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In this experiment, a general purpose BP neural network (BP) based on genetic algorithm (GA) was developed for predicting fiber properties. The experiment is based on the breaking force of cotton fibers, and the controlled variable method is used for sampling test to collect 878 datasets containing four eigenvalues. The first 850 items of this dataset were then utilized to train the designed BP, and the remaining 28 items were evaluated for error. Next, the model is parameterized using a genetic algorithm to reduce the overall network size, thus optimizing the fit. Finally, the improved model was evaluated using the same dataset. The results were obtained: the MAPE was reduced from 10.94% to 3.7869%, the MAE was reduced from 0.39586 to 0.14584, and the MSE was reduced from 0.32161 to 0.05201. The results show that this GA-BP has better results for nonlinear fitting, and it can make a better correspondence to the outliers in the dataset, and also produces a smaller error in the fitting results, the Overall, the method is effective.

Purpose Research on bone metastasis in esophageal cancer (EC) is relatively limited. Once bone metastasis occurs in patients, their prognosis is poor, and it severely affects their quality of life. Currently, there is a lack of convenient tumor markers for early identification of bone metastasis in EC. Our research aims to explore whether neutrophil-lymphocyte ratio (NLR) can predict bone metastasis in patients with EC. Methods Retrospective analysis of clinical indicators was performed on 604 patients with EC. They were divided into groups based on whether or not there was bone metastasis, and the patients’ coagulation-related tests, blood routine, tumor markers and other indicators were collected. The receiver operating characteristic curve (ROC) were used to determine the predictive ability of parameters such as NLR for bone metastasis in EC, and univariate and multivariate logistic regression analyses were conducted to determine the impact of each indicator on bone metastasis. Using binary logistic regression to obtain the predictive probability of NLR combined with tumor markers. Results ROC curves analysis suggested that the area under the curve (AUC) of the NLR was 0.681, with a sensitivity of 79.2% and a specificity of 52.6%, which can be used as a predictive factor for bone metastasis in EC. Multivariate logistic regression analysis showed that high NLR (odds ratio [OR]: 2.608, 95% confidence interval [CI]: 1.395-4.874, P = 0.003) can function as an independent risk factor for bone metastasis in patients with EC. Additionally, high PT, high APTT, high FDP, high CEA, high CA724, low hemoglobin, and low platelet levels can also predict bone metastasis in EC. When NLR was combined with tumor markers, the area under the curve was 0.760 (95% CI: 0.713-0.807, P

Abstract Objective Mice are routinely utilized as animal models of drug-induced liver injury (DILI), however, there are significant differences in the pathogenesis between mice and humans. This study aimed to compare gene expression between humans and mice in acetaminophen (APAP)-induced liver injury (AILI), and investigate the similarities and differences in biological processes between the two species. Methods A pair of public datasets (GSE218879 and GSE120652) obtained from GEO were analyzed using “Limma” package in R language, and differentially expressed genes (DEGs) were identified, including co-expressed DEGs (co-DEGs) and specific-expressed DEGS (specific-DEGs). Analysis of Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed analyses for specific-DEGs and co-DEGs. The co-DEGs were also used to construct transcription factor (TF)-gene network, gene-miRNA interactions network and protein-protein interaction (PPI) network for analyzing hub genes. Results Mouse samples contained 1052 up-regulated genes and 1064 down-regulated genes, while human samples contained 1156 up-regulated genes and 1557 down-regulated genes. After taking the intersection between the DEGs, only 154 co-down-regulated and 89 co-up-regulated DEGs were identified, with a proportion of less than 10%. It was suggested that significant differences in gene expression between mice and humans in drug-induced liver injury. Mouse-specific-DEGs predominantly engaged in processes related to apoptosis and endoplasmic reticulum stress, while human-specific-DEGs were concentrated around catabolic process. Analysis of co-regulated genes reveals showed that they were mainly enriched in biosynthetic and metabolism-related processes. Then a PPI network which contains 189 nodes and 380 edges was constructed from the co-DEGs and two modules were obtained by Mcode. We screened out 10 hub genes by three algorithms of Degree, MCC and MNC, including CYP7A1, LSS, SREBF1, FASN, CD44, SPP1, ITGAV, ANXA5, LGALS3 and PDGFRA. Besides, TFs such as FOXC1, HINFP, NFKB1, miRNAs like mir-744-5p, mir-335-5p, mir-149-3p, mir-218-5p, mir-10a-5p may be the key regulatory factors of hub genes. Conclusions The DEGs of AILI mice models and those of patients were compared, and common biological processes were identified. The signaling pathways and hub genes in co-expression were identified between mice and humans through a series of bioinformatics analyses, which may be more valuable to reveal molecular mechanisms of AILI.

