The long-term effects of the biolicit procedure for original and biosimilar GCSF and EPO products in Hungary

Aim: We aimed to analyse the long-term effects of the biosimilar bids of NEAK regarding GCSF and EPO drugs. Data and Methods: Our analysis is based on the data derived from the nationwide pharmaceutical database of NEAK. The treatment days and reimbursement figures of 12 months periods has been compared, from 01.07.2011-30.06.2014. and 01.07.2017.-30.06.2019. Results: In the 12 months preceding the price competition bid of biosimilar products 13974 patients received G-CSF, 7.49 billion HUF health insurance reimbursement has been paid, 5 years later the turnover of the product increased (314760, 340100 DOT value), whereas the reimbursement decreased (2.03 billion HUF, 1.95 billion HUF respectively). 12 months before the biosimial price competition, 4167 patients were treated with erythropoietin, resulting in 2.33 billion HUF of reimbursements, in the last 2 years the turnover increased (48727, 50813 DOT value respectively) with decreased reimbursement (1.004 billion HUF, 1.002 billion HUF respectively). Conclusions: The long-range analyses price competition bid of biosimilar products revealed that in case of products the health insurance reimbursement decreased despite the elevated turnover in a longer period as well. During the years following the start of the price competition bid the switch from original products to biosimilars could be observed

The anti-inflammatory effect of dimethyl trisulfide in experimental acute pancreatitis

Various organosulfur compounds, such as dimethyl trisulfide (DMTS), display anti-inflammatory properties. We aimed to examine the effects of DMTS on acute pancreatitis (AP) and its mechanism of action in both in vivo and in vitro studies. AP was induced in FVB/n mice or Wistar rats by caerulein, ethanol-palmitoleic acid, or L-ornithine-HCl. DMTS treatments were administered subcutaneously. AP severity was assessed by pancreatic histological scoring, pancreatic water content, and myeloperoxidase activity measurements. The behaviour of animals was followed. Pancreatic heat shock protein 72 (HSP72) expression, sulfide, and protein persulfidation were measured. In vitro acinar viability, intracellular Ca 2+ concentration, and reactive oxygen species production were determined. DMTS dose-dependently decreased the severity of AP. It declined the pancreatic infiltration of leukocytes and cellular damage in mice. DMTS upregulated the HSP72 expression during AP and elevated serum sulfide and low molecular weight persulfide levels. DMTS exhibited cytoprotection against hydrogen peroxide and AP-inducing agents. It has antioxidant properties and modulates physiological but not pathophysiological Ca 2+ signalling. Generally, DMTS ameliorated AP severity and protected pancreatic acinar cells. Our findings indicate that DMTS is a sulfur donor with anti-inflammatory and antioxidant effects, and organosulfur compounds require further investigation into this potentially lethal disease

Examination of fluoroquinolone susceptibility of Mycoplasma bovis strains by molecular biological methods

A Mycoplasma bovis sejtfal nélküli, szarvasmarhákat fertőző kórokozó, mely jelentős gazdasági károkat okoz világszerte, hús- és tejhasznosítású állományokban egyaránt. Jelenleg nem áll rendelkezésre hatékony vakcina ellene, ezért az antibiotikumos kezelés a védekezés legfontosabb eszköze. A kezelésre alkalmas antibiotikumok között szerepelnek a fluoroquinolonok, melyekkel szemben a széleskörű és okszerűtlen használatuk miatt sok esetben rezisztencia alakult ki. A gyors és hatékony kezelés érdekében szükségessé vált egy, a rezisztencia kimutatására alkalmas, gyors módszer kifejlesztése. Vizsgálatunk célja olyan megbízható és olcsó molekuláris biológiai teszt kifejlesztése volt, mely képes a fluoroquinolon rezisztenciával összefüggő pontmutációk, így a fluoroquinolon rezisztens M. bovis törzsek gyors elkülönítésére

Hogyan élnek az európai nyugdíjasok? Egyéni szintű különbözőségek vizsgálata SHARE-adatok alapján

