Kingosborne1683

This work investigates the possibility of using scales of sea bass Dicentrarchus labrax as a low-cost material for the adsorptive removal of methylene blue (MB) cationic dye in aqueous solutions. The physical-chemical characterizations of fish scales in natura (FS-in natura) revealed through thermogravimetry that they are composed of inorganic (hydroxyapatite) and organic (collagen) phases in relatively similar amounts. Spectroscopy analyses show that the interactions of MB with FS-in natura occur mainly in the organic phase layer of the adsorbent. The effects of initial MB concentration (5.0 × 10-4 and 5.0 × 10-3 mol L-1) and temperature (25-55 °C) on the adsorption efficiency of FS-in natura were evaluated. FS-in natura at MB concentration (5.0 × 10-3 and 5.0 × 10-4 mol L-1) exhibited the maximum adsorption capacities of 2.2 × 10-3 mol g-1 at 25 °C and 2.8 × 10-5 mol g-1 at 55 °C, respectively. The pseudo-second-order model represented the adsorption kinetics well, and the equilibrium isotherm data were better correlated using the Langmuir equation. The newly developed neural model demonstrated a high predictive capacity with an R-value greater than 0.99 and reduced values for mean squared error, root mean squared error, and mean absolute error equal to 0.003, 0.055, and 0.0348, respectively. The genetic algorithm was used to optimize the experimental conditions of the process. In conclusion, the sea bass scales have promising prospects as a low-cost alternative material for removing cationic dyes from aqueous solutions.Fibrolamellar hepatocellular carcinoma (FLC) is a rare subtype of hepatocellular carcinoma. Our study aimed to construct a nomogram to predict the cancer-specific survival (CSS) of FLC. Data of 200 FLC patients enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were divided into the training group and the validation group. Prognostic factors identified in the univariate and multivariate Cox regression analyses were used to construct the nomogram. The concordance index (C-index), calibration curves, time-dependent receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. As a result, age ≥ 59, N1 stage, M1 stage, tumor size ≤ 2.0 cm, and no surgery were significantly associated with lower CSS in multivariate Cox regression analysis. The calibration plot showed good consistency of the nomogram between predicted and observed outcomes in the training and validation groups. Compared with the TNM staging system, the prognostic evaluation model (PEM) showed a higher C-index (0.823 vs 0.656). The PEM also showed better predictive performance, with areas under the curve of 0.909 and 0.890 for predicting the 1- and 5-year survival. The AUCs of the TNM stage model for predicting 1- and 5-year survival were 0.629 and 0.787, respectively. In addition, the DCA curve showed that the nomogram had better clinical utility. Finally, we concluded that Age, N stage, M stage, tumor size, and surgery are independent prognostic factors for FLC. PEM established based on these five prognostic indicators can help predict the CSS of patients with FLC.The discovery of proteins that neutralise vascular endothelial growth factors, such as pegaptanib, ranibizumab and aflibercept, can inhibit the process of angiogenesis, thereby restoring eyesight in individuals with retinal vascular disorders. However, due to the posterior location and chronic nature of retinal diseases, a safe and effective intraocular protein delivery system is currently lacking. Thus, dissolving bilayer microneedles (MNs) with the potential to deliver proteins to the back of the eye in an efficient and minimally invasive manner were developed in this study. Saracatinib A model protein, ovalbumin (OVA), was incorporated into MNs fabricated from different polymers, including hyaluronic acid (HA), polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP). Optimised PVA/PVP MNs were demonstrated to be robust enough to pierce porcine sclera with > 75% of the needle length penetrating the sclera and dissolving within 150 s. SDS-PAGE and OVA-specific ELISA revealed that the bioactivity of the model protein was maintained during the manufacture of MNs. In hen's egg-chorioallantoic membrane test, MNs fabricated from all chosen polymers were classified as non-irritants. Furthermore, ex vivo permeation studies showed that optimised MNs could permeate 86.99 ± 7.37% of OVA through the sclera, twice that of the needle-free patch (42.16 ± 3.95%), highlighting the capability of MNs to circumvent physical barriers and promote protein delivery to the posterior segment of the eye. In this work, a novel, efficient and safe intraocular protein delivery system was successfully established.In the current decade, remarkable efforts have been made to develop a self-regulated, on-demand and controlled release drug delivery system driven by triboelectric nanogenerators (TENGs). TENGs have great potential to convert biomechanical energy into electricity and are suitable candidates for self-powered drug delivery systems (DDSs) with exciting features such as small size, easy fabrication, biocompatible, high power output and economical. This review exclusively explains the development and implementation process of TENG-mediated, self-regulated, on-demand and targeted DDSs. It also highlights the recently used TENG-driven DDSs for cancer therapy, infected wounds healing, tissue regeneration and many other chronic disorders. Moreover, it summarises the crucial challenges that are needed to be addressed for their universal applications. Finally, a roadmap to advance the TENG-based drug delivery system developments is depicted for the targeted therapies and personalised healthcare.Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https//github.com/GoodGuy398/GraphLoc . The proposed GraphLoc model consists of three parts. The first part is a graph convolutional network (GCN) module, which utilizes the predicted contact maps to construct protein graph, taking benefit of protein information accordingly. The second part is the multi-head attention module, which learns the weighted contribution of different amino acids in different feature representation subspace, and weighted average the feature map across all amino acid nodes. The last part is a fully connected layer that maps the flatten graph representation vector to another vector with a category number dimension, followed by a softmax layer to predict the protein subcellular localization.Programmed cell death is considered a key player in a variety of cellular processes that helps to regulate tissue growth, embryogenesis, cell turnover, immune response, and other biological processes. Among different types of cell death, apoptosis has been studied widely, especially in the field of cancer research to understand and analyse cellular mechanisms, and signaling pathways that control cell cycle arrest. Hallmarks of different types of cell death have been identified by following the patterns and events through microscopy. Identified biomarkers have also supported drug development to induce cell death in cancerous cells. There are various serological and microscopic techniques with advantages and limitations, that are available and are being utilized to detect and study the mechanism of cell death. The complexity of the mechanism and difficulties in distinguishing among different types of programmed cell death make it challenging to carry out the interventions and delay its progression. In this review, mechanisms of different forms of programmed cell death along with their conventional and unconventional methods of detection of have been critically reviewed systematically and categorized on the basis of morphological hallmarks and biomarkers to understand the principle, mechanism, application, advantages and disadvantages of each method. Furthermore, a very comprehensive comparative analysis has been drawn to highlight the most efficient and effective methods of detection of programmed cell death, helping researchers to make a reliable and prudent selection among the available methods of cell death assay. Conclusively, how programmed cell death detection methods can be improved and can provide information about distinctive stages of cell death detection have been discussed.