Hyperspectral image (HSI) reconstruction plays a crucial role in compressive spectral imaging with coded aperture snapshot spectrometry. Although HSI reconstruction has attracted much attention in recent years, it remains a challenging problem. Existing deep learning-based methods leverage all the spectral information to reconstruct the HSI images without considering the spectral redundancy of HSI images, leading to high computational costs. In this article, we present an efficient method named dynamic spectral guided spatial sparse transformer (DGST). Specifically, DGST consists of three core modules as follows. 1) spectral sparse multihead self-attention hybrid spatial feature enhancement (SSHE) module, which employs a top-k spectral sparsity method to filter noise and redundant spectral information while extracting spectral information from HSI. 2) Spatial information compensation module, which utilizes a multiscale approach to extract spatial information and compensates for the spatial information neglected by SSHE. 3) Mask-guided spatial sparse multihead self-attention hybrid spectral enhancement module, which dynamically generates masks to guide the filtering of irrelevant regions, reducing computational costs while focusing on spatial information reconstruction. Our DGST improves the quality of HSI reconstruction by integrating spatial–spectral details and global information. Extensive experiments on public HSI reconstruction benchmark datasets demonstrate that our approach achieves state-of-the-art performance in end-to-end hyperspectral reconstruction. The superior performance of the proposed DGST is showcased on real and simulated hyperspectral imaging datasets.

The globe is an organically linked whole, and in the pandemic era, COVID-19 has brought heavy public safety threats and economic costs to humanity as almost all countries began to pay more attention to taking steps to minimize the risk of harm to society from sudden-onset diseases. It is worth noting that in some low- and middle-income areas, where the environment for epidemic detection is complex, the causative and comorbid factors are numerous, and where public health resources are scarce. It is often more difficult than in other areas to obtain timely and effective detection and control in the event of widespread virus transmission, which, in turn, is a constant threat to local and global public health security. Pandemics are preventable through effective disease surveillance systems, with nonpharmacological interventions (NPIs) as the mainstay of the control system, effectively controlling the spread of epidemics and preventing larger outbreaks. However, current state-of-the-art NPIs are not applicable in low- and middle-income areas and tend to be decentralized and costly. Based on a 3-year case study of SARS-CoV-2 preventive detection in low-income areas in south-central China, we explored a strategic model for enhancing disease detection efficacy in low- and middle-income areas. For the first time, we propose an integrated and comprehensive approach that covers structural, social, and personal strategies to optimize the epidemic surveillance system in low- and middle-income areas. This model can improve the local epidemic detection efficiency, ensure the health care needs of more people, reduce the public health costs in low- and middle-income areas in a coordinated manner, and ensure and strengthen local public health security sustainably.

Abstract Accurate traffic flow prediction information can help traffic managers and drivers make more rational decisions and choices. To make an effective and accurate traffic flow prediction, we need to consider not only the spatio-temporal dependencies between data, but also the temporal correlation between data. However, most existing methods only consider temporal continuity and ignore temporal correlation. In this paper, we propose a multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation (LSTSC). In the model, we employed attention mechanisms to capture the spatio-temporal correlations of the sequences, and the model based on multiple decision forms demonstrated higher accuracy and reliability. The superiority of the model is demonstrated on two datasets, PeMS08 and PeMSD7(M), particularly for long-term predictions.

