An analysis of neurovascular disease markers in the hippocampus of Tupaia chinensis at different growth stages

IntroductionIt is considered that Tupaia chinensis can replace laboratory primates in the study of nervous system diseases. To date, however, protein expression in the brain of Tupaia chinensis has not been fully understood.MethodThree age groups of T. chinensis-15 days, 3 months and 1.5 years—were selected to study their hippocampal protein expression profiles.ResultsA significant difference was observed between the 15-day group and the other two age groups, where as there were no significant differences between the 3-month and 1.5-year age groups. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis found that differentially expressed proteins could be enriched in several pathways related to neurovascular diseases, such as metabolic pathways for Alzheimer's disease (AD), Huntington's disease, Parkinson's disease, and other diseases. The KEGG enrichment also showed that relevant protein involved in oxidative phosphorylation in the hippocampus of T. chinensis for 15days were downregulated, and ribosomal proteins (RPs) were upregulated, compared to those in the hippocampus of the other two age groups.DiscussionIt was suggested that when the hippocampus of T. chinensis developed from day 15 to 3 months, the expression of oxidatively phosphorylated proteins and RPs would vary over time. Meanwhile, the hippocamppal protein expression profile of T. chinensis after 3 months had become stable. Moreover, the study underlines that, during the early development of the hippocampus of T. chinensis, energy demand increases while protein synthesis decreases. The mitochondria of T. chinensis changes with age, and the oxidative phosphorylation metabolic pathway of mitochondria is closely related to neurovascular diseases, such as stroke and cerebral ischemia

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively

Is histogram manipulation always beneficial when trying to improve model performance across devices? Experiments using a Meibomian gland segmentation model

Meibomian gland dysfunction (MGD) is caused by abnormalities of the meibomian glands (MG) and is one of the causes of evaporative dry eye (DED). Precise MG segmentation is crucial for MGD-related DED diagnosis because the morphological parameters of MG are of importance. Deep learning has achieved state-of-the-art performance in medical image segmentation tasks, especially when training and test data come from the same distribution. But in practice, MG images can be acquired from different devices or hospitals. When testing image data from different distributions, deep learning models that have been trained on a specific distribution are prone to poor performance. Histogram specification (HS) has been reported as an effective method for contrast enhancement and improving model performance on images of different modalities. Additionally, contrast limited adaptive histogram equalization (CLAHE) will be used as a preprocessing method to enhance the contrast of MG images. In this study, we developed and evaluated the automatic segmentation method of the eyelid area and the MG area based on CNN and automatically calculated MG loss rate. This method is evaluated in the internal and external testing sets from two meibography devices. In addition, to assess whether HS and CLAHE improve segmentation results, we trained the network model using images from one device (internal testing set) and tested on images from another device (external testing set). High DSC (0.84 for MG region, 0.92 for eyelid region) for the internal test set was obtained, while for the external testing set, lower DSC (0.69–0.71 for MG region, 0.89–0.91 for eyelid region) was obtained. Also, HS and CLAHE were reported to have no statistical improvement in the segmentation results of MG in this experiment

Baicalein inhibits acinar-to-ductal metaplasia of pancreatic acinal cell AR42J via improving the inflammatory microenvironment

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers. Recent research has demonstrated that chronic pancreatitis (CP) is associated with an increased risk of PDAC, partly due to acinar-to-ductal metaplasia (ADM). Baicalein has been shown to exert anti-inflammatory and anti-tumor effects for CP or PDAC, respectively. The aim of our study was to investigate the effect of baicalein, and the putative underlying mechanism, on inflammatory cytokines-induced ADM of rat pancreatic acinar cell line AR42J. To investigate ADM and baicalein effects in vitro, AR42J were treated with recombinant rat Tumor Necrosis Factor alpha (rTNFα) with or without baicalein for 5 days. Results showed that rTNFα-induced AR42J cells switched their phenotype from dominantly amylase-positive acinar cells to dominantly cytokeratin 19-positive ductal cells. Moreover, expression of the transcripts for TNFα or Hes-1, a Notch target, was up-regulated in these cells. Interestingly, baicalein reduced the population of ADM as well as cytokines gene expression but not Hes-1. Baicalein inhibited NF-κB activation induced by rTNFα in AR42J, but no effect on Notch 1activation. Moreover, baicalein suppressed the secretion of TNFα and Nitric Oxide (NO) in macrophages stimulated with LPS and further inhibited ADM of conditional medium-treated AR42J cells. Baicalein also suppressed the inflammatory response of LPS-activated macrophages, thereby inhibited ADM of AR42J by altering their microenvironment. Taken together, our study indicates that baicalein reduces rTNFα-induced ADM of AR42J cells by inhibiting NF-κB activation. It also sheds new light on Chinese material medica therapy of pancreatitis and thereby prevention of PDAC

