Post-operative pain after root canal preparation with different apical finishing sizes a triple blinded split mouth clinical trial.

BACKGROUND: This is a triple-blinded, prospective split-mouth clinical trial. It is important to shed light on the effect of different apical preparation sizes regarding postoperative pain within the same patient with the same pulpal histological status. The aim is to compare and evaluate the severity of postoperative pain following apical enlargement with two different sizes after the IBF using the visual analogue scale. METHODS: Fifty "teeth" in 25 patients were assigned into two equal groups (25 per group) using E3 Azure rotary files; Group A was prepared two sizes greater than the Initial binding file (IBF) (the largest K file to bind at the actual working length) mesial canals, which were enlarged to 35#/0.04 and 40#/0.04 for the distal canals. Group B was prepared in three sizes larger than the IBF: 40#/0.04 for mesial canals and 45#/0.04 for the distal canals. On a modified VAS form, patients were questioned to indicate the degree of their pain and assisted in narrating their pain intensity during the following periods: 12, 24, and 72 h, and after a week. VAS data were non-parametric and analyzed using the signed-rank test for intergroup comparisons, Freidman's test, and the Nemenyi post hoc test for intragroup comparisons. The significance level was set at p < 0.05. RESULTS: showed that regardless of measurement time, enlargement of apical preparation was significantly associated with higher pain scores (p < 0.001). Within both groups, there was a significant reduction of measured pain score with time, with values measured after 12 and 24 h being significantly higher than values measured at other intervals (p < 0.001) and with values measured after three days being significantly higher than 1-week value (p < 0.001). CONCLUSION: The size of apical preparation had a significant effect on postoperative pain. TRIAL REGISTRATION NUMBER & DATE: NCT05847738, 08/05/2023.

Mechanistic insight into roles of α/ß-type small acid-soluble proteins, RecA, and inner membrane proteins during bacterial spore inactivation by ohmic heating.

AIM: Ohmic heating (OH) (i.e. heating by electric field) more effectively kills bacterial spores than traditional wet heating, yet its mechanism remains poorly understood. This study investigates the accelerated spore inactivation mechanism using genetically modified spores. METHODS AND RESULTS: We investigated the effects of OH and conventional heating (CH) on various genetically modified strains of Bacillus subtilis: isogenic PS533 (wild type_1), PS578 [lacking spores' α/ß-type small acid-soluble proteins (SASP)], PS2318 (lacking recA, encoding a DNA repair protein), isogenic PS4461 (wild type_2), and PS4462 (having the 2Duf protein in spores, which increases spore wet heat resistance and decreases spore inner membrane fluidity). Removal of SASP brought the inactivation profiles of OH and CH closer, suggesting the interaction of these proteins with the field. However, the reemergence of a difference between CH and OH killing for SASP-deficient spores at the highest tested field strength suggested there is also interaction of the field with another spore core component. Additionally, RecA-deficient spores yielded results like those with the wild-type spores for CH, while the OH resistance of this mutant increased at the lower tested temperatures, implying that RecA or DNA are a possible additional target for the electric field. Addition of the 2Duf protein markedly increased spore resistance both to CH and OH, although some acceleration of killing was observed with OH at 50 V/cm. CONCLUSIONS: In summary, both membrane fluidity and interaction of the spore core proteins with electric field are key factors in enhanced spore killing with electric field-heat combinations.

Safety and efficacy of beta-3 adrenergic agonists in treating neurogenic lower urinary tract dysfunction: A systematic review and meta-analysis.

