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[Effectiveness and also basic safety involving long-term levetiracetam treatment method within people with refractory epilepsy].
Broad-spectrum antivirals are more needed than ever to provide treatment options for novel emerging viruses and for viruses that lack therapeutic options or have developed resistance. A large number of viruses rely on charge-dependent non-specific interactions with heparan sulfate (HS), a highly sulfated glycosaminoglycan (GAG), for attachment to cell surfaces to initiate cell entry. As such, inhibitors targeting virion-HS interactions have potential to have broad-spectrum antiviral activity. Previous research has explored organic and inorganic small molecules, peptides, and GAG mimetics to disrupt virion-HS interactions. Here we report antiviral activities against both enveloped (the herpesvirus human cytomegalovirus) and non-enveloped (adenovirus) DNA viruses for four defined marine sulfated glycans a sulfated galactan from the red alga Botryocladia occidentalis; a sulfated fucan from the sea urchin Lytechinus variegatus, and a sulfated fucan and a fucosylated chondroitin sulfate from the sea cucumber Isostichopus badionotus. As evidenced by gene expression, time of addition, and treatment/removal assays, all four novel glycans inhibited viral attachment and entry, most likely through interactions with virions. The sulfated fucans, which both lack anticoagulant activity, had similar antiviral profiles, suggesting that their activities are not only due to sulfation content or negative charge density but also due to other physicochemical factors such as the potential conformational shapes of these carbohydrates in solution and upon interaction with virion proteins. The structural and chemical properties of these marine sulfated glycans provide unique opportunities to explore relationships between glycan structure and their antiviral activities.Protein kinases are involved in the transfer of phosphate group to serine, threonine, and tyrosine residues of a target protein. With No Lysine (WNK) kinase is a member of the serine/threonine protein kinase family, which has conserved catalytic lysine (K) residue in subdomain I instead of being in subdomain II.The WNKs family members in plants are stress inducible and have been validated for their role in abiotic stress tolerance. In the present study, we have characterized Arabidopsis overexpressed lines of OsWNK9 regulated by the constitutive promoter under arsenite stress. Moreover, we have performed In silico expression analysis of OsWNK9 under nutrient deficiency and heavy metal stress. Three independent transgenic Arabidopsis (OsWNK9-OX T11, T12,andT13) lines showed tolerance to arsenite stress compared to wild-type (WT) plants. Under arsenite stress, transgenic lines T11, T12 and T13 showed 56.46, 57.8 and 51.66 % increased biomass respectively, as compared to WT plants. All three ArabidopsisOsWNK9-OX lines exhibited higher proline content, increased antioxidant enzyme activities and lower hydrogen peroxide levels under arsenite stress. Besides, the total antioxidant capacity in terms of DPPH (2, 2-diphenyl-1-picrylhydrazyl) free radical scavenging percentage was increased by 8-15 % in three independent OsWNK9-OX lines compared with those of WT plants. Protein-protein interaction analysis of OsWNK9 predicted interaction partners with protein kinase and oxidative stress-responsive protein. Co-expression analysis of OsWNK9 in phosphate deficiency and arsenate stress condition predicted various proteins including membrane transporter and transcription factors. Taken together, our results, for the first time, provide evidence that OsWNK9 could positively mediate arsenite stress tolerance in plants.Declines across global fishery stocks forced aquaculture feed manufacturers to search for new and sustainable components. Therefore, the aim of study was assessing nutritional value and sensory properties of meat of common carp (Cyprinus carpio L.) fed for 116 days with two blends. The control feed contained 5% of fishmeal and vegetable oils (rapeseed and soybean) as sole fat sources. While in the experimental diet half of the fishmeal was replaced with a blend of microalgae (Spirulina sp., Chlorella sp.), macroalgae (Laminaria digitata) and vegetable oil was replaced with salmon oil. Proximate composition, energy value, fatty acid profile of meat, nutritional characteristics of fat and protein as well as culinary properties of fillets were assessed. Fillets of carp fed experimental diet had a higher level of protein, lower level of fat and energy value. Intramuscular fat of fish fed with the experimental diet had a better parameters of quality. Protein in the meat of fish from both groups was characterized by a high quality comparing to the protein standard. Our study showed that meat of carp fed with experimental feed enriched with sustainable and natural feed ingredients can be a sensorily attractive source of nutritious ingredients in the human diet.PCB 180 is a typical non-dioxin-like polychlorinated biphenyl (NDL-PCB). It is one of the most prevalent PCB-congeners found in human adipose tissue. However, the role of PCB 180 in obesity remains poorly understood. The aim of this study was to explore the adipogenic effect and mechanism of PCB 180. Significant enhancement in adipogenesis was observed when differentiating murine 3T3-L1 preadipocytes or human preadipocytes-visceral (HPA-v) that were exposed to PCB 180. Furthermore, exposure to PCB 180 during the first two days was critical to the adipogenic effect. According to results from sequential cell cycle analyses, cell counting, BrdU incorporation, and cyclin D1, cyclin B1, and p27 protein quantification, PCB 180 was found to enhance mitotic clonal expansion (MCE) during early adipogenic differentiation. Molecular mechanistic investigation revealed that PCB 180 promoted accumulation of the C/EBPβ protein, a key regulator that controls MCE. Finally, it was found that PCB 180 mitigated degradation of the C/EBPβ protein by repressing the SUMOylation and subsequent ubiquitination of C/EBPβ by the upregulation of SENP2. Momelotinib in vitro In summary, it was shown for the first time that PCB 180 facilitated adipogenesis by alleviating C/EBPβ protein SUMOylation. This result provides novel evidence regarding obesogenic effect of PCB 180.
