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BackgroundOn January 30, 2020, the World Health Organization’s Emergency Committee declared the rapid, worldwide spread of COVID-19 a global health emergency. Since then, tireless efforts have been made to mitigate the spread of the disease and its impact, and these efforts have mostly relied on nonpharmaceutical interventions. By December 2020, the safety and efficacy of the first COVID-19 vaccines were demonstrated. The large social media platform Twitter has been used by medical researchers for the analysis of important public health topics, such as the public’s perception on antibiotic use and misuse and human papillomavirus vaccination. The analysis of Twitter-generated data can be further facilitated by using Twitter’s built-in, anonymous polling tool to gain insight into public health issues and obtain rapid feedback on an international scale. During the fast-paced course of the COVID-19 pandemic, the Twitter polling system has provided a viable method for gaining rapid, large-scale, international public health insights on highly relevant and timely SARS-CoV-2–related topics. ObjectiveThe purpose of this study was to understand the public’s perception on the safety and acceptance of COVID-19 vaccines in real time by using Twitter polls. MethodsWe developed 2 Twitter polls to explore the public’s views on available COVID-19 vaccines. The surveys were pinned to the Digital Health and Patient Safety Platform Twitter timeline for 1 week in mid-February 2021, and Twitter users and influencers were asked to participate in and retweet the polls to reach the largest possible audience. ResultsThe adequacy of COVID-19 vaccine safety (ie, the safety of currently available vaccines; poll 1) was agreed upon by 1579 out of 3439 (45.9%) Twitter users. In contrast, almost as many Twitter users (1434/3439, 41.7%) were unsure about the safety of COVID-19 vaccines. Only 5.2% (179/3439) of Twitter users rated the available COVID-19 vaccines as generally unsafe. Poll 2, which addressed the question of whether users would undergo vaccination, was answered affirmatively by 82.8% (2862/3457) of Twitter users, and only 8% (277/3457) categorically rejected vaccination at the time of polling. ConclusionsIn contrast to the perceived high level of uncertainty about the safety of the available COVID-19 vaccines, we observed an elevated willingness to undergo vaccination among our study sample. Since people's perceptions and views are strongly influenced by social media, the snapshots provided by these media platforms represent a static image of a moving target. Thus, the results of this study need to be followed up by long-term surveys to maintain their validity. This is especially relevant due to the circumstances of the fast-paced pandemic and the need to not miss sudden rises in the incidence of vaccine hesitancy, which may have detrimental effects on the pandemic’s course.

BackgroundVirtual reality (VR) and augmented reality (AR) have recently become popular research themes. However, there are no published bibliometric reports that have analyzed the corresponding scientific literature in relation to the application of these technologies in medicine. ObjectiveWe used a bibliometric approach to identify and analyze the scientific literature on VR and AR research in medicine, revealing the popular research topics, key authors, scientific institutions, countries, and journals. We further aimed to capture and describe the themes and medical conditions most commonly investigated by VR and AR research. MethodsThe Web of Science electronic database was searched to identify relevant papers on VR research in medicine. Basic publication and citation data were acquired using the “Analyze” and “Create Citation Report” functions of the database. Complete bibliographic data were exported to VOSviewer and Bibliometrix, dedicated bibliometric software packages, for further analyses. Visualization maps were generated to illustrate the recurring keywords and words mentioned in the titles and abstracts. ResultsThe analysis was based on data from 8399 papers. Major research themes were diagnostic and surgical procedures, as well as rehabilitation. Commonly studied medical conditions were pain, stroke, anxiety, depression, fear, cancer, and neurodegenerative disorders. Overall, contributions to the literature were globally distributed with heaviest contributions from the United States and United Kingdom. Studies from more clinically related research areas such as surgery, psychology, neurosciences, and rehabilitation had higher average numbers of citations than studies from computer sciences and engineering. ConclusionsThe conducted bibliometric analysis unequivocally reveals the versatile emerging applications of VR and AR in medicine. With the further maturation of the technology and improved accessibility in countries where VR and AR research is strong, we expect it to have a marked impact on clinical practice and in the life of patients.