PURPOSE: To evaluate efficacy and safety of beta-3 adrenergic agonists in adults with neurogenic lower urinary tract dysfunction. MATERIALS AND METHODS: According to a protocol (CRD42022350079), we searched multiple data sources for published and unpublished randomized controlled trials (RCTs) up to 2nd August 2022. Two review authors independently screened studies and abstracted data from the included studies. We performed statistical analyses by using a random-effects model and interpreted them according to the Cochrane Handbook for Systematic Reviews of Interventions. We used GRADE guidance to rate the certainty of evidence (CoE). RESULTS: We found data to inform two comparisons: beta-3 adrenergic agonists versus placebo (4 RCTs) and anticholinergics (2 RCTs). Only mirabegron was used for intervention in all included studies. Compared to placebo, beta-3 adrenergic agonists may have a clinically unimportant effect on urinary symptoms score (mean difference [MD] -2.50, 95% confidence interval [CI] -4.78 to -0.22; I²=92%; 2 RCTs; 192 participants; low CoE) based on minimal clinically important difference of 3. We are very uncertain of the effects of beta-3 adrenergic agonists on quality of life (MD 10.86, 95% CI 1.21 to 20.50; I²=41%; 2 RCTs; 98 participants; very low CoE). Beta-3 adrenergic agonists may result in little to no difference in major adverse events (cardiovascular adverse events) (risk ratio 0.57, 95% CI 0.14 to 2.37; I²=0%; 4 RCTs; 310 participants; low CoE). Compared to anticholinergics, no study reported urinary symptom scores and quality of life. There were no major adverse events (cardiovascular adverse events) in either study group (1 study; 60 participants; very low CoE). CONCLUSIONS: Compared to placebo, beta-3 adrenergic agonists may have similar effects on urinary symptom scores and major adverse events. There were uncertainties about their effects on quality of life. Compared to anticholinergics, we are either very uncertain or have no evidence about urinary symptom scores, quality of life, and major adverse events.

Functionalized surface of PLGA nanoparticles in thermosensitive gel to enhance the efficacy of antibiotics against antibiotic resistant infections in endodontics: A randomized clinical trial.

Enterococcus faecalis plays the key role in endodontic infections and is responsible for the formation of biofilm on dentin, which causes a resistance against periradicular lesions treatment, consequently the aim of this study is to use nanoparticles entrapping anibacterial agents coated with chitosan that in authors previous study showed a successful in vitro biofilm inhibition, additionally incorporated in thermoresponsive gel.to benefit nanoparticles` small size, and the positive charge of their surfaces that binds with the negatively charged surface of bacterial cell causing its destruction, in addition to the sustained release pattern of the drug based nanoparticles in gel. Therefore, Ciprofloxacin hydrochloride (CIP) encapsulated in PLGA nanoparticles coated with chitosan (CIP-CS-PLGA-NPs), in addition to free CIP, were incorporated in Pluronic® 407/188 to form thermosensitive gels (F1) and (F2), respectively. The thermosensitive gels were tested with regards to rheology, gelling temperature and the release pattern of the drug. A clinical study of the efficacy of F1 and F2 as antibacterial treatments was conducted on patients followed by a comparative studies against CIP and Ca(OH)2 pastes in terms of biofilm inhibition assay and total bacterial reduction count and percent.The results revealed that F1 and F2 exhibited gelation temperature of 36.9 ± 0.3 °C and 36.0 ± 0.4 °C, viscosity was 15,000 ± 360.6 and 7023.3 ± 296.8 cP respectively. The cumulative release of F1 and F2 after 72 h was 50.03% ± 0.7345 and 77.98% ± 3.122 respectively. F1 was the most efficient treatment against recurrent E.faecalis infection in endodontics that was evident by the highest total bacterial reduction count and percent and biofilm inhibition percent that were recorded in the group treated with F1followed by the group treated with F2. Nanocarriers succeeded in carrying the drug deeply in the root canal and sustaining its effect to abolish the obstinate E. faecalis recurrent infection and its biofilm formation.

The power of progressive active learning in floorplan images for energy assessment.

Floorplan energy assessments present a highly efficient method for evaluating the energy efficiency of residential properties without requiring physical presence. By employing computer modelling, an accurate determination of the building's heat loss or gain can be achieved, enabling planners and homeowners to devise energy-efficient renovation or redevelopment plans. However, the creation of an AI model for floorplan element detection necessitates the manual annotation of a substantial collection of floorplans, which poses a daunting task. This paper introduces a novel active learning model designed to detect and annotate the primary elements within floorplan images, aiming to assist energy assessors in automating the analysis of such images-an inherently challenging problem due to the time-intensive nature of the annotation process. Our active learning approach initially trained on a set of 500 annotated images and progressively learned from a larger dataset comprising 4500 unlabelled images. This iterative process resulted in mean average precision score of 0.833, precision score of 0.972, and recall score of 0.950. We make our dataset publicly available under a Creative Commons license.

