Surveillance Tool Research Articles

Surveillance of human adenoviruses in water environments: Assessing the suitability of a locally developed quenching of unincorporated amplification signal reporters-loop-mediated isothermal amplification assay

The human adenovirus (HAdV) has shown greater environmental persistence, more water treatment resistance than bacteria, and cause infection even at low concentrations. HAdV causes gastrointestinal illnesses and is abundant in various environmental samples such as groundwater, surface water, recreational water, and drinking water. The detection of these pathogens calls for a more practical and affordable approach. A recent technology based on the quenching of unincorporated amplification signal reporters (QUASR) was adapted for the detection of human adenoviruses. This technology allows for non-inhibitory, single-step DNA detection in a closed tube. The QUASR-(loop-mediated isothermal amplification (LAMP) assay was previously optimized and was tested for its applicability to detect enteric HAdV in two areas where people can unintentionally be exposed to contaminated water. A total of 203 water samples were collected and tested using both real-time PCR and QUASR-LAMP assays. Results showed a higher positivity rate of 78.82 % (160/203) for QUASR-LAMP compared to qPCR with only 58.62 % (119/203). The sensitivity and specificity rates for QUASR-LAMP were calculated at 86.55 % and 32.14 %, respectively, when compared to qPCR. The QUASR-LAMP assay's ability to detect target analytes even at low concentrations can be attributed to its increased diagnostic sensitivity but lower specificity since there were samples that were positive in PCR but negative in the QUASR-LAMP assay. However, this characteristic does not diminish its utility as a valuable tool for the detection of HAdV. In fact, this attribute enhances its advantages in situations with constrained space and instrumentation requirements, making it suitable for rapid surveillance of important viruses. Finally, the capacity of the QUASR-LAMP assay to differentiate between positive and negative samples at a defined endpoint is highly beneficial for laboratory technicians who possess limited molecular biology expertise or experience. The QUASR-LAMP platform has demonstrated its usefulness as a diagnostic tool for surveillance of enteric adenoviruses in water sources.

INaturalist community observations provide valuable data on human-mosquito encounters.

Mosquitoes (Diptera: Culicidae) and the pathogens they transmit represent a threat to human and animal health. Low-cost and effective surveillance methods are necessary to enable sustainable monitoring of mosquito distributions, diversity, and human interactions. This study examined the use of iNaturalist, an online, community-populated biodiversity recording database, for passive mosquito surveillance in the United Kingdom (UK) and Ireland, countries under threat from the introduction of invasive mosquitoes and emerging mosquito-borne diseases. The Mozzie Monitors UK & Ireland iNaturalist project was established to collate mosquito observations in these countries. Data were compared with existing long-term mosquito UK datasets to assess representativeness of seasonal and distribution trends in citizen scientist-recorded observations. The project collected 738 observations with the majority recorded 2020-2022. Records were primarily associated with urban areas, with the most common species Culex pipiens and Culiseta annulata significantly more likely to be observed in urban areas than other species. Analysis of images uploaded to the iNaturalist project also provided insights into human-biting behavior. Our analyses indicate that iNaturalist provides species composition, seasonal occurrence, and distribution figures consistent with existing datasets and is therefore a useful surveillance tool for recording information on human interactions with mosquitoes and monitoring species of concern.

Use of Digital Tools in Arbovirus Surveillance: Scoping Review

Background The development of technology and information systems has led to important changes in public health surveillance. Objective This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance. Methods The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society). Results Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance. Conclusions In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools’ data, considering ethical aspects.

Dihydroartemisinin–Piperaquine Combination in the Treatment of Uncomplicated Plasmodium falciparum Malaria: Update on Clinical Failures in Africa and Tools for Surveillance

Dihydroartemisinin (or artenimol)–piperaquine is one of the six artemisinin-based combination therapies recommended in uncomplicated malaria treatment. However, artemisinin partial resistance has been reported in Cambodia, Laos, Vietnam, India, and, recently, in Africa. Polymorphisms in the Pfk13 gene have been described as molecular markers of artemisinin resistance and the amplification of the plasmepsine II/III (Pfpmp2/Pfpmp3) gene has been associated with piperaquine resistance. However, some therapeutic failures with this combination remain unexplained by strains’ characterization. We provide an overview on the use of dihydroartemisinin–piperaquine in malaria treatment and discuss tools available to monitor its efficacy.

