Advancing Medical Assistance: Developing an Effective Hungarian-Language Medical Chatbot with Artificial Intelligence

In recent times, the prevalence of chatbot technology has notably increased, particularly in the realm of medical assistants. However, there is a noticeable absence of medical chatbots that cater to the Hungarian language. Consequently, Hungarian-speaking people currently lack access to an automated system capable of providing assistance with their health-related inquiries or issues. Our research aims to establish a competent medical chatbot assistant that is accessible through both a website and a mobile app. It is crucial to highlight that the project’s objective extends beyond mere linguistic localization; our goal is to develop an official and effectively functioning Hungarian chatbot. The assistant’s task is to answer medical questions, provide health advice, and inform users about health problems and treatments. The chatbot should be able to recognize and interpret user-provided text input and offer accurate and relevant responses using specific algorithms. In our work, we put a lot of emphasis on having steady input so that it can detect all the diseases that the patient is dealing with. Our database consisted of sentences and phrases that a user would type into a chatbot. We assigned health problems to these and then assigned the categories to the corresponding cure. Within the research, we developed a website and mobile app, so that users can easily use the assistant. The app plays a particularly important role for users because it allows them to use the assistant anytime and anywhere, taking advantage of the portability of mobile devices. At the current stage of our research, the precision and validation accuracy of the system is greater than 90%, according to the selected test methods

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM

Morphological and molecular identification of nasopharyngeal bot fly larvae infesting red deer (Cervus elaphus) in Austria

Nasopharyngeal myiases are caused by larvae of bot flies (Diptera: Oestridae), which have evolved a high specificity for their hosts. Bot flies (n = 916) were collected from 137 (57.6 %) out of 238 red deer (Cervus elaphus) hunted in Vorarlberg and Tyrol (Western Austria). After being stored in 75 % ethanol, larvae were identified to species level and developmental stage using morphological and morphometric keys. Larvae were also molecularly characterized by polymerase chain reaction (PCR) amplification and partial sequencing of the mitochondrial cytochrome oxidase subunit I gene. Morphological and molecular analysis allowed identification of larvae as Cephenemyia auribarbis and Pharyngomyia picta. Genetic variations were also examined within the specimens collected in both geographical locations

Landscape structure affects distribution of potential disease vectors (Diptera: Culicidae)

Abstract Background Vector-pathogen dynamics are controlled by fluctuations of potential vector communities, such as the Culicidae. Assessment of mosquito community diversity and, in particular, identification of environmental parameters shaping these communities is therefore of key importance for the design of adequate surveillance approaches. In this study, we assess effects of climatic parameters and habitat structure on mosquito communities in eastern Austria to deliver these highly relevant baseline data. Methods Female mosquitoes were sampled twice a month from April to October 2014 and 2015 at 35 permanent and 23 non-permanent trapping sites using carbon dioxide-baited traps. Differences in spatial and seasonal abundance patterns of Culicidae taxa were identified using likelihood ratio tests; possible effects of environmental parameters on seasonal and spatial mosquito distribution were analysed using multivariate statistical methods. We assessed community responses to environmental parameters based on 14-day-average values that affect ontogenesis. Results Altogether 29,734 female mosquitoes were collected, and 21 of 42 native as well as two of four non-native mosquito species were reconfirmed in eastern Austria. Statistical analyses revealed significant differences in mosquito abundance between sampling years and provinces. Incidence and abundance patterns were found to be linked to 14-day mean sunshine duration, humidity, water–level maxima and the amount of precipitation. However, land cover classes were found to be the most important factor, effectively assigning both indigenous and non-native mosquito species to various communities, which responded differentially to environmental variables. Conclusions These findings thus underline the significance of non-climatic variables for future mosquito prediction models and the necessity to consider these in mosquito surveillance programmes

Molecular Identification of Hemoprotozoan Parasites in Camels (Camelus dromedarius) of Iran

Background: Although camels represent a valuable source of food, wool and hide in many countries, in-depth information about their vector-borne pathogens is scarce compared to other animals. The aim of the current study was to characterize vector-borne protozoa in the blood of dromedaries from Iran by molecular tools. Methods: From June to July 2014, 200 peripheral blood samples were collected from asymptomatic one-humped camels in two provinces of Kerman and Sistan- va-Baloochestan in central and southeastern Iran. Microscopic examination was performed on Giemsa-stained blood smears, and drops of blood were spotted on Whatman FTA® cards for further analyses. Genomic DNA was extracted from the cards, and PCR was carried out for the detection of piroplasms and trypanosomes, followed by sequence analysis of positive samples. Results: One sample was positive Trypanosoma spp. trypomastigotes in light microscopy. PCR results revealed one positive sample each with Theileria annulata and Trypanosoma evansi. Conclusion: Camels were identified as hosts for bovine Mediterranean theileriosis in the investigated area. The presence of Tr. evansi, the causative agent of surra disease, was also confirmed in camels of Iran. Further studies are recommended in order to investigate their impact on the health and productivity of camels and other livestock in this region

Screening blood-fed mosquitoes for the diagnosis of filarioid helminths and avian malaria

Abstract Background Both Dirofilaria repens and recently D. immitis are known to be endemic in Hungary. As one of several recent cases, the fatal case of a dog infested with D. immitis in Szeged, Southern Hungary, received attention from the media. Hence it was decided to catch mosquitoes in the garden where the dog lived to screen for filarioid helminths and Plasmodium spp. using molecular tools. Methods Mosquitoes were caught in Szeged, in the garden where the infected dog was kept, in July 2013 with M-360 electric mosquito traps and were stored in ethanol until further procedure. Female mosquitoes were classified to genus level by morphology. Each mosquito was homogenized and analyzed for filarioid helminths and avian malaria using standardized PCR techniques. Positive mosquito samples were further identified to species level by comparing a section of the mitochondrial COI gene to GenBank® entries. Results In this study, 267 blood-fed mosquitoes were caught in July 2013 in Szeged. Subsequent molecular screening revealed that not only D. immitis was present in the analyzed specimens but also DNA of D. repens, Setaria tundra and Plasmodium spp. was confirmed. Conclusions The analysis of blood-fed mosquitoes for the diagnosis of Dirofilaria spp. and other mosquito-borne pathogens seems to be an adequate technique to evaluate if filarioid helminths are present in a certain area. Usually only unfed female mosquitoes are analyzed for epidemiological studies. However, blood-fed mosquitoes can only be used for screening if a pathogen is present because the role of the mosquito as vector cannot be classified (blood of bitten host). Furthermore, Setaria tundra was confirmed for the first time in Hungary