A népesség elöregedésének problémája a 21. század egyik legnagyobb kihívása. Míg 1996-ban a nyugdíjasok száma a teljes népességhez viszonyítva az Európai Unióban 14,97 százalék volt, 2018-ra ez a szám 19,85 százalékra nőtt. Számos tanulmány szól a társadalmi idősödés különböző oldalairól, a magyar gazdasági, demog­rá­fiai szakirodalomban mégsem kap elég figyelmet a 65 év feletti korosztály. Tanulmányunkban ezt a szűkösséget kívánjuk enyhíteni az európai nyugdíjasok életminőségének bemutatásával, különbözőségeik feltárásával. Vizsgálatunkhoz a SHARE multidiszciplináris adatbank kérdőíves adatait használtuk fel, adatbázisunkat a 2017-es hullámból úgy állítottuk össze, hogy az 24 európai ország 17 726 nyugdíjasának adatait tartalmazza demográfiai ismérveikre, iskolázottságukra, egészségi állapotukra és befektetési szokásaikra vonatkozóan. Az adatok tükrében négy kutatási kérdés alapján vizsgálódtunk, és azt találtuk, hogy az Európai Unió nyugdíjasai általában jelentős különbségeket mutatnak családi állapotuk, lakóhelyük, iskolázottságuk, egészségi állapotuk és befektetési szokásaik szerint.* Journal of Economic Literature (JEL) kód: G53, I36, D14

Impact Evaluation of Score Classes and Annotation Regions in Deep Learning-Based Dairy Cow Body Condition Prediction

Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aims to investigate the accuracy with which supervised machine learning, specifically a deep convolutional neural network (CNN), can be used to retrieve body condition score (BCS) classes estimated by an expert. We recorded images of animals’ rumps in three large-scale farms using a simple action camera. The images were annotated with classes and three different-sized bounding boxes by an expert. A CNN pretrained model was fine-tuned on 12 and 3 BCS classes. Training in 12 classes with a 0 error range, the Cohen’s kappa value yielded minimal agreement between the model predictions and ground truth. Allowing an error range of 0.25, we obtained minimum or weak agreement. With an error range of 0.5, we had strong or almost perfect agreement. The kappa values for the approach trained on three classes show that we can classify all animals into BCS categories with at least moderate agreement. Furthermore, CNNs trained on 3 BCS classes showed a remarkably higher proportion of strong agreement than those trained in 12 classes. The prediction precision when training with various annotation region sizes showed no meaningful differences. The weights of our trained CNNs are freely available, supporting similar works

Bacterial colony size growth estimation by deep learning

Abstract The bacterial growth rate is important for pathogenicity and food safety. Therefore, the study of bacterial growth rate over time can provide important data from a medical and veterinary point of view. We trained convolutional neural networks (CNNs) on manually annotated solid medium cultures to detect bacterial colonies as accurately as possible. Predictions of bacterial colony size and growth rate were estimated from image sequences of independent Staphylococcus aureus cultures using trained CNNs. A simple linear model for control cultures with less than 150 colonies estimated that the mean growth rate was 60.3 $$\mu m/h$$ μ m / h for the first 24 h. Analyzing with a mixed effect model that also takes into account the effect of culture, smaller values of change in colony size were obtained (control: 51.0 $$\mu m/h$$ μ m / h , rifampicin pretreated: 36.5 $$\mu m/h$$ μ m / h ). An increase in the number of neighboring colonies clearly reduces the colony growth rate in the control group but less typically in the rifampicin-pretreated group. Based on our results, CNN-based bacterial colony detection and the subsequent analysis of bacterial colony growth dynamics might become an accurate and efficient tool for bacteriological work and research

