Abstract Background During the first epidemic wave, COVID-19 surveillance focused on quantifying the magnitude and the escalation of a growing global health crisis. The scientific community first assessed risk through basic indicators, such as the number of cases or rates of new cases and deaths, and later began using other direct impact indicators to conduct more detailed analyses. We aimed at synthesizing the scientific community’s contribution to assessing the direct impact of the COVID-19 pandemic on population health through indicators reported in research papers. Methods We conducted a rapid scoping review to identify and describe health indicators included in articles published between January 2020 and June 2021, using one strategy to search PubMed, EMBASE and WHO COVID-19 databases. Sixteen experts from European public health institutions screened papers and retrieved indicator characteristics. We also asked in an online survey how the health indicators were added to and used in policy documents in Europe. Results After reviewing 3891 records, we selected a final sample of 67 articles and 233 indicators. We identified 52 (22.3%) morbidity indicators from 33 articles, 105 severity indicators (45.1%, 27 articles) and 68 mortality indicators (29.2%, 51). Respondents from 22 countries completed 31 questionnaires, and the majority reported morbidity indicators (29, 93.5%), followed by mortality indicators (26, 83.9%). Conclusions The indicators collated here might be useful to assess the impact of future pandemics. Therefore, their measurement should be standardized to allow for comparisons between settings, countries and different populations.

Prostate cancer (PC) is one of the most commonly diagnosed malignancies worldwide and the second leading cause of mortality among men (1). Nowadays, radical prostatectomy is considered the primary therapeutic modality for treating patients with localized PC (stage pT2), providing a five-year survival rate of nearly 100% (2). Sexual dysfunction in men associated with PC treatment encompasses three distinct entities: erectile dysfunction (ED) and penile shortening; ejaculatory and orgasmic dysfunction; and psychosexual dysfunction, which pertains to sexual desire, intimacy, and mental health (3). Penile rehabilitation (PR) is defined as the use of any intervention or combination of procedures aimed not only at achieving an erection sufficient for satisfactory sexual intercourse but also at restoring erectile function to its preoperative level (4). Despite eff orts to preserve the neurovascular bundle during radical prostatectomy, ED remains a common outcome. Although prevalence rates of ED after the procedure vary widely, recent studies report rates as high as 85% (5). This is primarily due to the lack of control over factors that significantly influence the erection recovery, such as the patient’s age, preoperative erectile function, comorbidities, surgical approach (open, laparoscopic, or robot-assisted), surgical technique (non-, uni-, or bilateral nerve-sparing), and the surgeon’s skills and experience. The pathophysiology of postoperative ED is multifactorial. The primary mechanisms are believed to be damage to the cavernous nerves, whether through dis-section or neuropraxia, and vascular injury, which includes damage to the accessory pudendal arteries, hypoxia and fibrosis of the endothelium and smooth muscle, resulting in penile shortening (6-8). Although there is no consensus on the optimal approach to PR, accepted modalities include the use of phosphodiesterase type 5 inhibitors (PDE-5i; such as sildenafil, vardenafil, tadalafil) and vacuum erection de-vices (VED) or vacuum constriction devices (VCD) as first-line therapies. Second-line treatments involve prostaglandin E1 preparations for intracavernous, or intraurethral (MUSE – “Medicated Urethral System for Erection”) administration. The final therapeutic option is the implantation of penile prostheses (3-10).

Background/Objectives: The aim of this study was to evaluate brain metabolism using MR spectroscopy (MRS) after recovery from Coronavirus disease (COVID-19) and to test the impact of disease severity on brain metabolites. Methods: We performed MRS on 81 individuals (45 males, 36 females, aged 40–60), who had normal MRI findings and had recovered from COVID-19, classifying them into mild (17), moderate (36), and severe (28) groups based on disease severity during the acute phase. The study employed two-dimensional spectroscopic imaging above the corpus callosum, focusing on choline (Cho), creatine (Cr), and N-acetylaspartate (NAA). We analyzed Cho/Cr and NAA/Cr ratios as well as absolute concentrations using water as an internal reference. Results: Results indicated that the Cho/Cr ratio was higher with increasing disease severity, while absolute Cho and NAA/Cr ratios showed no significant differences across the groups. Notably, absolute Cr and NAA levels were significantly lower in patients with severe disease. Conclusions: These findings suggest that the severity of COVID-19 during the acute phase is associated with significant changes in brain metabolism, marked by an increase in Cho/Cr ratios and a reduction in Cr and NAA levels, reflecting substantial metabolic alterations post-recovery.