Abstract IA017: ctDNA based detection of MRD in colon cancer: Towards clinical implementation

Abstract Patient selection for adjuvant treatment remains a challenge with our current clinicopathologic criteria. While adjuvant chemotherapy has been shown to improve relapse risk and survival in stage III colon cancer and perhaps less so in stage II disease, the absolute benefit remains modest, risking the over-treatment of many patients and potential under-treatment of some. Additionally, beyond fluoropyrimidine and oxaliplatin, no new adjuvant therapy has been shown to improve survival over the past 2 decades, especially for MMR proficient tumors. The ability of ctDNA to detect minimal residual disease offers a paradigm shift to the conventional tissue- based risk assessment. Post-op ctDNA detection has been shown to be one of the strongest independent prognostic markers in curatively resected colorectal cancer. Alongside technological advancement, multiple randomized trials are investigating different applications of ctDNA MRD analysis in the clinic, from adjuvant treatment de-escalation in patients with ctDNA-negative result, treatment escalation in ctDNA-positive result to the role of ctDNA as a surveillance tool. Understanding the strengths and limitations of this test will help inform future trial design and optimize clinical implementation. Citation Format: Jeanne Tie. ctDNA based detection of MRD in colon cancer: Towards clinical implementation [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA017.

Abstract A012: Analytical validation of a cfDNA-based fragmentomic profiling assay for the detection and quantification of 14 HPV types in cervical cancer

Abstract Purpose: Human papillomavirus (HPV) is the primary cause of cervical cancer, one of the most common cancers affecting women globally. Despite advances in prevention and screening, there remains a critical need for reliable, non-invasive assays to detect and monitor HPV-driven cervical cancers. Liquid biopsy offers a promising solution to meet this need. Here, we report the analytical validation of a plasma-based fragmentomic profiling assay designed to detect and characterize circulating tumor tissue modified viral (TTMV)-HPV DNA from 14 HPV types associated with cervical cancer. Methods: A high-complexity, laboratory-developed test was developed that employs 68 primers and 34 probes to target 14 HPV types via droplet digital PCR (ddPCR). A quantitative algorithm was applied to integrate fragment counts to generate a TTMV-HPV DNA Score, reflective of the quantity of circulating tumor-derived HPV DNA. Analytical validation was performed by determining the limit of blank (LOB), limit of quantitation (LOQ), and limit of detection (LOD) for each HPV type. To measure the limits of assay performance, engineered samples were used for reference interval studies. Synthesized target-specific DNA regions of HPV16, HPV18, HPV31, HPV33, HPV35, HPV39, HPV45, HPV51, HPV52, HPV56, HPV58, HPV59, HPV66, and HPV68 were diluted to varying concentrations to perform assay validation. Results: The assay demonstrated excellent specificity, with LOBs ranging from 0 to 0.07 copies/µL across the 14 HPV types. LODs ranged from 0.20 to 1.02 copies/µL, indicating a high level of sensitivity. The assay also showed high accuracy and precision, with coefficients of variation (CV) below 20% for all HPV types across both intra- and inter-assay precision studies. Regression analysis confirmed strong linearity (R2 > 0.99) across a wide range of analyte concentrations. Conclusion: These results demonstrate that TTMV-HPV DNA assay accurately and reproducibly detects circulating HPV DNA for 14 HPV types commonly associated with cervical cancer, offering a valuable tool for both diagnosis and ongoing surveillance in clinical settings. Future studies will present the clinical validation in order to establish the prognostic utility of the assay in predicting the likelihood of HPV-driven cervical cancer in patients. Citation Format: Sunil Kumar, Joshua Hutcheson, David Conway, Michael Horan, Syandan Chakraborty, Chloe Wiseman, Alicia Gunning, Evgeny Skyrypkin, Cassin Williams, Catherine Del Vecchio Fitz, Piyush B Gupta. Analytical validation of a cfDNA-based fragmentomic profiling assay for the detection and quantification of 14 HPV types in cervical cancer [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A012.

Targeting c-MET for Endoscopic Detection of Dysplastic Lesions Within Barrett's Esophagus Using EMI-137 Fluorescence Imaging.

