Background: Two-dimensional echocardiography (2DE) Simpson methods is the most frequently used imaging modality to assess Left ventricular ejection fraction (LVEF). LVEF is an important predictor of morbidity and mortality in a wide range of patients and clinical scenarios. Despite its importance in prognosis and clinical decision making, most echocardiography laboratories currently determine EF primarily by visual estimation, which is highly experience-dependent and sensitive to intra- and inter-observer variability and suboptimal accuracy and repeatability. Over the last decade, 3-dimensional echocardiography (3DE) has become increasingly implemented in clinical practice. The automated 3D HeartModelA.I. tracks every frame over the cardiac cycle using 3D speckle technology. HeartModelA.I. is a fully automated program that simultaneously detects LA and LV endocardial surfaces using an adaptive analytics algorithm that consists of knowledge-based identification of initial global shape and orientation followed by patient-specific adaptation. Objective: The objective of the study was to compare the automated 3D HeartModelA.I echocardiography and 2D Simpson methods echocardiography in evaluation of the left ventricular ejection fraction and left ventricular volumes in patients with left heart dysfunction. Methods: The study prospectively enrolled 165 patients with symptoms of LV dysfunction (ischemic or nonischemic) and New York Heart Association (NYHA) functional class I-III, referred for an echocardiographic study to evaluate the LV volumes and LV ejection fraction (LVEF) during the period from March 2020 to March 2022. Echocardiographic images were acquired by experienced echocardiographers using a commercially available Philips EPIQ machine (Koninklijke Philips Ultrasound, USA) equipped with X5-1 Matrix probe for 2DE and DHM 3DE acquisitions, respectively. Results: 2D Simpson methods echocardiography results for estimated LVEF were 38.43 ± 1.70 in patients with NYHA class I-II, 30.53 ± 1.60 in patients with NYHA class III. Using 3D Heart Model, LVEF were 38.23 ± 1.71 in patients with NYHA class I-II and 30.27 ± 1.50 in patients with NYHA class III. The results of 2D Simpson methods echocardiography for estimated LVEDVi in NYHA class I-II and NYHA class III were 99.06 ± 6.36 ml/m2, 121.96 ± 2.93 ml/m2 respectively, LVESVi were 60.91 ± 3.91 ml/m2, 84.74 ± 2.70 ml/m2 respectively, for 3D Heart Model, LVEDVi in NYHA class I-II and NYHA class III were 100.07 ± 6.72, 121.38 ± 3.01 ml/m2 respectively, LVESVi were 61.75 ± 3.94 ml/m2, 84.73 ± 2.33 ml/m2 respectively. 2DE measurement of LV volumes and EF was completed in 6.1 ± 0.8 min. per patient. 3DE HeartModelA.I acquisition and analysis in most patients was completed in <3.2 min., an average time of 2.9 ± 1.3 min. per patient. The result of our study shows that the 3D HeartModelA.I. is a reliable and robust method for LVEF and LV volume analysis, which has similar results to 2D echocardiography performed by experienced sonographers. In this study, we found that 3DE DHM fully automated tool is also significantly faster than 2DE analysis and thus can help overcome the time-consuming nature and its present a strong argument for its incorporation into the clinical workflow. In this study, we found that 3DE DHM fully automated tool is also significantly faster than 2DE analysis and thus can help overcome the time-consuming nature and its present a strong argument for its incorporation into the clinical workflow. Conclusion: 3D DHM provides fast and accurate LV volumes and LVEF quantitation, as it avoids geometric assumptions and left ventricular foreshortening, has better reproducibility and has incremental value to predict adverse outcomes in comparison with conventional 2DE. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome.

Obstetric shock (OS) has been defined as a life-threatening cardiovascular collapse syndrome associated with pregnancy, childbirth and puerperium (obstetrics causes), and is the most significant cause of high maternal mortality (MM) throughout human history. Shock in obstetrics (SIO) refers to indirect causes of non-obstetrics causes in pregnancy, childbirth and puerperium (polytrauma, aesthetic incidents, cardiovascular or cerebrovascular incidents, other septic syndromes). The goals of OS treatment are: to quickly detect the location or cause of bleeding / injury / inflammation, prevent the progression of shock, prevent massive transfusions, preserve the uterus (and adnexa), and preserve fertility if possible. Surgical treatment of septic shock includes exploratory laparotomy (laparoscopy), ectomy or resection of the necrotized organ, abdominal lavage with multiple drainages, continuous peritoneal drainage with lavation, extensive triple antibiosis per admission or per antibiogram and thromboprophylaxis. OS seems to remain a permanent miasma in practical clinical obstetrics, which we will not be able to influence, because we have obviously caused today's increase in MM from haemorrhagic OS by iatrogenic increase in the number of caesarean sections, especially elective ones.