The occurrence of a short-circuit fault in a transformer results in a rapid increase in the short-circuit current, and the temperature of the transformer winding rises rapidly. At present, many scholars use analytical formulas to solve for the average temperature of transformer short-circuit for two seconds, but the transient change of the temperature of transformer short-circuit for two seconds is less considered. In order to counter this phenomenon, this paper establishes a two-dimensional planar power transformer model, simulates its fluid-temperature coupling field, and obtains its temperature distribution results. In this thesis, the steady-state fluid-temperature field of 40000 kVA/110 kV oil-immersed power transformers under rated working condition is analyzed. Secondly, on the basis of static analysis, the temperature characteristics after two seconds of short circuit is analyzed numerically. Finally, by comparing the simulated value with the formula calculation value, the calculation results were verified.

Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map.

The immune system and the tumor have been at each other’s throats for so long that the neoplasm has learned to avoid detection and avoid being attacked, which is called immune evasion. Malignant tumors, such as gastric cancer (GC), share the ability to evade the body’s immune system as a defining feature. Immune evasion includes alterations to tumor-associated antigens (TAAs), antigen presentation mechanisms (APMs), and the tumor microenvironment (TME). While TAA and APM are simpler in nature, they both involve mutations or epigenetic regulation of genes. The TME is comprised of numerous cell types, cytokines, chemokines and extracellular matrix, any one of which might be altered to have an effect on the surrounding ecosystem. The NF-kB, MAPK, PI3K/AKT, JAK/STAT, Wnt/β-catenin, Notch, Hippo and TGF-β/Smad signaling pathways are all associated with gastric cancer tumor immune evasion. In this review, we will delineate the functions of these pathways in immune evasion.

Geranylgeranylacetone (GGA) exerts cytoprotective activity against various toxic stressors via the thioredoxin (TRX) redox system; however, its effect on skin inflammation and molecular mechanism on inducing the TRX of GGA is still unknown. We investigated the effects of GGA in a murine irritant contact dermatitis (ICD) model induced by croton oil. Both a topical application and oral administration of GGA induced TRX production and Nrf2 activation. GGA ameliorated ear swelling, neutrophil infiltration, and inhibited the expression of TNF-α, IL-1β, GM-CSF, and 8-OHdG. GGA’s cytoprotective effect was stronger orally than topically in mice. In vitro studies also showed that GGA suppressed the expression of NLRP3, TNF-α, IL-1β, and GM-CSF and scavenged ROS in PAM212 cells after phorbol myristate acetate stimulation. Moreover, GGA induced endogenous TRX production and Nrf2 nuclear translocation in PAM212 cells (dependent on the presence of ROS) and activated the PI3K-Akt signaling pathway. GGA significantly downregulated thioredoxin-interacting protein (TXNIP) levels in PAM212 cells treated with or without Nrf2 siRNA. After knocking down Nrf2 in PAM212 cells, the effect of GGA on TRX induction was significantly inhibited. This suggests that GGA suppress ICD by inducing endogenous TRX, which may be regulated by PI3K/Akt/Nrf2 mediation of the TRX redox system.

Liver fibrosis resulting from chronic liver damage is becoming one of the major threats to health worldwide. Active saponin constituents isolated from Gynostemma pentaphyllum were found to possess a protective effect in liver diseases. Here, we obtained a naturally abundant gypenoside, XLVI, and evaluated its liver protection activity in both animal and cellular models. The results showed that it ameliorated acute and chronic liver injuries and lightened the process of fibrogenesis in vivo. XLVI can inhibit TGF-β-induced activation of hepatic stellate cells and ECM deposition in vitro. The underlying mechanism study verified that it upregulated the protein expression of protein phosphatase 2C alpha and strengthened the vitality of the phosphatase together with a PP2Cα agonist gypenoside NPLC0393. These results shed new light on the molecular mechanisms and the potential therapeutic function of the traditional herb Gynostemma pentaphyllum in the treatment of liver fibrosis.