Efficacy of using chitosan and chitosan nanoparticles as final irrigating solutions on smear layer removal and mineral content of intraradicular dentin.

Background: This study evaluated the effect of using chitosan, nano-chitosan, and ethylenediaminetetraacetic acid (EDTA) as final irrigating solutions on smear layer cleanliness and Ca/P ratio of dentin. Methodology: Forty-eight decoronated human single-rooted teeth were used. They were divided randomly into four groups (n = 12) based on the final irrigating solution used as follows: (a) control group (IA; n = 6) normal saline, (IB; n = 6) were left unprepared; group II - 0.2% chitosan; group III - 0.2% nano-chitosan; and group IV - 17% EDTA. Samples were prepared using ProTaper Next and irrigated with 2.6% NaOCl 5 ml after each instrument using 31-gauge needle. Final rinse was used 5 ml/3 min according to the assigned group. The specimens were prepared for evaluation. Results: Best smear layer removal was observed in group IV. No statistically significant differences (P > 0.05) were observed between the experimental groups (II, III, and IV) coronally; however, a statistically significant difference (P < 0.05) was observed between groups II and IV at middle and apical thirds. Intragroup comparison showed that apical third exhibited the highest mean smear layer score among all experimental groups. The highest mean Ca/P ratio was in the 0.2% nano-chitosan group, while the highest calcium loss was in the 17% EDTA group. Conclusions: 17% EDTA is a potent chelating agent that can successfully remove the smear layer but compromises the Ca/p ratio of dentin. However, 0.2% chitosan and its nanoparticles have comparable chelating effects and induce remineralization of the root canal dentin.

A logarithmically amortising temperature effect for supervised learning of wheat solar disinfestation of rice weevil Sitophilus oryzae (Coleoptera: Curculionidae) using plastic bags.

This work investigates the effectiveness of solar heating using clear polyethylene bags against rice weevil Sitophilus oryzae (L.), which is one of the most destructive insect pests against many strategic grains such as wheat. In this paper, we aim at finding the key parameters that affect the control heating system against stored grain insects while ensuring that the wheat grain quality is maintained. We provide a new benchmark dataset, where the experimental and environmental data was collected based on fieldwork during the summer in Canada. We measure the effectiveness of the solution using a novel formula to describe the amortising temperature effect on rice weevil. We adopted different machine learning models to predict the effectiveness of our solution in reaching a lethal heating condition for insect pests, and hence measure the importance of the parameters. The performance of our machine learning models has been validated using a 10-fold cross-validation, showing a high accuracy of 99.5% with 99.01% recall, 100% precision and 99.5% F1-Score obtained by the Random Forest model. Our experimental study on machine learning with SHAP values as an eXplainable post-hoc model provides the best environmental conditions and parameters that have a significant effect on the disinfestation of rice weevils. Our findings suggest that there is an optimal medium-sized grain amount when using solar bags for thermal insect disinfestation under high ambient temperatures. Machine learning provides us with a versatile model for predicting the lethal temperatures that are most effective for eliminating stored grain insects inside clear plastic bags. Using this powerful technology, we can gain valuable information on the optimal conditions to eliminate these pests. Our model allows us to predict whether a certain combination of parameters will be effective in the treatment of insects using thermal control. We make our dataset publicly available under a Creative Commons Licence to encourage researchers to use it as a benchmark for their studies.

Highlights of Cardiovascular Disease Prevention Studies Presented at the 2022 American Heart Association Scientific Sessions.