Esophageal cancer (EC) carries a poor prognosis with 5-year overall survival of less than 20%. Barrett's esophagus (BE) increases the risk of esophageal adenocarcinoma (EAC). The aim of this study was to investigate the ability of EMI-137, a mesenchymal-epithelial transition factor (c-MET)-targeting optical imaging tracer, to detect dysplasia in BE. c-MET expression in human esophageal tissue was investigated using Gene Expression Omnibus (GEO) datasets, tissue microarrays and BE biopsies. EMI-137 was tested in a dual xenograft mouse model bearing OE33 (c-MET high expression) and FLO-1 (c-MET low expression) tumors. Fluorescence molecular endoscopy (FME) was performed in a mouse model of Barrett's-like metaplasia and dysplasia (L2-IL1β). Tumors and organs-of-interest were evaluated through ex vivo fluorescence imaging. MET mRNA expression analyses and c-MET immunostaining confirmed upregulation of c-MET in BE and EAC compared to normal epithelium. There was strong accumulation of EMI-137 in OE33 xenografts 3 h post injection decreasing by more than 50% on co-injection of a 10-fold molar excess of unlabeled EMI-137. The target-to background ratio (TBR) at 3 h p.i. for OE33 and FLO-1 tumors was 10.08 and 1.42, respectively. FME of L2-IL1β mice showed uptake of EMI-137 in dysplastic lesions within BE with a TBR of 1.9 in vivo, and greater than 2 in ex vivo fluorescence imaging. EMI-137 accumulates in dysplastic lesions within BE and in c-MET positive EAC. EMI-137 imaging has potential as a screening and surveillance tool for patients with BE and as a means to detecting dysplasia and EAC.

ARTIFICIAL INTELLIGENCE IN CRIMINAL JUSTICE MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW

This systematic review, based on 37 articles, explores the role of artificial intelligence (AI) in criminal justice, focusing on its applications in predictive policing, judicial risk assessments, and surveillance, as well as the associated ethical and regulatory challenges. AI has demonstrated substantial potential for improving efficiency and accuracy in criminal justice systems, from optimizing law enforcement resource allocation to providing data-driven risk assessments that support judicial decisions. However, the review identifies significant ethical issues, especially related to algorithmic bias, which can perpetuate existing societal inequalities and disproportionately affect marginalized communities. Concerns around transparency and accountability are prevalent, as the "black-box" nature of many AI algorithms complicates public understanding and trust in AI-driven outcomes. Surveillance tools, including facial recognition and behavioral analysis, enhance real-time threat detection but raise privacy and civil rights concerns, highlighting the need for regulatory oversight. Gaps in legal frameworks suggest the urgency for standardized policies that address data privacy, algorithmic fairness, and accountability in AI applications. The findings underscore that interdisciplinary collaboration, transparent practices, and comprehensive regulatory measures are essential to responsibly integrate AI into criminal justice, balancing technological advancements with justice, equity, and public trust.

Atypical cutaneous leishmaniasis: a new challenge to VL elimination in South-East Asia

Visceral leishmaniasis (VL) caused by L. donovani in South-East Asian endemic countries including India, Nepal and Bangladesh has been the primary focus of the ongoing VL elimination program. With a major reduction in VL cases resulting from the elimination program during the last two decades, the efforts are now focused on the challenges posed by potential reservoirs within the asymptomatic cases, HIV-co-infection VL cases and Post Kala-azar Dermal Leishmaniasis (PKDL) cases that continue to sustain the parasite transmission cycle in known and newer endemic zones. This article brings attention to a new potential parasite reservoir in the form of atypical cutaneous leishmaniasis (ACL) cases caused by novel L. donovani genetic variants. L. donovani mediated ACL is an emerging phenomenon in recent endemic sites that now justify a need for implementing molecular surveillance tools to identify region-specific L. donovani variants with dermotropic capabilities and potential to revert to visceral disease. A timely detection of novel ACL causing L. donovani genetic lineages in South-East Asian endemic regions is necessary to halt the spread of ACL and is potentially crucial for the sustainability of the advances made by the VL elimination.

