A water-soluble red-emitting fluorescence probe for detecting hazardous hydrazine in environmental waters and biosystems.

Hydrazine (N2H4), a crucial chemical raw material, enhances people's lives and fosters human progress. Hydrazine usage or leakage has caused environmental contamination, affecting water, soil, and living beings. Hydrazine simultaneously presents a possible risk to human health due to its carcinogenic properties. Thus, quick and precise detection of hydrazine is crucial in environmental studies and biological contexts. We prepared a red-emitting fluorescence turn-on probe (XT-HZ) to detect hydrazine specifically. The probe has a low detecting limit for hydrazine (63 nM) with excitation wavelength at 570 nm and emission wavelength at 625 nm. Besides, the probe XT-HZ had excellent water solubility, high selectivity, and good sensitivity for detecting hydrazine. Finally, probe XT-HZ was applied in the imaging of N2H4 in living cells, zebrafish and environmental water samples.

Comparison of ERIC carbapenem-resistant Enterobacteriaceae test, BD Phoenix CPO detect panel, and NG-test CARBA 5 for the detection of main carbapenemase types of carbapenem-resistant Enterobacterales.

BACKGROUND: This study aimed to assess the performance of three commercial panels, the ERIC Carbapenem-Resistant Enterobacteriaceae Test (ERIC CRE test), the NG-Test CARBA 5 (NG CARBA 5), and the BD Phoenix CPO Detect Panel (CPO panel), for the detection of main types of carbapenemases among carbapenem-resistant Enterobacterales (CRE). METHODS: We collected 502 isolates of carbapenem-resistant Enterobacterales (CRE) demonstrating intermediate or resistant profiles to at least one carbapenem antibiotic (ertapenem, imipenem, meropenem, or doripenem). Carbapenemase genes and their specific types were identified through multiplex PCR and sequencing methods. Subsequently, the ERIC CRE test, CPO panel, and NG CARBA 5 assay were conducted on these isolates, and the results were compared with those obtained from multiplex PCR. RESULTS: The results indicated that the ERIC CRE test exhibited an overall sensitivity and specificity of 98.1% and 93.6%, respectively, which were comparable to 99.1% and 90.6% for the NG CARBA 5. However, the CPO panel demonstrated a sensitivity of only 56.2% in identifying Ambler classes, exhibiting the poorest sensitivity for class A. Moreover, while the ERIC CRE test outperformed the NG CARBA 5 in identifying multi-gene isolates with multiple carbapenemase-encoding genes, the CPO panel failed to accurately classify these isolates. CONCLUSIONS: Our findings support the utilization of the ERIC CRE test as one of the methods for detecting carbapenemases in clinical laboratories. Nonetheless, further optimization is imperative for the CPO panel to enhance its accuracy in determining carbapenemase classification and address limitations in detecting multi-gene isolates.

Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study.

BACKGROUND: The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively. OBJECTIVE: This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy. METHODS: We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi). RESULTS: In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings. CONCLUSIONS: Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.

Impact of Audio Data Compression on Feature Extraction for Vocal Biomarker Detection: Validation Study.