Objectives Evidence on the current status of gender equity in academic rheumatology in Europe and potential for its improvement is limited. The EULAR convened a task force to obtain empirical evidence on the potential unmet need for support of female rheumatologists, health professionals and non-clinical scientists in academic rheumatology. Methods This cross-sectional study comprised three web-based surveys conducted in 2020 among: (1) EULAR scientific member society leaders, (2) EULAR and Emerging EULAR Network (EMEUNET) members and (3) EULAR Council members. Statistics were descriptive with significance testing for male/female responses assessed by χ2 test and t-test. Results Data from EULAR scientific member societies in 13 countries indicated that there were disproportionately fewer women in academic rheumatology than in clinical rheumatology, and they tended to be under-represented in senior academic roles. From 324 responses of EULAR and EMEUNET members (24 countries), we detected no gender differences in leadership aspirations, self-efficacy in career advancement and work–life integration as well as the share of time spent on research, but there were gender differences in working hours and the levels of perceived gender discrimination and sexual harassment. There were gender differences in the ranking of 7 of 26 factors impacting career advancement and of 8 of 24 potential interventions to aid career advancement. Conclusions There are gender differences in career advancement in academic rheumatology. The study informs a EULAR task force developing a framework of potential interventions to accelerate gender-equitable career advancement in academic rheumatology.

Nonlinear handheld detection of magnetic nanoparticles is used to assess the lymph node status of cancer patients. Joint sensitivity and resolving power of nonlinear handheld detection can be maximized by optimizing the frequency of the excitation field, which is strongly influenced by Brownian and Néel relaxation. The characteristic frequency of magnetic nanoparticles that defines sensitivity and resolving power is usually assessed by AC susceptometry. In this study, we used SPaQ data to predict handheld detection performance for magnetic nanoparticles with various particle sizes. SPaQ assesses dynamics by measuring the derivative of the magnetization originating from magnetic nanoparticles activated by an alternating excitation field. The ratio between the maximum signal difference and full-width-at-half-maximumis used to estimate the optimal excitation frequency. Thereupon, it was shown that a particle with a combination of Brownian and Néel relaxation is superior in nonlinear handheld detection compared to Brownian or Néel only particles. Moreover, the optimal excitation frequency is generally established at a slightly higher frequency compared to the characteristic frequency assessed by AC susceptometry. Consequently, this insight into the consequences of the dynamic behavior of magnetic nanoparticles under an alternating magnetic field enables the optimization of nonlinear handheld detection for specific clinical applications.

The expression pattern of the markers p19, Ki-67, MSX1, MSX2, PDL1, pRB, and CYCLINA2 was quantitatively and semiquantitatively analyzed in histologic sections of the developing and postnatal human eye at week 8, in retinoblastoma, and in various uveal melanomas post hoc studies by double immunofluorescence. The p19 immunoreactivity characterized retinal and/or choroidal cells in healthy and tumor tissues: expression was lower in the postnatal retina than in the developing retina and retinoblastoma, whereas it was high in epithelioid melanomas. Ki67 expression was high in the developing eye, retinoblastoma, and choroidal melanomas. MSX1 and MSX2 expression was similar in the developing eye and retinoblastoma, whereas it was absent in the postnatal eye. Their different expression was evident between epithelioid and myxoid melanomas. Similarly, PDL1 was absent in epithelioid melanomas, whereas it was highly expressed in developing and tumor tissues. Expression of pRB and CYCA2 was characteristic of developing and tumorous eye samples but not of the healthy postnatal eye. The observed expression differences of the analyzed markers correlate with the origin and stage of cell differentiation of the tissue samples. The fine balance of expression could play a role in both human eye development and ocular tumorigenesis. Therefore, understanding their relationship and interplay could open new avenues for potential therapeutic interventions and a better understanding of the mechanisms underlying the developmental plasticity of the eye and the development of neoplasms.

