Pregnancy Rates of Holstein Friesian Cows with Cavitary or Compact Corpus Luteum.

Cavitary corpora lutea are commonly observed during the estrous cycle in bovines. Since the quality of the corpus luteum (CL) is extremely important during embryo transfer when embryos are implanted into the recipient, the ultrasonographic examination of the CL is becoming more and more important in terms of the outcome of the procedure. In the present study, a total of 2477 ultrasonographic transrectal diagnoses were performed, and data were collected between the years of 2018 and 2020 in a large-scale Holstein Friesian dairy farm in Hungary. In 91.1% (n = 2257) and in 8.9% (n = 220) of the cases, compact CLs and cavitary CLs, respectively, were diagnosed at pregnancy diagnosis. The presence of a cavitary CL on the ovary at pregnancy diagnosis increased the odds of remaining open after pregnancy by 21 times compared to the presence of a compact CL (OR = 21.0, p < 0.001) in the cows. The presence of cavitary CL was not influenced either by month or season. Ovarian cysts were detected in 196 cases (8.0%) in the examined animals. The presence of a cavitary CL decreased by 9 times when an ovarian cyst was also diagnosed (OR = 9.0, 1.6% vs. 9.5%, p < 0.001). The presence of an ovarian cyst decreased the odds of established pregnancy by 81 times (OR = 81.1, p < 0.001). Based on our results, the presence of a cavitary CL between days 31 and 42 after artificial insemination is associated with a smaller chance of conception in Holstein Friesian cows. The presence of an ovarian cyst decreases the occurrence of cavitary CL and the chance of conception.

Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey.

BACKGROUND: The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences. METHODS: A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist's and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents' electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions. RESULTS: Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (p = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task (p = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen. CONCLUSIONS: Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.

Fractional calculus in mathematical oncology.

Even though, nowadays, cancer is one of the leading causes of death, too little is known about the behavior of this disease due to its unpredictability from one patient to another. Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, improvements are still necessary on these models. The primary objective of the present research is to prove the efficiency of fractional order calculus in mathematical oncology, more specifically in tumor growth modeling. For this, a generalization of the four most used differential equation models in tumor volume measurements fitting is realized, using the corresponding fractional order equivalent. Are established the fractional order Exponential, Logistic, Gompertz, General Bertalanffy-Pütter and Classical Bertalanffy-Pütter models for a treated and untreated dataset. The obtained results are compared by Mean Squared Error (MSE) with the integer order correspondent of each model. The results prove the superiority of the fractional order models. The MSE of fractional order models are reduced at least at half in comparison with the MSE of the integer order equivalent. It is demonstrated in this way that fractional order deterministic models can offer a good starting point in finding a proper mathematical model for tumor evolution prediction. Fractional calculus is a suitable method in this case due to its memory property, aspect that particularly characterizes biological processes.

Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning.

Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m-3 with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%.

Usp5, Usp34, and Otu1 deubiquitylases mediate DNA repair in Drosophila melanogaster.

Ubiquitylation is critical for preventing aberrant DNA repair and for efficient maintenance of genome stability. As deubiquitylases (DUBs) counteract ubiquitylation, they must have a great influence on many biological processes, including DNA damage response. To elucidate the role of DUBs in DNA repair in Drosophila melanogaster, systematic siRNA screening was applied to identify DUBs with a reduced survival rate following exposure to ultraviolet and X-ray radiations. As a secondary validation, we applied the direct repeat (DR)-white reporter system with which we induced site-specific DSBs and affirmed the importance of the DUBs Ovarian tumor domain-containing deubiquitinating enzyme 1 (Otu1), Ubiquitin carboxyl-terminal hydrolase 5 (Usp5), and Ubiquitin carboxyl-terminal hydrolase 34 (Usp34) in DSB repair pathways using Drosophila. Our results indicate that the loss of Otu1 and Usp5 induces strong position effect variegation in Drosophila eye following I-SceI-induced DSB deployment. Otu1 and Usp5 are essential in DNA damage-induced cellular response, and both DUBs are required for the fine-tuned regulation of the non-homologous end joining pathway. Furthermore, the Drosophila DR-white assay demonstrated that homologous recombination does not occur in the absence of Usp34, indicating an indispensable role of Usp34 in this process.

Towards personalized medicine by mathematical modeling of tumors.

In this open letter, we give some insights on the potential use of mathematical modeling in understanding cancer research. The article is written in a form that can be understood by a larger public, not only by specialists.

[Mathematical and computational methods to model chemotherapy- induced resistance]. / Matematikai és számítógépes módszerek a kemoterápiás kezelések során kialakuló rezisztencia modellezésére.

Chemotherapy plays an important role in the treatment of cancer. While clinical chemotherapy protocols can lead to remission in some patients, in many cases tumor progression occurs despite continued treatment. In the present study we summarize mathematical approaches to model tumor growth and response to treatment, focusing on anticancer therapy resistance. We present results obtained at the recently founded Cybermedical Competence Center at Óbuda University, focusing on the development of a new therapy optimization concept that aims to optimize traditional chemotherapy.

