Eutrophication of coastal areas is a global problem. A full-scale coastal remediation project was initiated in Björnöfjärden bay in the Stockholm archipelago in 2011. Measures to reduce external nutrient inputs from the surrounding catchment (15 km2) targeted agriculture, on-site wastewater treatment facilities, and horse keeping. The effects were evaluated at 22 water quality monitoring stations over 11 years (2012–2022) to determine temporal trends in nutrient concentrations, spatial correlations within and between monitored sub-catchments, and effects of individual mitigation measures at local and catchment scale. The effect of individual measures varied from no significant effect to significant nutrient decreases (21% reduction in dissolved P concentrations in one lime filter) or increases (11% higher concentrations in total P in one constructed wetland). However, few significant trends were detected at sub-catchment outlet stations. Tailored placement, design, dimensioning, and maintenance of implemented mitigation measures are needed to improve their nutrient retention effect.

Background After 25 years of implementing the Medical Devices Directive (MDD), in 2017, the new Medical Devices Regulation (MDR) came into force, establishing stricter requirements for post-market surveillance of the safety and performance of medical devices (MD). For electrocardiogram (ECG) devices, which are crucial for monitoring cardiac activities, these requirements are essential to ensure the reliability and accuracy of diagnosing cardiac conditions and timely treatment. Objective This study aims to enhance post-market surveillance of ECG devices by leveraging Machine Learning (ML) algorithms to predict the operational status of these devices. Specifically, the research focuses on classifying the success or failure of ECG device operations based on performance and safety parameters. The ultimate goal is to improve the management strategies of ECG devices in healthcare institutions, ensuring optimal functionality and increasing the reliability of diagnostic procedures. Method During the inspection process of ECG devices conducted by an accredited laboratory in accordance with ISO 17020 standard in numerous healthcare institutions in Bosnia and Herzegovina, a total of 5577 samples were collected. Various machine learning algorithms, including Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (NB), and Support Vector Machine (SVM), were employed for result comparison and selection of the most accurate algorithm. Results All algorithms demonstrated good performance, but the Random Forest (RF) algorithm stood out, achieving 100% accuracy in predicting the success/unsuccess status of the device. While the results of this research are specific to the collected data from EKG devices, the developed algorithms can be applied to other similar datasets, offering opportunities for broader use in the medical environment. Conclusion Implementing machine learning algorithms for automated systems in healthcare institutions can significantly enhance the quality of patient diagnosis and treatment. Additionally, these systems can optimize costs associated with managing medical devices. Improved post-market surveillance using ML can address challenges related to ensuring device reliability and safety.

Electrochemical biosensors transduce chemical reactions into measurable electrical signals by incorporating recognition components. Although they are capable of detecting a broad range of target molecules, their application in complex matrices, such as food, at minimum or no sample preparation, is challenging and requires the introduction of innovative and effective strategies. This review explores the recent advances in electrochemical biosensors for on-site food safety and quality analysis. We first discuss the presence of chemical contaminants and biohazards in food and the need for robust, rapid, low-cost, and point-of-care (POC) analytical techniques. We then address the critical aspects of sensitivity and selectivity of electrochemical biosensors in detecting chemical and biological contaminants in real food samples. We finally investigate the major drawbacks of these biosensors and provide future perspectives on the field.

Background and Objectives: Depression is a common mental problem in the older population and has a significant impact on recovery and general well-being. A comprehensive understanding of the prevalence of depressive symptoms and their effects on functional outcomes is essential for improving care strategies. The primary aim of this study was to determine the prevalence of depressive symptoms in older patients undergoing geriatric rehabilitation and to assess their specific impact on their functional abilities. Materials and Methods: A retrospective study was conducted at the Lucerne Cantonal Hospital in Wolhusen, Switzerland, spanning from 2015 to 2020 and including 1159 individuals aged 65 years and older. The presence of depressive symptoms was assessed using the Geriatric Depression Scale (GDS) Short Form, while functional abilities were evaluated using the Functional Independence Measure (FIM) and the Tinetti test. Data analysis was performed using TIBCO Statistica 13.3, with statistical significance set at p < 0.05. Results: Of the participants, 22.9% (N = 266) exhibited depressive symptoms, with no notable differences between genders. Although all patients showed functional improvements, the duration of rehabilitation was prolonged by two days (p = 0.012, d = 0.34) in those with depressive symptoms. Alarmingly, 76% of participants were classified as at risk of falling based on the Tinetti score. However, no significant correlation was found between the GDS and Tinetti scores at admission (p = 0.835, r = 0.211) or discharge (p = 0.336, r = 0.184). The results from the non-parametric Wilcoxon matched-pairs test provide compelling evidence of significant changes in FIM scores when comparing admission scores to those at discharge across all FIM categories. Conclusions: Depressive symptoms are particularly common in geriatric rehabilitation patients, leading to prolonged recovery time and increased healthcare costs. While depressive symptoms showed no correlation with mobility impairments, improvements in functional status were directly associated with reduced GDS scores. Considering mental health during admission and planning is critical in optimizing rehabilitation outcomes.