Understanding Variation Among Medical Device Reporting Sources: A Study of the MAUDE Database

BackgroundIncreasing medical device usage raises concerns regarding unexpected, potentially life-threatening events that pose public health risks. Such events are reported to the Food and Drug Administration (FDA), and cataloged in the Manufacturer and User Facility Device Experience (MAUDE) database, a vital tool for post market surveillance that requires information of high quality and integrity, particularly concerning reporting sources. PurposeTo analyze reporting behavior among different reporters, including manufacturers, distributors, and user facilities, by examining differences in reported factors, namely: (1) device types, (2) product problem attribution, and (3) selection of Device Problem Codes (DPCs) associated with the root causes of events. MethodsData spanning from 2005 to 2022 was retrieved from the MAUDE database using Python. Reports were grouped by reporter type and divided according to device type, and the reporter's indication of association with a product problem. Furthermore, events were classified by their respective DPCs, which were manually grouped into four categories: device issues, user issues, clinical issues, or unknown. FindingsThe analysis revealed significant variations among reporters across all examined aspects (P < 0.00001 in all comparisons according to the proportion test). Manufacturers predominantly focused on infusion pumps (10.1%) and Implant, Endosseous, Root-Form (IER) devices (7.6%), with a product problem indication rate of 29.2% in their reports. Device issues codes were the most frequently observed category in their reports, comprising 36.3%, followed by unknown codes (32%) and clinical codes (19.3%). Distributors, on the other hand, primarily reported on IER devices (89%) and exhibited the lowest product problem indication rate at 2.7%. Clinical issues codes predominated in their reports, constituting 85.7%, followed by unknown codes (6.7%). User facilities concentrated on intravascular sets (4.7%), Electrosurgical, Cutting & Coagulation & Accessories (4.2%), and Ventricular (Assist) Bypass (4.1%). Remarkably, their product problem indication rate was the highest at 56.7%. They predominantly reported device issues codes (54.3%), followed by use codes (30.8%), and unknown codes (11.4%) ImplicationsThe notable variation among different reporters underscores the importance of incorporating diverse sources when analyzing the database, particularly in cases where majority of reports originate from manufacturers. Decision-makers must approach database information comprehensively, considering data sources and diverse perspectives to inform regulatory decisions effectively. Developing strategies that encourage various reporters to contribute their unique and complementary viewpoints is advisable.

Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD).

Signs and symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) are present at preschool ages and often not identified for early intervention. We aimed to use machine learning to detect ADHD early among kindergarten-aged children using population-level administrative health data and a childhood developmental vulnerability surveillance tool: Early Development Instrument (EDI). The study cohort consists of 23,494 children born in Alberta, Canada, who attended kindergarten in 2016 without a diagnosis of ADHD. In a four-year follow-up period, 1,680 children were later identified with ADHD using case definition. We trained and tested machine learning models to predict ADHD prospectively. The best-performing model using administrative and EDI data could reliably predict ADHD and achieved an Area Under the Curve (AUC) of 0.811 during cross-validation. Key predictive factors included EDI subdomain scores, sex, and socioeconomic status. Our findings suggest that machine learning algorithms that use population-level surveillance data could be a valuable tool for early identification of ADHD.

Cattle, sheep, and goat humoral immune responses against SARS-CoV-2

Following the emergence of SARS-CoV-2 in late 2019, several species of domestic and wild animals have been found to be susceptible to SARS-CoV-2 infection through experimental inoculation and animal surveillance activities. Detection of SARS-CoV-2 specific antibodies in animals is an important surveillance tool since viral shedding in animals can only be detected for a short period of time. In this study, convenience serum samples were collected from 691 cattle, 698 sheep, and 707 goats from several regions in the United States, between 2019 to 2022. The samples were evaluated for the presence of SARS-CoV-2 specific antibodies using two commercial enzyme-linked immunosorbent assays (ELISA); one based on the inhibition of the SARS-CoV-2 receptor-binding domain (sVNT) and the other based on the nucleocapsid protein (N-ELISA) of SARS-CoV-2. Positive samples from the sVNT were additionally evaluated using a conventional virus neutralization test (VNT) employing the Wuhan-like SARS-CoV-2 USA/WA1/2020 isolate. Our results indicate that ∼1% (6/691) of cattle, ∼2% (13/698) of sheep, and ∼2.5% (18/707) of goat serum samples were positive when using the sVNT, whereas ∼4% of cattle (25/691) and sheep (27/698), and 2.5% (18/707) of goat serum samples tested positive with the N-ELISA. None of the sVNT positive cattle, sheep, or goat serum samples had detectable neutralizing antibody activity (<1:8) against the SARS-CoV-2 USA/WA1/2020 isolate by the VNT. Our results indicate low seropositivity in cattle, sheep, and goats in the U.S., indicating the importance to continue monitoring for SARS-CoV-2 prevalence in animal species that are in close contact with humans.