BACKGROUND: Vocal biomarkers, derived from acoustic analysis of vocal characteristics, offer noninvasive avenues for medical screening, diagnostics, and monitoring. Previous research demonstrated the feasibility of predicting type 2 diabetes mellitus through acoustic analysis of smartphone-recorded speech. Building upon this work, this study explores the impact of audio data compression on acoustic vocal biomarker development, which is critical for broader applicability in health care. OBJECTIVE: The objective of this research is to analyze how common audio compression algorithms (MP3, M4A, and WMA) applied by 3 different conversion tools at 2 bitrates affect features crucial for vocal biomarker detection. METHODS: The impact of audio data compression on acoustic vocal biomarker development was investigated using uncompressed voice samples converted into MP3, M4A, and WMA formats at 2 bitrates (320 and 128 kbps) with MediaHuman (MH) Audio Converter, WonderShare (WS) UniConverter, and Fast Forward Moving Picture Experts Group (FFmpeg). The data set comprised recordings from 505 participants, totaling 17,298 audio files, collected using a smartphone. Participants recorded a fixed English sentence up to 6 times daily for up to 14 days. Feature extraction, including pitch, jitter, intensity, and Mel-frequency cepstral coefficients (MFCCs), was conducted using Python and Parselmouth. The Wilcoxon signed rank test and the Bonferroni correction for multiple comparisons were used for statistical analysis. RESULTS: In this study, 36,970 audio files were initially recorded from 505 participants, with 17,298 recordings meeting the fixed sentence criteria after screening. Differences between the audio conversion software, MH, WS, and FFmpeg, were notable, impacting compression outcomes such as constant or variable bitrates. Analysis encompassed diverse data compression formats and a wide array of voice features and MFCCs. Wilcoxon signed rank tests yielded P values, with those below the Bonferroni-corrected significance level indicating significant alterations due to compression. The results indicated feature-specific impacts of compression across formats and bitrates. MH-converted files exhibited greater resilience compared to WS-converted files. Bitrate also influenced feature stability, with 38 cases affected uniquely by a single bitrate. Notably, voice features showed greater stability than MFCCs across conversion methods. CONCLUSIONS: Compression effects were found to be feature specific, with MH and FFmpeg showing greater resilience. Some features were consistently affected, emphasizing the importance of understanding feature resilience for diagnostic applications. Considering the implementation of vocal biomarkers in health care, finding features that remain consistent through compression for data storage or transmission purposes is valuable. Focused on specific features and formats, future research could broaden the scope to include diverse features, real-time compression algorithms, and various recording methods. This study enhances our understanding of audio compression's influence on voice features and MFCCs, providing insights for developing applications across fields. The research underscores the significance of feature stability in working with compressed audio data, laying a foundation for informed voice data use in evolving technological landscapes.

Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors.

PURPOSE: A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses. MATERIALS AND METHODS: This study analyzed 340 breast masses identified through ultrasound in 308 patients. The masses were divided into training (n = 260) and validation (n = 80) groups. The AI-based analysis employed the Samsung Ultrasound AI system (S-detect). Univariate and multivariate analyses were conducted to construct nomograms using logistic regression. The AI-Nomogram was based solely on AI results, while the ClinAI- Nomogram incorporated additional clinical factors. Both nomograms underwent internal validation with 1000 bootstrap resamples and external validation using the independent validation group. Performance was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. RESULTS: The ClinAI-Nomogram, which incorporates patient age, AI-based mass size, and AI-based diagnosis, outperformed an existing AI-Nomogram in differentiating benign from malignant breast masses. The ClinAI-Nomogram surpassed the AI-Nomogram in predicting malignancy with significantly higher AUC scores in both training (0.873, 95% CI: 0.830-0.917 vs. 0.792, 95% CI: 0.748-0.836; p = 0.016) and validation phases (0.847, 95% CI: 0.763-0.932 vs. 0.770, 95% CI: 0.709-0.833; p < 0.001). Calibration curves further revealed excellent agreement between the ClinAI-Nomogram's predicted probabilities and actual observed risks of malignancy. CONCLUSION: The ClinAI- Nomogram, combining AI alongside clinical data, significantly enhanced the differentiation of benign and malignant breast masses in clinical AI-facilitated ultrasound examinations.

Half-volume validation of the NGM Detect™ PCR Amplification Kit and its application on degraded casework samples.

The NGM Detect™ PCR Amplification Kit was designed particularly for genotyping degraded casework samples. This study aimed to validate the half-volume amplification of the kit and to present its successful long-term application. The validation was performed in accordance to the corresponding guidelines of the Scientific Working Group on DNA analysis methods and the European Network of Forensic Science Institutes. For validation parameters, such as sensitivity, reproducibility, and repeatability, polymerase chain reactions (PCR) were set up both manually and robotically, applying 29 cycles. For PCRs with sub-optimal DNA input (≤0.5 ng) the cycle numbers were increased to 31. Regardless of the PCR preparation method, the optimal 0.5 ng DNA input produced optimal allelic peak heights with no allelic dropout. The first alleles that failed to amplify started to appear at the level of 0.0375 ng input DNA, although the manually prepared PCRs produced fewer missing alleles. In this case, the raised cycle number produced 1.9% and 4.4% of dropout for manually and for robotically set up PCRs, respectively. In the case of 84 degraded casework samples, PCRs were prepared only by hand. The kit was able to provide informative profiles for 78.57%, 70.37%, and 69.77% for lowly, moderately, and highly degraded samples, respectively. Allelic dropouts were 26.05%, 44.88%, and 51.23% for the same groups. According to our results, we strongly recommend using the NGM Detect™ Kit in half-volume PCR system and encourage the usage of the kit in the particular cases when other kits fail to produce a complete DNA profile.