The article discusses the results of scientific and economic experience carried out in the winter - in the spring period, which showed that when raising newborns and calves in dispensary houses in the open air, it is necessary to strictly comply with a number of favorable conditions; calving of cows should take place in stalls: newborn calves in must be kept in a place with their mothers for 24 hours; Calves should be provided with more careful care and their own - temporary feeding, daily addition of dry straw bedding. Young animals raised in the open air, subject to all these specified conditions, are significantly less susceptible to gastrointestinal diseases, grow better and use high-quality roughage more.

The conscious need of transition to an environmentally friendly way of society life on a global scale in recent years outlines a new direction in the development and activity of humanity. A number of documents at the global and European levels are a clear sign of countries' determination t change significantly their attitude to the natural resources use in order to limit climate change and global warming of the planet, to ensure an environmentally friendly way of life and activity and the long-term preservation and well-being of human civilization. Small and medium-sized businesses in Bulgaria are a huge potential resource that can be a powerful engine for realizing the ecological transformation of the country's economy. Current progress, financial ability and access to financial resources as well as the outlooks of small and medium companies in Bulgaria in the green transition are analyzed in this study. SMEs are classified in 3 clusters in terms of their progress in the transition process using TwoStep Cluster analysis with a set of indicators identified by authors. It has been established that despite the existing difficulties and the great inertia in the process of transition to an ecological economy of small and medium-sized companies in particular, more or less sustainable steps leading to the reduction of carbon emissions are observed, in the production of cleaner energy as well as elements of circularity in production processes and consumption. The transition appears to be taking place more quickly in medium-sized companies operating in manufacturing, logistics and transport, while small, mostly family-owned, companies with businesses in retail or service are progressing more slowly in this process. All the companies need of more serious and adequate support in the way of ecologization both by state regulations that to be applied clearly and transparently and by adequate financing. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD. [ABSTRACT FROM AUTHOR]

Canine mammary tumors (CMTs) represent a prevalent malignancy in female dogs. MicroRNAs (miRNAs) have emerged as critical regulators of gene expression and are implicated in various cancer types, including CMTs. This study aimed to investigate the altered miRNA expression patterns in CMTs and their potential role in tumorigenesis. We analyzed miRNA profiles in a cohort of CMT samples and matched normal tissues using a custom canine panel microarray slide (Agilent technology). The bioinformatics analysis overlapped the altered miRNA signature in CMT with human breast cancer miRNA (TCGA patient cohort). The biological significance of this altered miRNA signature was evaluated using Ingenuity Pathway Analysis. Our results revealed a distinctive miRNA expression signature associated with CMTs compared to normal mammary tissues, and when overlapped with human breast cancer miRNA data (TCGA cohort), we identified a common signature composed of one overexpressed transcript and eight downregulated transcripts. In conclusion, our study provides comprehensive insights into the altered miRNA expression patterns in CMTs, shedding light on their potential contribution to the pathogenesis of these tumors. Further investigation into the specific roles of these dysregulated miRNAs is warranted to elucidate their precise involvement in CMT progression and to explore their therapeutic implications. [ABSTRACT FROM AUTHOR]

The aim of the study was to evaluate the effect of pomegranate juice consumption on bone histomorphometric parameters using an animal model. The animals (rats) were divided into 7 experimental groups. The groups differed in the concentration of pomegranate juice administered. The control group received only water, the other groups were supplied with commercial or freshly squeezed pomegranate juice at concentrations of 10%, 25% and 50%. To study the structure of the proximal end of the tibia, a Skyscan 1174 X-ray microtomography device, equipped with a 1.3Mp FW VDS camera, was used. The proximal end (metaphysis) of the left tibia was examined. The following bone tissue parameters were analysed: bone mineral density (BMD) (mm3), the number of trabeculae (Tb.N) (mm-1), trabecular separation (Tb.Sp) (mm) and trabecular thickness (Tb. Th) (mm), BS/BV(mm-1). It was found that 25% pomegranate juice, both freshly squeezed and commercial, significantly increased bone density (BMD), increased trabecular thickness (Tb.TH), and reduced the distance between trabeculae (Tb.SP) compared to the control group and groups receiving juice at other concentrations. The highest BMD, the thickest trabeculae and the smallest distance between trabeculae were observed after the application of fresh 25% pomegranate juice. Thus, it can be concluded that pomegranate juice, especially freshly squeezed, improves bone density. [ABSTRACT FROM AUTHOR]

