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Sommer Mcmillan posted an update 6 months ago
Gliomas are often associated with symptoms including seizures. Most patients with high-grade gliomas are treated with radiotherapy or radio-chemotherapy. Since irradiation causes inflammation, it may initially aggravate symptoms. Studies focusing on seizure activity during radiotherapy for gliomas are not available. Such knowledge may improve patient monitoring and anti-epileptic treatment. This study evaluates seizure activity during radiotherapy for high-grade gliomas.
The primary objective this prospective interventional study is the evaluation of seizure activity during a course of radiotherapy for high-grade gliomas. Progression of seizure activity is defined as increased frequency of seizures by > 50%, increased severity of seizures, or initiation/increase by ≥25% of anti-epileptic medication. Seizure frequency up to 6 weeks following radiotherapy and electroencephalography activity typical for epilepsy will also be evaluated. Patients keep a seizure diary during and up to 6 weeks following radiofor a larger prospective trial and will likely lead to closer patient monitoring and better anti-epileptic treatment.
clinicaltrials.gov ( NCT04552756 ); registered on 16th of September, 2020.
clinicaltrials.gov ( NCT04552756 ); registered on 16th of September, 2020.
The dose perturbation effect of immobilization devices is often overlooked in intensity-modulated radiation therapy (IMRT) for breast cancer (BC). This retrospective study assessed the dosimetric effects of supine immobilization devices on the skin using a commercial treatment planning system.
Forty women with BC were divided into four groups according to the type of primary surgery groups A and B included patients with left and right BC, respectively, who received 50 Gy radiotherapy in 25 fractions after radical mastectomy, while groups C and D included patients with left and right BC, respectively, who received breast-conservation surgery (BCS) and 40.05 Gy in 15 fractions as well as a tumor bed simultaneous integrated boost to 45 Gy. A 0.2-cm thick skin contour and two sets of body contours were outlined for each patient. Dose calculations were conducted for the two sets of contours using the same plan. The dose differences were assessed by comparing the dose-volume histogram parameter results and by pr dose attenuation and skin dose increment.
This study does not report on interventions in human health care.
This study does not report on interventions in human health care.
Green care farms, which offer care for people with dementia in a farm setting, have been emerging in the Netherlands. The aim of this study was to 1) implement green care farms which use rice farming in Japan, 2) explore the positive experiences of rice farming care, and 3) compare the effect of rice farming care to that of usual care on well-being and cognitive ability.
We developed a new method of green care farm in Japan which uses rice farming, a farming that is practiced all over East Asia. The participants were 15 people with dementia (mean age = 75.6 ± 9.8 years) who participated in a one-hour rice farming care program once a week for 25 weeks. We also collected qualitative data on the positive experiences of study participants after the program. As a reference data, we also collected the corresponding data of the usual care group which included 14 people with dementia (mean age = 79.9 ± 5.8 years) who were attending the near-by day-care.
The mean participation rate on the rice farming care group was 72.1%. After the intervention, participants reported experiencing enjoyment and connection during the program. It also changed the staff’s view on dementia. The green care farm group showed a significant improvement in well-being but no significant difference in cognitive function compared to the usual care group.
Green care farms by using rice farming is promising care method which is evidence-based, empowerment-oriented, strengths-based, community-based dementia service, which also delivers meaningful experience for the people with dementia in East Asia.
UMIN, UMIN000025020 , Registered 1 April 2017.
UMIN, UMIN000025020 , Registered 1 April 2017.
Aedes aegypti mosquito, the principal global vector of arboviral diseases, lays eggs and undergoes larval and pupal development to become adult mosquitoes in fresh water (FW). It has recently been observed to develop in coastal brackish water (BW) habitats of up to 50% sea water, and such salinity tolerance shown to be an inheritable trait. Genomics of salinity tolerance in Ae. aegypti has not been previously studied, but it is of fundamental biological interest and important for controlling arboviral diseases in the context of rising sea levels increasing coastal ground water salinity.
BW- and FW-Ae. aegypti were compared by RNA-seq analysis on the gut, anal papillae and rest of the carcass in fourth instar larvae (L4), proteomics of cuticles shed when L4 metamorphose into pupae, and transmission electron microscopy of cuticles in L4 and adults. Genes for specific cuticle proteins, signalling proteins, moulting hormone-related proteins, membrane transporters, enzymes involved in cuticle metabolism, and covide new information on molecular and ultrastructural changes associated with salinity adaptation in FW mosquitoes. Changes in cuticles of larvae and adults of salinity-tolerant Ae. aegypti are expected to reduce the efficacy of insecticides used for controlling arboviral diseases. Expansion of coastal BW habitats and their neglect for control measures facilitates the spread of salinity-tolerant Ae. D-Cycloserine mouse aegypti and genes for salinity tolerance. The transmission of arboviral diseases can therefore be amplified in multiple ways by salinity-tolerant Ae. aegypti and requires appropriate mitigating measures. The findings in Ae. aegypti have attendant implications for the development of salinity tolerance in other fresh water mosquito vectors and the diseases they transmit.
With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads.
In this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains.
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