Rapid quantitative authentication and analysis of camellia oil adulterated with edible oils by electronic nose and FTIR spectroscopy.

Camellia oil, recognized as a high-quality edible oil endorsed by the Food and Agriculture Organization, is confronted with authenticity issues arising from fraudulent adulteration practices. These practices not only pose health risks but also lead to economic losses. This study proposes a novel machine learning framework, referred to as a transformer encoder backbone with a support vector machine regressor (TES), coupled with an electronic nose (E-nose), for detecting varying adulteration levels in camellia oil. Experimental results indicate that the proposed TES model exhibits the best performance in identifying the adulterated concentration of camellia oi, compared with five other machine learning models (the support vector machine, random forest, XGBoost, K-nearest neighbors, and backpropagation neural network). The results obtained by E-nose detection are verified by complementary Fourier transform infrared (FTIR) spectroscopy analysis for identifying functional groups, ensuring accuracy and providing a comprehensive assessment of the types of adulterants. The proposed TES model combined with E-nose offers a rapid, effective, and practical tool for detecting camellia oil adulteration. This technique not only safeguards consumer health and economic interests but also promotes the application of E-nose in market supervision.

Retrospective Evaluation of the Effect of Lumbar Sympathetic Blockade on Pain Scores, Fontaine Classification, and Collateral Perfusion Status in Patients with Lower Extremity Peripheral Arterial Disease.

Background and Objectives: The aim of this retrospective study was to evaluate the effect of lumbar sympathetic block (LSB) on pain scores, Fontaine Classification, and collateral perfusion status in patients with lower extremity peripheral artery disease (PAD), in whom revascularization is impossible. Material and Methods: Medical records of 21 patients with PAD who underwent LSB with a combination of local anesthetics, steroids, and patient follow-up forms containing six-month follow-ups between January 2020 and March 2021 were retrospectively reviewed. Numeric Rating Scale (NRS), Pain Detect Questionnaire (PDQ) scores, Fontaine Classification Stages, and collateral perfusion status (collateral diameter and/or development of neovascularization) evaluated by arterial color Doppler Ultrasound (US) from the medical records and follow-up forms of the patients were reviewed. Results: NRS and PDQ scores were significantly lower, and regression of the Fontaine Classification Stages was significantly better after the procedure at the first, third, and sixth month than at the baseline values (p < 0.001). Only four patients (19%) had collaterals before the procedure. An increase in the collateral diameter after LSB was noted in three out of four patients. Before the procedure, 17 patients had no prominent collateral. However, in thirteen of these patients, after LSB, neovascularization was detected during the six-month follow-up period (three patients in the first month, seven patients in the third month, and thirteen patients in the sixth month). The number of patients evolving neovascularization after LSB was found to be statistically significant at the third and sixth months compared to the initial examination (p < 0.001). Conclusions: LSB with the use of local anesthetic and steroids in patients with lower extremity PAD not only led to lower NRS and PDQ scores, but also resulted in regressed Fontaine Classification Stages and better collateral perfusion status.

ViroISDC: a method for calling integration sites of hepatitis B virus based on feature encoding.

BACKGROUND: Hepatitis B virus (HBV) integrates into human chromosomes and can lead to genomic instability and hepatocarcinogenesis. Current tools for HBV integration site detection lack accuracy and stability. RESULTS: This study proposes a deep learning-based method, named ViroISDC, for detecting integration sites. ViroISDC generates corresponding grammar rules and encodes the characteristics of the language data to predict integration sites accurately. Compared with Lumpy, Pindel, Seeksv, and SurVirus, ViroISDC exhibits better overall performance and is less sensitive to sequencing depth and integration sequence length, displaying good reliability, stability, and generality. Further downstream analysis of integrated sites detected by ViroISDC reveals the integration patterns and features of HBV. It is observed that HBV integration exhibits specific chromosomal preferences and tends to integrate into cancerous tissue. Moreover, HBV integration frequency was higher in males than females, and high-frequency integration sites were more likely to be present on hepatocarcinogenesis- and anti-cancer-related genes, validating the reliability of the ViroISDC. CONCLUSIONS: ViroISDC pipeline exhibits superior precision, stability, and reliability across various datasets when compared to similar software. It is invaluable in exploring HBV infection in the human body, holding significant implications for the diagnosis, treatment, and prognosis assessment of HCC.