Impact of phosphatidylethanol in the surveillance for alcohol use in post-liver transplant population: A retrospective study.

Alcohol-related liver disease (ALD) is the leading indication for liver transplantation in the United States. The aim of this study was to describe the impact of phosphatidylethanol (PEth) in the surveillance for alcohol use after liver transplantation. We conducted a single-center retrospective study to assess the impact of phosphatidylethanol (PEth) for the surveillance of alcohol use and its correlation to health outcomes. We compared orthotopic liver transplant (OLT) recipients for ALD transplanted between 2016 and 2018, before the introduction of PEth, to those transplanted between 2019 and 2022, after the introduction of PEth. Alcohol relapse versus nonrelapse cohorts were also compared. Follow-up time for all cohorts was limited to 3 years post-OLT. Continuous variables were analyzed with an independent t-test and categorical variables with Fischer's exact test and chi-square test. The Kaplan-Meier method and log-rank test were used to assess alcohol-free survival. We reviewed 263 patients who were transplanted for ALD; 46 (17.5%) patients were noted to have at least one episode of alcohol relapse after their transplant. Patients with alcohol relapse had more frequent episodes of elevated liver enzymes compared with nonrelapsed patients (4.35 episodes vs. 2.46 episodes respectively, p < 0.001). The number of hospitalizations was also noted to be elevated among relapsed versus nonrelapsed patients; however, this was not statistically significant (2.85 vs. 2.50 respectively, p = 0.307). When comparing relapse rates before and after the introduction of PEth, relapses were notably detected more frequently after the introduction of PEth (17% vs. 7%, p = 0.012). No difference was noted in rates of mortality between patients who did or did not relapse. Overall, PEth is an effective surveillance tool in the postliver transplant population to monitor for alcohol relapse. Early detection of relapse can lead to opportunities for early intervention to avoid alcohol-related complications.

A Study on Implementation and Evalutation Methods in Recruitment and Selection Process

The study examines implementation and evaluation methods used in the recruitment and selection process, focusing on best practices that improve candidate fit and organizational effectiveness. Technological advancements, changing workforce demographics, and evolving employer branding strategies have dramatically changed the recruiting landscape. Using a mixing approach, this study evaluates the current practices of many companies by combining quantitative data and qualitative data.The structured interviews of the candidate's surveillance tools, predictive analysis, and the improvement of the selection process are emphasized by major conclusions. He also emphasizes the importance of the employer and the experience of candidates who attracts the best talent, and the evaluation methods such as feedback loops and productivity measurements are optimized tactical adoption and are the goals of the organization. This is key to alignment so that a global strategy focused on data use and choice will promote more diverse and inclusive workplaces, in addition to improving workforce levels. The study concludes with a recommendation that educational institutions systematically evaluate their hiring practices to implement best practices and adapt to an ever-changing job market.

Wastewater Surveillance of SARS-CoV-2 in Slovenia: Key Public Health Tool in Endemic Time of COVID-19

With the reclassification of COVID-19 as an endemic disease and the relaxation of measures, Slovenia needed a complementary system for monitoring SARS-CoV-2 infections. This article provides an overview of the epidemiological situation of SARS-CoV-2 in Slovenia using a wastewater surveillance system, demonstrating its usefulness as a complementary tool in epidemiological surveillance. This study found that estimated SARS-CoV-2 infections in Slovenia peaked in September 2022 and showed a declining trend with subsequent lower peaks in March–April and December 2023, mirroring the trends observed from clinical data. Based on both surveillance systems, the most prevalent variant in 2022 was BA.5. By 2023, BQ.1 and other Omicron variants increased in prevalence. By the end of 2023, XBB sublineages and the BA.2.86 variant had become predominant, demonstrating consistent dynamic shifts in variant distribution across both monitoring methods. This study found that wastewater surveillance at wastewater treatment plants in Slovenia effectively tracked SARS-CoV-2 infection trends, showing a moderate to strong correlation with clinical data and providing early indications of changes in infection trends and variant emergence. Despite limitations during periods of low virus concentration, the system proved significant in providing early warnings of infection trends and variant emergence, thus enhancing public health response capabilities.