Objective: The aim of this study was to investigate the molecular characteristics and in vitro susceptibility to bedaquiline of Mycobacterium tuberculosis (MTB) isolates from Shaanxi, China. Methods: The minimum inhibitory concentration (MIC) of bedaquiline was determined using the microplate alamarBlue assay for 518 MTB isolates from Shaanxi. Isolates with MIC values of bedaquiline ≥0.12 μg/mL were sequenced for the atpE, Rv0678, and pepQ genes. Drug susceptibility testing and spoligotyping were also conducted for all strains. Results: Ten (1.93%) bedaquiline-resistant strains were isolated from 518 tuberculosis patients. The resistance rate of bedaquiline was not correlated to sex, age, treatment history, region, or genotype. Five bedaquiline-resistant isolates and one bedaquiline-susceptible isolate were found to carry Rv0678 mutations; six mutation types were identified, including G5T, A263G, C185T, G19deletion, C265T, and T323C. No mutations within the atpE and pepQ genes were observed. Conclusions: Bedaquiline showed strong in vitro antibacterial activity against MTB isolates, and the Rv0678 gene serves as the major mechanism contributing to bedaquiline resistance among MTB isolates from Shaanxi, China. Three novel mutation types (G19deletion, C265T, and T323C) of the Rv0678 gene were associated with resistance to bedaquiline. Furthermore, in addition to the current three resistance-associated genes (atpE, Rv0678, and pepQ), other mechanisms of resistance to bedaquiline may exist that need further study.

Abstract Background Myotonic dystrophy type 1 (DM1) is the most common disease that can cause muscle weakness and atrophy among adults. Normal pressure hydrocephalus (NPH) is characterized by the triad of gait disturbance, cognitive impairment and urinary incontinence. The association between DM1 and NPH is extremely rare. We report a Chinese female patient with DM1 in association with NPH. Case presentation The patient presented with a history of 3-year of walking instability and cognitive impairment. Her brain MRI showed ventriculomegaly with normal cerebrospinal fluid (CSF) pressure and the CSF tap-test was positive, which indicated the diagnosis of probable NPH. DM1 was confirmed by genetic testing. Conclusions Four patients with DM1-NPH association were found before. The association between NPH and DM1 may not be just a coincidence, NPH may occur in DM1 later in life and it is vital to recognize the association as a shunt surgery may improve patients’ quality of life.

Abstract Genome‐wide association studies have identified 3p21.1 as a robust risk locus for schizophrenia. However, the underlying molecular mechanisms remain elusive. Here a functional regulatory variant (rs2535629) is identified that disrupts CTCF binding at 3p21.1. It is confirmed that rs2535629 is also significantly associated with schizophrenia in Chinese population and the regulatory effect of rs2535629 is validated. Expression quantitative trait loci analysis indicates that rs2535629 is associated with the expression of three distal genes (GLT8D1, SFMBT1, and NEK4) in the human brain, and CRISPR‐Cas9‐mediated genome editing confirmed the regulatory effect of rs2535629 on GLT8D1, SFMBT1, and NEK4. Interestingly, differential expression analysis of GLT8D1, SFMBT1, and NEK4 suggested that rs2535629 may confer schizophrenia risk by regulating SFMBT1 expression. It is further demonstrated that Sfmbt1 regulates neurodevelopment and dendritic spine density, two key pathological characteristics of schizophrenia. Transcriptome analysis also support the potential role of Sfmbt1 in schizophrenia pathogenesis. The study identifies rs2535629 as a plausibly causal regulatory variant at the 3p21.1 risk locus and demonstrates the regulatory mechanism and biological effect of this functional variant, indicating that this functional variant confers schizophrenia risk by altering CTCF binding and regulating expression of SFMBT1, a distal gene which plays important roles in neurodevelopment and synaptic morphogenesis.

The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads and the dynamic trend of time patterns, traditional forecasting methods still have limitations in obtaining spatial–temporal correlation, which makes it difficult to extract more valid information. In order to improve the accuracy of the forecasting, this paper proposes a multi-scale temporal dual graph convolution network for traffic flow prediction (MD-GCN). Firstly, we propose a gated temporal convolution based on a channel attention and inception structure to extract multi-scale temporal dependence. Then, aiming at the complexity of the traffic spatial structure, we develop a dual graph convolution module including the graph sampling and aggregation submodule (GraphSAGE) and the mix-hop propagation graph convolution submodule (MGCN) to extract the local correlation and global correlation between neighbor nodes. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods.

