Background White spot lesions (WSLs) are a common complication after orthodontic treatment. The aim of this study was to characterize and compare the antimicrobial properties of selenium-containing vs. fluoride-containing orthodontic materials. Material/Methods Antibacterial efficacy of orthodontic materials (SeLECT Defense bonding agent, Adhesive agent, Band Cement, Transbond Plus SEP bonding agent, Transbond Plus Adhesive agent, Fuji I Band cement, Fuji Ortho LC Adhesive agent, Ortho Solo Bonding agent, Transbond XT bonding agent, and Transbond XT primer) was tested with the inhibition of 2 bacterial strains: S. mutans (ATCC 10449) and L. acidophilus (ATCC 4356). The antimicrobial efficacy of the materials was measured by agar diffusion test. The diameters of inhibition zones around each disk were measured in millimeters (mm). Results Materials containing selenium and fluoride showed significant differences from the negative control (both p<0.001). Orthodontic materials containing fluoride as a potential antimicrobial agent showed larger zones of inhibition in total (9.1±2.6 mm), the selenium group was the second-most effective (4.7±4.9 mm), and the group without any potential antimicrobial agent showed the least antimicrobial effect (0.9±1.0 mm). Materials from the group with no antibacterial agent were not significantly different from the negative control group (p>0.05). Conclusions Materials containing selenium carried the most significance when comparing microorganisms with the agent, since they were the only ones showing difference between the 2 microorganisms. They showed statistically significant difference in efficacy against S. mutans, and poor antimicrobial effect against L. acidophilus. These data suggest that orthodontic materials containing selenium might have the potential to prevent WSLs due to their antimicrobial properties.

BACKGROUND The Emergency Severity Index (ESI) is a widely used tool to triage patients in emergency departments. The ESI tool is used to assess all complaints and has significant limitation for accurately triaging patients with suspected acute coronary syndrome (ACS). OBJECTIVE We evaluated the accuracy of ESI in predicting serious outcomes in suspected ACS and aimed to assess the incremental reclassification performance if ESI is supplemented with a clinically validated tool used to risk-stratify suspected ACS. METHODS We used existing data from an observational cohort study of patients with chest pain. We extracted ESI scores documented by triage nurses during routine medical care. Two independent reviewers adjudicated the primary outcome, incidence of 30-day major adverse cardiac events. We compared ESI with the well-established modified HEAR/T (patient History, Electrocardiogram, Age, Risk factors, but without Troponin) score. RESULTS Our sample included 750 patients (age, 59 ± 17 years; 43% female; 40% black). A total of 145 patients (19%) experienced major adverse cardiac event. The area under the receiver operating characteristic curve for ESI score for predicting major adverse cardiac event was 0.656, compared with 0.796 for the modified HEAR/T score. Using the modified HEAR/T score, 181 of the 391 false positives (46%) and 16 of the 19 false negatives (84%) assigned by ESI could be reclassified correctly. CONCLUSION The ESI score is poorly associated with serious outcomes in patients with suspected ACS. Supplementing the ESI tool with input from other validated clinical tools can greatly improve the accuracy of triage in patients with suspected ACS.

Like early work on human intergroup interaction, previous research on people’s willingness to interact with robots has focused mainly on effects of anxiety. However, existing findings suggest that other negative emotions as well as some positive emotions also have effects. This article systematically examines the roles of positive and negative emotions in predicting willingness to interact with robots, using an integrative analysis of data across five studies that use diverse interaction conditions and several types of robots. We hypothesize and find that positive emotions account for more variance than negative emotions. Practically, the findings suggest new strategies for interventions, aimed at increasing positive emotions to increase willingness to engage in intergroup interaction. No existing work has examined whether positive emotions are stronger predictors than negative emotions for willingness for human intergroup interaction, an important topic for future research.