During the period 2018-2020 in the experimental field of the Agricultural Institute, Shumen, on calcic chernozem soil type, a field experiment with triticale (x Triticosecale Wittmack) was conducted. The experiment was based on the block method, in four replications with a size of the experimental plot of 10 m2. Sowing was carried out in October with a sowing rate of 500 germinate able seeds/m2 after the predecessor sugar beet (Beta vulgaris L.). In the appropriate periods treatments were made in 4 variants: 1) Control-untreated variant. 2) Treatment with biodynamic preparations: Preparation 500 (5% solution) + Fladen preparation (1.5% solution) + preparation 501 (1.5 g for 300 l H2O/ha). 3) Treatment with biological preparations: Heliosulfur (500 ml/ha) + Free N (500 ml/ha). 4) Combined treatment with the same biodynamic and biological preparations. The following indicators were studied: plant height, number of spikelet's per spike, number of grains per spike, grain weight per spike. After the statistical analysis for the first year of the study, a proven positive difference was found in all indicators in the combined biodynamic + biological method of cultivation, as well as in the stand-alone biological. In the second year of the study, a proven positive difference was obtained in all studied indicators in the variants treated with biodynamic preparations and the combination of biodynamic + biological preparations, with statistical significance for p<0.001. A positive correlation was found between all variants of the observed indicators at p<0.05. [ABSTRACT FROM AUTHOR]

Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study is aimed at the creation of a novel algorithm to identify and distinguish white flowering honey plants, such as black locust (Robinia pseudo-acacia) and to determine the areas occupied by this forest species in mixed forest ecosystems using UAV-based RGB imaging. In our study, to determine the plant cover of black locust in mixed forest ecosystems we used a DJI (Da-Jiang Innovations, Shenzhen, China) Phantom 4 Multispectral drone with 6 multispectral cameras with 1600 × 1300 image resolution. The monitoring was conducted in the May 2023 growing season in the village of Yuper, Northeast Bulgaria. The geographical location of the experimental region is 43°32′4.02″ N and 25°45′14.10″ E at an altitude of 223 m. The UAV was used to make RGB and multispectral images of the investigated forest massifs, which were thereafter analyzed with the software product QGIS 3.0. The spectral images of the observed plants were evaluated using the newly created criteria for distinguishing white from non-white colors. The results obtained for the scanned area showed that approximately 14–15% of the area is categorized as white-flowered trees, and the remaining 86–85%—as non-white-flowered. The comparison of the developed algorithm with the Enhanced Bloom Index (EBI) approach and with supervised Support Vector Machine (SVM) classification showed that the suggested criterion is easy to understand for users with little technical experience, very accurate in identifying white blooming trees, and reduces the number of false positives and false negatives. The proposed approach of detecting and mapping the areas occupied by white flowering honey plants, such as black locust (Robinia pseudo-acacia) in mixed forest ecosystems is of great importance for beekeepers in determining the productive potential of the region and choosing a place for an apiary. [ABSTRACT FROM AUTHOR]

Natural products (NPs) have played a significant role in modern drug discovery, serving as a source of inspiration and ideas for drug design. NPs have led to advancements in the treatment of various diseases, including infectious, cardiovascular, cancer, and multiple sclerosis. Pharmaceutical companies are shifting towards biotechnological methods to develop medicines for complex diseases. NPs offer a wider chemical space compared to synthetic compounds, with unique structural features and a diverse range of bioactive compounds. In the context of kidney disease treatment, both NPs and synthetic drugs have made significant contributions to pharmacotherapy. NPs have the potential to provide unique structural motifs for future drug development. [Extracted from the article]

Adverse drug reactions continue to be not only one of the most urgent problems in clinical medicine, but also a social problem. The aim of this study was a bibliometric analysis of the use of digital technologies to prevent adverse drug reactions and an overview of their main applications to improve the safety of pharmacotherapy. The search was conducted using the Web of Science database for the period 1991–2023. A positive trend in publications in the field of using digital technologies in the management of adverse drug reactions was revealed. A total of 72% of all relevant publications come from the following countries: the USA, China, England, India, and Germany. Among the organizations most active in the field of drug side effect management using digital technologies, American and Chinese universities dominate. Visualization of publication keywords using VOSviewer software 1.6.18 revealed four clusters: "preclinical studies", "clinical trials", "pharmacovigilance", and "reduction of adverse drug reactions in order to improve the patient's quality of life". Molecular design technologies, virtual models for toxicity modeling, data integration, and drug repurposing are among the key digital tools used in the preclinical research phase. Integrating the application of machine learning algorithms for data analysis, monitoring of electronic databases of spontaneous messages, electronic medical records, scientific databases, social networks, and analysis of digital device data into clinical trials and pharmacovigilance systems, can significantly improve the efficiency and safety of drug development, implementation, and monitoring processes. The result of combining all these technologies is a huge synergistic provision of up-to-date and valuable information to healthcare professionals, patients, and health authorities. [ABSTRACT FROM AUTHOR]