Sleep disturbance remains insufficiently characterized in many dermatoses.
To investigate the prevalence, burden, and factors associated with sleep disturbance in dermatologic patients.
We recruited 800 patients and recorded pruritus characteristics and sociodemographic and clinical parameters. Validated questionnaires were used to assess sleep disturbance, psychological distress, health-related quality of life, and work productivity.
Two thirds of patients met criteria of poor sleep, which was associated with psychological distress, diminished health-related quality of life, and lost work productivity. Patients with average and maximum pruritus on the visual analog scale exceeding 5 and 6.5 points, respectively, were at high risk of suffering pruritus-related sleep disturbance. Overall pruritus intensity and its nocturnal exacerbation contributed independently to sleep disturbance. Psychological distress was of even higher impact on sleep than pruritus and almost a third of the relationship between pruritus intensity and sleep was mediated by psychological distress.
Sleep disturbance is prevalent in dermatologic patients and constitutes a considerable burden.
Dermatologic patients with intense pruritus and psychological distress should be examined for sleep disorders. Adequate antipruritic therapy and complementary psychotherapy in affected patients may help them regain restorative sleep.
Dermatologic patients with intense pruritus and psychological distress should be examined for sleep disorders. Adequate antipruritic therapy and complementary psychotherapy in affected patients may help them regain restorative sleep.
In this work, we explore the possibility of decoding Imagined Speech (IS) brain waves using machine learning techniques.
We design two finite state machines to create an interface for controlling a computer system using an IS-based brain-computer interface. To decode IS signals, we propose a covariance matrix of Electroencephalogram channels as input features, covariance matrices projection to tangent space for obtaining vectors from matrices, principal component analysis for dimension reduction of vectors, an artificial neural network (ANN) as a classification model, and bootstrap aggregation for creating an ensemble of ANN models.
Based on these findings, we are first to use an IS-based system to operate a computer and obtain an information transfer rate of 21-bits-per-minute. The proposed approach can decode the IS signal with a mean classification accuracy of 85% on classifying one long vs. short word. Our proposed approach can also differentiate between IS and rest state brain signals with a mean classification accuracy of 94%.
After comparison, we show that our approach performs equivalent to the state-of-the-art approach (SOTA) on decoding long vs. short word classification task. We also show that the proposed method outperforms SOTA significantly on decoding three short words and vowels with an average margin of 11% and 9%, respectively.
These results show that the proposed approach can decode a wide variety of IS signals and is practically applicable in a real-time environment.
These results show that the proposed approach can decode a wide variety of IS signals and is practically applicable in a real-time environment.
Neonatal seizures are a common occurrence in clinical settings, requiring immediate attention and detection. Previous studies have proposed using manual feature extraction coupled with machine learning, or deep learning to classify between seizure and non-seizure states.
In this paper a deep learning based approach is used for neonatal seizure classification using electroencephalogram (EEG) signals. The architecture detects seizure activity in raw EEG signals as opposed to common state-of-art, where manual feature extraction with machine learning algorithms is used. The architecture is a two-dimensional (2D) convolutional neural network (CNN) to classify between seizure/non-seizure states.
The dataset used for this study is annotated by three experts and as such three separate models are trained on individual annotations, resulting in average accuracies (ACC) of 95.6 %, 94.8 % and 90.1 % respectively, and average area under the receiver operating characteristic curve (AUC) of 99.2 %, 98.4 % and 96.7 % respectively. The testing was done using 10-cross fold validation, so that the performance can be an accurate representation of the architectures classification capability in a clinical setting. After training/testing of the three individual models, a final ensemble model is made consisting of the three models. The ensemble model gives an average ACC and AUC of 96.3 % and 99.3 % respectively.
This study outperforms previous studies, with increased ACC and AUC results coupled with use of small time windows (1 s) used for evaluation.
The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.
The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.