ADINA ELENA STANCIU, NAFIJA SERDAREVIC, MARCEL MARIAN STANCIU*, LAURA MAZILU, OVIDIU BRATU , MIRELA GHERGHE, SILVIU CRISTIAN VOINEA, DAN CRISTIAN GHEORGHE Institute of Oncology Bucharest, Department of Carcinogenesis and Molecular Biology, 252 Fundeni, 022338, Bucharest, Romania Institute for Clinical Chemistry and Biochemistry, University of Sarajevo Clinics Center, Bolnicka 25, 71000, Sarajevo, Bosnia and Herzegovina University Politehnica of Bucharest, Electrical Engineering Faculty, 313 Splaiul Independentei, 060042, Bucharest, Romania University Ovidius Constanța, Faculty of Medicine, 124 Mamaia Str., 900527, Constanța, Romania Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari, 050474, Bucharest, Romania

With the edge computing becoming an increasingly adopted concept in system architectures, it is expected its utilization will be additionally heightened when combined with deep learning (DL) techniques. The idea behind integrating demanding processing algorithms in Internet of Things (IoT) and edge devices, such as Deep Neural Network (DNN), has in large measure benefited from the development of edge computing hardware, as well as from adapting the algorithms for use in resource constrained IoT devices. Surprisingly, there are no models yet to optimally place and use machine learning in edge computing. In this paper, we propose the first model of optimal placement of Deep Neural Network (DNN) Placement and Inference in edge computing. We present a mathematical formulation to the DNN Model Variant Selection and Placement (MVSP) problem considering the inference latency of different model-variants, communication latency between nodes, and utilization cost of edge computing nodes. We evaluate our model numerically, and show that for low load increasing model co-location decreases the average latency by 33% of millisecond-scale per request, and for high load, by 21%.

Background: Kidney is the most common site of genitourinary trauma. 50% of all urinary injuries is kidney.Kidney is also affected in 8-12% of all blunt and penetrating trauma to abdomen. 80-90% of renal injury is caused by blunt injury GY. Children,  compared to adults, have  at a higher risk of renal injury from blunt trauma due to a variety of anatomic factors including decreased perirenal fat, weaker abdominal muscles, and a less ossified thoracic cage. While there are strong trends toward non-operative management of blunt renal trauma, there are no explicit guidelines for high grade injuries. Organ preservation in children is always a primary goal with solid organ injury. Aim of the work: The aim of the retrospective study is to show the specificity of kidney injury in children as well as the specificity of surgical treatment. Material and Methods: All 19 patients under the age of 18 who were admitted to Clinic for Pediatic surgery in Sarajevo with a diagnosis of renal trauma were retrospectively reviewed .The Echo an CT were used to identify patients with a renal injury. The time period examined was between January 1, 1999- 2019. Inclusion criteria were either a diagnosis of renal trauma or a diagnosis of blunt abdominal trauma and hematuria. Exclusion criterion was death due to an additional traumatic injury. The mechanism of injury (fall, car accident , assault) injury grade (I-V), the presence of hematuria, and demographic data to include age, weight, and sex, were recorded and reviewed.  In addition, amount of blood product required, hematocrit nadir prior to transfusion to assist in ascertaining whether transfusion was necessary, surgical interventions performed, and hospital length of stay were also retrospectively analyzed. Due to the low sample size we used descriptive as opposed to inferential statistics in our analysis. Result: Demographics include male to female ratio of 13:6 and the average age of patients was 11.9 + 4.6 years. Of the nineteen patients who underwent review, eleven (57,89%) children presented with a grade III renal injury, five  with a grade IV injury and three with  grade V injury. Six patients presented with gross hematuria and 3 with microscopic hematuria. Only four patients (22%) required blood transfusions, with the average hematocrit nadir being 31 + 5.3% (24.8-37.8). One of the two patients transfused had a concomitant grade IV splenic laceration with a hematocrit nadir of 24.8% and clinical symptoms consistent with shock. Conclusions:The specificity of the child's anatomy is an aggravating prognostic factor (the kidney is larger in relation to the body cavity than in adults, less protected against the ribs, the muscles of the body and the lower abdomen, the less developed peritoneal and retroperitoneal fatty tissue).It is recommended to initiate conservative treatment (leaching, infusion solution, monitoring) and possibly delayed surgical treatment.Indications for early surgicaly treatment are reserved only for patients with bleeding (absolute) and extravasation (relative).If it is necessary surgical treatment sould be  maximally preserve kidney tissue.

Automatic modulation classification (AMC) is one of the most critical technologies for non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have attracted significant attention due to their preferable performance. However, the study of most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are only a few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Therefore, we propose in this work a convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN/ZF-AMC method achieves better performance than the artificial neural network (ANN) with high order cumulants (HOC)-based AMC method under the condition of the perfect channel state information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the CNN/ZF-AMC method. Simulation results demonstrated that the classification performance is not only influenced by the imperfect CSI, but also associated with the number of the transmit and receive antennas.

Atrial fibrillation (AF) is common amongst the elderly, but this group tends to be suboptimally treated. Limited data are available on the stroke prevention strategies in the elderly, especially in the Balkan region.

The human gut microbiota has now been associated with drug responses and efficacy, while chemical compounds present in these drugs can also impact the gut bacteria. However, drug–microbe interactions are still understudied in the clinical context, where polypharmacy and comorbidities co-occur. Here, we report relations between commonly used drugs and the gut microbiome. We performed metagenomics sequencing of faecal samples from a population cohort and two gastrointestinal disease cohorts. Differences between users and non-users were analysed per cohort, followed by a meta-analysis. While 19 of 41 drugs are found to be associated with microbial features, when controlling for the use of multiple medications, proton-pump inhibitors, metformin, antibiotics and laxatives show the strongest associations with the microbiome. We here provide evidence for extensive changes in taxonomy, metabolic potential and resistome in relation to commonly used drugs. This paves the way for future studies and has implications for current microbiome studies by demonstrating the need to correct for multiple drug use. Here, via a metagenomics analysis of population-based and disease cohorts, Vich Vila et al. study the impact of 41 commonly used medications on the taxonomic structures, metabolic potential and resistome of the gut microbiome, underscoring the importance of correcting for multiple drug use in microbiome studies.

INTRODUCTION The Atrial fibrillation Better Care (ABC) pathway provides a useful way of simplifying decision-making considerations in a holistic approach to atrial fibrillation management. OBJECTIVES To evaluate adherence to ABC pathway and to determine major gaps in adherence to ABC pathway in patients in BALKAN-AF survey. PATIENTS AND METHODS In this ancillary analysis, patients in BALKAN-AF survey were divided into groups: "A (Avoid stroke)+B (Better symptom control)+C (Cardiovascular and comorbidity risk management)"-adherent and non-adherent "A+B+C" management.  Results: Of 2,712 enrolled patients, 1,013 (43.8%) patients with mean (SD) age of 68.8 (10.2) years and mean CHA2DS2-VASc score of 3.4 (1.8) had "A+B+C"-adherent management and 1,299 (56.2%) had non-adherent-"A+B+C" management. Independent predictors of increased "A+B+C"-adherent management were: capital city [odds ratio (OR) 1.23, 95% confidence interval (CI) 1.03-1.46, p = 0.02], treatment by cardiologist (OR 1.34, 95% CI 1.08-1.66, p = 0.01), hypertension (OR 2.20, 95% CI 1.74-2.77, p <0.001), diabetes mellitus (OR 1.28, 95% CI 1.05-1.57, p = 0.01) and multimorbidity (the presence of two or more long-term conditions) (OR 1.85, 95% CI 1.43-2.38, p <0.001). Independent predictors of decreased "A+B+C"-adherent management were: age ≥80 years (OR 0.61, 95% CI 0.48-0.76, p < 0.001) and history of bleeding (OR 0.50, 95% CI 0.33-0.75, p = 0.001). CONCLUSIONS Physicians' adherence to integrated AF management based on the ABC pathway was suboptimal in our study. Addressing the identified clinical  and system-related factors associated with non-adherent-"A+B+C" management using targeted approaches is needed to optimize treatment of AF patients in the Balkan region.

Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog mode of operation observed in our silicon oxide memristors and apply this to the problem of edge detection. We demonstrate how a potential divider exploiting this analog behavior can prove a scalable solution to edge detection. We confirm its behavior experimentally and simulate its performance on a standard testbench. We show good performance comparable to existing memristor based work with a benchmark score of 0.465 on the BSDS500 dataset, while simultaneously maintaining a lower component count.