with high incidence rates in humans. The feasibility of 10 commercial antibodies (four polyclonal and six monoclonal antibodies) was tested using the 18 serotypes associated with 67.5% Salmonella infection cases in the United States of America (U.S.A) in 2016. Ab 3 (polyclonal; eight of 18 serotypes), Ab 8 (monoclonal; 13 of 18 serotypes), and Ab 9 (monoclonal; 10 of 18 serotypes) antibodies exhibited high detection rates in western blotting and combinations of two antibodies (Ab 3+8, Ab 3+9, and Ab 8+9) were applied to dot-blot assays. The combination of Ab 3+8 identified 15 of the tested 18 serotypes in 3 h, i.e., S. Enteritidis, S. Typhimurium, S. Javiana, S. I 4,[5],12:i:-, S. Infantis, S. Montevideo, S. Braenderup, S. Thompson, S. Saintpaul, S. Heidelberg, S. Oranienburg, S. Bareilly, S. Berta, S. Agona, and S. Anatum, which were responsible for 53.7% Salmonella infections in the U.S. in 2016. This cost-effective and rapid method can be utilized as an on-site colorimetric method for Salmonella detection.

Fabrication and Application of Ag@SiO2/Au Core-Shell SERS Composite in Detecting Cu2+ in Water Environment

A sensitive and simple method for detecting Cu2+ in the water source was proposed by using surface-enhanced Raman scattering spectroscopy (SERS) based on the Ag@SiO2/Au core-shell composite. The Ag@SiO2 SERS tag was synthesized by a simple approach, in which Ag nanoparticles were first embedded with Raman reporter PATP and next coated with a SiO2 shell. The Ag@SiO2 nanoparticles had strong stability even in a high-concentration salty solution, and there were no changes to their properties and appearance within one month. The Ag@SiO2/Au composite was fabricated through a controllable self-assemble process. L-cysteine was decorated on the surface of a functionalized Ag@SiO2/Au composite, as the amino and carboxyl groups of it can form coordinate covalent bond with Cu2+, which shows that the Ag@SiO2/Au composite labelled with L-cysteine has excellent performance for the detection of Cu2+ in aqueous media. In this study, the SERS detection of Cu2+ was carried out using Ag@SiO2 nanoparticles, and the limit of detection (LOD) as low as 0.1 mg/L was achieved.

Sensitivity of antenatal ultrasound in diagnosing posterior placenta accreta spectrum disorders

OBJECTIVES

Optimal management of placenta accreta spectrum (PAS) requires antenatal diagnosis. We sought to evaluate the sensitivity of ultrasound findings suggestive of PAS in detecting posterior PAS.

METHODS

Cohort study of patients with posterior placentation and pathology-confirmed PAS from 2011 to 2020 at a tertiary center. Patients were excluded if ultrasound images were unavailable. Ultrasounds were reviewed for presence of lacunae, hypervascularity, myometrial thinning, loss of the hypoechoic zone, bridging vessels, abnormal uterine serosa-bladder interface, placental bulge, placental extension into/beyond the myometrium, and an exophytic mass. Risk factors, postpartum outcomes, and ultrasound findings were compared by antepartum suspicion for PAS. Sensitivity was calculated for each ultrasound finding.

RESULTS

Thirty-three patients were included. PAS was not suspected antenatally in 70 % (23/33). Patients with unsuspected PAS were more likely to be non-Hispanic, have in vitro fertilization, no prior Cesarean deliveries, no placenta previa, and delivered later in gestation. Depth of invasion and estimated blood loss were less for unsuspected PAS, but there was no difference in hysterectomy between groups. Ultrasound findings were less frequently seen in those who were not suspected antenatally: lacunae 17.4 vs. 100 % (p<0.001), hypervascularity 8.7 vs. 80 % (p<0.001), myometrial thinning 4.4 vs. 70 % (p<0.001), and placental bridging vessels 0 vs. 60 % (p<0.001). There was poor sensitivity (0-42.4 %) for all findings.

CONCLUSIONS

Posterior PAS is less likely to be detected antenatally due to a lower sensitivity of typical ultrasound findings in the setting of a posterior placenta. Further studies are needed to better identify reliable markers of posterior PAS.

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.

Development of real-time PCR assay for quantitative detection of Clostridium septicum

Cellulitis is an important disease in commercial turkey farms associated with significant economic loss. Although the etiology of cellulitis is not fully elucidated, Clostridium septicum (C. septicum) is one of the main causes of this infectious disease. In this study, we report the development of a quantitative real-time PCR (qRT PCR) assay targeting the alpha-toxin gene (csa), which involves a prior 15-cyle PCR using a nested pair of primers to increase the detection sensitivity. Additionally, the TaqMan probe was employed to increase the target-specificity of the assay. The performance of our nested qRT-PCR assay was evaluated using Clostridium isolates from turkey farms, representing both septicum and non-septicum species, as well as sponge swab samples from turkey farms. Our step-by-step development of the assay showed that the csa gene is a suitable target for specific detection of C. septicum strains and that the inclusion of nested PCR step significantly increased the detection sensitivity of the final qRT PCR assay. The performance of the assay was also validated by a high correlation of the threshold cycle numbers of the qRT PCR assay with the relative abundance of C. septicum read counts in 16S rRNA gene microbiota profiles of the C. septicum-containing samples from turkey farms.

Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis: Comparative Case Analysis

BACKGROUND

Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots.

OBJECTIVE

This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research.

METHODS

To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one's phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots).

RESULTS

Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness.

CONCLUSIONS

Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual's psychological processes in the time leading up to a suicidal crisis.

Erratum: Sharp detection of oscillation packets in rich time-frequency representations of neural signals

[This corrects the article DOI: 10.3389/fnhum.2023.1112415.].

Using EpiCore to Enable Rapid Verification of Potential Health Threats: Illustrated Use Cases and Summary Statistics

BACKGROUND

The proliferation of digital disease-detection systems has led to an increase in earlier warning signals, which subsequently have resulted in swifter responses to emerging threats. Such highly sensitive systems can also produce weak signals needing additional information for action. The delays in the response to a genuine health threat are often due to the time it takes to verify a health event. It was the delay in outbreak verification that was the main impetus for creating EpiCore.

OBJECTIVE

This paper describes the potential of crowdsourcing information through EpiCore, a network of voluntary human, animal, and environmental health professionals supporting the verification of early warning signals of potential outbreaks and informing risk assessments by monitoring ongoing threats.

METHODS

This paper uses summary statistics to assess whether EpiCore is meeting its goal to accelerate the time to verification of identified potential health events for epidemic and pandemic intelligence purposes from around the world. Data from the EpiCore platform from January 2018 to December 2022 were analyzed to capture request for information response rates and verification rates. Illustrated use cases are provided to describe how EpiCore members provide information to facilitate the verification of early warning signals of potential outbreaks and for the monitoring and risk assessment of ongoing threats through EpiCore and its utilities.

RESULTS

Since its launch in 2016, EpiCore network membership grew to over 3300 individuals during the first 2 years, consisting of professionals in human, animal, and environmental health, spanning 161 countries. The overall EpiCore response rate to requests for information increased by year between 2018 and 2022 from 65.4% to 68.8% with an initial response typically received within 24 hours (in 2022, 94% of responded requests received a first contribution within 24 h). Five illustrated use cases highlight the various uses of EpiCore.

CONCLUSIONS

As the global demand for data to facilitate disease prevention and control continues to grow, it will be crucial for traditional and nontraditional methods of disease surveillance to work together to ensure health threats are captured earlier. EpiCore is an innovative approach that can support health authorities in decision-making when used complementarily with official early detection and verification systems. EpiCore can shorten the time to verification by confirming early detection signals, informing risk-assessment activities, and monitoring ongoing events.

Detection of Parasites in the Field: The Ever-Innovating CRISPR/Cas12a

The rapid and accurate identification of parasites is crucial for prompt therapeutic intervention in parasitosis and effective epidemiological surveillance. For accurate and effective clinical diagnosis, it is imperative to develop a nucleic-acid-based diagnostic tool that combines the sensitivity and specificity of nucleic acid amplification tests (NAATs) with the speed, cost-effectiveness, and convenience of isothermal amplification methods. A new nucleic acid detection method, utilizing the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) nuclease, holds promise in point-of-care testing (POCT). CRISPR/Cas12a is presently employed for the detection of Plasmodium falciparum, Toxoplasma gondii, Schistosoma haematobium, and other parasites in blood, urine, or feces. Compared to traditional assays, the CRISPR assay has demonstrated notable advantages, including comparable sensitivity and specificity, simple observation of reaction results, easy and stable transportation conditions, and low equipment dependence. However, a common issue arises as both amplification and cis-cleavage compete in one-pot assays, leading to an extended reaction time. The use of suboptimal crRNA, light-activated crRNA, and spatial separation can potentially weaken or entirely eliminate the competition between amplification and cis-cleavage. This could lead to enhanced sensitivity and reduced reaction times in one-pot assays. Nevertheless, higher costs and complex pre-test genome extraction have hindered the popularization of CRISPR/Cas12a in POCT.