A highly efficient rice green tissue protoplast system for transient gene expression and studying light/chloroplast-related processes

<p>Abstract</p> <p>Background</p> <p>Plant protoplasts, a proven physiological and versatile cell system, are widely used in high-throughput analysis and functional characterization of genes. Green protoplasts have been successfully used in investigations of plant signal transduction pathways related to hormones, metabolites and environmental challenges. In rice, protoplasts are commonly prepared from suspension cultured cells or etiolated seedlings, but only a few studies have explored the use of protoplasts from rice green tissue.</p> <p>Results</p> <p>Here, we report a simplified method for isolating protoplasts from normally cultivated young rice green tissue without the need for unnecessary chemicals and a vacuum device. Transfections of the generated protoplasts with plasmids of a wide range of sizes (4.5-13 kb) and co-transfections with multiple plasmids achieved impressively high efficiencies and allowed evaluations by 1) protein immunoblotting analysis, 2) subcellular localization assays, and 3) protein-protein interaction analysis by bimolecular fluorescence complementation (BiFC) and firefly luciferase complementation (FLC). Importantly, the rice green tissue protoplasts were photosynthetically active and sensitive to the retrograde plastid signaling inducer norflurazon (NF). Transient expression of the GFP-tagged light-related transcription factor OsGLK1 markedly upregulated transcript levels of the endogeneous photosynthetic genes <it>OsLhcb1</it>, <it>OsLhcp</it>, <it>GADPH </it>and <it>RbcS</it>, which were reduced to some extent by NF treatment in the rice green tissue protoplasts.</p> <p>Conclusions</p> <p>We show here a simplified and highly efficient transient gene expression system using photosynthetically active rice green tissue protoplasts and its broad applications in protein immunoblot, localization and protein-protein interaction assays. These rice green tissue protoplasts will be particularly useful in studies of light/chloroplast-related processes.</p

Application of a nomogram model for the prediction of 90-day poor outcomes following mechanical thrombectomy in patients with acute anterior circulation large-vessel occlusion

BackgroundThe past decade has witnessed advancements in mechanical thrombectomy (MT) for acute large-vessel occlusions (LVOs). However, only approximately half of the patients with LVO undergoing MT show the best/independent 90-day favorable outcome. This study aimed to develop a nomogram for predicting 90-day poor outcomes in patients with LVO treated with MT.MethodsA total of 187 patients who received MT were retrospectively analyzed. Factors associated with 90-day poor outcomes (defined as mRS of 4–6) were determined by univariate and multivariate logistic regression analyzes. One best-fit nomogram was established to predict the risk of a 90-day poor outcome, and a concordance index was utilized to evaluate the performance of the model. Additionally, 145 patients from a single stroke center were retrospectively recruited as the validation cohort to test the newly established nomogram.ResultsThe overall incidence of 90-day poor outcomes was 45.16%, affecting 84 of 186 patients in the training set. Moreover, five variables, namely, age (odds ratio [OR]: 1.049, 95% CI [1.016–1.083]; p = 0.003), glucose level (OR: 1.163, 95% CI [1.038–1.303]; p = 0.009), baseline National Institute of Health Stroke Scale (NIHSS) score (OR: 1.066, 95% CI [0.995–1.142]; p = 0.069), unsuccessful recanalization (defined as a TICI grade of 0 to 2a) (OR: 3.730, 95% CI [1.688–8.245]; p = 0.001), and early neurological deterioration (END, defined as an increase of ≥4 points between the baseline NIHSS score and the NIHSS score at 24 h after MT) (OR: 3.383, 95% CI [1.411–8.106]; p = 0.006), were included in the nomogram to predict the potential risk of poor outcomes at 90 days following MT in LVO patients, with a C-index of 0.763 (0.693–0.832) in the training set and 0.804 (0.719–0.889) in the validation set.ConclusionThe proposed nomogram provided clinical evidence for the effective control of these risk factors before or during the process of MT surgery in LVO patients