Mesterséges neurális hálózatok az állatitermék-előállításban

A szerzők bemutatják a mesterséges intelligencia használatának lehetőségeit, amelynek a térnyerése az agráriumot sem kerüli el. A mesterséges neurális háló- zat az agráriumbeli gépi tanulási módszerek között a leghatékonyabb gépi tanulási eszköz. Az mesterséges neurális hálózatok bonyolult matematikai modellek, amelyek betaníthatók az adatokban (pl. képekben) rejlő mintázatok (pl. állati testrészek) felismerésére. A tanítást követően pedig e mintázatok predikciójára használhatók. Jelen közleményükben a szerzők összefoglalják az mesterséges neurális hálózat általános szerkezetét és működését. Bemutatnak továbbá felhasználási lehetőségeket kifejezetten az állatitermék-előállítás területéről vett példákon keresztül. | The rise of artificial intelligence (AI) is not going unnoticed in the agricultural sector. The processing of the large amounts of data (’big data’) generated in animal production is increasingly being done using artificial intelligence, particularly machine learning (ML). Machine learning is a branch of AI, in which algorithms are automatically trained to solve a task of interest using a given dataset. There are several sub-areas of ML, of which we focus on artificial neural networks (ANNs), the most successfully used in agriculture. The basic units of an ANN are artificial neurons. These are connected to each other similarly to synapses in the brain, forming a network. ANNs can be considered complex mathematical models that can make predictions from given data after a learning process, taking into account millions of parameters. Because they are pretty flexible, these networks have a wide range of applications in many fields. One such field is a subset of agriculture, namely animal production. In our work, we outline the general structure and operation of ANNs. We provide insight into the metrics widely used to indicate the accuracy of prediction and their calculation methods. Possible applications are illustrated with examples specifically from the field of food production. The wide range of applications is illustrated by the fact that the works cited also respond to the challenges faced by aquacultures and beekeepers, in addition to the problems of cattle, pig and poultry farms. Despite their many good features, ANNs cannot solve all problems, regardless of type. Therefore, in our work we also concern about the limitations of the method. Our work contributes to the definition of artificial intelligence, machine learning, and artificial neural networks in the context of agriculture

Statistical control charts in the animal production

SUMMARY Background: In our age, the information society, the computer based technol ogies, including the data analytical procedures are part of each sites of the life. In the production animal sector more and more data are generated day by day. Unfortunately, only a small piece of this huge amount of data is transformed to information, that can be interpreted, used by professionals in decisions. Objectives: The present work demonstrates a relatively simple statistical approach that may help to improve the production, animal health and welfare measures in animal production sector. Materials and methods: Since the 1920s in the industry sector the Statistical Process Control (SPC) is a widely used toolbox to help the producers to improve the efficiency and profitability of production processes. The components of the SPC toolbox are the „magnificent seven”: histogram, check sheet, pareto chart, cause-and-effect diagram, defect concentration diagram, scatter diagram, con trol chart. The last one (also called as Shewhart chart) is the most widely used tool of SPC. Through a case study on piglet weaning weight the authors summa rizes the most important momentums of the creation and interpretation of the control charts. In the example a real world based simulated dataset was used to construct the control charts. Beside this a short review is presented to demon strate control chart applications in animal production areas. Results and Discussion: The presented case study helps the reader to under stand how the statistical control charts can improve the surveillance of animal production and animal health. The authors emphasise that in the „big data” age it is necessary to develop the computational, data analytical skills of veter inarians working on farms, to be able to convert the accumulating raw data to professionally usable information

A glimpse of antimicrobial resistance gene diversity in kefir and yoghurt

Abstract Antimicrobial resistance (AMR) is a global threat gaining more and more practical significance every year. The main determinants of AMR are the antimicrobial resistance genes (ARGs). Since bacteria can share genetic components via horizontal gene transfer, even non-pathogenic bacteria may provide ARG to any pathogens which they become physically close to (e.g. in the human gut). In addition, fermented food naturally contains bacteria in high amounts. In this study, we examined the diversity of ARG content in various kefir and yoghurt samples (products, grains, bacterial strains) using a unified metagenomic approach. We found numerous ARGs of commonly used fermenting bacteria. Even with the strictest filter restrictions, we identified ARGs undermining the efficacy of aminocoumarins, aminoglycosides, carbapenems, cephalosporins, cephamycins, diaminopyrimidines, elfamycins, fluoroquinolones, fosfomycins, glycylcyclines, lincosamides, macrolides, monobactams, nitrofurans, nitroimidazoles, penams, penems, peptides, phenicols, rifamycins, tetracyclines and triclosan. In the case of gene lmrD, we detected genetic environment providing mobility of this ARG. Our findings support the theory that during the fermentation process, the ARG content of foods can grow due to bacterial multiplication. The results presented suggest that the starting culture strains of fermented foods should be monitored and selected in order to decrease the intake of ARGs via foods