This study investigates the use of deep learning algorithms to predict the discharge coefficient (Cd) of contaminated multi-hole orifice flow meters with circular opening. Datasets (MHO1 and MHO2) were obtained from computational fluid dynamic simulations for two circular multi-hole orifice flow meters of different geometries. To evaluate the performance and generalization capabilities of different models, three distinct scenarios, each involving different dataset configurations and normalization techniques were designed. For each scenario, three deep learning models (feedforward neural networks, convolutional neural network, and recurrent neural network) were implemented and evaluated based on their performance metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). For all three scenarios eight models for each neural network model were developed (FFNN – four models, CNN – two models, RNN – two models). The same structure of models was used across all scenarios to ensure consistency in the evaluation process. Key input parameters include geometrical and flow variables such as β – parameter, contamination thickness, radial distance, Reynolds number, and orifice diameters. Results demonstrate the effectiveness of deep learning in accurately predicting discharge coefficient for different contamination conditions and different geometries. This study showed that deep learning models can be used for prediction of discharge coefficients for multi-hole orifice flow meters of similar geometry, based on data obtained from one orifice flow meter for different contamination parameters.

INTRODUCTION AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction (LVEF <40%) is not yet well established. What is the sensitivity and specificity of artificial-based electrocardiogram to diagnose heart failure with low ejection fraction (cut-off of 40%. AIMS We conducted a meta-analysis and systematic review to evaluate the accuracy of artificial intelligence electrocardiograms in estimating an ejection fraction below 40%. METHODS We searched PubMed, Embase, and Cochrane Library for studies evaluating the performance of AI ECGs in diagnosing heart failure with reduced ejection fraction. We computed true positives, true negatives, false positives, and false negatives events to estimate pooled sensitivity, specificity, and area under the curve, using R software version 4.3.1, under a random-effects model. RESULTS We identified 9 studies, including patients with a paired artificial intelligence-enabled electrocardiogram with an echocardiography. patients had an ejection fraction below 40% according to the echocardiogram. The AI-ECG data yielded areas under the receiver operator of, the sensitivity of), specificity of, and area under the curve of. The mean/median age ranged from 60±9 to 68.05± 11.9 years. CONCLUSIONS In this systematic review and meta-analysis, the use of electrocardiogram-based artificial intelligence models demonstrated high sensitivity and specificity to estimate a left ventricular ejection fraction below 40%.

OBJECTIVE To systematically evaluate timely reporting of clinical trial results at medical universities and university hospitals in the Nordic countries. STUDY DESIGN AND SETTING In this cross-sectional study, we included trials (regardless of intervention) registered in the EU Clinical Trials Registry and/or ClinicalTrials.gov, completed 2016-2019, and led by a university with medical faculty or university hospital in Denmark, Finland, Iceland, Norway, or Sweden. We identified summary results posted at the trial registries, and conducted systematic manual searches for results publications (e.g., journal articles, preprints). We present proportions with 95% confidence intervals (CI), and medians with interquartile range (IQR). PROTOCOL https://osf.io/wua3r RESULTS: Among 2,112 included clinical trials, 1,650 (78.1%, 95%CI 76.3-79.8%) reported any results during our follow-up; 1,097 (51.9%, 95%CI 49.8-54.1%) reported any results within 2 years of the global completion date; and 48 (2.3%, 95%CI 1.7-3.0%) posted summary results in the registry within 1 year. Median time from global completion date to results reporting was 690 days (IQR 1,103). 856/1,681 (50.9%) of ClinicalTrials.gov-registrations were prospective. Denmark contributed approximately half of all trials. Reporting performance varied widely between institutions. CONCLUSION Missing and delayed results reporting of academically led clinical trials is a pervasive problem in the Nordic countries. We relied on trial registry information, which can be incomplete. Institutions, funders, and policy makers need to support trial teams, ensure regulation adherence, and secure trial reporting before results are permanently lost.

