MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction.

Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP .

SELDNet: Sequenced encoder and lightweight decoder network for COVID-19 infection region segmentation.

Segmenting regions of lung infection from computed tomography (CT) images shows excellent potential for rapid and accurate quantifying of Coronavirus disease 2019 (COVID-19) infection and determining disease development and treatment approaches. However, a number of challenges remain, including the complexity of imaging features and their variability with disease progression, as well as the high similarity to other lung diseases, which makes feature extraction difficult. To answer the above challenges, we propose a new sequence encoder and lightweight decoder network for medical image segmentation model (SELDNet). (i) Construct sequence encoders and lightweight decoders based on Transformer and deep separable convolution, respectively, to achieve different fine-grained feature extraction. (ii) Design a semantic association module based on cross-attention mechanism between encoder and decoder to enhance the fusion of different levels of semantics. The experimental results showed that the network can effectively achieve segmentation of COVID-19 infected regions. The dice of the segmentation result was 79.1%, the sensitivity was 76.3%, and the specificity was 96.7%. Compared with several state-of-the-art image segmentation models, our proposed SELDNet model achieves better results in the segmentation task of COVID-19 infected regions.

A study on spinal level, length, and branch type of the inferior mesenteric artery and the position relationship between the inferior mesenteric artery, left colic artery, and inferior mesenteric vein.

BACKGROUND: This study was aimed to explore the clinical application of dual-energy computed tomography (DECT) monoenergetic plus (mono+) imaging to evaluate anatomical variations in the inferior mesenteric artery (IMA). METHODS: The clinical and imaging data of 212 patients who had undergone total abdominal DECT were retrospectively analyzed. The post-processing mono+ technique was used to obtain 40-keV single-level images in the arterial phase. Three-dimensional reconstruction was performed to evaluate the relationship between the IMA root position and the spinal level, IMA length, and IMA branch type, as well as the position of the left colic artery (LCA) and inferior mesenteric vein (IMV) at the IMA root level. RESULTS: The IMA root was located at the L3 level in 78.3% of cases and at the L2/L3 level in 3.3%. The highest vertebral level of IMA origin was L2 (4.2%), and the lowest was L4 (7.1%). The distance from the IMA root to the level of the sacral promontory was 99.58 ± 13.07 mm, which increased with the elevation of the IMA root at the spinal level. Of the patients, 53.8% demonstrated Type I IMA, 23.1% Type II, 20.7% Type III, and 2.4% Type IV. The length of the IMA varied from 13.6 to 66.0 mm. 77.3% of the IMAs belonged to Type A, the adjacent type, and 22.7% to Type B, the distant type. CONCLUSION: DECT mono+ can preoperatively evaluate the anatomical characteristics of the IMA and the positional relationship between the LCA and IMV at the IMA root level, which would help clinicians plan individualized surgery for patients.

A clinical study of inferior mesenteric artery typing in laparoscopic radical resections with left colonic artery preservation of rectal cancer.

OBJECTIVES: An investigation of the effects of different types of the inferior mesenteric artery (IMA) on laparoscopic left colic artery (LCA) radical resection of rectal cancer was conducted. METHODS: Clinical data were collected from 92 patients who underwent laparoscopic radical resection of rectal cancer with preservation of the LCA at Nantong University's Second Affiliated Hospital. All patients underwent full-abdominal dual-energy CT enhancement examination before surgery and 3D post-processing reconstruction of the IMA. Two radiologists with >3 years of experience in abdominal radiology jointly conducted the examination. A total of three types of IMA were identified among the patients: IMA type I (the LCA arising independently from the IMA), type II (LCA and sigmoid colon artery [SA] branching from a common trunk from IMA), and type III (LCA, SA, and superior rectal artery [SRA] branching from the IMA at the same point). The baseline data, pathological results, and intra-operative and post-operative indicators of the groups were analyzed. RESULTS: The proportions of type I, type II, and type III IMA were 58.70% (54/92), 18.48% (17/92), and 22.82% (21/92), respectively. IMA typing was consistent with the preoperative CT evaluation results. The intra-operative blood loss of type III IMA patients [median (interquartile spacing), M (P25, P75): 52.00 (39.50, 68.50) ml] was higher than that of type I and II IMA patients [35.00 (24.00, 42.00) and 32.00 (25.50, 39.50) ml, respectively] (P<0.05). The incidence of anastomotic fistula in type III IMA patients (4 cases, 19.05%) was higher than that in non-type III IMA patients (1 case, 1.41%) (X2=6.679, P=0.010). The incidence of postoperative complications among the three types of IMA was not significantly different (P>0.05). CONCLUSIONS: Among rectal cancer patients undergoing laparoscopic LCA preservation, type III IMA patients had more intraoperative bleeding and a higher incidence of postoperative anastomotic fistula. However, this did not increase the risk of overall postoperative complications.

