Neural reactivations during sleep determine network credit assignment.

A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship

Formulation and In-vitro evaluation of liposomal drug delivery system of metformin HCl

Metformin is widely used for the treatment of diabetes; the intention of the present study was to formulate Metformin HCl liposomes for a sustained drug delivery system. It have the advantages of dose reduction, less dosing frequency, minimize the side effect, prolong the action of drug and thus achieve better patient compliance. The liposomes were prepared by physical dispersion and ether injection method. Soya lecithin and cholesterol were used for encapsulating the drug, it facilitates to release the medicaments in sustained manner. Chloroform, ether and methanol were used as a solvent. Phosphate buffer pH 6.8 was used as a hydration medium for loading the drug. The final liposome was evaluated in various quality parameters of drug entrapment efficiency, morphological analysis, particle size analysis, in-vitro drug release studies and stability studies. In the two methods of metformin liposome formulation the ether injection method showed prolonged action when compared to physical dispersion method. In the parameters of drug entrapment and stability physical dispersion method was shows better results. Keywords: Physical dispersion, ether injection, soya lecithin, cholesterol, morphological analysis, metformin

Deciphering Millet Diversity: Proteomic Clusters and Phylogenetic Insights

Millets are renowned for their climatic resilience and possess high nutritive value with wide genetic variations. In countries like India and Africa, millets are part of many people's regular diets with rich sources of protein, dietary fiber, polyphenols, minerals, vitamins, and other nutrients. The proteomic signatures of several millet species, including Fonio, Finger, Proso, Sorghum, and Foxtail millet, were examined in this study. We have performed orthologous analysis to discover both common and distinctive protein clusters among these species by using the OrthoFinder algorithm in conjunction with visualization tools. A total of 16,247 clusters were shared by all species, offering light on similar evolutionary or adaptation mechanisms. The strong representation of Gene Ontology (GO) categories related to osmotic stress, water deprivation, and temperature stresses in the research further highlighted the millets' powerful adaptative responses to various environmental difficulties. Intricate signaling mechanisms for wound, defense, and growth are also revealed by their efficient photosynthetic capacities. However, each species' distinctive clusters, particularly those in Finger millet, highlighted how it differed from other millets. The evolutionary links were further clarified by a phylogenetic tree built using the Maximum likelihood approach and the JTT+CAT evolutionary model, with Foxtail and Proso millets showing a closer kinship. The research sheds light on the complex genetic network of millets, evolutionary histories, and potential adaptive processes. The identification of 2,277 clusters, which are mainly shared by foxtail, proso, fonio, and sorghum millets and support the distinct evolutionary history of finger millet, was especially important. These millets' strong adaptive mechanisms, which are on display in clusters related to different response mechanisms, demonstrate their evolutionary skill and point to prospective directions for crop improvement and resilience techniques

Robust neuroprosthetic control from the stroke perilesional cortex.

Intracortical brain-machine interfaces (BMIs) may eventually restore function in those with motor disability after stroke. However, current research into the development of intracortical BMIs has focused on subjects with largely intact cortical structures, such as those with spinal cord injury. Although the stroke perilesional cortex (PLC) has been hypothesized as a potential site for a BMI, it remains unclear whether the injured motor cortical network can support neuroprosthetic control directly. Using chronic electrophysiological recordings in a rat stroke model, we demonstrate here the PLC's capacity for neuroprosthetic control and physiological plasticity. We initially found that the perilesional network demonstrated abnormally increased slow oscillations that also modulated neural firing. Despite these striking abnormalities, neurons in the perilesional network could be modulated volitionally to learn neuroprosthetic control. The rate of learning was surprisingly similar regardless of the electrode distance from the stroke site and was not significantly different from intact animals. Moreover, neurons achieved similar task-related modulation and, as an ensemble, formed cell assemblies with learning. Such control was even achieved in animals with poor motor recovery, suggesting that neuroprosthetic control is possible even in the absence of motor recovery. Interestingly, achieving successful control also reduced locking to abnormal oscillations significantly. Our results thus suggest that, despite the disrupted connectivity in the PLC, it may serve as an effective target for neuroprosthetic control in those with poor motor recovery after stroke