Abstract Let S be a groupoid (magma) with zero 0, and let R=⊕s∈SRs be a contracted S-graded ring, that is, an S-graded ring with R0=0. By G(HR) we denote the undirected power graph of a multiplicative subsemigroup HR=∪s∈SRs of R, and by G*(HR)a graph obtained from G(HR) by removing 0 and its incident edges. If Re is a nonzero ring component of R, then G*(Re) denotes a subgraph of G*(HR), induced by Re*. In this paper we address a problem raised in [Abawajy, J., Kelarev, A., Chowdhury, M.: Power Graphs: A Survey. Electron. J. Graph Theory Appl. 1(2), 125–147 (2013)]. Namely, let S be torsion-free, that is, sn=tn implies s = t for all s, t∈S, and all positive integers n, and let S be 0-cancellative, that is, for all s, t, u∈S,su=tu≠0 implies s=t, and us=ut≠0 implies s=t. Also, let R be semisimple Artinian. We prove that if G*(Re) is connected for every nonzero ring component Re of R, then the connected components of G*(HR) are precisely the graphs G*(Re).

The targeted compounds in this research, resveratrol analogs 1–14, were synthesized as mixtures of isomers by the Wittig reaction using heterocyclic triphenylphosphonium salts and various benzaldehydes. The planned compounds were those possessing the trans-configuration as the biologically active trans-resveratrol. The pure isomers were obtained by repeated column chromatography in various isolated yields depending on the heteroaromatic ring. It was found that butyrylcholinesterase (BChE) was more sensitive to the heteroaromatic resveratrol analogs than acetylcholinesterase (AChE), except for 6, the methylated thiophene derivative with chlorine, which showed equal inhibition toward both enzymes. Compounds 5 and 8 achieved the highest BChE inhibition with IC50 values of 22.9 and 24.8 μM, respectively. The same as with AChE and BChE, methylated thiophene subunits of resveratrol analogs showed better enzyme inhibition than unmethylated ones. Two antioxidant spectrophotometric methods, DPPH and CUPRAC, were applied to determine the antioxidant potential of new heteroaromatic resveratrol analogs. The molecular docking of these compounds was conducted to visualize the ligand-active site complexes’ structure and identify the non-covalent interactions responsible for the complex’s stability, which influence the inhibitory potential. As ADME properties are crucial in developing drug product formulations, they have also been addressed in this work. The potential genotoxicity is evaluated by in silico studies for all compounds synthesized.

This research explores into the utilization of synthetic data within image classification tasks and evaluates its efficiency in comparison to the utilization of real data. To facilitate this investigation, we employ the CIFAKE dataset, comprising the well-established CIFAR10 dataset and an equivalent number of images synthetically generated using the Latent Diffusion Model (LDM). The increasing demand for diverse and abundant labeled datasets has prompted the emergence of synthetic data as a potential solution to address data scarcity. Within this study, we scrutinize the performance of image classification models trained on both real and synthetic datasets. To ensure a comprehensive evaluation, we alternately apply test data across different models. Our analysis encompasses diverse factors, including classification accuracy, generalization capabilities, and robustness in various scenarios. The findings provide valuable insights into the efficacy of synthetic data as a viable alternative or complement to real data in the realm of image classification.

Artificial intelligence, Machine Learning, and Deep Learning are increasingly making significant contributions to the field of medicine. Individual patient conditions, disease localization, and various influencing factors underscore the complexity of disease diagnosis and treatment planning. Introducing new technologies can revolutionize medical diagnostics, facilitating swift and accurate assessments. Among the noninvasive diagnostic methods, Magnetic Resonance Imaging (MRI) stands out, particularly in tumor diagnosis. UNet, renowned for its effectiveness in medical image analysis, serves as a robust model for semantic segmentation, as does DeepLabV3+. However, these models are inherently complex, and their inference process can be time-consuming. By leveraging the OpenVINO toolkit, the inference process is significantly reduced. In this study, nearly a 2-fold acceleration is achieved in inference time with the DeepLabV3+ model and a roughly 1.2-fold improvement with the UNet model on CPU. Moreover, when employing GPU with FP16 precision, the acceleration reached almost 2.5fold for UNet and nearly 3-fold for DeepLabV3+, showcasing the substantial performance enhancements attainable through optimized hardware utilization.

Noise removal in image processing and computer vision is a crucial preprocessing step employing a spectrum of techniques. In recent years, autoencoders exhibit remarkable efficacy in mapping noisy images to clean counterparts, capturing intricate relationships for effective noise removal. Motivated by the challenges posed by noise in real-world images, this research focuses on the denoising preprocessing step, crucial for tasks like object detection and segmentation. The study explores the application of autoencoders in removing artificially added noise from images within the MNIST dataset. The MNIST dataset’s simplicity and historical significance facilitate focused examinations on specific aspects, such as the impact of different types and levels of noise. The efficacy of autoencoders for noise removal is assessed through the evaluation of results using various metrics, including SSIM, PSNR, MSE, and RMSE. In one remarkable instance, the reconstruction process achieved an impressive peak SSIM score of 99.06%, showcasing the efficacy of the method in preserving image fidelity despite the challenging presence of noise. This comprehensive analysis provides valuable insights into the performance and effectiveness of autoencoders in the context of noise reduction in various domains.