BACKGROUND Arboviruses are increasingly affecting Europe, partly due to the effects of climate change. This increase in range and impact emphasises the need to improve preparedness for emerging arboviral infections that often co-circulate and might have overlapping clinical syndromes. We aimed to strengthen surveillance networks for four clinically relevant arboviruses in southeast Europe. METHODS This study reports an in-depth analysis of the MERMAIDS-ARBO prospective observational study in adults (ie, aged ≥18 years) hospitalised with an arbovirus-compatible disease syndrome in 21 hospitals in seven countries in southeast Europe over four arbovirus seasons (May 1-Oct 31, 2016-19) to obtain arbovirus prevalence outcomes. The main objectives of the MERMAIDS-ARBO study, describing the clinical management and outcomes of four arboviruses endemic to southeast Europe, including Crimean-Congo haemorrhagic fever virus (CCHFV), tick-borne encephalitis virus (TBEV), Toscana virus, and West Nile virus (WNV), are reported elsewhere. In this analysis, given the challenges associated with arbovirus diagnostics, we developed a diagnostic algorithm accounting for serology outcomes and sample timing to study arbovirus prevalence in southeast Europe. Serum samples were collected on days 0, 7, 28, and 60 after hospital admission and tested for anti-CCHFV IgG and IgM antibodies with ELISAs (confirmed with an indirect immunofluorescence test) and for IgG and IgM antibodies specific to TBEV, Toscana virus, and WNV with custom-printed protein microarrays (confirmed with virus neutralisation tests). All acute-phase samples were tested by PCR for all four viruses. Descriptive analyses were performed for virus-reactive cases by geography and year, and possible factors (eg, age, sex, and insect bites) associated with virus reactivity were assessed. FINDINGS Of 2896 individuals screened, 913 were eligible for inclusion, of whom 863 (514 men, 332 women, and 17 unknown) had samples sent to the study reference laboratories and were included in molecular and serological analyses. Some individuals had insufficient clinical data to be included in the clinical analysis, but met the eligibility criteria for and were included here. Serum sampling was incomplete (eg, samples missing from one or more timepoints or no data on time since symptom onset) for 602 (70%) patients, and the timing of collection was often heterogeneous after symptom onset up to 40 days (average median delay of 5-6 days across all timepoints), affecting the ability to diagnose arbovirus infection by serology. By use of an interpretation table incorporating timing and completeness of sampling, one (<1%) participant had a confirmed recent infection with CCHFV, ten (1%) with TBEV, 40 (5%) with Toscana virus, and 52 (6%) with WNV. Most acute confirmed infections of Toscana virus were found in Albania (25 [63%] of 40), whereas WNV was primarily identified in Romania (36 [69%] of 52). Albania also had the highest overall Toscana virus seropositivity (168 [60%] of 282), mainly explained by patients confirmed to be exposed or previously exposed (104 [62%] of 168). Patients without antibodies to WNV or Toscana virus were significantly younger than patients with antibodies (mean difference -8·48 years [95% CI -12·31 to -4·64] for WNV, and -6·97 years [-9·59 to -4·35] for Toscana virus). We found higher odds of Toscana virus reactivity in men (odds ratio 1·56 [95% CI 1·15 to 2·11]; p=0·0055), WNV reactivity with mosquito bites versus no mosquito bites (2·47 [1·54 to 3·97]; p=0·0002), and TBEV reactivity with tick bites versus no tick bites (2·21 [1·19 to 4·11]; p=0·018). INTERPRETATION This study shows that despite incomplete and heterogeneous data, differential diagnosis of suspected arbovirus infections is possible, and the diagnostic interpretation algorithm we propose could potentially be used to strengthen routine diagnostics in clinical settings in areas at risk for arboviral diseases. Our data highlight potential hotspots for arbovirus surveillance and risk factors associated with these particular arbovirus infections. FUNDING European Commission and Versatile Emerging infectious disease Observatory. TRANSLATIONS For the Greek, Albanian, Romanian, Bosnian, Serbian, and Croatian translation of the summary see Supplementary Materials section.