COVID-19 CT image recognition algorithm based on transformer and CNN.

Novel corona virus pneumonia (COVID-19) broke out in 2019, which had a great impact on the development of world economy and people's lives. As a new mainstream image processing method, deep learning network has been constructed to extract medical features from chest CT images, and has been used as a new detection method in clinical practice. However, due to the medical characteristics of COVID-19 CT images, the lesions are widely distributed and have many local features. Therefore, it is difficult to diagnose directly by using the existing deep learning model. According to the medical features of CT images in COVID-19, a parallel bi-branch model (Trans-CNN Net) based on Transformer module and Convolutional Neural Network module is proposed by making full use of the local feature extraction capability of Convolutional Neural Network and the global feature extraction advantage of Transformer. According to the principle of cross-fusion, a bi-directional feature fusion structure is designed, in which features extracted from two branches are fused bi-directionally, and the parallel structures of branches are fused by a feature fusion module, forming a model that can extract features of different scales. To verify the effect of network classification, the classification accuracy on COVIDx-CT dataset is 96.7%, which is obviously higher than that of typical CNN network (ResNet-152) (95.2%) and Transformer network (Deit-B) (75.8%). These results demonstrate accuracy is improved. This model also provides a new method for the diagnosis of COVID-19, and through the combination of deep learning and medical imaging, it promotes the development of real-time diagnosis of lung diseases caused by COVID-19 infection, which is helpful for reliable and rapid diagnosis, thus saving precious lives.

Genome cloning and genetic evolution analysis of eight duck-sourced novel goose parvovirus strains in China in 2023.

Introduction: There are three major categories of waterfowl parvoviruses, namely goose parvovirus (GPV), Muscovy duck parvovirus, and novel goose parvovirus (NGPV). NGPV can infect both Cherry Valley ducks and mule ducks, resulting in short beaks and dwarfism syndrome, and the incidence of short beaks and dwarfism syndrome rises annually, posing a significant threat to the waterfowl breeding and the animal husbandry. Therefore, clarifying the biological characteristics and genetic evolution of NGPV is very important for the prevention and control of NGPV. Methods: Ducks with short beaks and dwarfism syndrome from Shandong and Henan Province were investigated by dissection and the tissue samples were collected for study. The NGPV genome was amplified by PCR, and the genome was analyzed for genetic evolution. Results: Eight strains of NGPV were isolated, which were designated as HZ0512, HZ0527, HZ0714, HZ0723, HZ0726, HZ0811, HZ0815, and HN0403. The nucleotide homology among these strains ranged from 99.9% to 100%. The eight strains, along with other NGPVs, belong to GPV. The eight strains showed a 92.5%-98.9% nucleotide homology with the classical GPV, while a 96.0%-99.9% homology with NGPV.Therefore, it can be deduced that there have been no major mutations of NGPV in Shandong and Henan provinces in recent years. Discussion: This study lays a theoretical foundation for further studying the genetic evolution and pathogenicity of NGPV, thereby facilitating the prevention and control of NGPV.

Genome-wide identification of cold shock proteins (CSPs) in sweet cherry (Prunus avium L.) and exploring the differential responses of PavCSP1 and PavCSP3 to low temperature and salt stress.

BACKGROUND: Cold shock proteins (CSPs) are ubiquitous nucleic acid-binding proteins involved in growth, development, and stress response across various organisms. While extensively studied in many species, their regulatory roles in sweet cherry (Prunus avium L.) remain unclear. OBJECTIVE: To identify and analyze CSP genes (PavCSPs) in sweet cherry genome, and explore the differential responses of PavCSP1 and PavCSP3 to low temperature and salt stress. METHODS: Three methods were employed to identify and characterize CSP in sweet cherry genomes. To explore the potential functions and evolutionary relationships of sweet cherry CSP proteins, sequence alignment and phylogenetic tree incorporating genes from five species were conducted and constructed, respectively. To investigate the responses to abiotic stresses, cis-acting elements analysis and gene expression patterns to low-temperature and salt stress were examined. Moreover, transgenic yeasts overexpressing PavCSP1 or PavCSP3 were generated and their growth under stress conditions were observed. RESULTS: In this study, three CSP genes (PavCSPs) were identified and comprehensively analyzed. The quantitative real-time PCR revealed diverse expression patterns, with PavCSP1-3 demonstrating a particular activity in the upper stem and all members were responsive to low-temperature and salt stress. Further investigation demonstrated that transgenic yeasts overexpressing PavCSP1 or PavCSP3 exhibited improved growth states following high-salt and low-temperature stress. CONCLUSION: These findings elucidated the responses of PavCSP1 and PavCSP3 to salt and low-temperature stresses, laying the groundwork for further functional studies of PavCSPs in response to abiotic stresses.