Rift Valley Fever virus M and L genome segment detection: a comparison of field-deployable reverse transcription insulated isothermal PCR (RT-iiPCR) and laboratory-based multiplex reverse transcription real-time PCR

Rift Valley Fever phlebovirus (RVFV) is a mosquito-borne zoonotic pathogen that causes major agricultural and public health problems in Africa and the Arabian Peninsula. It is considered a potential agro-bioterrorism agent for which limited countermeasures are available. To address diagnostic needs, here we describe a rapid and sensitive molecular method immediately employable at sites of suspected outbreaks in animals that commonly precede outbreaks in humans. The strategy involves the concurrent detection of two of the three RVFV genome segments (large and medium) using reverse transcription insulated isothermal PCR (RT-iiPCR) performed on a portable, touch screen nucleic acid analyzer, POCKIT. The analytical sensitivity for both the RT-iiPCR and a laboratory-based L and M multiplex reverse transcription real-time PCR assay was estimated at approximately 0.1-3 copies/reaction using synthetic RNA or viral RNA. The diagnostic sensitivity and specificity of detection of RVFV on the POCKIT, determined using sera from sheep and cattle (n = 181) experimentally infected with two strains of RVFV (SA01 and Ken06), were 93.8% and 100% (kappa = 0.93), respectively. Testing of ruminant field sera (n = 193) in two locations in Africa demonstrated 100% diagnostic sensitivity and specificity. We conclude that the POCKIT dual-gene RVFV detection strategy can provide reliable, sensitive, and specific point-of-need viral RNA detection. Moreover, the field detection of RVFV in vectors or susceptible animal species can aid in the surveillance and epidemiological studies to better understand and control RVFV outbreaks.

IMPORTANCE

The content of this manuscript is of interest to the diverse readership of the Journal of Clinical Microbiology, including research scientists, diagnosticians, healthcare professionals, and policymakers. Rift Valley Fever virus (RVFV) is a zoonotic mosquito-borne pathogen that causes major agricultural and public health problems. Current and most sensitive diagnostic approaches that are molecular-based are performed in highly specialized molecular diagnostic laboratories. To address diagnostic needs, we developed a novel, rapid, and sensitive molecular method using a portable PCR machine, POCKIT, capable of immediate deployment at sites of suspected outbreaks. Here, we demonstrate that field-deployable RVFV detection can provide reliable, sensitive, and specific point-of-need viral RNA detection that could be used for diagnostic investigations and epidemiological studies, and can be performed in the field.

Solid-Phase Extraction and Enhanced Amplification-Free Detection of Pathogens Integrated by Multifunctional CRISPR-Cas12a

Public healthcare demands effective and pragmatic diagnostic tools to address the escalating challenges in infection management in resource-limited areas. Recent advances in clustered regularly interspaced short palindromic repeat (CRISPR)-based biosensing promise the development of next-generation tools for disease diagnostics, including point-of-care (POC) testing for infectious diseases. The currently prevailing strategy of developing CRISPR/Cas-based diagnostics exploits only the target identification and trans-cleavage activity of a CRISPR-Cas12a/Cas13a system to provide diagnostic results, and they need to be combined with an additional preamplification reaction to enhance sensitivity. In contrast to this dual-function strategy, here, we present a new approach that collaboratively integrates the triple functions of CRISPR-Cas12a: target identification, sequence-specific enrichment, and signal generation. With this approach, we develop a nucleic acid assay termed Solid-Phase Extraction and Enhanced Detection Assay integrated by CRISPR-Cas12a (SPEEDi-CRISPR) that negates the need for preamplification but significantly improves the detection of limit (LOD) from the pM to fM level. Specifically, using Cas12a-coated magnetic beads, this assay combines efficient solid-phase extraction and enrichment of DNA targets enabled by the sequence-specific affinity of CRISPR-Cas12a with fluorogenic detection by activated Cas12a on beads. SPEEDi-CRISPR, for the first time, leverages the possibility of employing CRISPR/Cas12a in nucleic acid extraction and integrates the ability of both enrichment and detection of CRISPR/Cas into a single platform. Our proof-of-concept studies revealed that the SPEEDi-CRISPR assay has great specificity to distinguish HPV-18 from HPV-16, and Parvovirus B19, in addition to being able to detect HPV-18 at a concentration as low as 2.3 fM in 100 min and 4.7 fM in 60 min. Furthermore, we proved that this assay can be coupled with two point-of-care testing strategies: the smartphone-based fluorescence detector and the lateral flow assay. Overall, these results suggested that our assay could pave a new way for developing CRISPR diagnostics.

The Role of Soil Abundance of TxtAB in Potato Common Scab Disease Severity

Common scab is an economically costly soilborne disease of potato endemic in many potato-growing regions. The disease is caused by species of Streptomyces bacteria that produce the phytotoxin thaxtomin A. The primary disease management tool available to growers is planting resistant cultivars, but no cultivar is fully resistant to common scab, and partially resistant cultivars are often not the preferred choice of growers because of agronomic or market considerations. Therefore, growers would benefit from knowledge of the presence and severity of common scab infestations in field soils to make informed planting decisions. We implemented a quantitative PCR diagnostic assay to enable field detection and quantification of all strains of Streptomyces that cause common scab in the United States through amplification of thaxtomin A biosynthetic genes. Greenhouse trials confirmed that pathogen abundance was highly correlated with disease severity for five distinct phytopathogenic Streptomyces species, although the degree of disease severity was dependent on the pathogen species. Correlations between the abundance of the thaxtomin biosynthetic genes from field soil with disease on tubers at field sites across four U.S. states and across 2 years were not as strong as correlations observed in greenhouse assays. We also developed an effective droplet digital PCR diagnostic assay that also has potential for field quantification of thaxtomin biosynthetic genes. Further improvement of the PCR assays and added modeling of other environmental factors that impact disease outcome, such as soil composition, can aid growers in making informed planting decisions.

Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study

BACKGROUND

Atrial fibrillation (AF) represents a hazardous cardiac arrhythmia that significantly elevates the risk of stroke and heart failure. Despite its severity, its diagnosis largely relies on the proficiency of health care professionals. At present, the real-time identification of paroxysmal AF is hindered by the lack of automated techniques. Consequently, a highly effective machine learning algorithm specifically designed for AF detection could offer substantial clinical benefits. We hypothesized that machine learning algorithms have the potential to identify and extract features of AF with a high degree of accuracy, given the intricate and distinctive patterns present in electrocardiogram (ECG) recordings of AF.

OBJECTIVE

This study aims to develop a clinically valuable machine learning algorithm that can accurately detect AF and compare different leads' performances of AF detection.

METHODS

We used 12-lead ECG recordings sourced from the 2020 PhysioNet Challenge data sets. The Welch method was used to extract power spectral features of the 12-lead ECGs within a frequency range of 0.083 to 24.92 Hz. Subsequently, various machine learning techniques were evaluated and optimized to classify sinus rhythm (SR) and AF based on these power spectral features. Furthermore, we compared the effects of different frequency subbands and different lead selections on machine learning performances.

RESULTS

The light gradient boosting machine (LightGBM) was found to be the most effective in classifying AF and SR, achieving an average F1-score of 0.988 across all ECG leads. Among the frequency subbands, the 0.083 to 4.92 Hz range yielded the highest F1-score of 0.985. In interlead comparisons, aVR had the highest performance (F1=0.993), with minimal differences observed between leads.

CONCLUSIONS

In conclusion, this study successfully used machine learning methodologies, particularly the LightGBM model, to differentiate SR and AF based on power spectral features derived from 12-lead ECGs. The performance marked by an average F1-score of 0.988 and minimal interlead variation underscores the potential of machine learning algorithms to bolster real-time AF detection. This advancement could significantly improve patient care in intensive care units as well as facilitate remote monitoring through wearable devices, ultimately enhancing clinical outcomes.

[Detection, Generation, and Control of Disinfection By-products of Reclaimed Water]

At the time when water resources are in short supply,wastewater recycling is both an important environmental protection strategy and also a resource strategy. Disinfection is essential to ensure the biological safety of reclaimed wastewater by killing pathogens and preventing the spread of waterborne diseases. However,the disinfection process could inevitably produce toxic disinfection byproducts(DBPs)due to the reaction between the disinfectants and wastewater organic matters. Regarding wastewater DBPs,this study reviewed their identification methods,formation conditions(including precursors,the effect of water quality,disinfectants,and operational parameters on DBPs),and control methods(including source control,process control,and end control). In addition,future research trends of wastewater DBPs were discussed.

The histologic features, molecular features, detection and management of serrated polyps: a review

The serrated pathway to colorectal cancers (CRCs) is a significant pathway encompassing five distinct types of lesions, namely hyperplastic polyps (HPs), sessile serrated lesions (SSLs), sessile serrated lesions with dysplasia (SSL-Ds), traditional serrated adenomas (TSAs), and serrated adenoma unclassified. In contrast to the conventional adenoma-carcinoma pathway, the serrated pathway primarily involves two mechanisms: BRAF/KRAS mutations and CpG island methylator phenotype (CIMP). HPs are the most prevalent non-malignant lesions, while SSLs play a crucial role as precursors to CRCs, On the other hand, traditional serrated adenomas (TSAs) are the least frequently encountered subtype, also serving as precursors to CRCs. It is crucial to differentiate these lesions based on their unique morphological characteristics observed in histology and colonoscopy, as the identification and management of these serrated lesions significantly impact colorectal cancer screening programs. The management of these lesions necessitates the crucial steps of removing premalignant lesions and implementing regular surveillance. This article provides a comprehensive summary of the epidemiology, histologic features, molecular features, and detection methods for various serrated polyps, along with recommendations for their management and surveillance.