Robust Neuroprosthetic Control from the Stroke Perilesional Cortex

Intracortical brain-machine interfaces (BMIs) may eventually restore function in those with motor disability after stroke. However, current research into the development of intracortical BMIs has focused on subjects with largely intact cortical structures, such as those with spinal cord injury. Although the stroke perilesional cortex (PLC) has been hypothesized as a potential site for a BMI, it remains unclear whether the injured motor cortical network can support neuroprosthetic control directly. Using chronic electrophysiological recordings in a rat stroke model, we demonstrate here the PLC's capacity for neuroprosthetic control and physiological plasticity. We initially found that the perilesional network demonstrated abnormally increased slow oscillations that also modulated neural firing. Despite these striking abnormalities, neurons in the perilesional network could be modulated volitionally to learn neuroprosthetic control. The rate of learning was surprisingly similar regardless of the electrode distance from the stroke site and was not significantly different from intact animals. Moreover, neurons achieved similar task-related modulation and, as an ensemble, formed cell assemblies with learning. Such control was even achieved in animals with poor motor recovery, suggesting that neuroprosthetic control is possible even in the absence of motor recovery. Interestingly, achieving successful control also reduced locking to abnormal oscillations significantly. Our results thus suggest that, despite the disrupted connectivity in the PLC, it may serve as an effective target for neuroprosthetic control in those with poor motor recovery after stroke

Robust neuroprosthetic control from the stroke perilesional cortex.

Intracortical brain-machine interfaces (BMIs) may eventually restore function in those with motor disability after stroke. However, current research into the development of intracortical BMIs has focused on subjects with largely intact cortical structures, such as those with spinal cord injury. Although the stroke perilesional cortex (PLC) has been hypothesized as a potential site for a BMI, it remains unclear whether the injured motor cortical network can support neuroprosthetic control directly. Using chronic electrophysiological recordings in a rat stroke model, we demonstrate here the PLC's capacity for neuroprosthetic control and physiological plasticity. We initially found that the perilesional network demonstrated abnormally increased slow oscillations that also modulated neural firing. Despite these striking abnormalities, neurons in the perilesional network could be modulated volitionally to learn neuroprosthetic control. The rate of learning was surprisingly similar regardless of the electrode distance from the stroke site and was not significantly different from intact animals. Moreover, neurons achieved similar task-related modulation and, as an ensemble, formed cell assemblies with learning. Such control was even achieved in animals with poor motor recovery, suggesting that neuroprosthetic control is possible even in the absence of motor recovery. Interestingly, achieving successful control also reduced locking to abnormal oscillations significantly. Our results thus suggest that, despite the disrupted connectivity in the PLC, it may serve as an effective target for neuroprosthetic control in those with poor motor recovery after stroke

Lactate Dehydrogenase Levels in the Saliva of Cigarette and E-Cigarette Smokers (Vapers):A Comparative Analysis

BACKGROUND: We examined the lactate dehydrogenase (LDH) enzyme levels in the saliva of vapers (e-cigarette users) and compared the data with cigarette smokers and a control group of non-smokers and non-vapers. METHODS: Subjects were recruited among those responding to a social media announcement or patients attending the SEGi Oral Health Care Centre between May and December 2019, and among some staff at the centre. Five ml of unstimulated whole saliva was collected and salivary LDH enzyme activity levels were measured with a LDH colorimetric assay kit. Salivary LDH activity level was determined for each group and compared statistically. RESULTS: Eighty-eight subjects were categorized into three groups (control n=30, smokers n=29, and vapers n=29). The mean ± standard deviation (SD) values for salivary LDH activity levels for vapers, smokers, and control groups were 35.15 ± 24.34 mU/ml, 30.82 ± 20.73 mU/ml, and 21.45 ± 15.30 mU/ml, respectively. The salivary LDH activity levels of smoker and vaper groups were significantly higher than in the control group (p = 0.031; 0.017). There was no significant difference of salivary LDH activity level in vapers when compared with smokers (p= 0.234). CONCLUSION: Our findings showed higher LDH levels in the saliva of vapers when compared with controls, confirming cytotoxic and harmful effects of e-cigarettes on the oral mucosa

Healthcare Professional in the Loop (HPIL) : Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia