Abstract Age estimation is a key component in forensic analysis, be it in legal proceedings or archeological research. Current methods in forensic odontology are based on manual measurements of a wide array of morphometric parameters, typically from dental x-ray images, and occasionally from material remains. While those parameters follow a set progression during human development, thereby allowing current methods to precisely estimate the age of juveniles, estimation for adults and seniors proves to be more difficult. In this study, we explore the applicability of deep learning to the problem of chronological age estimation. We determine the best convolutional neural network model derived from state-of-the-art architectures, we determine the best performing model parameters using pretrained general-purpose vision model parameters as the starting point, and we perform ablation experiments to highlight which anatomical regions of the dental system contribute the most to the estimation. The proposed approach attains the lowest estimation error in literature for adult and senior subjects, which we verify on one of the largest datasets of panoramic dental x-ray images in literature. The dataset consists of 4035 panoramic dental x-ray images of male and female subjects with ages between 19 and 90 years. This study also evaluates the feasibility of the proposed model for age estimations of individual teeth, achieving an estimation error competitive with current methods while being fully automated. The estimation error is verified on our dataset of 76416 individual tooth images, which is the largest dataset to date in forensic odontology literature. Unlike current methods, dental alterations, decay, illnesses, or missing teeth do not pose a problem to the proposed model. With a median estimation error of 2.95 years for panoramic dental x-ray images and 4.68 years for individual teeth, and by deriving the model from state-of-the-art architectures, verifying those results on the largest dataset in forensic odontology literature and demonstrating the importance of different anatomical regions of the dental system for estimation, this study sets the baseline for future research of automated chronological age estimation in forensic odontology.

The International Organisation for Animal Health (OIE), from the onset of COVID-19 pandemic, promoted One Health in global and national responses. The OIE accentuated the role of the veterinary profession due to testing capacity of animal health laboratories and expertise. Veterinary Faculty Sarajevo through its Veterinary Institute participates in the national veterinary service with diagnostic and advisory roles. It has proactively enhanced the scope and quality of laboratories, including strengthening the interdisciplinarity and internationality. Development achieved through earlier pandemic threats resulted in having laboratory and technical facilities for molecular SARS-CoV-2 detection in the wake of the unveiling COVID-19 pandemic (early 2020). From confirmation of the first COVID-19 cases in Bosnia and Herzegovina (BiH), our staff participated in crisis response teams and, so far, held over sixty media addresses promoting public awareness and science based information. Our laboratories were included in the official detection system and were the first to sequence SARS-CoV-2, then to establish the Alpha COVID-19 variant in BiH human samples and to substantiate one-way virus transmission from humans to pets. The aim of this paper is to describe our activities as a participant in the response to the COVID-19 pandemic, alongside faced challenges and gained experiences.

Many wild animal populations are considered endangered due to anthropogenic activities. Wildlife and nature habitat preservation requires holistic and science based approaches supported by adequate regulations. One of the means for wildlife preservation is undoubtedly heath monitoring and investigation of infectious diseases of the wild animal populations, particularly if spillover effects are considered. Even though the theoretical background is well researched, implementation of disease prevention and control measures in wildlife populations entails more challenges than in domestic animal populations. Immediate signs of health disorders in wildlife often become evident when the infectious agent is well established in an area. Additionally, due to unrestricted and often long-range movement of wildlife, diseases are easily spread across borders. Brown bears, indigenous in Europe, are classified by EU regulations as endangered, almost extinct and rare. The wild bear population in Bosnia and Herzegovina shares a genetic lineage with bear populations of neighbouring Croatia, Serbia and Montenegro and is one of the few remaining fragments of bear populations in Europe. The aim of this paper is to describe implemented activities for health and telemetric monitoring of wild bears in the Nature Park Skakavac, Canton Sarajevo, Bosnia and Herzegovina.

Simple Summary CD8+ T cells are prominent decidual cells in the third trimester of healthy human pregnancy. They have a cytotoxic capacity which may control invasion of extravillous trophoblast and therefore affect placentation and play the role in development of preeclampsia. In this study, we examined the expression of CD8+ T cells in decidual tissue and peripheral blood of women with severe and mild preeclampsia in comparison to gestational age-matched healthy pregnancies. Additionally, the expression of cytotoxic proteins in CD8+ T cells was examined in order to specify their subpopulations. Abstract In our study, we aimed to establish expression of cytotoxic CD8+ T cells in the decidua basalis and the maternal peripheral blood (mPBL) of severe and mild preeclampsia (PE) and compare to healthy pregnancies. Decidual tissue and mPBL of 10 women with mild PE, 10 women with severe PE, and 20 age-matched healthy pregnancy controls were analyzed by double immunofluorescence and qPCR, respectively. By double immunofluorescence staining, we found a decreased total number of cells/mm2 in decidua basalis of granulysin (GNLY)+ (p ˂ 0.0001), granzyme B (GzB)+(p ˂ 0.0001), GzB+CD8+(p ˂ 0.0001), perforin (PRF1)+ (p ˂ 0.0001), and PRF1+CD8+ (p ˂ 0.01) in the severe PE compared to control group. Additionally, we noticed the trend of lower mRNA expression for GNLY, granzyme A (GZMA), GzB, and PRF1 in CD8+ T cells of mPBL in mild and severe PE, with the latter marker statistically decreased in severe PE (p ˂ 0.001). Forkhead box P3 (FOXP3) mRNA in CD8+ T cells mPBL was increased in mild PE (p ˂ 0.001) compared to controls. In conclusion, severe PE is characterized by altered expression of cytotoxic CD8+ T cells in decidua and mPBL, suggesting their role in pathophysiology of PE and fetal-maternal immune tolerance.

Abstract Pneumonia is a leading cause of mortality in limited resource settings (LRS), which are common in low- and middle-income countries (LMICs). Accurate referrals can reduce the devastating impact of pneumonia, especially in LRS. Discriminating pneumonia from other respiratory conditions based only on symptoms is a major challenge. Machine learning has shown promise in overcoming the diagnostic difficulties of pneumonia (i.e., low specificity of symptoms, lack of accessible diagnostic tests and varied clinical presentation). Many scientific papers are now focusing on deep-learning methods applied to clinical images, which is unaffordable for initial patient referral in LMICs. The current study used a dataset of 4500 patients (1500 patients affected by bronchitis, 3000 by pneumonia) from a middle-income country, containing information on subject population characteristics, symptoms and laboratory test results. Manual feature selection was performed, focusing on clinical symptoms that are easily measurable in LRS and in community settings. Three common machine learning methods were tested and compared: logistic regression; decision tree and support vector machine. Models were developed through a holdout process of training-validation and testing. We focused on six clinically relevant, easily interpreted patient symptoms as best indicators for pneumonia. Our final model was a decision tree, achieving an AUC of 93%, with the advantage of being fully intelligible and easily interpreted. The performance achieved suggested that intelligible machine learning models can enhance symptom-based referral of pneumonia in LRS and in community settings.

BACKGROUND In this cross-sectional, international study, we aimed to analyze vector-borne and zoonotic infections (VBZI), which are significant global threats. METHOD VBZIs' data between May 20-28, 2018 was collected. The 24 Participatingcountries were classified as lower-middle, upper-middle, and high-income. RESULTS 382 patients were included. 175(45.8%) were hospitalized, most commonly in Croatia, Egypt, and Romania(P = 0.001). There was a significant difference between distributions of VBZIs according to geographical regions(P < 0.001). Amebiasis, Ancylostomiasis, Blastocystosis, Cryptosporidiosis, Giardiasis, Toxoplasmosis were significantly more common in the Middle-East while Bartonellosis, Borreliosis, Cat Scratch Disease, Hantavirus syndrome, Rickettsiosis, Campylobacteriosis, Salmonellosis in Central/East/South-East Europe; Brucellosis and Echinococcosis in Central/West Asia; Campylobacteriosis, Chikungunya, Tick-borne encephalitis, Visceral Leishmaniasis, Salmonellosis, Toxoplasmosis in the North-Mediterranean; CCHF, Cutaneous Leishmaniasis, Dengue, Malaria, Taeniasis, Salmonellosis in Indian Subcontinent; Lassa Fever in West Africa. There were significant regional differences for viral hemorrhagic fevers(P < 0.001) and tick-borne infections(P < 0.001), and according to economic status for VBZIs(P < 0.001). The prevalences of VBZIs were significantly higher in lower-middle income countries(P = 0.001). The most similar regions were the Indian Subcontinent and the Middle-East, the Indian Subcontinent and the North-Mediterranean, and the Middle-East and North-Mediterranean regions. CONCLUSIONS Regional and socioeconomic heterogeneity still exists for VBZIs. Control and eradication of VBZIs require evidence-based surveillance data, and multidisciplinary efforts.

