Search

Freshwater crayfish are found in 20 out of 53 Prefectures of Greece (38%) and they form isolated populations in relatively pristine water bodies. Three indigenous crayfish species (ICS) occur in Greek waters: Astacus astacus, Astacus leptodactylus and Austropotamobius torrentium, as well as one non-indigenous crayfish species (NICS) Pacifastacus leniusculus. Greece represents probably the most southern natural distribution limit for A. astacus and A. torrentium in the world. Concerning NICS, P. leniusculus was imported, at least in two cases, from Sweden and Germany during the early and late 1980’s respectively, in order to replace A. astacus stocks impoverished by human activities. Recently, Cherax quadricarinatus specimens have been seen in a restaurant’s aquarium and pet shop, raising fears about uncontrolled imports of alien pet species. During our 2004-2005 survey, we found that P. leniusculus established not only a thriving population in the artificial Lake Agra (Region of Central Macedonia), but also they co-occur with A. torrentium there. It is also suspected that A. astacus may co-occur there with these two species, as three large male specimens of A. astacus were caught in a creek close to the lake. Concerning A. leptodactylus, there are three sets of preserved samples from the River Evros (state border with Turkey in Thrace today), dated back to the 19th century, but no recent information about occurrence was available. But the accidental catch of six specimens of A. leptodactylus during May 2005 in that river re-documented the occurrence of the species. Finally, a deep-dwelling A. torrentium population with bluish legs was found to live in depths up to 7,800 meters inside the Aggitis Cave in northeastern Greece. There is a lot of work to be done on geographic distribution, population dynamics, genetics, reproduction, nutrition, diseases, and farming of crayfish species in Greece. Moreover, several actions should be undertaken to reform the existing legislation, in order to protect the native species and their habitats.

There is a trend of significantly more people to be employed in the agro-food sector in Southern Europe lately, mainly due to the changes occurred in the E.U. economy the last years. However, there is definitely a challenge that has to be met in order to obtain competitive agricultural products, and has to do with the management of natural resources (soil, water and energy). Eleagro (developed by GeoAnalysis S.A.) is developing a tool for remote crop monitoring with the use of Unmanned Aerial Vehicles (UAVs) and satellites, aiming at using these technologies for precision agriculture management. Remote sensing is advantageous over traditional methods of plant and soil sampling, because it offers the potential for quick area mapping of soil and plant properties and allows for quantification of crop variation (e.g. nutrient deficiency or sufficiency, pest and disease attacks and infestations, water stress and faulty irrigation equipment detection and weed infestation). Remote sensing in agriculture is predicted to be in the near future the main methodology of data acquisition from agricultural field parcels, due to the physical evolution of satellite technology providing better spatial resolution and lower cost image acquisition. The creation of crop information with the use of remote sensing techniques is expected to aid in the connection between the crop management knowledge obtained through long experimentation and the spatial analysis of the problems that need to be managed. Thus, treatments available at the disposal of the growers have to be targeted towards specific management zones in the field and should not be broadcasted uniformly over the whole area, increasing the cost of plant protection and damaging the environment. The same principle applies for plant nutrition. GeoAnalysis S.A. also, along with crop monitoring, is developing pest management techniques, using natural or bio-physical means for the reduction of the agro-ecosystem pathogenesis. The present work has assessed thermal imagery and plant analysis as tools for early detection of Verticillium wilt, in accordance with an 11-point calibration scale, which was based on visual observation of the disease symptoms. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study is to emphasize the necessity and possibilities of early intervention and physiotherapy rehabilitation of premature infants, as they are reflected in five-year olds according to the gross motor function measure (GMFM) and gross motor performance measure (GMPM) evaluation scales for gross function and quality of movement. In addition, the present study examined the importance of using assessment tools for children who have received or not therapeutic intervention, through which both the child's abilities and appropriateness of the help received by him/her are evaluated based on individual needs., Material and Methods: Our specific exploratory process was carried out through a literature review as well as a process of primary research, in order to obtain and collect all necessary information and data which would finally lead us to the nearest and best conclusions. Our goal was to collect 20 complete and graded GMFM and 20 GMPM assessment tests, so that our research was based on a satisfactory sample of participants. In the next year, the scores received by participants were recorded and analyzed using the statistical software program SPSS (Superior Performance Software System). The analysis was performed through descriptive and inductive statistical analysis in the SPSS statistical program. Specifically, the SPSS version 20.0 and specifically the one-way ANOVA variance analysis and the Tukey's parametric test were used for the statistical analysis of the results., Results: The use of physiotherapy care was found to be important for premature infants, as the level of statistical significance was set at p <0.05, while the data were reported as average. The final overall scores of the evaluations (on average) were higher in the group who received early intervention and specialized physiotherapy intervention from the first day after birth., Conclusions: The effect of physiotherapy on premature infants is positive in five-year-old children, who have completed almost all their developmental stages at that age. These benefits become apparent not only in a better handling of kinetic patterns and sequences but also in the ability to execute kinetic models, conquer developmental motor stages and perform them with quality in terms of alignment, sequence, synergy of movements, separation and stability.