Impact of artificial intelligence on healthcare-associated infection control: a systematic review

Abstract Background Healthcare-associated infections (HAIs) represent a significant public health concern, correlating with increased morbidity, mortality rates and healthcare expenditures. While artificial intelligence (AI) systems offer transformative potential in enhancing HAIs detection and control practices, the actual performance effectiveness of these systems remains uncertain. This systematic review updates a previously published study from 2020 and evaluates the performance of AI-based tools for surveillance, detection, and control of HAIs. Methods PRISMA 2020 guidelines were applied. The study protocol has been registered in PROSPERO (ID: CRD42024513145). PubMed, Embase, Scopus and Web of Science were searched for experimental and observational studies assessing the performance of AI-based tools to detect and control HAIs, published in English. Results From 8,701 articles initially identified, 4,212 records were removed due to duplication. Out of 4,489 papers screened, 147 were included. Studies reported performance measures including sensitivity, specificity, positive and negative predictive values, area under the receiver operating characteristic curve, accuracy, precision, F1 score. Significant heterogeneity was found in the types of technology, infections targeted, health care settings and data sources between studies. Conclusions The increase in published evidence since the previous review reflects the growing interest and use of new technologies such as Large Language Models, showing promising performance in surveillance, early diagnosis and prediction of HAIs. However, the observed heterogeneity in study designs, targeted infections, healthcare settings, and data sources underscores the need for standardised methodologies and robust validation processes to ensure the reliability and comparability of results across different studies. Key messages • The use of AI-based tools has the potential to enhance surveillance, detection, and control of healthcare-associated infections, offering a transformative impact on healthcare systems. • Standardised methodologies and validation processes are needed to ensure comparable results across studies and to maximise the real-world impact of AI tools in HAI surveillance and control efforts.

Collaborative development of open-source outbreak detection tools: Needs, concept and implementation

Abstract Issue The early detection of infectious disease outbreaks through surveillance is critical to initiate targeted public health interventions. Automated outbreak detection helps to identify trends and unusual events. Yet, there is no overview of how many EU countries use outbreak detection systems and what factors hindered their implementation. Description of the problem One aim of the Joint Action UNITED4Surveillance (U4S) is to identify EU countries’ requirements for outbreak detection, and based on these, develop and deploy a tool for the timely and accurate detection of outbreaks. Through surveys, interviews and an in-person workshop involving public health experts, we identified uses cases, collected functional and technical requirements for a tool. Subsequently the tool development started in August 2023 as an open-source software process on GitHub. Results Despite high interest, only three of the 21 countries that took part in the survey have implemented outbreak detection tools, even though national surveillance systems offer the potential for their use. Reasons for this are limited resources and low prioritization. Experts identified gastrointestinal diseases as one major potential use case and emphasised the necessity of flexible outputs. Tailored to these requirements, data scientists from five European public health institutes developed an open-source R package for signal detection. The tool generates signals based on national surveillance data and enables the user to analyses these results using a dashboard with flexible exploration and generation of a report. The tool is now being piloted locally by ten European public health authorities and its use will be evaluated. Lessons There is a need to use automated outbreak detection tools for routine surveillance of infectious diseases in EU countries but often lack resources. Projects like U4S can bring together expertise to build sustainable tools and supporting material that can address the identified needs. Key messages • Collaborative and open-source development leads to tools that are well tailored to the needs of the community and foster a high uptake in the usage of these tools. • The tool enhances surveillance and outbreak detection in countries without the capacities to develop and implement outbreak detection methods for their surveillance systems.

Establishing normal global longitudinal strain values in heart transplant patients: a novel approach to enhancing detection of rejection and cardiac allograft vasculopathy

Abstract In the realm of heart transplantation, accurate cardiac monitoring is pivotal for patient outcomes. This study pioneers in filling a critical void by delineating the normative values of Global Longitudinal Strain (GLS), specifically tailored for heart transplant recipients, distinguishing them from the general population. We conducted a retrospective analysis on a cohort of 110 patients who underwent heart transplantation, meticulously compiling echocardiographic data over the first 18 months post-operation. Our research focused on two primary indicators of cardiac function: Ejection Fraction (EF) and Global Longitudinal Strain (GLS). The data indicated that EF values remained within the normal range and relatively stable throughout the monitoring period. In contrast, GLS values exhibited a gradual increase over time, suggesting a post-transplant myocardial adaptation process. However, these GLS values did not reach the established reference ranges for non-transplanted, healthy hearts, highlighting the distinctive physiological landscape of transplant recipients. Our study elucidates the superior sensitivity of GLS in detecting subtle myocardial changes that might precede clinically apparent dysfunction, advocating its utility as a crucial surveillance tool for this demographic. By pinpointing the modified GLS benchmarks applicable to post-heart transplant patients, our findings advocate for a recalibrated echocardiographic assessment protocol. Such a protocol can significantly enhance the surveillance for post-transplant complications, most notably graft rejection and the onset of cardiac allograft vasculopathy (CAV). The study posits that incorporating these refined GLS thresholds into routine post-transplant evaluations could facilitate early intervention, potentially mitigating adverse outcomes and improving the long-term prognosis for heart transplant recipients. Thus, our research underscores the need for updated clinical guidelines that integrate these novel echocardiographic insights, paving the way for improved patient-centric cardiac care in the transplant setting.