Taking into consideration its physico-chemical properties, digestate should be used primarily as a fertiliser. The possible ways of using digestate as a fertiliser in agriculture were identified, and digestate collected from an agricultural biogas plant was tested for its macroelement and heavy metal content. The research was conducted on Haplic LUVISOLS soil according FAO classification. The area of the land plots was 75 m2. All measurements were carried out in ten replicates. Seed yield was determined at 2.6 t ha−1. The thousand-seed weight was similar in the three growing seasons, and averaged 171.49 g to 184.44 g for the three years under analysis. For the control object, the average thousand-seed weight from the three years of the experiment was 168.56 g. This parameter was significantly influenced by the year of analysis. The highest protein content was obtained in 2022 (an average of 20.3%), which was significantly higher than in 2021 (20.13%) and 2020 (20.12%). The analysis showed an increase in the average value for the three harvest years regarding the fat content of the multiflora bean seeds depending on the post-harvest digestate dose, ranging from 0.47% to 0.61%. In the control object, the average fat content for the three harvest years under analysis was 0.41%. The year under analysis had no significant impact on fat content. A positive correlation was found between the digestate dose and protein, fat, and carbohydrate contents per 100 g of beans. Increasing the dose resulted in statistically significant differences from the lower dose. The obtained results show an increase in macroelement content depending on the digestate dose applied. The average carbohydrate content per 100 g of beans for the three years under analysis ranged from 49.78 g to 54.01 g, while the calcium content per 100 g of beans ranged from 109.23 mg to 124.00 mg. In contrast, the magnesium content in 100 g of bean ranged from 129.91 g to 137.01 mg, the phosphorus content in 100 g of bean from 366.99 mg to 387.00 mg, and the potassium content in 100 g of bean from 1341.20 mg to 1394.06 mg. Statistical analysis revealed statistically significant differences except for potassium, where no differences were found for the two highest doses. In addition, no differences were found in the average phosphorus and potassium content between the years under analysis. The study showed an increase in yield depending on the amount of digestate applied. The highest dose used in the experiment provided the most nitrogen and macronutrients, with a positive effect on yield velocity, protein and fat content, micronutrients, and macronutrients in beans. [ABSTRACT FROM AUTHOR]

Several models on honeybee population dynamics have been considered in the past decades, which explain that the growth of bee colonies is highly dependent on the availability of food and social inhibition. The phenomenon of the Colony Collapse Disorder (CCD) and its exact causes remain unclear and here we are interested on the factor of social immunity. We work with the mathematical model in [1]. The core model, consisting of four nonlinear ordinary differential equations with unknown functions: brood and nurses B, iB, N and iN represent the number of healthy brood, infected brood, healthy nurses, and infected nurses, respectively. First, this model implements social segregation. Highrisk individuals such as foragers are limited to contact only nectar-receivers, but not other vulnerable individuals (nurses and brood) inside the nest. Secondly, it includes the hygienic behavior, by which healthy nurses actively remove infected workers and brood from the colony. We aim to study the dynamics and the long-term behavior of the proposed model, as well as to discuss the effects of crucial parameters associated with the model. In the first stage, we study the model equilibria stability in dependence of the reproduction number. In the second stage, we investigate the inverse problem of parameters identification in the model based on finite number time measurements of the population size. The conjugate gradient method with explicit Frechet derivative of the cost functional is proposed for the numerical solution of the inverse problem. Computational results with synthetic and realistic data are performed and discussed. [ABSTRACT FROM AUTHOR]

We analyze a mathematical model of the effects of soil contamination by heavy metals, which is expressed as systems of nonlinear ordinary differential equations (ODEs). The model is based on the symmetry dynamics of heavy metals soil–plant interactions. We aim to study this symmetric process and its long-term behavior, as well as to discuss the role of two crucial parameters, namely the flux of the hydrogen protons to the soil in rainfall events W (t) , and the available water for roots p (t) . We study the boundedness and positivity of the solution. Further, a parameter identification analysis of the model is presented. Numerical experiments with synthetic and realistic data of honeybee population are discussed. [ABSTRACT FROM AUTHOR]