Chemotherapy is among the limited choices approved for the treatment of hepatocellular carcinoma (HCC) at intermediate and advanced stages. Preferential and prolonged drug exposure in diseased sites is required to maximize the therapeutic index of the drug. Here, we report an injectable supramolecular peptide hydrogel as an intraperitoneal depot for localized and sustained release of triptolide for the treatment of orthotopic HCC. We chose peptide amphiphile C16-GNNQQNYKD-OH-based nanofibers as gelators and carriers for triptolide. Sustained triptolide release from the hydrogel was achieved over 14 days in vitro, with higher accumulation in and cytotoxicity against human HCC Bel-7402 in comparison with L-02 fetal hepatocytes. After intraperitoneal injection, the hydrogel showed prolonged retention over 13 days and preferential accumulation in the liver, realizing HCC growth inhibition by 99.7 ± 0.1% and animal median survival extension from 19 to 43 days, without causing noticeable pathological changes in the major organs. These results demonstrate that injectable peptide hydrogel can be a potential carrier for localized chemotherapy of HCC. KEY WORDS: Self-assembly, Hydrogel, Peptide amphiphile, Triptolide, Hepatocellular carcinoma

Abstract Background Mild cognitive impairment (MCI) is a transition state between asymptomatic stage and dementia. Amnestic MCI (aMCI) patients who mainly present with memory deficits are highly likely to progress to Alzheimer’s disease (AD). At present, no broadly effective drug therapy is available to prevent the progression from memory deficit to dementia. Cognitive control training, which has transfer effects on multiple cognitive capacities including memory function in healthy old adults, has not yet been applied to aMCI. Methods/Design In this single-center, randomized double-blind placebo-controlled study, 70 aMCI patients will be recruited and randomly assigned to the training and control groups. The intervention is an Internet-based cognitive control training program performed for 30 min daily, five days per week, for 12 consecutive weeks. Neuropsychological assessment and structural and functional magnetic resonance imaging (MRI) will be performed at baseline and outcome. Primary outcomes are changes of episodic memory retrieval function. Secondary outcome measures are neuroplasticity changes measured by functional and structural MRI. Discussion In this study, an Internet-based cognitive control training program is adopted to investigate whether cognitive control training can enhance the retrieval of episodic memory in aMCI patients. The combination of multi-modal MRI and neuropsychological tests could have a good sensitivity in evaluating the effects of cognitive control training and could also uncover the underlying neural underpinning. Trial registration ClinicalTrials.gov, NCT03133052. Registered on 21 April 2017.

Background: Several vascular risk factors, including hypertension, diabetes, body mass index, and smoking status are found to be associated with cognitive decline and the risk of Alzheimer's disease (AD). We aimed to investigate whether an aggregation of vascular risk factors modulates the amplitude of low-frequency fluctuation (ALFF) in patients with mild cognitive impairment (MCI).Methods: Forty-three MCI patients and twenty-nine healthy controls (HCs) underwent resting-state functional MRI scans, and spontaneous brain activity was measured by the ALFF technique. The vascular risk profile was represented with the Framingham Heart Study general cardiovascular disease (FHS-CVD) risk score, and each group was further divided into high and low risk subgroups. Two-way ANOVA was performed to explore the main effects of diagnosis and vascular risk and their interaction on ALFF.Results: The main effect of diagnosis on ALFF was found in left middle temporal gyrus (LMTG) and left superior parietal gyrus (LSPG), and the main effect of risk on ALFF was detected in left fusiform gyrus (LFFG), left precuneus (LPCUN), and left cerebellum posterior lobe (LCPL). Patients with MCI exhibited increased ALFF in the LMTG and LSPG than HCs, and participants with high vascular risk showed increased ALFF in the LFFG and LCPL, while decreased ALFF in the LPCUN. An interaction between diagnosis (MCI vs. HC) and FHS-CVD risk (high vs. low) regarding ALFF was observed in the left hippocampus (LHIP). HCs with high vascular risk showed significantly increased ALFF in the LHIP than those with low vascular risk, while MCI patients with high vascular risk showed decreased ALFF in the LHIP than HCs with high vascular risk. Interestingly, the mean ALFF of LHIP positively correlated with word recall test in HCs with high vascular risk (rho = 0.630, P = 0.016), while negatively correlated with the same test in MCI patients with high vascular risk (rho = −0.607, P = 0.001).Conclusions: This study provides preliminary evidence highlighting that the aggregation of vascular risk factors modulates the spontaneous brain activity in MCI patients, and this may serve as a potential imaging mechanism underlying vascular contribution to AD.