Website: https://www.selleckchem.com/products/Cyt387.html
Broad-spectrum antivirals are more needed than ever to provide treatment options for novel emerging viruses and for viruses that lack therapeutic options or have developed resistance. A large number of viruses rely on charge-dependent non-specific interactions with heparan sulfate (HS), a highly sulfated glycosaminoglycan (GAG), for attachment to cell surfaces to initiate cell entry. As such, inhibitors targeting virion-HS interactions have potential to have broad-spectrum antiviral activity. Previous research has explored organic and inorganic small molecules, peptides, and GAG mimetics to disrupt virion-HS interactions. Here we report antiviral activities against both enveloped (the herpesvirus human cytomegalovirus) and non-enveloped (adenovirus) DNA viruses for four defined marine sulfated glycans a sulfated galactan from the red alga Botryocladia occidentalis; a sulfated fucan from the sea urchin Lytechinus variegatus, and a sulfated fucan and a fucosylated chondroitin sulfate from the sea cucumber Isostichopus badionotus. As evidenced by gene expression, time of addition, and treatment/removal assays, all four novel glycans inhibited viral attachment and entry, most likely through interactions with virions. The sulfated fucans, which both lack anticoagulant activity, had similar antiviral profiles, suggesting that their activities are not only due to sulfation content or negative charge density but also due to other physicochemical factors such as the potential conformational shapes of these carbohydrates in solution and upon interaction with virion proteins. The structural and chemical properties of these marine sulfated glycans provide unique opportunities to explore relationships between glycan structure and their antiviral activities.Protein kinases are involved in the transfer of phosphate group to serine, threonine, and tyrosine residues of a target protein. With No Lysine (WNK) kinase is a member of the serine/threonine protein kinase family, which has conserved catalytic lysine (K) residue in subdomain I instead of being in subdomain II.The WNKs family members in plants are stress inducible and have been validated for their role in abiotic stress tolerance. In the present study, we have characterized Arabidopsis overexpressed lines of OsWNK9 regulated by the constitutive promoter under arsenite stress. Moreover, we have performed In silico expression analysis of OsWNK9 under nutrient deficiency and heavy metal stress. Three independent transgenic Arabidopsis (OsWNK9-OX T11, T12,andT13) lines showed tolerance to arsenite stress compared to wild-type (WT) plants. Under arsenite stress, transgenic lines T11, T12 and T13 showed 56.46, 57.8 and 51.66 % increased biomass respectively, as compared to WT plants. All three ArabidopsisOsWNK9-OX lines exhibited higher proline content, increased antioxidant enzyme activities and lower hydrogen peroxide levels under arsenite stress. Besides, the total antioxidant capacity in terms of DPPH (2, 2-diphenyl-1-picrylhydrazyl) free radical scavenging percentage was increased by 8-15 % in three independent OsWNK9-OX lines compared with those of WT plants. Protein-protein interaction analysis of OsWNK9 predicted interaction partners with protein kinase and oxidative stress-responsive protein. Co-expression analysis of OsWNK9 in phosphate deficiency and arsenate stress condition predicted various proteins including membrane transporter and transcription factors. Taken together, our results, for the first time, provide evidence that OsWNK9 could positively mediate arsenite stress tolerance in plants.Declines across global fishery stocks forced aquaculture feed manufacturers to search for new and sustainable components. Therefore, the aim of study was assessing nutritional value and sensory properties of meat of common carp (Cyprinus carpio L.) fed for 116 days with two blends. The control feed contained 5% of fishmeal and vegetable oils (rapeseed and soybean) as sole fat sources. While in the experimental diet half of the fishmeal was replaced with a blend of microalgae (Spirulina sp., Chlorella sp.), macroalgae (Laminaria digitata) and vegetable oil was replaced with salmon oil. Proximate composition, energy value, fatty acid profile of meat, nutritional characteristics of fat and protein as well as culinary properties of fillets were assessed. Fillets of carp fed experimental diet had a higher level of protein, lower level of fat and energy value. Intramuscular fat of fish fed with the experimental diet had a better parameters of quality. Protein in the meat of fish from both groups was characterized by a high quality comparing to the protein standard. Our study showed that meat of carp fed with experimental feed enriched with sustainable and natural feed ingredients can be a sensorily attractive source of nutritious ingredients in the human diet.PCB 180 is a typical non-dioxin-like polychlorinated biphenyl (NDL-PCB). It is one of the most prevalent PCB-congeners found in human adipose tissue. However, the role of PCB 180 in obesity remains poorly understood. The aim of this study was to explore the adipogenic effect and mechanism of PCB 180. Significant enhancement in adipogenesis was observed when differentiating murine 3T3-L1 preadipocytes or human preadipocytes-visceral (HPA-v) that were exposed to PCB 180. Furthermore, exposure to PCB 180 during the first two days was critical to the adipogenic effect. According to results from sequential cell cycle analyses, cell counting, BrdU incorporation, and cyclin D1, cyclin B1, and p27 protein quantification, PCB 180 was found to enhance mitotic clonal expansion (MCE) during early adipogenic differentiation. Molecular mechanistic investigation revealed that PCB 180 promoted accumulation of the C/EBPβ protein, a key regulator that controls MCE. Finally, it was found that PCB 180 mitigated degradation of the C/EBPβ protein by repressing the SUMOylation and subsequent ubiquitination of C/EBPβ by the upregulation of SENP2. Momelotinib in vitro In summary, it was shown for the first time that PCB 180 facilitated adipogenesis by alleviating C/EBPβ protein SUMOylation. This result provides novel evidence regarding obesogenic effect of PCB 180.