BACKGROUND After introducing interleukin(IL)-1/IL-6 inhibitors, some Still and Still-like patients developed unusual often fatal pulmonary disease. This complication was associated with scoring as DReSS (drug reaction with eosinophilia and systemic symptoms) implicating these inhibitors, although DReSS can be difficult to recognize in the setting of systemic inflammatory disease. OBJECTIVE We sought to facilitate recognition of IL-1/IL-6 inhibitor-DReSS in systemic inflammatory illnesses (Still/Still-like) by looking at timing and reaction-associated features. We evaluated outcomes of stopping or not-stopping IL-1/IL-6-inhibitors after DReSS reaction began. METHODS In an international study collaborating primarily with pediatric specialists, we characterized features of 89 drug-reaction cases versus 773 drug-exposed controls and compared outcomes of 52 cases stopping IL-1/IL-6-inhibitors to 37 cases not-stopping these drugs. RESULTS Before reaction began, drug-reaction cases and controls were clinically comparable, except for younger disease onset age for reaction cases with pre-existing cardiothoracic comorbidities. After reaction began, increased rates of pulmonary complications and macrophage activation syndrome (MAS), differentiated drug-reaction cases from drug-tolerant controls (p=4.7x10-35; p=1.1x10-24, respectively). Initial DReSS feature was typically reported 2-8 weeks after initiating IL-1/IL-6-inhibition. In drug-reaction cases stopping versus not-stopping IL-1/IL-6-inhibitor treatment, reaction related features were indistinguishable, including pulmonary complication rates [75%(39/52] versus [76%(28/37)]. Those stopping subsequently required fewer medications for treatment of systemic inflammation, had decreased rates of MAS, and improved survival (p=0.005, multivariate regression). Resolution of pulmonary complications occurred in 67%(26/39) of drug-reaction cases who stopped and in none who continued inhibitors. CONCLUSIONS In systemic inflammatory illnesses, recognition of IL-1/IL-6-inhibitor-associated reactions followed by avoidance of IL-1/IL-6-inhibitors significantly improved outcomes.

Case summary A 1-year-old domestic shorthair queen with five neonates was referred for umbilical cord entwinement in three kittens 24 h after parturition. The owner noticed the kittens were stuck to each other 3 h before admission. Despite a conservative treatment approach, prolonged ischaemia led to dry gangrenous changes in one of the kitten’s metatarsi. Relevance and novel information This and other neonatal complications in cats are rarely reported. Primiparity is a known factor contributing to postpartum complications. Furthermore, inexperienced owners require more assistance in mitigating these challenges. Therefore, further research and collaboration among breeders, owners and veterinary professionals are imperative in order to accurately determine the prevalence of this condition in kittens and develop effective strategies to address it.

BACKGROUND The proliferation of sexting among adolescents around the world today has woven a complex tapestry of sexual expression and exploration. However, its implications extend beyond consensual engagement, occasionally manifesting as a form of cyberviolence. Varied prevalence rates further complicate our understanding of the extent of youth sexting worldwide. Therefore, this study aims to provide a tool to measure sexting in young people from different countries by validating the Sexting Behaviours and Motives Questionnaire (SBM-Q), a comprehensive instrument that captures the diversity of consensual and non-consensual sexting behaviors and motives in different countries. METHOD A total of 4739 students, aged 15 to 25, participated. They were from Spain (1563), Croatia (1598), and Bosnia and Herzegovina (1578). Confirmatory factor analyses and multigroup analyses were conducted. RESULTS The validity of the instrument was confirmed, endorsing its six-factor structure, which includes the dimensions of sending, reasons for sending, victimization by non-consensual forwarding, receiving, forwarding, and reasons for forwarding. Internal consistency across the three countries further underscores the robustness of the SBM-Q. CONCLUSIONS This validated questionnaire provides a reliable measure for understanding sexting behaviors and motives in different countries. Cultural nuances are discussed.

Thermal insulation materials play a vital role in minimising energy loss in building operation and also affect the amount of greenhouse gas emissions associated with heating and cooling. In this context, it is becoming an increasingly important milestone to find suitable thermal insulation materials that not only meet the technical requirements but also minimise their environmental impact. The trend towards the use of eco-friendly materials for thermal insulation reflects the construction industry’s desire to contribute to environmental protection and the transition to more sustainable models of building construction and renovation. For more than 20 years, a number of research teams have been investigating the possibility of replacing synthetically produced materials such as mineral wool and polystyrene foam with natural fibre-based insulation materials. These alternatives include wood as a traditional, easily renewable raw material. This, together with the low energy intensity of processing and manufacturing wood materials, contributes to its low carbon footprint. Compared to traditional synthetic insulation materials, which are often energy intensive to produce, wood is a more environmentally friendly choice. However, with many European countries now facing a potential shortage of higher quality wood, it is necessary to look for alternative sources of wood, including in the field of thermal insulation materials, materials with a lower carbon footprint that can be produced from lower quality wood or from wood waste that would otherwise only have an energy use. The paper is devoted to the study and use of suitable wood waste and secondary raw materials from spruce wood (coarse wood chips, sawdust and wood flour) for the development of modern thermal insulations with the aim of an environmentally friendly and less energy-intensive production process compared to conventional insulants.