Effects of ATPM-ET, a novel κ agonist with partial μ activity, on physical dependence and behavior sensitization in mice

Aim: To investigate the effects of ATPM-ET [(−)-3-N-Ethylaminothiazolo [5,4-b]-N-cyclopropylmethylmorphinan hydrochloride] on physical dependence and behavioral sensitization to morphine in mice. Methods: The pharmacological profile of ATPM-ET was characterized using competitive binding and GTPγS binding assays. We then examined the antinociceptive effects of ATPM-ET in the hot plate test. Morphine dependence assay and behavioral sensitization assay were used to determine the effect of ATPM-ET on physical dependence and behavior sensitization to morphine in mice. Results: The binding assay indicated that ATPM-ET ATPM-ET exhibited a high affinity to both κ- and μ-opioid receptors with Ki values of 0.15 nmol/L and 4.7 nmol/L, respectively, indicating it was a full κ-opioid receptor agonist and a partial μ-opioid receptor agonist. In the hot plate test, ATPM-ET produced a dose-dependent antinociceptive effect, with an ED50 value of 2.68 (2.34–3.07) mg/kg. Administration of ATPM-ET (1 and 2 mg/kg, sc) prior to naloxone (3.0 mg/kg, sc) injection significantly inhibited withdrawal jumping of mice. In addition, ATPM-ET (1 and 2 mg/kg, sc) also showed a trend toward decreasing morphine withdrawal-induced weight loss. ATPM-ET (1.5 and 3 mg/kg, sc) 15 min before the morphine challenge significantly inhibited the morphine-induced behavior sensitization (P<0.05). Conclusion: ATPM-ET may have potential as a therapeutic agent for the treatment of drug abuse

Formulation of Diblock Polymeric Nanoparticles through Nanoprecipitation Technique

Nanotechnology is a relatively new branch of science that involves harnessing the unique properties of particles that are nanometers in scale (nanoparticles). Nanoparticles can be engineered in a precise fashion where their size, composition and surface chemistry can be carefully controlled. This enables unprecedented freedom to modify some of the fundamental properties of their cargo, such as solubility, diffusivity, biodistribution, release characteristics and immunogenicity. Since their inception, nanoparticles have been utilized in many areas of science and medicine, including drug delivery, imaging, and cell biology1-4. However, it has not been fully utilized outside of "nanotechnology laboratories" due to perceived technical barrier. In this article, we describe a simple method to synthesize a polymer based nanoparticle platform that has a wide range of potential applications

Dynamic Transcriptome Analysis Reveals Potential Long Non-coding RNAs Governing Postnatal Pineal Development in Pig

Postnatal development and maturation of pineal gland is a highly dynamic period of tissue remodeling and phenotype maintenance, which is genetically controlled by programmed gene expression regulations. However, limited molecular characterization, particularly regarding long noncoding RNAs (lncRNA), is available for postnatal pineal at a whole transcriptome level. The present study first characterized the comprehensive pineal transcriptome profiles using strand-specific RNA-seq to illustrate the dynamic mRNA/lncRNA expression at three developmental stages (infancy, puberty, and adulthood). The results showed that 21,448 mRNAs and 8,166 novel lncRNAs were expressed in pig postnatal pineal gland. Among these genes, 3,573 mRNAs and 851 lncRNAs, including the 5-hydroxytryptamine receptors, exhibited significant dynamic regulation along maturation process, while the expression of homeobox genes didn’t show significant differences. Gene Ontology analysis revealed that the differentially expressed genes (DEGs) were significantly enriched in ion transport and synaptic transmission, highlighting the critical role of calcium signaling in postnatal pineal development. Additionally, co-expression analysis revealed the DEGs could be grouped into 12 clusters with distinct expression patterns. Many differential lncRNAs were functionally enriched in co-expressed clusters of genes related to ion transport, transcription regulation, DNA binding, and visual perception. Our study first provided an overview of postnatal pineal transcriptome dynamics in pig and demonstrated that dynamic lncRNA regulation of developmental transitions impact pineal physiology