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions.

Although the functional connectivity of patients with disorders of consciousness (DOC) has been widely examined, less is known about brain white matter connectivity. The aim of this study was to explore structural network alterations for the diagnosis and prognosis of patients with chronic DOC. Eleven DOC patients and 11 sex- and age-matched controls were included in the study. Participants underwent diffusion magnetic resonance imaging (MRI) and T1-weighted structural MRI at 7 tesla (7 T). Graph-theoretical analysis and network-based statistics were used to analyze the group differences. Two patients were scanned twice for a longitudinal study to examine the relationship between connectome metrics and the patients' prognoses. Compared with healthy controls, DOC patients showed significantly elevated transitivity (p

BackgroundDetermining the early changes of brain structure that occur from vegetative state/unresponsive wakefulness syndrome (VS/UWS) to a minimally conscious state (MCS) is important for developing our understanding of the processes underlying disorders of consciousness (DOC), particularly during spontaneous recovery from severe brain damage.ObjectiveThis study used a multi-modal neuroimaging approach to investigate early structural changes during spontaneous recovery from VS/UWS to MCS.MethodsThe Coma Recovery Scale-Revised (CRS-R) score, 24-h electroencephalography (EEG), and ultra-high field 7-T magnetic resonance imaging were used to investigate a male patient with severe brain injury when he was in VS/UWS compared to MCS. Using white matter connectometry analysis, fibers in MCS were compared with the same fibers in VS/UWS. Whole-brain analysis was used to compare all fibers showing a 10% increase in density with each other as a population.ResultsBased on connectometry analysis, the number of fibers with increased density, and the magnitude of increase in MCS compared to VS/UWS, was greatest in the area of the temporoparietal junction (TPJ), and was mostly located in the right hemisphere. These results are in accordance with the active areas observed on 24-h EEG recordings. Moreover, analysis of different fibers across the brain, showing at least a 10% increase in density, revealed that altered white matter connections with higher discriminative weights were located within or across visual-related areas, including the cuneus_R, calcarine_R, occipital_sup_R, and occipital_mid_R. Furthermore, the temporal_mid_R, which is related to the auditory cortex, showed the highest increase in connectivity to other areas. This was consistent with improvements in the visual and auditory components of the CRS-R, which were greater than other improvements.ConclusionThese results provide evidence to support the important roles for the TPJ and the visual and auditory sensory systems in the early recovery of a patient with severe brain injury. Our findings may facilitate a much deeper understanding of the mechanisms underlying conscious-related processes and enlighten treatment strategies for patients with DOC.

The lesser grain borer, Rhyzopertha dominica (Fabricius) is a primary pest of starch-containing stored products worldwide. Here, we report characterization of mitogenome of R. dominica and its phylogenetic position. Rhyzopertha dominica complete mitochondrial genome (GenBank accession number MN527959) from Jingziguan town consisted of a circular DNA molecule of 15,862 bp (with 74.36% A + T content). The mitogenome comprised of 13 protein-coding genes (PCGs), and 22 tRNA and two rRNA genes. PCGs had typical ATN (Met) initiation codons and were terminated by typical TAN stop codons.

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Central poststroke pain (CPSP) is a medical complication that arises poststroke and significantly impacts the quality of life and social functioning of affected individuals. Despite ongoing research, the exact pathomechanisms of CPSP remain unclear, and practical treatments are still unavailable. Our review aims to systematically analyse current clinical and preclinical studies on CPSP, which is critical for identifying gaps in knowledge and guiding the development of effective therapies. The review will clarify the clinical characteristics, evaluation scales and contemporary therapeutic approaches for CPSP based on clinical investigations. It will particularly emphasise the CPSP model initiated by stroke, shedding light on its underlying mechanisms and evaluating treatments validated in preclinical studies. Furthermore, the review will not only highlight methodological limitations in animal trials but also offer specific recommendations to researchers to improve the quality of future investigations and guide the development of effective therapies. This review is expected to provide valuable insights into the current knowledge regarding CPSP and can serve as a guide for future research and clinical practice. The review will contribute to the scientific understanding of CPSP and help develop effective clinical interventions.