Design and Optimization of a yst-PCR to Detect Yersinia enterocolitica in Meat Food

In this study, a polymerase chain reaction (PCR) directed to the yst chromosomal gene (yst-PCR) was used as a rapid, sensitive, and specific method to detect Yersinia enterocolitica strains belonging to different biotypes in foods; a competitive Internal Amplification Control (cIAC) is also developed. The cIAC had a molecular weight of 417 bp and was detected until a concentration of 0.85 ng/μL. No other strains of other Yersinia species, nor Enterobacteriales order were detected by this PCR. In pure culture, the detection limit (DL) of the yst-PCR was lower for ystA+ strain (10 colony-forming unit [CFU]/mL) than for ystB+ strain (1 × 102 CFU/mL); which was the concentration detected in Y. enterocolitica inoculated minced meat. The proposed protocol included an enrichment step in peptone sorbitol bile (PSB) broth at 25°C for 24 h followed by isolation on Mac Conkey agar and chromogenic medium. An aliquot of the PSB broth homogenate and a loopful from the confluent zone of solid media were collected to perform DNA extraction for yst-PCR, and typical colonies were characterized by biochemical assays. Among 30 non-contaminated food samples, 4 samples were yst-positive and no Y. enterocolitica isolates were obtained. It is suggested that this yst-PCR could be used in the investigation of Y. enterocolitica in foods.

Development of Multiplex Molecular Assays for Simultaneous Detection of Dengue Serotypes and Chikungunya Virus for Arbovirus Surveillance

The major arboviruses mainly belong to the Bunyaviridae, Togaviridae, and Flaviviridae families, among which the chikungunya virus and dengue virus have emerged as global public health problems. The main objective of this study was to develop specific, sensitive, and cost-effective molecular multiplex RT-PCR and RT-qPCR assays for the rapid and simultaneous detection of CHIKV and the four serotypes of DENV for arbovirus surveillance. Specific primers for all viruses were designed, and one-step multiplex RT-PCR (mRT-PCR) and RT-qPCR (mRT-qPCR) were developed using reference strains of the CHIKV and DENV serotypes. The specificity of the test for all the viruses was confirmed through sequencing. The standard curves showed a high correlation coefficient, R2 = 0.99, for DENV-2 and DENV-3; R2 = 0.98, for DENV-4; and CHIKV; R2 = 0.93, for DENV-1. The limits of detection were calculated to be 4.1 × 10-1 copies/reaction for DENV-1, DENV-3, and CHIKV and 4.1 × 101 for DENV-2 and DENV-4. The specificity and sensitivity of the newly developed mRT-PCR and mRT-qPCR were validated using positive serum samples collected from India and Burkina Faso. The sensitivity of mRT-PCR and mRT-qPCR are 91%, and 100%, respectively. The specificity of both assays was 100%. mRT-PCR and mRT-qPCR assays are low-cost, and a combination of both will be a useful tool for arbovirus surveillance.

Recent Advances in Fluorescent Probes for Cancer Biomarker Detection

Many important biological species have been identified as cancer biomarkers and are gradually becoming reliable targets for early diagnosis and late therapeutic evaluation of cancer. However, accurate quantitative detection of cancer biomarkers remains challenging due to the complexity of biological systems and the diversity of cancer development. Fluorescent probes have been extensively utilized for identifying biological substances due to their notable benefits of being non-invasive, quickly responsive, highly sensitive and selective, allowing real-time visualization, and easily modifiable. This review critiques fluorescent probes used for detecting and imaging cancer biomarkers over the last five years. Focuses are made on the design strategies of small-molecule and nano-sized fluorescent probes, the construction methods of fluorescence sensing and imaging platforms, and their further applications in detection of multiple biomarkers, including enzymes, reactive oxygen species, reactive sulfur species, and microenvironments. This review aims to guide the design and development of excellent cancer diagnostic fluorescent probes, and promote the broad application of fluorescence analysis in early cancer diagnosis.

AI-based preeclampsia detection and prediction with electrocardiogram data

Introduction

More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.

Methods

Ten-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis.

Results

The UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00).

Discussion

We conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.

First report of Meloidogyne hapla on hemp (Cannabis sativa) in Oregon

Hemp is a crop that has gained interest in Washington and Oregon. As with other crops, hemp production faces challenges due to biotic factors, including plant-parasitic nematodes. During a survey for plant-parasitic nematodes associated with hemp, Meloidogyne sp. was found in a composite root sample collected in Oregon. Morphological characterization of second-stage juveniles identified the nematode as Meloidogyne hapla. Molecular identification confirmed the population as M. hapla. To our knowledge, this is the first report of M. hapla on hemp in the Pacific Northwest of the United States.