The management of high flow arteriovenous fistula and possible solutions.

The development of a high flow rate arteriovenous fistula (AVF) can expose the patient to development of heart failure due to increased cardiac preload and pulmonary hypertension. AVF flow measurement (Qa) is considered a screening tool for AVF surveillance, aiming to evaluate the access dysfunction and prevent complications, like a non-maturation, suspected stenosis, high-flow AVF, and distal ischemia. In the upper arm AVF, a high Qa may develops, which can expose the patient to the risk of high-output heart failure and ischemia. Although, the exact threshold to define high-flow access is not universally accepted, AVF with a Qa of 1-1.5 L/min or cardio-pulmonary recirculation (Qa/CO) >20% are considered at risk. In our work we describe the treatment performed in three patients with high flow AVF treated with DRIL technique with interposition of a Prosthetic Patch, revascularization procedures such as distal inflow revision or RUDI and with innovative technique a "tench snout," removal the previous anastomosis and reconstruction of the integrity of the radial artery at the terminal in pre and post anastomosis. A PTFE prosthetic segment measuring 5 cm in length and 5 mm in diameter was interposed, terminally anastomosed with the efferent cephalic vein and terminally lateral with the radial artery, reducing the anastomosis to approximately 4 mm. All treated patients showed a clear improvement in the clinical picture in particularly heart failure. The calculation of the post-intervention flow rate approximately 1500 mL/min. The patient on hemodialysis with arteriovenous fistula must be constantly monitored with clinical examination, monitoring during the hemodialysis session and color Doppler ultrasound of the AVF with calculation of the flow rate. The surgical technique used for flow reduction is chosen on the surgical experience of each operator with the main objective of preserving the autologous AVF.

Evaluation of Fecal Sample Pooling for Real-Time RT-PCR Testing SARS-CoV-2 in Animals

During the COVID-19 pandemic, veterinary diagnostic laboratories tested both human and animal samples and needed to ensure that they could accurately perform large numbers of diagnostic tests in a timely manner. Sample pooling, a methodology used effectively for over 80 years as a surveillance tool for screening large numbers of potentially infected individuals, was employed. Given its sensitivity, real-time polymerase chain reaction (PCR) is more suitable for employing this strategy, as compared to other less sensitive testing methods. In this study, we evaluated the capability of detecting SARS-CoV-2 in both 5-sample and 10-sample pools of feces using real-time reverse transcriptase polymerase chain reaction (rRT-PCR) as well as determined the level of sensitivity. A blinded method test (BMT) by an independent laboratory was conducted to assess the five-sample fecal pool. To complement detection capability, the stability of the genome within a PBS fecal suspension was measured under various time and temperature conditions across a 28-day period. Our results showed that the limit of detection for 5-sample and 10-sample fecal pools is 12.8 and 6.4 genome copies in a 25 µL PCR, respectively. The 5-sample and 10-sample pooling resulted in a cycle threshold (Ct) value loss of 2.35 and 3.45, as compared to Ct values of known positive individual samples, but consistent detection was still achieved in pools containing positive samples with an original Ct below 36 and 34, respectively. The simulation of clinical five-sample pooling showed that all positive samples could be detected regardless of the number (1–3) of positive samples in each pool. The BMT results demonstrated excellent sensitivity (100 copies/reaction) in five-sample pools for the detection of SARS-CoV-2 RNA even though a fecal matrix effect was observed. Finally, our results show that the SARS-CoV-2 genome remains stable over a wide range of time and temperature variations. Overall, our findings provide solid data to scale up SARS-CoV-2 testing capacity in veterinary diagnostic laboratories.