Objectives Early diagnostic separation between glioblastoma (GBM) and solitary metastases (MET) is important for patient management but remains challenging when based on imaging only. The objective of this study was to assess whether amide proton transfer weighted (APTw) MRI alone or combined with dynamic susceptibility contrast (DSC) MRI parameters, including cerebral blood volume (CBV), cerebral blood flow (CBF), and leakage parameter (K2) measurements, can differentiate GBM from MET. Methods APTw MRI and DSC-MRI were performed on 18 patients diagnosed with GBM (N = 10) or MET (N = 8). Quantitative parameter maps were calculated, and regions-of-interest (ROIs) were placed in whole tumor, contrast-enhanced tumor (ET), edema, necrosis and normal-appearing white matter (NAWM). The mean and max of the APTw signal, CBF, leakage-corrected CBV and K2 were obtained from each ROI. Except for K2, all were normalized to NAWM (nAPTwmean/max, nCBFmean/max, ncCBVmean/max,). Receiver Operating Characteristic (ROC) curves and area-under-the-curve (AUC) were assessed for different parameter combinations. Statistical analyses were performed using Mann–Whitney U test. Results When comparing GBM to MET, nAPTmax, nCBFmax, ncCBVmax and ncCBVmean were significantly increased (p < 0.05) in ET with AUC being 0.81, 0.83, 0.85, and 0.83, respectively. Combinations of nAPTwmax + ncCBVmax, nAPTwmean + ncCBVmean, nAPTwmax + nCBFmax, nAPTwmax + K2max and nAPTwmax + ncCBVmax + K2max in ET showed significant prediction in differentiating GBM and MET (AUC = 0.92, 0.82, 0.92, 0.85, and 0.92 respectively). Conclusion When assessed in Gd-enhanced tumor areas, nAPTw MRI signal intensity alone or combined with DSC-MRI parameters, was an excellent predictor for differentiating GBM and MET. However, the small cohort warrants future studies.

Background: Midwives are globally recognized as health professionals who specialize in the care of women in labor with a vital role in maternal and newborn health care. Midwives specialize in the care of women in labor and play a key role globally in managing normal vaginal birth, caring for pregnant women including supporting women and their families, providing consultations, managing normal birth for low-risk pregnant women and helping them maintain a healthy pregnancy. Despite the fact that the midwifery profession is an autonomous profession, in some countries there are many struggles to achieve recognition within its formal scope of work. The role of the midwife/midwife remains unclear in many countries due to poorly articulated policies and a lack of regulatory frameworks, which results in a lack of public clarity regarding the role of the midwife. Objective: The purpose of this expert report is to present the role of the midwife in protecting the health of mothers before, during and after childbirth, to clearly define their role and importance, and the need to improve midwifery as a profession in order to reduce the number of caesarean sections. Methods: This systematic review includes a comprehensive literature search of published scientific articles, in English, from 2020 to 2024, using electronic databases considered most relevant to the topics; CINAHL, EMBASE and PubMed. In this systematic review and meta-analysis, we included studies on the role of midwives in different countries, including Thailand, the United States, Australia, Canada, the UK, the Netherlands, Bosnia and Herzegovina, Slovenia, Croatia and Serbia, to arrive at results on what the role of midwives is in these countries. Citations without abstracts and/or full text, anonymous reports, editorials, case reports, case series and qualitative studies were excluded. Results: In the Law on Health Care of the FBiH, and the Law on Nursing and Midwifery of the FBiH, the role of the midwife is insufficiently defined and she is not given sufficient authority to work. For childbirth in BiH, in addition to midwives, a doctor must always be present. In European and foreign countries, the role of the midwife is put in the foreground during childbirth, so there are also hospitals where women give birth and are cared for by midwives. Midwife-led care, an approach that is already widely practiced in developed countries; however, it is a relatively new approach in lower-income countries. In midwife-led care, a midwife who is well known to the mother provides care for the low-risk pregnant woman during antenatal care, delivery and the postnatal period, rather than being cared for by different medical staff led by an obstetrician. The primary focus of care led by midwives is to support a healthy physiological pregnancy and birth and to empower women to give birth naturally with little or no regular intervention. Conclusion: It is very worrying for midwifery as a profession that there is currently a lack of visibility of midwives in practice within their scope of practice in Bosnia and Herzegovina. More research is needed on demonstrating the value of midwives as a primary role in the context of midwifery practice in Bosnia and Herzegovina.

Terrestrial laser scanners (TLS) are widely employed in structural health monitoring (SHM) of large objects due to their superior capabilities compared to traditional geodetic methods. TLS provides rapid and detailed data on the geometric properties of objects, enabling various types of analyses. In this study, TLS was utilized to examine the minaret of the Bjelave Mosque, located in Sarajevo, Bosnia and Herzegovina. The inclination of the minaret was assessed using principal component analysis (PCA) and linear regression (LR) applied to sampled data from four edges of the minaret’s body. The geodetically determined inclination values were used as input data for subsequent static and pushover analyses conducted in DIANA FEA, where the minaret was modeled. The analyses indicate that the inclination increases stress and strain, leading to larger cracks and reduced structural capacity, as demonstrated by the pushover analysis curves. This study highlights the combined impact of structural inclination, water infiltration, and settlement on the minaret’s integrity and proposes these findings as a basis for future maintenance and strengthening measures.

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.