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Significant progress has been made in the field of gene therapy, but effective treatments for brain tumors remain challenging due to their complex nature. Current treatment options have limitations, especially due to their inability to cross the blood-brain barrier (BBB) and precisely target cancer cells. Therefore options that are safer, more effective, and capable of specifically targeting cancer cells are urgently required as alternatives. This current study aimed to develop highly biocompatible natural biopolymeric chitosan nanoparticles (CNPs) as potential gene delivery vehicles that can cross the BBB and serve as gene or drug delivery vehicles for brain disease therapeutics. The efficiency of the CNPs was evaluated via in vitro transfection of Green Fluorescent Protein (GFP)-tagged plasmid in HEK293-293 and brain cancer MG-U87 cell lines, as well as within in vivo mouse models. The CNPs were prepared via a complex coacervation method, resulting in nanoparticles of approximately 260 nm in size. In vitro cytotoxicity analysis revealed that the CNPs had better cell viability (85%) in U87 cells compared to the chemical transfection reagent (CTR) (72%). Moreover, the transfection efficiency of the CNPs was also higher, as indicated by fluorescent emission microscopy (20.56% vs. 17.79%) and fluorescent-activated cell sorting (53% vs. 27%). In vivo assays using Balb/c mice revealed that the CNPs could efficiently cross the BBB, suggesting their potential as efficient gene delivery vehicles for targeted therapies against brain cancers as well as other brain diseases for which the efficient targeting of a therapeutic load to the brain cells has proven to be a real challenge. [ABSTRACT FROM AUTHOR]

Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain's active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin ( Δ H B O 2 ) and deoxygenated haemoglobin ( Δ H H B ). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both Δ H B O 2 and Δ H H B measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of 68.51 % ± 9.02 % in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of Δ H B O 2 and Δ H H B . These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS. [ABSTRACT FROM AUTHOR]

Earthworms are economically and environmentally very beneficial and their correct identification is very vital. In this study, an attempt has been made to taxonomically identify earthworm species based on morphological characters. The earthworms were collected by simple random sampling from eighteen study sites of Poonch division Pakistan, between April through October 2019 by diging and hand sorting methods were fixed in 10% formaldehyde solution and studied by using a stereomicroscope. The samples were then identified with the help of identification keys provided by Julka (2008) and Chih-Han (2016). The data gathered by studying various external morphological characteristics was statistically assessed by using the one-factor analysis of variance (ANOVA). The research findings revealed that a total of eleven earthworm species classified under two families and six genera were recognized. Moreover, a significant statistical relationship (Tukey test, P < 0.05) was recorded between each of the morphological variables like length, weight and total number of segments. Eisenia fetida was the dominant species of Poonch division as it was reported from most of the sites, whereas Amynthus hupeiensis and Perionyx excavatus were not previously reported in the country. By keeping in mind the ecological significance of earthworms, the country’s research centers should focus on preparing an atlas for advance identification in the future. [ABSTRACT FROM AUTHOR]