"Notification! You May Have Cancer." Could Smartphones and Wearables Help Detect Cancer Early?

This viewpoint paper considers the authors' perspectives on the potential role of smartphones, wearables, and other technologies in the diagnosis of cancer. We believe that these technologies could be valuable additions in the pursuit of early cancer diagnosis, as they offer solutions to the timely detection of signals or symptoms and monitoring of subtle changes in behavior that may otherwise be missed. In addition to signal detection, technologies could assist symptom interpretation and guide and facilitate access to health care. This paper aims to provide an overview of the scientific rationale as to why these technologies could be valuable for early cancer detection, as well as outline the next steps for research and development to drive investigation into the potential for smartphones and wearables in this context and optimize implementation. We draw attention to potential barriers to successful implementation, including the difficulty of the development of signals and sensors with sufficient utility and accuracy through robust research with the target group. There are regulatory challenges; the potential for innovations to exacerbate inequalities; and questions surrounding acceptability, uptake, and correct use by the intended target group and health care practitioners. Finally, there is potential for unintended consequences on individuals and health care services including unnecessary anxiety, increased symptom burden, overinvestigation, and inappropriate use of health care resources.

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.

A colorimetric and fluorescent probe of lignocellulose nanofiber composite modified with Rhodamine 6G derivative for reversible, selective and sensitive detection of metal ions in wastewater.

Heavy metal ions have extremely high toxicity. As the top of food chain, human beings certainly will accumulate them by ingesting food and participating other activities, which eventually result in the damage to our health. Therefore, it is very meaningful and necessary to design a simple, portable, stable and efficient material for heavy metal ions detection. Based on the spirolactam Rhodamine 6G (SRh6G) fluorescent probe, we prepared two types of nanocomposite materials (membrane and aerogel) by vacuum filtration and freeze-drying methods with lignocellulose nanofiber (CNF) as a carrier, polyvinyl alcohol (PVA) and glutaraldehyde (GA) as the cross-linkers. Then the microstructure, chemical composition, wetting property, fluorescence intensity and selectivity of as-prepared SRh6G/PVA/CNF would be characterized and analyzed. Results showed that SRh6G/PVA/CNF nanocomposites would turn red in color under strong acidic environment and produced orange fluorescence under ultraviolet light. Besides, they were also to detect Al3+, Cu2+, Hg2+, Fe3+ and Ag+ through color and fluorescence variations. We had further tested its sensitivity, selectivity, adsorption, fluorescence limits of detection (LOD) to Fe3+ and Cu2+. The test towards real water samples (hospital wastewater, Songhua River and tap water) proved that SRh6G/PVA/CNF nanocomposites could detect the polluted water with low concentrations of Fe3+ and Cu2+. In addition, SRh6G/PVA/CNF nanocomposites have excellent mechanical property, repeatability, superhydrophilicity and underwater superoleophobicity, which may offer a theoretical reference for the assembly strategy and detection application of cellulose-based fluorescent probe.

Detecting child sexual abuse in child and adolescent psychiatry: a survey study of healthcare professionals' assessment practice.

BACKGROUND: Research shows that only around half of all survivors of child sexual abuse (CSA) disclose the abuse during childhood and adolescence. This is worrying, as CSA is related to substantial suffering later in life. The proportion of children and adolescents who have been exposed to CSA is significantly higher in Child and Adolescent Psychiatry (CAP) than in the general population. Healthcare professionals report that uncovering CSA is a complex and challenging task. However, we know little about how they proceed when uncovering CSA. More knowledge of healthcare personnel's experience is therefore necessary to facilitate and increase CSA disclosure. The study aims to explore how CAP healthcare professionals in Norway proceed when assessing and detecting CSA, how they experience this work, and what hinders or facilitates their efforts. METHODS: The study employed a mixed method approach. Data was collected through an anonymous online survey, generating both quantitative and qualitative data. The sample consisted of 111 healthcare professionals in CAP, of whom 84% were women, with a mean age of 40.7 years (range 24-72; sd = 10.8). Mean years of CAP clinical experience were 8.3 years (range 0-41; sd = 7.5). The quantitative data was analysed using descriptive statistics, correlations, and independent sample t-tests, while the qualitative data was analysed using a team-based qualitative content analysis. RESULTS: The results showed that detection of CSA was viewed as an important, but complex task in CAP, and the existing procedures were deemed to be insufficient. The therapists mostly felt confident about how to proceed when they suspected or detected CSA, yet they seldom detected CSA. In their initial assessment they applied standardised procedures, but if their suspicion of possible CSA persisted, they seemed to rely more on clinical judgement. Specific challenges and facilitators for CSA detection were identified, both in the individual and in the organisation. CONCLUSIONS: The study highlights the challenges and complexities healthcare professionals and the CAP system face when assessing CSA, which may account for the low detection rate. The results show that healthcare professionals believe room for clinical autonomy and targeted competence development may improve CSA detection. Additionally, the findings suggest a need for CAP to define roles and responsibilities within and between agencies.