Development of a lateral flow assay for rapid and accurate detection of chicken anemia virus

Significant challenges to poultry health are posed by chicken anemia virus (CAV), which induces immunosuppression and causes increased susceptibility to secondary infections. The effective management and containment of CAV within poultry stocks require precise and prompt diagnosis. However, a deficiency persists in the availability of low-cost, rapid, and portable CAV detection devices. In this study, an immunochromatographic lateral-flow test strip-based assay was developed for CAV detection using in-house generated monoclonal antibodies (MABs) against CAV viral protein 1 (VP1). The recombinant truncated VP1 protein (Δ60VP1), with amino acid residues 1 to 60 of the native protein deleted, was produced via a prokaryotic expression system and utilized for immunizing BALB/c mice. Subsequently, high-affinity MABs against Δ60VP1 were generated and screened using conventional hybridoma technology combined with serial dilution assays. Two MABs, MAB1, and MAB3, both binding to distinct epitopes of Δ60VP1, were selected for the development of a lateral-flow assay. Sensitivity analysis demonstrated that the Δ60VP1 antigen could be detected by our homemade lateral-flow assay at concentrations as low as 625 ng/mL, and this sensitivity was maintained for at least 6 mo. The assay exhibited high specificity, as evidenced by its lack of reactivity with surrogate recombinant proteins and the absence of cross-reactivity with other chicken viruses and viral antigens. Comparative analysis with quantitative PCR data demonstrated substantial agreement, with a Kappa coefficient of 0.66, utilizing a sample set comprising 305 clinical chicken serum samples. In conclusion, the first lateral-flow assay for CAV detection was developed in this study, utilizing 2 specific anti-VP1 MABs. It is characterized by simplicity, rapidity, sensitivity, and specificity.

N-glycolylneuraminic acid in red meat and processed meat is a health concern: A review on the formation, health risk, and reduction

One of the most consistent epidemiological associations between diet and human disease risk is the impact of consuming red meat and processed meat products. In recent years, the health concerns surrounding red meat and processed meat have gained worldwide attention. The fact that humans have lost the ability to synthesize N-glycolylneuraminic acid (Neu5Gc) makes red meat and processed meat products the most important source of exogenous Neu5Gc for humans. As our research of Neu5Gc has increased, it has been discovered that Neu5Gc in red meat and processed meat is a key factor in many major diseases. Given the objective evidence of the harmful risk caused by Neu5Gc in red meat and processed meat to human health, there is a need for heightened attention in the field of food. This updated review has several Neu5Gc aspects given including biosynthetic pathway of Neu5Gc and its accumulation in the human body, the distribution of Neu5Gc in food, the methods for detecting Neu5Gc, and most importantly, a systematic review of the existing methods for reducing the content of Neu5Gc in red meat and processed meat. It also provides some insights into the current status and future directions in this area.

Establishment of RPA-Cas12a-Based Fluorescence Assay for Rapid Detection of Feline Parvovirus

Feline parvovirus (FPV) is highly infectious for cats and other Felidae and often causes severe damage to young kittens. In this study, we incorporated recombinase polymerase amplification (RPA) and Cas12a-mediated detection and developed an RPA-Cas12a-based real-time or end-point fluorescence detection method to identify the NS1 gene of FPV. The total time of RPA-Cas12a-based fluorescence assay is approximately 25 min. The assay presented a limit of detection (LOD) of 1 copies/μl (25 copies/per reaction), with no cross-reactivity with several feline pathogens. The clinical performance of the assay was examined using total genomic DNA purified from 60 clinical specimens and then compared to results obtained with qPCR detection of FPV with 93.3% positive predictive agreement and 100% negative predictive agreement. Together, the rapid reaction, cost-effectiveness, and high sensitivity make the RPA-Cas12a-based fluorescence assay a fascinating diagnostic tool that will help minimize infection spread through instant detection of FPV.

Validation of rapid detection methods for Salmonella enterica in green chile

The objective of this study is to validate the US Food and Drug Administration (FDA) rea-time polymerase chain reaction (qPCR) assay, the Neogen Amplified Nucleic Single Temperature Reaction (ANSR) assay, and the Vitek ImmunoDiagnostic Assay System (VIDAS) SLM procedure against the FDA cultural procedure for Salmonella detection in green chile pepper. Green chile was artificially contaminated with Salmonella according to the FDA guidelines (FDA. Guidelines for the Validation of Microbiological Methods for the FDA Foods Program, 3rd Edition. 2019. www.fda.gov/media/83812/download?attachment (17 March 2024, date last accessed)) at a fractional recovery level (where 50%-25% tests positive and at a level +1 log greater for each organism tested). Enriched samples were tested directly by the ANSR Salmonella test and by qPCR, and were subcultured into Rappaport-Vassiliadis and tetrathionate brilliant green broth for cultural detection and qPCR. For the VIDAS-SLM assay, the selective enrichments were further cultured in M broth before testing. Presumptive salmonellae were confirmed with biochemical tests, serology, and qPCR. All three rapid assays were compared favorably with the FDA-BAM (Bacteriological Analytical Manual) method. No significant differences at P < .05 were found between the procedures using McNemar's χ2 test. The three procedures were found to be rapid and reliable alternatives to cultural detection of Salmonella enterica in green chile.