Lower Limb Amputations and Revascularisation Procedures in the Hungarian Population: A 14 Year Retrospective Cohort Study.

OBJECTIVE: The aim of this study was to assess the long term trends of lower limb amputation and revascularisation in Hungary over 14 years. METHODS: This was a retrospective cohort study that included all patients who underwent lower limb amputation or revascularisation over a 14 year period (2004-2017) in Hungary. Inpatient administrative data claims covering the entire beneficiary population were incorporated. Lower limb amputations (both minor and major) and revascularisation procedures (both open and endovascular) were identified in the claims files. Incidence rates were calculated and time trends were assessed via a generalised additive model. RESULTS: From 2004 to 2017, a total of 121 351 lower limb amputations (61 154 minor; 60 197 major) and 149 355 revascularisation procedures (89 243 open; 60 112 endovascular) were detected in 140 581 patients. The number of minor amputations decreased moderately in the last few years of the study period, while major amputations showed a slight decline (15%) beginning after 2013, which was more marked (22%) following adjustment for age. While the crude incidence of open vascular surgery procedures decreased by 31% (from 74.5/105 to 51.4/105), endovascular procedures showed growth by 79% (from 33.7/105 to 60.4/105) over the whole observation period. CONCLUSION: Observed amputation and revascularisation trends in Hungary are similar to the international experience. The major difference is a more than one decade lag in the starting point of the decline of amputations and in the move towards endovascular procedures. The number of amputations is more than twofold higher and the number of revascularisations is close to half that reported internationally. This comprehensive report of two vascular care performance indicators reveals an east/west vascular health divide in Europe and indicates the need to improve amputation prevention.

Reinforcement learning-based control of tumor growth under anti-angiogenic therapy.

BACKGROUND AND OBJECTIVES: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration. METHODS: A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient. RESULTS: The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to  ±â€¯20% of tumor growth parameters changes. CONCLUSION: The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm3 in a reasonable short period compared to the literature.

A Feature Ranking and Selection Algorithm for Brain Tumor Segmentation in Multi-Spectral Magnetic Resonance Image Data.

Accuracy is the most important quality marker in medical image segmentation. However, when the task is to handle large volumes of data, the relevance of processing speed rises. In machine learning solutions the optimization of the feature set can significantly reduce the computational load. This paper presents a method for feature selection and applies it in the context of a brain tumor detection and segmentation problem in multi-spectral magnetic resonance image data. Starting from an initial set of 104 features involved in an existing ensemble learning solution that employs binary decision trees, a reduced set of features is obtained using a iterative algorithm based on a composite criterion. In each iteration, features are ranked according to the frequency of usage and the correctness of the decisions to which they contribute. Lowest ranked features are iteratively eliminated as long as the segmentation accuracy is not damaged. The final reduced set of 13 features provide the same accuracy in the whole tumor segmentation process as the initial one, but three times faster.

Effect of physical activity on cardiac autonomic function of dairy cows on commercial dairy farms.

Interbeat interval data were collected from 219 Holstein cows in 2 smaller-scale farms and 3 larger-scale farms to investigate the effects of posture (standing vs. lying), rumination (rumination vs. no rumination) and feeding on baseline values of heart rate (HR) and heart rate variability (HRV) parameters reflecting vagal and sympathetic activity. A General Linear Model was used for detecting factors (parity, milking technology, herd size) having possible effects on HRV calculated for undisturbed lying posture. Basal values of cardiac parameters were also compared between larger and smaller-scale farms. Neither parity nor milking technology affected HRV parameters. Sympathetic activity increased in the order of lying, ruminating when lying, standing, ruminating when standing and feeding on both sizes of farms. Vagal activity decreased in that order in both lower- and larger-scale farms. Rumination caused an increase in HR and a decrease in vagal tone in both lower- and larger-scale farms and an increase in sympathetic activity during lying in both farm sizes. Basal vagal activity was lower in larger-scale farms compared to smaller-scale farms, while greater sympathetic activity was found in cows housed on larger-scale farms. Our findings demonstrate that reference values of HRV parameters in lactating dairy cows cannot be generally defined for Holstein cattle as they are affected by physical activity and herd size. Higher HR and sympathetic activity at rest in larger-scale farms compared to farms with lower cow population might be associated with higher levels of social stress and therefore should be considered as a potential welfare concern.

The Centrioles, Centrosomes, Basal Bodies, and Cilia of Drosophila melanogaster.

Centrioles play a key role in the development of the fly. They are needed for the correct formation of centrosomes, the organelles at the poles of the spindle that can persist as microtubule organizing centers (MTOCs) into interphase. The ability to nucleate cytoplasmic microtubules (MTs) is a property of the surrounding pericentriolar material (PCM). The centriole has a dual life, existing not only as the core of the centrosome but also as the basal body, the structure that templates the formation of cilia and flagellae. Thus the structure and functions of the centriole, the centrosome, and the basal body have an impact upon many aspects of development and physiology that can readily be modeled in Drosophila Centrosomes are essential to give organization to the rapidly increasing numbers of nuclei in the syncytial embryo and for the spatially precise execution of cell division in numerous tissues, particularly during male meiosis. Although mitotic cell cycles can take place in the absence of centrosomes, this is an error-prone process that opens up the fly to developmental defects and the potential of tumor formation. Here, we review the structure and functions of the centriole, the centrosome, and the basal body in different tissues and cultured cells of Drosophila melanogaster, highlighting their contributions to different aspects of development and cell division.