The detect consensus report on Attention Deficit/Hyperactivity Disorder and its management among Turkish children and adolescents (Detect: consensus report on ADHD among Turkish youth).

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common and heritable neurodevelopmental disorders which may last through the life-span. A consensus report on diagnosis and management of ADHD among Turkish youth was prepared previously. However, the participants as well as the management options were rather limited and developments in the past decade necessitated a revision and update of the consensus. Therefore, this review aims to summarize the consensus among Child and Adolescent Psychiatrists from Türkiye on the nature and management of pediatric ADHD. For those aims, the etiology of ADHD, diagnostic and evaluation process, epidemiology, developmental presentations, differential diagnoses and comorbidities, course/outcome and pharmacological as well as non-pharmacological management options were reviewed and suggestions for clinical practice are presented. Since ADHD is a chronic disorder with wide-ranging effects on functionality that is frequently accompanied by other mental disorders, a multidimensional therapeutic approach is recommended. However, since the disorder has neurobiological basis, pharmacotherapy represents the mainstay of treatment. Additional therapies may include psychosocial therapy, behavioral therapy, school-based therapeutic approaches, and family education. This review provides recommendations for ADHD at the national and global levels. It contains information about ADHD that will contribute to and facilitate clinicians' decision-making processes. It is advisable to consider this guideline in clinical practice.

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.

In situ monitoring of cytoplasmic dopamine levels by noble metals decorated carbon fiber tips.

Dopamine (DA), a catecholamine neurotransmitter, is crucial in brain signal transmission. Monitoring cytoplasmic DA levels can reflect changes in metabolic factors and provide valuable information for researching the mechanisms involved in neurodegenerative diseases. However, the in-situ detection of intracellular DA is constrained by its low contents in small-sized single cells. In this work, we report that noble metal (Au, Pt)-modified carbon fiber micro-nanoelectrodes are capable of real-time detection of DA in single cells with excellent sensitivity, selectivity, and anti-contamination capabilities. Notably, noble metals can be modified on the electrode surface through electrochemical deposition to enhance the conductivity of the electrode and the oxidation current of DA by 50 %. The nanosensors can work stably and continuously in rat adrenal pheochromocytoma cells (PC12) to monitor changes in DA levels upon K+ stimulation. The functionalized carbon fibers based nanosensors will provide excellent prospects for DA analysis in the brains of living animals.

Research progress of N1-methyladenosine RNA modification in cancer.

N1-methyladenosine (m1A) is a post-transcriptionally modified RNA molecule that plays a pivotal role in the regulation of various biological functions and activities. Especially in cancer cell invasion, proliferation and cell cycle regulation. Over recent years, there has been a burgeoning interest in investigating the m1A modification of RNA. Most studies have focused on the regulation of m1A in cancer enrichment areas and different regions. This review provides a comprehensive overview of the methodologies employed for the detection of m1A modification. Furthermore, this review delves into the key players in m1A modification, known as the "writers," "erasers," and "readers." m1A modification is modified by the m1A methyltransferases, or writers, such as TRMT6, TRMT61A, TRMT61B, TRMT10C, NML, and, removed by the demethylases, or erasers, including FTO and ALKBH1, ALKBH3. It is recognized by m1A-binding proteins YTHDF1, TYHDF2, TYHDF3, and TYHDC1, also known as "readers". Additionally, we explore the intricate relationship between m1A modification and its regulators and their implications for the development and progression of specific types of cancer, we discuss how m1A modification can potentially facilitate the discovery of novel approaches for cancer diagnosis, treatment, and prognosis. Our summary of m1A methylated adenosine modification detection methods and regulatory mechanisms in various cancers provides useful insights for cancer diagnosis, treatment, and prognosis. Video Abstract.