Rapid and sensitive detection of Mycoplasma synoviae using RPA combined with Pyrococcus furiosus Argonaute

Mycoplasma synoviae (MS) is an important pathogen in laying hens and causes serious economic losses in poultry production. Rapid, accurate and specific detection is important for the prevention and control of MS. Argonaute from Pyrococcus furiosus (PfAgo) is emerging as a nucleic acid detector that works via "dual-step" sequence-specific cleavage. In this study, an MS detection method combining recombinase polymerase amplification (RPA) and PfAgo was established. Through elaborate design and screening of RPA primers and PfAgo gDNA and condition optimization, amplification and detection procedures can be completed within 40 min, whereas the results were superficially interpreted under UV and blue light. The sensitivity for MS detection was 2 copies/µL, and the specificity results showed no cross reaction with other pathogens. For the detection of 31 clinical samples, the results of this method and qPCR were completely consistent. This method provides a reliable and convenient method for the on-site detection of MS that is easy to operate without complex instruments and equipment.

Global Genetic Diversity of Tobacco Ringspot Virus Including Newly Reported Isolates from Cotton (Gossypium hirsutum) in Oklahoma

Cotton is one of the most salient cash crops globally and in the United States. Lately, several virus-like diseases have been reported from cotton in the United States such as the tobacco ringspot virus (TRSV) in Oklahoma. TRSV has been reported from various hosts worldwide with minimal phylogenetic examination. In this study, complete genome sequences of four TRSV isolates from cotton were isolated, and the genetic diversity was investigated along with additional available TRSV isolates retrieved from GenBank. Phylogenetic analysis based on the complete RNA1 and RNA2 sequences distributed all TRSV isolates into three major phylogenetic clades exhibiting a differential clade composition depending on the segment. The TRSV cotton isolates exhibited differential grouping between the RNA1 and RNA2 analyses. Additionally, monophyletic subclades of isolates appeared to be conserved between both segments. Thirty-five recombination events in RNA1 and 23 in RNA2 were identified with implications in the variation of the phylogenetic analyses. Furthermore, multiple hypotheses of TRSV evolution were generated based on the phylogenetic analyses, but to test them, more complete genomes of TRSV will be needed. This study provides the first complete genome analysis of TRSV isolates infecting cotton in the United States and a detailed analysis of global TRSV isolates.

An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal

One of the epileptic patients' challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients.

Molecular Detection and Characterization of Coxiella burnetii in Aborted Samples of Livestock in China

Coxiella burnetii is the causative agent of zoonotic Q fever. Animals are the natural reservoirs of C. burnetii, and domestic livestock represent the major sources of human infection. C. burnetii infection in pregnant females may causes abortion during late pregnancy, whereby massive shedding of C. burnetii with abortion products becomes aerosolized and persists in the environment. Therefore, monitoring and surveillance of this infection in livestock is important for the prevention of the C. burnetii transmission. Previous serological surveys have shown that C. burnetii infection is endemic in livestock in China. However, few data are available on the diagnosis of C. burnetii as a cause of abortion by molecular methods in livestock. To get a better understanding of the impact of C. burnetii infection on domestic livestock in China, a real-time PCR investigation was carried out on collected samples from different domestic livestock suffering abortion during 2021-2023. A total of 338 samples collected from eight herds of five livestock species were elected. The results showed that 223 (66%) of the collected samples were positive for C. burnetii DNA using real-time PCR. For the aborted samples, 82% (128/15) of sheep, 81% (34/42) of goats, 44% (15/34) of cattle, 69% (18/26) of camels, and 50% (17/34) of donkeys were positive for C. burnetii. Besides, 44% (8/18) and 4% (1/25) of asymptomatic individuals of sheep and donkey were also positive for C. burnetii. In addition, the positive samples were further confirmed by amplification and sequencing of the C. burnetii-specific isocitrate dehydrogenase (icd) gene. Phylogenetic analysis based on specific gene fragments of icd genes revealed that the obtained sequences in this study were clustered into two different groups associated with different origin of hosts and geographic regions. This is the first report confirming that C. burnetii exists in aborted samples of sheep, goats, cattle, donkeys and camels in China. Further studies are needed to fully elucidate the epidemiology of this pathogen in livestock as well as the potential risks to public health.

The Added Value of MRI-Based Targeted Biopsy in Biopsy-Naïve Patients: A Propensity-Score Matched Comparison

BACKGROUND

Multiparametric Magnetic Resonance Imaging (mpMRI)-based targeted biopsy has shown to be beneficial in detecting Clinically Significant Prostate Cancer (csPCa) and avoiding diagnosis of Non-csPCa (ncsPCa); however, its role in the treatment of biopsy-naïve patients is still under discussion.