The Expression of Checkpoint and DNA Repair Genes in Head and Neck Cancer as Possible Predictive Factors.

DNA damage response failure may influence the efficacy of DNA-damaging treatments. We determined the expression of 16 genes involved in distinct DNA damage response pathways, in association with the response to standard therapy. Twenty patients with locoregionally advanced, squamous cell head and neck carcinoma were enrolled. The treatment included induction chemotherapy (iChT) with docetaxel, cisplatin and 5-fluorouracil followed by concomitant chemoradiotherapy (ChRT) or radiotherapy (RT) alone. The volumetric metabolic therapeutic response was determined by [18F]FDG-PET/CT. In the tumor and matched normal tissues collected before treatment, the gene expressions were examined via the quantitative real-time polymerase chain reaction (qRT-PCR). The down-regulation of TP53 was apparently associated with a poor response to iChT, its up-regulation with complete regression in 2 cases. 7 cases with down-regulated REV1 expression showed complete regression after ChRT/RT, while 1 case with REV1 overexpression was resistant to RT. The overexpression of WRN was an independent predictor of tumor relapse. Our results suggest that an altered expression of REV1 predicts sensitivity to RT, while WRN overexpression is an unfavorable prognostic factor.

Tumor Volume Estimation and Quasi-Continuous Administration for Most Effective Bevacizumab Therapy.

BACKGROUND: Bevacizumab is an exogenous inhibitor which inhibits the biological activity of human VEGF. Several studies have investigated the effectiveness of bevacizumab therapy according to different cancer types but these days there is an intense debate on its utility. We have investigated different methods to find the best tumor volume estimation since it creates the possibility for precise and effective drug administration with a much lower dose than in the protocol. MATERIALS AND METHODS: We have examined C38 mouse colon adenocarcinoma and HT-29 human colorectal adenocarcinoma. In both cases, three groups were compared in the experiments. The first group did not receive therapy, the second group received one 200 µg bevacizumab dose for a treatment period (protocol-based therapy), and the third group received 1.1 µg bevacizumab every day (quasi-continuous therapy). Tumor volume measurement was performed by digital caliper and small animal MRI. The mathematical relationship between MRI-measured tumor volume and mass was investigated to estimate accurate tumor volume using caliper-measured data. A two-dimensional mathematical model was applied for tumor volume evaluation, and tumor- and therapy-specific constants were calculated for the three different groups. The effectiveness of bevacizumab administration was examined by statistical analysis. RESULTS: In the case of C38 adenocarcinoma, protocol-based treatment did not result in significantly smaller tumor volume compared to the no treatment group; however, there was a significant difference between untreated mice and mice who received quasi-continuous therapy (p = 0.002). In the case of HT-29 adenocarcinoma, the daily treatment with one-twelfth total dose resulted in significantly smaller tumors than the protocol-based treatment (p = 0.038). When the tumor has a symmetrical, solid closed shape (typically without treatment), volume can be evaluated accurately from caliper-measured data with the applied two-dimensional mathematical model. CONCLUSION: Our results provide a theoretical background for a much more effective bevacizumab treatment using optimized administration.

Model-based angiogenic inhibition of tumor growth using modern robust control method.

Cancer is one of the most destructive and lethal illnesses of the modern civilization. In the last decades, clinical cancer research shifted toward molecular targeted therapies which have limited side effects in comparison to conventional chemotherapy and radiation therapy. Antiangiogenic therapy is one of the most promising cancer treatment methods. The dynamical model for tumor growth under angiogenic stimulator/inhibitor control was posed by Hahnfeldt et al. in 1999; it was investigated and partly modified many times. In this paper, a modified version of the originally published model is used to describe a continuous infusion therapy. In order to generalize individualized therapies a robust control method is proposed using H(∞) methodology. Uncertainty weighting functions are determined based on the real pathophysiological case and simulations are performed on different tumor volumes to demonstrate the robustness of the proposed method.

Towards unified electromagnetic tracking system assessment-static errors.

Recent advances in Image-Guided Surgery allows physicians to incorporate up-to-date, high quality patient data in the surgical decision making, and sometimes to directly perform operations based on pre- or intra-operatively acquired patient images. Electromagnetic tracking is the fastest growing area within, where the position and orientation of tiny sensors can be determined with sub-millimeter accuracy in the field created by a generator. One of the major barriers to the wider spread of electromagnetic tracking solutions is their susceptibility to ferromagnetic materials and external electromagnetic sources. The research community has long been engaged with the topic to find engineering solutions to increase measurement reliability and accuracy. This article gives an overview of related experiments, and presents our recommendation towards a robust method to collect representative data about electromagnetic trackers.