Reaction of diphenylmethanol (4) with n-butyllithium and subsequent treatment with selenium resulted in 12H-dibenzo[d,g][1,2,3]triselenocin-12-ol (5) comprising a novel heterocyclic ring system. The title compound 5 was analyzed by 1H-NMR, 13C-NMR and HPLC. Additionally, the structure of 5 was confirmed by single crystal X-ray diffraction.

Summary This project aims to assess and analyse the perception and impact of the COVID-19 pandemic in Benin. The applied research methodology was interdisciplinary and combined field studies that used ethnographic and social research methods with coding and data analysis, leading to theoretical dilemmas, which were analysed from the viewpoint of bioethical reflection. Furthermore, biomedical engineering approaches were used to assess the preparedness to COVID-19. Despite the preparedness to COVID-19 due to the promoted governmental measures, a peculiar management of the pandemic emerged. The latter, although noteworthy, did not overcome the typical challenges of medical locations in low-resource settings. This, together with the controversial spread of information and local beliefs, caused significant economic and social consequences, exceeding the benefits related to the containment of the virus. This research highlights how the emotion of fear, in this specific situation, was herald of dramatic consequences, rather than having a heuristic and empowering effect.

Aim To analyse biochemical markers as possible predictors of death before discharge in cooled newborns following perinatal asphyxia. Methods A total of 91 infants that underwent therapeutic hypothermia after perinatal asphyxia were included. Inclusion criteria for therapeutic hypothermia were Sarnat stage 2 or 3. Data were collected from medical histories regarding gender, gestational age, birth weight, Apgar and Sarnat score; additionally, gas analyses, liver and cardiac enzymes before, and in the first 12 hours after starting therapeutic hypothermia, were evaluated. The patients' characteristics were compared between two groups, survivors and non-survivors. Results Statistical difference was not found between groups regarding gender, gestational age, birth weight, delivery type, 1st and 5th minute Apgar score, seizures, alanine aminotransferase (ALT), creatine kinase (CK), troponin and fibrinogen level. Groups were significantly different regarding acid-base balance (p=0.012), base excess (BE) (p=0.025), lactate (p=0.002), aspartate aminotransferaze (AST), (p=0.011), lactate dehydrogenase (LDH) (p=0.006), activated partial thromboplastin clotting time (aPTT) (p=0.001) and international normalized ratio (INR) (p=0.001). Conclusion Acid-base balance, BE, lactate, AST, LDH, aPTT and INR were significantly higher in the group of cooled newborns after perinatal asphyxia (non-survivors), and can serve as predictors of death before discharge. Combining diagnostic modalities raises a chance for accurate prediction of outcomes of asphyxiated infants.

The proliferation of mobile communication systems, arrival of high-speed broadband networks and more complex network topologies have exacerbated cyber-threats. Cyber-warfare has become an aspect of modern war-fare that can no longer be overlooked. In recent years, network intrusions launched using the Internet have seriously undermined the security systems of many nations. Classifying malicious network traffic is the first step in network intrusion detection. In this paper, we propose three models using semi-supervised learning-based malicious traffic classification (MTC) methods that effectively improve the classification of traffic using a small proportion of labeled traffic data. Employing three different deep neural networks as feature extraction networks respectively, the proposed models use transductive transfer learning and domain adaptive ideas, and ladder networks as classification layers. Experimental results are provided to validate the proposed methods.

Malware detection is an important step in network security. Traditional malware detection methods suffer from the ability to learn, understand, process, and apply characteristics of network traffic data accurately, and in a relatively short space of time. It also cannot learn new tasks without forgetting the old tasks. In this paper, we propose a self-evolving malware detection (SEMD) method using network traffic and incremental learning. Incremental learning (IL) method is one of the important methods in deep learning, which can learn new tasks without forgetting the old tasks. Its loss function draws lessons from the idea of knowledge distillation. Experimental results show that the proposed method can recognize both old tasks and new tasks (overcoming the problem of catastrophic network forgetting). The performance of the proposed SEMD method is also better than the general method of incremental learning without self-evolving.