METHODS

After identifying predictors for the diagnosis of csPCa via Multivariate Logistic Regression Analysis (MLRA), a propensity-score (1:1 nearest neighbor) matched comparison was performed between a Systematic-Only Biopsy (SOB) cohort and a mpMRI-based Combined (systematic + targeted) Biopsy (CB) cohort from two tertiary urologic centers (SOB: Department of Urology, University General Hospital of Heraklion, University of Crete, School of Medicine, Heraklion, Crete, Greece; CB: LKH Hall in Tirol, Austria). Only biopsy-naïve patients were included in the study. The study period for the included patients was from February 2018 to July 2023 for the SOB group and from July 2017 to June 2023 for the CB group. The primary outcome was the diagnosis of csPCa (≥ISUP 2); secondary outcomes were overall cancer detection, the added value of targeted biopsy in csPCa detection, and the reduction in ncsPCa diagnosis with CB compared to SOB. To estimate the Average Treatment effect of the Treated groups (ATT), cluster-robust standard errors were used to perform g-computation in the matched sample. p-values < 0.05 with a two-sided 95% confidence interval were considered statistically significant.

RESULTS

Matching achieved well-balanced groups (each n = 140 for CB and SOB). In the CB group, 65/140 (46.4%) patients were diagnosed with csPCa compared to 44/140 (31.4%) in the SOB group (RR 1.48, 95%-CI: 1.09-2.0, p = 0.01). In the CB group, 4.3% (6/140) and 1.4% (2/140) of csPCa cases were detected with targeted-only and systematic-only biopsy cores, respectively. In the CB group, 22/140 (15.7%) patients were diagnosed with ncsPCa compared to 33/140 (23.6%) in the SOB group (RR = 0.67, 95% CI: 0.41-1.08, p = 0.1). When comparing SOB to CB (ATT), the marginal OR was 0.56 (95% CI: 0.38-0.82, p = 0.003) for the diagnosis of csPCa and 0.75 (95% CI: 0.47-1.05, p = 0.085) for the diagnosis of overall cancer (≥ISUP 1).

CONCLUSION

The CB approach was superior to the SOB approach in detecting csPCa, while no additional detection of ncsPCa was seen. Our results support the application of mpMRI for biopsy-naïve patients with suspicions of prostate cancer.

Noninvasive detection of Zika virus in mosquito excreta sampled from wild mosquito populations in French Guiana

Arboviruses can be difficult to detect in the field due to relatively low prevalence in mosquito populations. The discovery that infected mosquitoes can release viruses in both their saliva and excreta gave rise to low-cost methods for the detection of arboviruses during entomological surveillance. We implemented both saliva and excreta-based entomological surveillance during the emergence of Zika virus (ZIKV) in French Guiana in 2016 by trapping mosquitoes around households of symptomatic cases with confirmed ZIKV infection. ZIKV was detected in mosquito excreta and not in mosquito saliva in 1 trap collection out of 85 (1.2%). One female Ae. aegypti L. (Diptera: Culicidae) was found with a ZIKV systemic infection in the corresponding trap. The lag time between symptom onset in a ZIKV-infected individual living near the trap site and ZIKV detection in this mosquito was 1 wk. These results highlight the potential of detection in excreta from trapped mosquitoes as a sensitive and cost-effective method to non invasively detect arbovirus circulation.

Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.

On-Site Bioaerosol Sampling and Airborne Microorganism Detection Technologies

Bioaerosols are small airborne particles composed of microbiological fragments, including bacteria, viruses, fungi, pollens, and/or by-products of cells, which may be viable or non-viable wherever applicable. Exposure to these agents can cause a variety of health issues, such as allergic and infectious diseases, neurological disorders, and cancer. Therefore, detecting and identifying bioaerosols is crucial, and bioaerosol sampling is a key step in any bioaerosol investigation. This review provides an overview of the current bioaerosol sampling methods, both passive and active, as well as their applications and limitations for rapid on-site monitoring. The challenges and trends for detecting airborne microorganisms using molecular and immunological methods are also discussed, along with a summary and outlook for the development of prompt monitoring technologies.

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Entropy Sharing in Ransomware: Bypassing Entropy-Based Detection of Cryptographic Operations

This study presents a groundbreaking approach to the ever-evolving challenge of ransomware detection. A lot of detection methods predominantly rely on pinpointing high-entropy blocks, which is a hallmark of the encryption techniques commonly employed in ransomware. These blocks, typically difficult to recover, serve as key indicators of malicious activity. So far, many neutralization techniques have been introduced so that ransomware utilizing standard encryption can effectively bypass these entropy-based detection systems. However, these have limited capabilities or require relatively high computational costs. To address these problems, we introduce a new concept entropy sharing. This method can be seamlessly integrated with every type of cryptographic algorithm and is also composed of lightweight operations, masking the high-entropy blocks undetectable. In addition, the proposed method cannot be easily nullified, contrary to simple encoding methods, without knowing the order of shares. Our findings demonstrate that entropy sharing can effectively bypass entropy-based detection systems. Ransomware utilizing such attack methods can cause significant damage, as they are difficult to detect through conventional detection methods.