Scholastic factors (academic achievement) are hypothesized to be important determinants of health-related behaviors in adolescents, but there is a lack of knowledge on their influence on physical activity levels (PAL), especially considering the COVID-19 pandemic and the imposed lockdown. This study aimed to investigate the associations between scholastic factors and PAL before and during the pandemic lockdown. The participants were adolescents form Bosnia and Herzegovina (n = 525, 46% females), who were observed prospectively at the baseline (before the pandemic lockdown) and during the lockdown in 2020 (follow-up). The scholastic factors (grade point average, behavioral grade, school absences, unexcused absences) were evidenced at the baseline (predictors). The outcome (PAL) was evaluated using the Physical Activity Questionnaire for Adolescents at the baseline and the follow-up. Gender, age, parental/familial conflict, and sport participation were observed as confounders. No significant influence of the predictors on PAL were evidenced at the baseline or at the follow-up. The scholastic variables were significantly associated with the changes of PAL which occurred due to pandemic lockdown, with a lower risk for negative changes in PAL among adolescents who were better in school (OR = 0.56, 95%CI: 0.34–0.81, and OR = 0.66, 95%CI: 0.34–0.97, for the grade point average and behavioral grade, respectively). Students who do well in school are probably more aware of the health benefits of proper PAL, and therefore are devoted to the maintenance of their PAL even during the home-confinement of lockdown. Public health authorities should focus more on helping adolescents to understand the importance and benefits of proper PAL throughout the school system.

Miltefosine is an alkylphosphocholine agent with a broad spectrum of antiparasitic properties. For over two decades now, miltefosine remains the first and only oral drug licensed and used in treatment of the neglected tropical disease leishmaniasis. An extensive review on the pharmacology of miltefosine was last published in 2012, while additional data on the clinical pharmacokinetics (PK) and pharmacodynamics (PD) of miltefosine became available in the past decade, along with ongoing and future studies in this area. Miltefosine PK are characterized by slow absorption and elimination resulting in accumulation of drug in plasma until the end of treatment. Several recent studies established exposure-response relationships for various treatment regimens of miltefosine in the treatment of visceral and cutaneous leishmaniasis, leading to the identification of PK parameters predictive of clinical relapse and outcome. This review provides an update on the most recent developments in the area of clinical pharmacology of miltefosine, including a discussion of the current dosing regimens.

Healthcare users and providers increasingly utilize social media to interact with one another. For a future understanding of when and how these interactions supplement or replace offline doctor-patient interactions, it is essential to understand who interacts, about what, and how these interactions can be categorized in a taxonomy. We draw on affordance theory and employ a mixed-methods approach to study social media interactions among healthcare users and providers. We first engage in qualitative content analysis, which is followed by cluster analysis. We identify five archetypal interactions and categorize these in a taxonomy that adds to current literature on how social media is utilized in the healthcare context. We also provide a clear and systematic overview of the interactions in different social media categories that can stimulate future research regarding doctor-patient interactions. Furthermore, we identify a new and distinct type of social media enabled interaction in healthcare, namely lifestyle support, focusing on prevention.

Abstract In recent years, significant achievements have been made with respect to the development of SF6-free gas insulated substations (GIS). In parallel, the interest in installing SF6-free GIS by utilities increased steadily and tenders for new substations or upgrades, which regularly also include alternative technologies. The excellent performance of SF6 was unequivocally accepted by all vendors and users so that the community became used to single technology solutions. This is no longer the case with alternative gas mixtures, and multiple technological solutions are available. However, from the present body of literature it is not possible to make a full and comparative evaluation of different alternative gas switchgear, i.e. circuit-breakers and disconnectors. Thus, the High-Voltage Laboratory of ETH Zürich started investigations and measurements of basic experiments that allow an unbiased comparison of properties of alternative gas mixtures relevant for switching. The two main purposes of these investigations are to define a set of measurements that allow an estimation and comparison of switching performance with different gas mixtures, independent of a specific interruption nozzle geometry, drive system, electrostatic design, and other design specific features, and to perform (some of) these measurements comparing pure SF6, with air, pure CO2, CO2/O2 mixture, and further specific gas mixtures that are currently proposed by manufacturers for SF6 replacement. The basic analysis behind the definition of measurements will be given in detail and the design principles of the chosen test devices and the derived test currents and diagnostics will be introduced. Test results themselves will not be given, rather they will be the subject of separate future publications.