Image detection model construction of Apolygus lucorum and Empoasca spp. based on improved YOLOv5.

BACKGROUND: The polyphagous mirid bug Apolygus lucorum (Meyer-Dür) and the green leafhopper Empoasca spp. Walsh are small pests that are widely distributed and important pests of many economically important crops, especially kiwis. Conventional monitoring methods are expensive, laborious and error-prone. Currently, deep learning methods are ineffective at recognizing them. This study proposes a new deep-learning-based YOLOv5s_HSSE model to automatically detect and count them on sticky card traps. RESULTS: Based on a database of 1502 images, all images were collected from kiwi orchards at multiple locations and times. We trained the YOLOv5s model to detect and count them and then changed the activation function to Hard swish in YOLOv5s, introduced the SIoU Loss function, and added the squeeze-and-excitation attention mechanism to form a new YOLOv5s_HSSE model. Mean average precision of this model in the test dataset was 95.9%, the recall rate was 93.9% and the frames per second was 155, which are higher than those of other single-stage deep-learning models, such as SSD, YOLOv3 and YOLOv4. CONCLUSION: The proposed YOLOv5s_HSSE model can be used to identify and count A. lucorum and Empoasca spp., and it is a new, efficient and accurate monitoring method. Pest detection will benefit from the broader applications of deep learning. © 2024 Society of Chemical Industry.

Zero- to One-Dimensional Zn24 Supraclusters: Synthesis, Structures and Detection Wavelength.

A zinc supracluster [Zn24(ATZ)18(AcO)30(H2O)1.5]·(H2O)3.5 (Zn24), and a 1D zinc supracluster chain {[Zn24(ATZ)18(AcO)30(C2H5OH)2(H2O)3]·(H2O)2.5}n (1-D⊂Zn24) with molecular diameters of 2 nm were synthesized under regulatory solvothermal conditions or the micro bottle method. In an N,N-dimethylformamide solution of Zn24, Fe3+, Ni2+, Cu2+, Cr2+ and Co2+ ions exhibited fluorescence-quenching effects, while the rare earth ions Ce3+, Dy3+, Er3+, Eu3+, Gd3+, Ho3+, La3+, Nd3+, Sm3+, and Tb3+showed no obvious fluorescence quenching. In ethanol solution, the Zn24 supracluster can be used to selectively detect Ce3+ ions with excellent efficiency (limit of detection (LOD) = 8.51 × 10-7 mol/L). The Zn24 supracluster can also detect wavelengths between 302 and 332 nm using the intensity of the emitted light.

Diagnostic performance of CL Detect rapid-immunochromatographic test for cutaneous leishmaniasis: a systematic review and meta-analysis.

BACKGROUND: Sensitive, robust, and fast point-of-care tests are needed for cutaneous leishmaniasis (CL) diagnosis. The recently developed CL Detect rapid test (InBios) for detecting Leishmania peroxidoxin antigen has been evaluated in several studies. However, diagnostic performances were controversial. Therefore, this systematic review and meta-analysis aimed to determine the pooled sensitivity and specificity of CL Detect for CL diagnosis. METHODS: PubMed, Scopus, EMBASE, ScienceDirect, and Google Scholar were sources of articles. We included studies reporting the diagnostic accuracy of CL Detect and CL-suspected patients in the English language. The methodological qualities of the included studies were appraised using the quality assessment of diagnostic accuracy studies-2 (QUADAS-2). Meta-analysis was conducted using Stata 14.2 and R software. RESULTS: A total of 9 articles were included. The study sample size ranged from 11 to 274. The sensitivities of the individual studies ranged from 23 to 100%, and the specificities ranged from 78 to 100%. Pooled sensitivity and specificity were 68% (95% CI, 41-86%) and 94% (95% CI, 87-97%), respectively. AUC displayed 0.899. Pooled sensitivity was lower (47%, 95% CI, 34-61%) when PCR was used as a reference than microscopy (83%, 95% CI, 39-97%). Pooled sensitivity was lower (48%, 95% CI, 30-67%) for all lesion durations compared to ≤ 4 months (89%, 95% CI, 43-99%). CONCLUSIONS: CL Detect has poor sensitivity and does not meet the minimal sensitivity of 95% of target product profiles designed for CL point-of-care tests. Currently, the CL Detect test looks unsuitable for CL diagnosis, despite its high specificity. Findings are limited by the low number of studies available. Further large-scale studies are recommended. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022323497.