Background/Objectives: Congenital heart disease (CHD), affecting approximately 1% of live births, has transitioned to a chronic condition due to advances in diagnostics and surgery, resulting in an increasing adult congenital heart disease (ACHD) population. This study characterizes the clinical and demographic profiles of ACHD patients in Serbia, focusing on congenital anomalies, mortality rates, and key clinical factors to identify opportunities for improving care and outcomes. Methods: This observational single-center study was conducted at the Cardiovascular Institute “Dedinje” in Belgrade, Serbia, involving patients diagnosed or treated for CHD between 2006 and 2022. Results: A total of 1532 patients were included in the study, with common diagnoses including atrial septal defects (ASD) (47.65%) and ventricular septal defects (VSD) (13.19%). The mean patient age was 48.31 years, with a slight predominance of females (57.21%). The complexity of CHD was categorized as mild (54.6%), moderate (36.5%), and severe (6.3%). The mortality rate was 4.2%, with higher rates observed in conditions like Ebstein anomaly (17.78%) and congenital aortic stenosis (11.76%). Conclusions: This study provides a comprehensive overview of the current state of ACHD management in Serbia, highlighting the high prevalence of ASD and VSD among patients, the challenges associated with moderate and severe CHD, and the notable mortality rates for certain conditions. The findings underscore the importance of improving early detection, individualized treatment plans, and multidisciplinary care to enhance patient outcomes in this growing population.

BACKGROUND Arboviruses have expanded into new regions in Europe, yet data indicate gaps in disease notifications and a risk of further spread. We aimed to report on prevalence, clinical management, and outcomes of endemic arbovirus infections in southeast Europe. METHODS In this prospective observational study (MERMAIDS-ARBO), we enrolled adults (age ≥18 years) hospitalised with an arbovirus-compatible disease syndrome within 21 days of symptom onset across 21 hospitals in seven countries in southeast Europe over four arbovirus seasons (May 1-Oct 31, during 2016-19). We obtained data from case report forms completed by site investigators on admission and discharge. Participants were excluded if they had non-infectious CNS disorders, symptoms of another confirmed cause, an identified focal source of infection, or symptoms caused by recurrence of a pre-existing condition. The primary outcome was the proportion of participants with confirmed or probable acute infections with West Nile virus (WNV), tick-borne encephalitis virus (TBEV), Crimean-Congo haemorrhagic fever virus (CCHFV), or Toscana virus (TOSV), per reference laboratory criteria. Secondary outcomes were the proportions of patients treated with antivirals, antibiotics, or corticosteroids; the proportion of patients requiring intensive care; hospital length of stay; and mortality. FINDINGS Of 2896 adults screened for eligibility, 929 were recruited and 913 met protocol-defined eligibility criteria (median age 43·1 years [IQR 29·5-59·7]; 550 [60%] men, 361 [40%] women, and two [<1%] with missing data). 530 (58%) participants presented with suspected meningitis, encephalitis, or both, and 318 (35%) with fever plus myalgia, fever plus arthralgia, or both. 820 (90%) reported no international travel within 21 days before symptom onset. 727 (80%) were administered antibiotics, 379 (42%) corticosteroids, and 222 (24%) antivirals. The median length of hospital stay was 9 days (IQR 6-14), and 113 (12%) required intensive care. Of 847 participants with a reference laboratory sample who met full eligibility criteria for analysis, 110 (13%) were diagnosed with 114 confirmed or probable acute arbovirus infections (four had coinfections or cross-reactivity): one (<1%) with CCHFV, 16 (2%) with TBEV, 44 (5%) with TOSV, and 53 (6%) with WNV. There was one death (<1%) of an individual with WNV. Of the 110 participants, 49 (45%) had a local clinician-attributed arbovirus discharge diagnosis. INTERPRETATION Our data highlight the need to strengthen arbovirus surveillance systems for the early detection of emerging and re-emerging outbreaks, including investments to increase awareness of arbovirus infections among clinicians, to improve access to specialist diagnostics, and to develop effective and accessible vaccines and treatments to protect populations and health systems in southeast Europe. FUNDING European Commission and Versatile Emerging infectious disease Observatory. TRANSLATIONS For the Greek, Albanian, Romanian, Bosnian, Serbian, and Croatian translation of the summary see Supplementary Materials section.