We consider an inverse problem of recovering the mortality rate in the honey bee difference equation model, that tracks a forage honeybee leaving and entering the hive each day. We concentrate our analysis to the model without pesticide contamination in the symmetric spatial environment. Thus, the mathematical problem is formulated as a symmetric inverse problem for reaction coefficient at final time constraint. We use the overspecified information to transform the inverse coefficient problem to the forward problem with non-local terms in the differential operator and the initial condition. First, we apply semidiscretization in space to the new nonsymmetric differential operator. Then, the resulting non-local nonsymmetric system of ordinary differential equations (ODEs) is discretized by three iterative numerical schemes using different time stepping. Results of numerical experiments which compare the efficiency of the numerical schemes are discussed. Results from numerical tests with synthetic and real data are presented and discussed, as well. [ABSTRACT FROM AUTHOR]

This study conducted a comprehensive patent and bibliometric analysis to elucidate the evolving scientific landscape surrounding the development and application of pulse oximeters, including in the field of digital medicine. Utilizing data from the Lens database for the period of 2000–2023, we identified the United States, China, the Republic of Korea, Japan, Canada, Australia, Taiwan, and the United Kingdom as the predominant countries in patent issuance for pulse oximeter technology. Our bibliometric analysis revealed a consistent temporal trend in both the volume of publications and citations, underscoring the growing importance of pulse oximeters in digitally-enabled medical practice. Using the VOSviewer software(version 1.6.18), we discerned six primary research clusters: (1) measurement accuracy; (2) integration with the Internet of Things; (3) applicability across diverse pathologies; (4) telemedicine and mobile applications; (5) artificial intelligence and deep learning; and (6) utilization in anesthesiology, resuscitation, and intensive care departments. The findings of this study indicate the prospects for leveraging digital technologies in the use of pulse oximetry in various fields of medicine, with implications for advancing the understanding, diagnosis, prevention, and treatment of cardio-respiratory pathologies. The conducted patent and bibliometric analysis allowed the identification of technical solutions to reduce the risks associated with pulse oximetry: improving precision and validity, technically improved clinical diagnostic use, and the use of machine learning. [ABSTRACT FROM AUTHOR]

Individuals with spinal cord injury (SCI) have higher infection rates compared to those without SCI. In this review, the immune status difference between individuals with and without traumatic SCI is investigated by examining their peripheral immune cells and markers. PubMed, Cochrane, EMBASE, and Ovid MEDLINE were searched without language or date restrictions. Studies reporting peripheral immune markers' concentration and changes in functional capabilities of immune cells that compared individuals with and without SCI were included. Studies with participants with active infection, immune disease, and central nervous system (CNS) immune markers were excluded. The review followed the PRISMA guidelines. Effect estimates were measured by Weighted Mean Difference (WMD) using a random-effects model. Study quality was assessed using the National Heart, Lung, and Blood Institute Quality Assessment Tool. Fifty-four studies (1813 with SCI and 1378 without SCI) contributed to the meta-analysis. Leukocytes (n = 23, WMD 0.78, 95% CI 0.17; 1.38, I2 83%), neutrophils (n = 11, WMD 0.76, 95% CI 0.09; 1.42, I2 89%), C-reactive protein (CRP) (n = 12, WMD 2.25, 95% CI 1.14; 3.56, I2 95%), and IL6 (n = 13, WMD 2.33, 95% CI 1.20; 3.49, I2 97%) were higher in individuals with SCI vs. without SCI. Clinical factors (phase of injury, completeness of injury, sympathetic innervation impairment, age, sex) and study-related factors (sample size, study design, and serum vs. plasma) partially explained heterogeneity. Immune cells exhibited lower functional capability in individuals with SCI vs. those without SCI. Most studies (75.6%) had a moderate risk of bias. The immune status of individuals with SCI differs from those without SCI and is clinically influenced by the phase of injury, completeness of injury, sympathetic innervation impairment, age, and sex. These results provide information that is vital for monitoring and management strategies to effectively improve the immune status of individuals with SCI. [ABSTRACT FROM AUTHOR]