62 research outputs found

    Somatotypes and hand-grip strength analysis of elite cadet sambo athletes

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    The objectives of this research were to establish somatotype and hand-grip strength between elite cadet male and female sambo athletes divided by weight categories. A total of 97 elite cadet sambo athletes, participants of the World Cadets Sambo Championships 2018 participated in the study. Male and female sambo athletes were divided by official weight categories. Anthropometrical variables were taken in order to calculate somatotypes and hand-grip strength. A one-way analysis of variance and Tukey's post hoc tests were used to compare group differences by weight categories. Results of this study provide the first description of somatotype and hand-grip strength of elite male and female cadet sambo athletes in relation to weight category. A typical somatotype in male sambo athletes was endomorphic mesomorphs with a predominance of musculoskeletal tissue, while female athletes differed concerning weight category. Overall, an increase in handgrip strength across weight categories was noted. Hand-grip strength increases linearly from the lightest to the heaviest weight category except in -66 and -84 kg in male athletes. Differences in handgrip strength of female athletes were detected between the lightest group and last six groups in all three variables in favor of last six as well as -44 and kg -48 kg compared with the heaviest. To the best of our knowledge, this study provides the first normative data of somatotype and hand-grip strength analyses in relation to age, gender, and weight categories of cadet sambo athletes. The anthropometric profile of sambo athletes changed according to their weight category. Mesomorphy was the most dominant somatotype component in male athletes, while female had three different types of somatotype component in relation to weight category. In conclusion, we found differences in hand-grip strength related to weight category, which can be linked to the muscle mass of athletes. Future studies should focus on somatotype and strength handgrip values of international compared to national level sambo athletes

    Prevalence and clinical implications of the HPV16 infection in oral cancer in Montenegro - Evidence to support the immunization program

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    Oral squamous cell carcinoma (OSCC) makes 85-95% of all malignances in the oral cavity. Increasing evidence shows that the Human Papillomaviruses (HPVs) are preferentially associated with some oropharyngeal and OSCCs, namely the genotype 16. The aim of the present study was to determine the prevalence and clinical implications of HPV16 infection in oral squamous cell carcinoma in population of Montenegro.This study included 60 patients with OSCC (localized on the lower lip, tongue or/and floor of the mouth), surgically treated at the Clinical Centre of Montenegro from 2012 to 2018. Surgically obtained formalin-fixed and paraffin-embedded specimens were used for histopathological analysis and HPV16 genome detection using standard Polymerase Chain Reaction (primers for detection of E6 gene). Each individual was further followed up for the period of three years and for different clinico-pathological characteristics, including disease free interval (DFI).The prevalence of HPV16 infection in OSCCs was 23.3% and the infection was significantly more common in female patients (P = 0.038). No significant correlation was detectable between HPV16 infection and the patients' age (P = 0.302), tumor site (P = 0.125), tumor grade (P = 0.363) and disease stage (P = 0.995). Observing the total sample the DFI was not significantly different for HPV16-positive versus HPV16-negative patients (P = 0.427), but a gender-based difference in DFI was observed, with the significantly shorter DFI (Log Rank test, P = 0.003) in HPV16 positive female patients compared to male patients (P = 0.003).The results obtained in this study provide scientific evidence for the development of national HPV vaccination program in Montenegro

    Convolutional Neural Operators for robust and accurate learning of PDEs - datasets

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    <p>We made the datasets as clean as we could. It is now clear which data sets correspong to in-distribution and out-of-distributions testings.</p><p> </p><p><strong>Please refer to out github page to get the script that loads the datasets: </strong></p><blockquote><p>https://github.com/bogdanraonic3/ConvolutionalNeuralOperator/tree/main </p></blockquote><p> </p><p>We also added additional datasets corresponding to Darcy flow (it was not there in the previous version).</p><p> </p&gt
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