To summarize current evidence and recent developments in the surgical treatment of drug-resistant generalized epilepsy.

Current surgical treatments of drug-resistant generalized epilepsy include vagus nerve stimulation (VNS), deep brain stimulation (DBS) and corpus callosotomy (CC). Neurostimulation with VNS and/or DBS has been shown to be effective in reducing seizure frequency in patients with generalized epilepsy. DBS for generalized epilepsy is primarily consisted of open-loop stimulation directed at the centromedian (CM) nucleus in the thalamus, though closed-loop stimulation and additional targets are being explored. CC can be effective in treating some seizure types and can be performed using traditional surgical techniques or with the less invasive methods of laser ablation and radiosurgery. This current literature supports the use of VNS, DBS and CC, alone or in combination, as palliative treatments of drug-resistant generalized epilepsy.

Current surgical treatments of drug-resistant generalized epilepsy include vagus nerve stimulation (VNS), deep brain stimulation (DBS) and corpus callosotomy (CC). Neurostimulation with VNS and/or DBS has been shown to be effective in reducing seizure frequency in patients with generalized epilepsy. DBS for generalized epilepsy is primarily consisted of open-loop stimulation directed at the centromedian (CM) nucleus in the thalamus, though closed-loop stimulation and additional targets are being explored. CC can be effective in treating some seizure types and can be performed using traditional surgical techniques or with the less invasive methods of laser ablation and radiosurgery. This current literature supports the use of VNS, DBS and CC, alone or in combination, as palliative treatments of drug-resistant generalized epilepsy.