PURPOSE OF REVIEW: Summarize selected late-breaking science on cardiovascular (CV) disease prevention presented at the 2022 scientific session of the American Heart Association (AHA). RECENT FINDINGS: The PROMINENT trial compared pemafibrate to a placebo in patients with type 2 diabetes mellitus (DM) and mild-to-moderate hypertriglyceridemia and high-density lipoprotein cholesterol (HDL-C)<40 mg/dL who were already on guideline-directed statin therapy. The RESPECT-EPA trial compared purified eicosapentaenoic acid (EPA) and statin therapy to statin therapy alone for secondary prevention of atherosclerotic CV disease (ASCVD). SPORT compared the efficacy of low-dose statin therapy with a placebo and six commonly used dietary supplements on lipid and inflammatory markers. Data from long-term follow-up of the FOURIER-OLE study was presented to evaluate the efficacy of very low low-density lipoprotein cholesterol (LDL-C) levels with proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. Patient-level meta-analyses evaluated the association of statin therapy with new-onset DM and worse glycemic control. PROMPT-LIPID evaluated if automated electronic alerts to physicians with guideline-based recommendations improved the management of hyperlipidemia in patients at very high risk. NOTIFY-1 trial evaluated if notifying physicians and patients about coronary artery calcium (CAC) scores in non-ECG gated computed tomography scans led to increased prescription of statin therapy for primary ASCVD prevention. The DCP trial compared hydrochlorothiazide and chlorthalidone for blood pressure control and CV outcomes in hypertension. The CRHCP study compared the effectiveness of a village doctor for hypertension management and CV outcomes in rural areas of China. The QUARTET USA trial compared the effectiveness and safety of 4 antihypertensive medications in ultra-low doses with angiotensin-receptor blocker monotherapy. The late-breaking science presented at the 2022 scientific session of the AHA paves the way for future pragmatic trials and provides meaningful information to guide management strategies in cardiovascular disease prevention.

XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification.

Of the various tumour types, colorectal cancer and brain tumours are still considered among the most serious and deadly diseases in the world. Therefore, many researchers are interested in improving the accuracy and reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised learning has been proven to be an effective solution when dealing with datasets with insufficient data annotations. However, medical image datasets often suffer from data irregularities, making the recognition task even more challenging. The class decomposition approach has provided a robust solution to such a challenging problem by simplifying the learning of class boundaries of a dataset. In this paper, we propose a robust self-supervised model, called XDecompo, to improve the transferability of features from the pretext task to the downstream task. XDecompo has been designed based on an affinity propagation-based class decomposition to effectively encourage learning of the class boundaries in the downstream task. XDecompo has an explainable component to highlight important pixels that contribute to classification and explain the effect of class decomposition on improving the speciality of extracted features. We also explore the generalisability of XDecompo in handling different medical datasets, such as histopathology for colorectal cancer and brain tumour images. The quantitative results demonstrate the robustness of XDecompo with high accuracy of 96.16% and 94.30% for CRC and brain tumour images, respectively. XDecompo has demonstrated its generalization capability and achieved high classification accuracy (both quantitatively and qualitatively) in different medical image datasets, compared with other models. Moreover, a post hoc explainable method has been used to validate the feature transferability, demonstrating highly accurate feature representations.

Tailoring Screening Guidelines for Retinopathy of Prematurity in Egypt: An Exploratory Multicentric Study.

Background: Retinopathy of prematurity (ROP) is increasing in incidence in developing nations, including Egypt. Secondary prevention requires timely detection through the development of regional screening guidelines, which should be preceded by large-scale studies to characterize the population at risk. Methods: A prospective, multicentric exploratory study that included five large tertiary institutions in an urban Egyptian setting. All infants born with gestational age (GA) < 37 weeks and/or birth weight (BW) ≤ 2000 grams were screened. More mature and heavier infants with unstable clinical course were also included. The primary outcome measure was the rate of ROP and high-risk disease occurrence in relation to underlying risk factors. Results: Of the 768 eyes (384 screened infants), 347 eyes (45.2%) had stage 1 or higher disease, and 43 eyes (5.6%) had high-risk disease. Eyes with stage 1 or higher ROP and treatment-requiring ROP had a mean (± SD) GA of 33.4 (± 2.6) weeks and 32.8 (± 3.2) weeks, and BW of 1842.3 (± 570.1) grams and 1747.6 ± (676.2) grams, respectively. Treatment-requiring eyes belonged to infants that had significantly lower GA and significantly higher prevalence of co-morbidities than non-treatment-requiring eyes. Conclusion: The incidence of ROP and high-risk disease in an urban Egyptian setting are similar to those in comparable settings elsewhere and locally. This exploratory study supports tailoring local screening criteria for ROP, and may aid the future development of national guidelines.

PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes.

This paper presents a set of methods, jointly called PGraphD*, which includes two new methods (PGraphDD-QM and PGraphDD-SS) for drift detection and one new method (PGraphDL) for drift localisation in business processes. The methods are based on deep learning and graphs, with PGraphDD-QM and PGraphDD-SS employing a quality metric and a similarity score for detecting drifts, respectively. According to experimental results, PGraphDD-SS outperforms PGraphDD-QM in drift detection, achieving an accuracy score of 100% over the majority of synthetic logs and an accuracy score of 80% over a complex real-life log. Furthermore, PGraphDD-SS detects drifts with delays that are 59% shorter on average compared to the best performing state-of-the-art method.

Effect of COVID-19 Pandemic on Neonatal Sepsis.

BACKGROUND: There is a sudden rise in infectious diseases, with special concern to the most recent SARS-CoV 2 outbreak. A retrospective study was conducted to study the effect of this outbreak on neonatal sepsis as a global issue that poses a challenge for pediatric management and to identify its risk factors, microbial profile, and mortality rate at King Faisal Medical Complex, Taif, KSA, a COVID-19-tertiary care segregation hospital. METHODS: This research included 111 neonates with a culture-proven diagnosis of neonatal sepsis (4 and 62 cases during 2019 and 2020, respectively). RESULTS: During 2019 early onset sepsis (EOS) occurred in 6/49 (12.2%) while in 2020 22/62 (35.5%), and during 2019 late onset sepsis (LOS) occurred in 43/49 (87.7%) while in 2020 40/62 (64.5%). Premature rupture of membrane was the major neonatal risk factor for EOS during 2019 and 2020 with proportions of 4 (66.7%), 20 (90.9%); respectively. As regards LOS, the peripherally inserted central catheters and peripheral lines were the top neonatal risk factors. In the two-year outbreak, the most prevalent causative organism for EOS neonates was Escherichia coli and for LOS neonates it was Klebsiella. There was non-significant change in the mortality rate of neonatal sepsis between 2019 and 2020. However, the mortality rate was higher in EOS 9/22 (40.9%) in 2020 in comparison to 2/6 (33.3%) in 2019. CONCLUSIONS: Neonatal sepsis remains a major health problem causing serious morbidity and mortality, and health care policy makers have to implement EOS preventive measures.

MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification.

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model. MCUa model consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUa model has achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models.

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images.

Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection.

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this article, we propose a novel deep convolutional neural network, which we called self-supervised super sample decomposition for transfer learning (4S-DT) model. The 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabeled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition (CD) layer to simplify the local structure of the data. The 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream CD mechanism. We used 50000 unlabeled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. The 4S-DT has achieved a high accuracy of 99.8% on the larger of the two datasets used in the experimental study and an accuracy of 97.54% on the smaller dataset, which was enriched by augmented images, out of which all real COVID-19 cases were detected.

Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences.

BACKGROUND: Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. METHODS: In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge - to explain AdaBoost classification - with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost's adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model's decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting. RESULTS: Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods. CONCLUSIONS: The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models.

Assessing the impact of a combination of sofosbuvir and daclatasvir treatment for hepatitis C virus infection on heart rate, rhythm and heart rate variability using 24-hour ECG monitoring.

BACKGROUND: Direct-acting antiviral agents (DAAs) cure patients with hepatitis C virus (HCV) infection. Concerns have arisen the occurrence of significant bradyarrhythmias during treatment with DAAs. The aim of this study was to assess the impact of a DAA combination for the treatment of HCV infection on heart rate, rhythm, and heart rate variability (HRV) using 24-h ECG monitoring. RESULTS: A prospective randomized study of 50 treatment-naïve patients with HCV infection treated with a combination of sofosbuvir 400 mg daily and daclatasvir 60 mg daily for 12 weeks. Surface ECG and 24-h ECG monitoring were performed at baseline and after completion of therapy to assess PR interval, corrected QT interval (QTc), minimum heart rate (HR), maximum HR, average HR, HRV time-domain and frequency-domain measures, significant pauses, tachycardias, bradycardias, premature atrial contractions (PACs), and premature ventricular contraction (PVCs). No differences were detected in all examined parameters between baseline and after completion of treatment. PR interval was 154 ± 25.95 vs 151.4 ± 23.82 ms, respectively (p = 0.124). QTc interval was 397.34 ± 29.38 vs 395.04 ± 30.23 ms, respectively (p = 0.403). No differences were detected for minimum HR, maximum HR, average HR, HRV time-domain and frequency-domain measures, the occurrence of significant pauses, sinus tachycardia episodes, sinus bradycardia episodes, PACs, and PVCs. No episodes of bradyarrhythmias, syncope, and atrial fibrillation, supraventricular, or ventricular tachycardias were reported or detected. CONCLUSION: In non-cardiac patients receiving no cardioactive medications, the combination of sofosbuvir and daclatasvir for the treatment of HCV infection has no effect on HR, rhythm, conductivity, or HRV. No symptomatic bradycardias, tachycardias, or syncope were reported or detected using 24-h ECG monitoring.

Functionalized Core-Shell Yttrium Oxide Nanoparticles as Antioxidants Agents in Heat Stressed Rats.

Herein, we assess the antioxidant potential of core-shell yttrium oxide nanoparticles functionalized with ethylene glycol methacrylate phosphate (EGMP). The antioxidant properties of these nanoparticles were investigated in heat stressed models of 21 rats (heat stressed group, group B). Different samples of blood, serum, and tissue homogenate were collected at different time intervals in order to measure oxidative biomarkers such as enzymatic antioxidants (SODs, GPX, GST, GR, and TAC) and oxidative byproducts (MDA, PC, and 8-OHdG). Liver specimens of prophylactic group and heat stressed ones were also histopathologically examined 2 h post NPs injection. The measurements of oxidative biomarkers were complementary with histopathological findings and confirmed the antioxidant properties of poly EGMP yttrium oxide NPs.

Antimicrobial resistance patterns and molecular resistance markers of Campylobacter jejuni isolates from human diarrheal cases.

The aim of this study is to characterize the antimicrobial resistance of Campylobacter jejuni recovered from diarrheal patients in Belgium, focusing on the genetic diversity of resistant strains and underlying molecular mechanisms of resistance among Campylobacter jejuni resistant strains isolated from diarrheal patients in Belgium. Susceptibility profile of 199 clinical C. jejuni isolates was determined by minimum inhibitory concentrations against six commonly-used antibiotics (ciprofloxacin, nalidixic acid, tetracycline, streptomycin, gentamicin, and erythromycin). High rates of resistance were observed against nalidixic acid (56.3%), ciprofloxacin (55.8%) and tetracycline (49.7%); these rates were similar to those obtained from different national reports in broilers intended for human consumption. Alternatively, lower resistance rates to streptomycin (4.5%) and erythromycin (2%), and absolute sensitivity to gentamicin were observed. C. jejuni isolates resistant to tetracycline or quinolones (ciprofloxacin and/or nalidixic acid) were screened for the presence of the tetO gene and the C257T mutation in the quinolone resistance determining region (QRDR) of the gyrase gene gyrA, respectively. Interestingly, some of the isolates that displayed phenotypic resistance to these antimicrobials lacked the corresponding genetic determinants. Among erythromycin-resistant isolates, a diverse array of potential molecular resistance mechanisms was investigated, including the presence of ermB and mutations in the 23S rRNA gene, the rplD and rplV ribosomal genes, and the regulatory region of the cmeABC operon. Two of the four erythromycin-resistant isolates harboured the A2075G transition mutation in the 23S rRNA gene; one of these isolates exhibited further mutations in rplD, rplV and in the cmeABC regulatory region. This study expands the current understanding of how different genetic determinants and particular clones shape the epidemiology of antimicrobial resistance in C. jejuni in Belgium. It also reveals many questions in need of further investigation, such as the role of other undetermined molecular mechanisms that may potentially contribute to the antimicrobial resistance of Campylobacter.