Background: Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship. Data sets of consistent and reliable inter-center dosimetry findings are required to characterize this relationship., Purpose: We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177 Lu-DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data., Methods: Pretreatment and posttreatment data for 20 patients with NETs treated with 177 Lu-DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects., Results: We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68 Ga-DOTATOC positron emission tomography (PET)/CT and posttherapy 177 Lu-DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68 Ga-DOTATOC PET/CT and any posttherapy 177 Lu-DOTATATE treatment cycle SPECT/CT scans as well as any 177 Lu-DOTATATE SPECT/CT treatment cycle and the consequent 177 Lu-DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from -0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68 Ga-DOTATOC PET/CT and first 177 Lu-DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%-96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 → C3 in spleen and left kidney, and Ga,C.1 → C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet-based features proved to have high correlated predictive value, whereas non-linear-based ML regression algorithms proved to be more capable than the linear-based of producing precise prediction in our case., Conclusions: The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision-making, especially regarding dose escalation issues., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Objective: A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for accurate prediction of absorbed doses per organ prior to the imaging acquisition considering only personalized anatomical characteristics of any new pediatric patient. Approach: GATE Monte Carlo simulations were performed using a population of computational pediatric models to calculate the specific absorbed dose rates (SADRs) in several organs. A simulated dosimetry database was developed for 28 pediatric phantoms (age range 2-17 years old, both genders) and 5 different radiopharmaceuticals. Machine Learning regression models were trained on the produced simulated dataset, with leave one out cross validation for the prediction model evaluation. Hyperparameter optimization and ensemble learning techniques for a variation of input features were applied for achieving the best predictive power, leading to the development of a SADR prediction toolkit for any new pediatric patient for the studied organs and radiopharmaceuticals. Main results . SADR values for 30 organs of interest were calculated via Monte Carlo simulations for 28 pediatric phantoms for the cases of five radiopharmaceuticals. The relative percentage uncertainty in the extracted dose values per organ was lower than 2.7%. An internal dosimetry prediction toolkit which can accurately predict SADRs in 30 organs for five different radiopharmaceuticals, with mean absolute percentage error on the level of 8% was developed, with specific focus on pediatric patients, by using Machine Learning regression algorithms, Single or Multiple organ training and Artificial Intelligence ensemble techniques. Significance: A large simulated dosimetry database was developed and utilized for the training of Machine Learning models. The developed predictive models provide very fast results (<2 s) with an accuracy >90% with respect to the ground truth of Monte Carlo, considering personalized anatomical characteristics and the biodistribution of each radiopharmaceutical. The proposed method is applicable to other medical dosimetry applications in different patients' populations., (Creative Commons Attribution license.)

Background: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers. These areas can be (and already are) supported by High-Performance-Computing (HPC), High-Performance Data Analytics (HPDA), and AI (together "HPC+")., Objective: This overview article aims to show state-of-the-art examples of studies supported by the National Competence Centres (NCCs) in HPC+ within the EuroCC project, employing HPC, HPDA and AI for medical applications., Method: The included studies on different applications of HPC in the medical sector were sourced from the National Competence Centres in HPC and compiled into an overview article. Methods include the application of HPC+ for medical image processing, high-performance medical and pharmaceutical data analytics, an application for pediatric dosimetry, and a cloud-based HPC platform to support systemic pulmonary shunting procedures., Results: This article showcases state-of-the-art applications and large-scale data analytics in the medical sector employing HPC+ within surgery, medical image processing in diagnostics, nutritional support of patients in hospitals, treating congenital heart diseases in children, and within basic research., Conclusion: HPC+ support scientific fields from research to industrial applications in the medical area, enabling researchers to run faster and more complex calculations, simulations and data analyses for the direct benefit of patients, doctors, clinicians and as an accelerator for medical research.

Temporal lobe epilepsy (TLE) has been modeled in mice using pilocarpine induction, with variable results depending on specific strains. To allow efficient xenotransplantation for the purpose of optimizing potential cell-based therapy of human TLE, we have determined the optimal dosing strategy to produce spontaneous recurring seizures in immunodeficient NodScid mice. Multiple 100mg/kg injections of pilocarpine have been shown to be more effective than single 300-400mg/kg injections for inducing spontaneous seizures in NodScid mice. Under our optimal conditions, 88.1 ± 2.9% of the mice experienced status epilepticus (SE) with a survival rate of 61.8 ± 5.9%. Surviving SE mice displayed spontaneous recurrent seizures at a frequency of 2.8 ± 0.9 seizures/day for a duration of 41.1 ± 3.5s. The widely used method of a single injection of pilocarpine was significantly less efficient in inducing seizures in NodScid mice. Therefore, we have determined that a multiple injection "ramping up" of 100mg/kg of pilocarpine is optimal for inducing TLE-like spontaneous seizures in NodScid mice. Using this method, mice with SE efficiently developed SRS and expressed mossy fiber sprouting, a signature histopathological feature of TLE., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Seizure disorders debilitate more than 65,000,000 people worldwide, with temporal lobe epilepsy (TLE) being the most common form. Previous studies have shown that transplantation of GABA-releasing cells results in suppression of seizures in epileptic mice. Derivation of interneurons from human pluripotent stem cells (hPSCs) has been reported, pointing to clinical translation of quality-controlled human cell sources that can enhance inhibitory drive and restore host circuitry. In this study, we demonstrate that hPSC-derived maturing GABAergic interneurons (mGINs) migrate extensively and integrate into dysfunctional circuitry of the epileptic mouse brain. Using optogenetic approaches, we find that grafted mGINs generate inhibitory postsynaptic responses in host hippocampal neurons. Importantly, even before acquiring full electrophysiological maturation, grafted neurons were capable of suppressing seizures and ameliorating behavioral abnormalities such as cognitive deficits, aggressiveness, and hyperactivity. These results provide support for the potential of hPSC-derived mGIN for restorative cell therapy for epilepsy., (Copyright © 2014 Elsevier Inc. All rights reserved.)