Sleep disturbance remains insufficiently characterized in many dermatoses.
To investigate the prevalence, burden, and factors associated with sleep disturbance in dermatologic patients.
We recruited 800 patients and recorded pruritus characteristics and sociodemographic and clinical parameters. Validated questionnaires were used to assess sleep disturbance, psychological distress, health-related quality of life, and work productivity.
Two thirds of patients met criteria of poor sleep, which was associated with psychological distress, diminished health-related quality of life, and lost work productivity. Patients with average and maximum pruritus on the visual analog scale exceeding 5 and 6.5 points, respectively, were at high risk of suffering pruritus-related sleep disturbance. Overall pruritus intensity and its nocturnal exacerbation contributed independently to sleep disturbance. Psychological distress was of even higher impact on sleep than pruritus and almost a third of the relationship between pruritus intensity and sleep was mediated by psychological distress.
Sleep disturbance is prevalent in dermatologic patients and constitutes a considerable burden.
Dermatologic patients with intense pruritus and psychological distress should be examined for sleep disorders. Adequate antipruritic therapy and complementary psychotherapy in affected patients may help them regain restorative sleep.
Dermatologic patients with intense pruritus and psychological distress should be examined for sleep disorders. Adequate antipruritic therapy and complementary psychotherapy in affected patients may help them regain restorative sleep.
In this work, we explore the possibility of decoding Imagined Speech (IS) brain waves using machine learning techniques.
We design two finite state machines to create an interface for controlling a computer system using an IS-based brain-computer interface. To decode IS signals, we propose a covariance matrix of Electroencephalogram channels as input features, covariance matrices projection to tangent space for obtaining vectors from matrices, principal component analysis for dimension reduction of vectors, an artificial neural network (ANN) as a classification model, and bootstrap aggregation for creating an ensemble of ANN models.
Based on these findings, we are first to use an IS-based system to operate a computer and obtain an information transfer rate of 21-bits-per-minute. The proposed approach can decode the IS signal with a mean classification accuracy of 85% on classifying one long vs. short word. Our proposed approach can also differentiate between IS and rest state brain signals with a mean classification accuracy of 94%.
After comparison, we show that our approach performs equivalent to the state-of-the-art approach (SOTA) on decoding long vs. short word classification task. We also show that the proposed method outperforms SOTA significantly on decoding three short words and vowels with an average margin of 11% and 9%, respectively.
These results show that the proposed approach can decode a wide variety of IS signals and is practically applicable in a real-time environment.
These results show that the proposed approach can decode a wide variety of IS signals and is practically applicable in a real-time environment.
Neonatal seizures are a common occurrence in clinical settings, requiring immediate attention and detection. Previous studies have proposed using manual feature extraction coupled with machine learning, or deep learning to classify between seizure and non-seizure states.
In this paper a deep learning based approach is used for neonatal seizure classification using electroencephalogram (EEG) signals. The architecture detects seizure activity in raw EEG signals as opposed to common state-of-art, where manual feature extraction with machine learning algorithms is used. The architecture is a two-dimensional (2D) convolutional neural network (CNN) to classify between seizure/non-seizure states.
The dataset used for this study is annotated by three experts and as such three separate models are trained on individual annotations, resulting in average accuracies (ACC) of 95.6 %, 94.8 % and 90.1 % respectively, and average area under the receiver operating characteristic curve (AUC) of 99.2 %, 98.4 % and 96.7 % respectively. The testing was done using 10-cross fold validation, so that the performance can be an accurate representation of the architectures classification capability in a clinical setting. After training/testing of the three individual models, a final ensemble model is made consisting of the three models. The ensemble model gives an average ACC and AUC of 96.3 % and 99.3 % respectively.
This study outperforms previous studies, with increased ACC and AUC results coupled with use of small time windows (1 s) used for evaluation.
The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.
The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.
Website: https://www.selleckchem.com/products/Cyt387.html
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