DKPE-GraphSYN: a drug synergy prediction model based on joint dual kernel density estimation and positional encoding for graph representation.

Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-to-end learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model's superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer.

Disrupted functional connectivity of the striatum in patients with diffuse axonal injury: a resting-state functional MRI study.

Diffuse axonal injury (DAI) disrupts the integrity of white matter microstructure and affects brain functional connectivity, resulting in persistent cognitive, behavioral and affective deficits. Mounting evidence suggests that altered cortical-subcortical connectivity is a major contributor to cognitive dysfunction. The functional integrity of the striatum is particularly vulnerable to DAI, but has received less attention. This study aimed to investigate the alteration patterns of striatal subdivision functional connectivity. Twenty-six patients with DAI and 27 healthy controls underwent resting-state fMRI scans on a 3.0 T scanner. We assessed striatal subdivision functional connectivity using a seed-based analysis in DAI. Furthermore, a partial correlation was used to measure its clinical association. Compared to controls, patients with DAI showed decreased functional connectivity between the right inferior ventral striatum and right inferior frontal gyrus, as well as the right inferior parietal lobule, between the left inferior ventral striatum and right inferior frontal gyrus, between the right superior ventral striatum and bilateral cerebellar posterior lobe, between the bilateral dorsal caudal putamen and right anterior cingulate gyrus, and between the right dorsal caudal putamen and right inferior parietal lobule. Moreover, decreased functional connectivity was observed between the left dorsal caudate and the right cerebellar posterior lobe, while increased functional connectivity was found between the left dorsal caudate and right inferior parietal lobule. Correlation analyses showed that regions with functional connectivity differences in the DAI group correlated with multiple clinical scoring scales, including cognition, motor function, agitated behavior, and anxiety disorders. These findings suggest that abnormalities in cortico-striatal and cerebellar-striatal functional connectivity are observed in patients with DAI, enriching our understanding of the neuropathological mechanisms of post-injury cognitive disorders and providing potential neuroimaging markers for the diagnosis and treatment of DAI.

Research Note: Complete genome cloning and genetic evolution analysis of four Cherry Valley duck circovirus strains in China in 2022.

In recent years, with the expansion of duck breeding industry in China, the infection rate of duck circovirus (DuCV) in duck and the mixed infection rate of DuCV with other diseases increased significantly, which seriously endanger the development of duck breeding industry. To study the epidemic status of duck circovirus in China, analyze the virus's genetics and evolution, and establish a foundation for scientific prevention and control of duck circovirus, our laboratory collected 4 disease materials preliminarily diagnosed as duck circovirus infections. Conventional PCR was used to amplify 4 strains of duck circovirus with a full length of 1993bp, and their sequences were compared and analyzed. The analysis showed that the 4 DuCVs had typical circovirus characteristics, including 3 major ORFs: ORFV1 (Rep protein), ORFC1 (Cap protein), ORFC2 (apoptosis-related protein), and a stem ring structure. The 4 strains were compared with 22 other reference strains, and the results revealed that all 4 strains belonged to the DuCV-I type represented by the German strain AY228555. Furthermore, the homology between the 4 DuCVs and the reference strains was up to 98.6%, which help us to understand the genotype and genetic variation of DuCV in these regions and provide a reference for the prevention and control of DuCV.

Research Note: Genetic characterization of novel duck reoviruses from Shandong Province, China in 2022.

Since 2005, novel duck reoviruses have been outbreaks in duck breeding areas such as central China and South China. In recent years, the incidence rate of this disease is still increasing, bringing serious economic losses to waterfowl breeding industry. This study isolated 3 novel duck reoviruses (NDRV-SDLS, NDRV-SDWF, and NDRV-SDYC) from sick ducks in 3 local duck farms in Shandong Province. The study aimed to investigate the characteristics of these viruses. The virus is inoculated into duck embryo fibroblasts, where the virus replicates to produce syncytium and dies within 3 to 5 d. The viruses were also isolated from infected ducks, and RT-PCR amplified the whole genomes after passage purification in duck embryos. The resulting whole genome was analyzed for genetic evolution. The total length of the gene sequencing was 23,418 bp, divided into 10 fragments. Gene sequence comparison showed that the 3 strains had high similarity with novel duck reoviruses (NDRV) but low similarity with chicken-origin reovirus (chicken ARV) and Muscovy duck reovirus (MDRV), especially in the σC segment. Phylogenetic analysis of the 10 fragments showed that the 3 isolates constituted the same evolutionary clade as other DRV reference strains and were far related to ARV and MDRV in different evolutionary clades. The results of all 10 segments indicate that the isolates are in the evolutionary branch of NDRV, suggesting that the novel waterfowl reovirus is the dominant circulating strain in Shandong. This study complements the gene bank information of NDRV and provides references for vaccine research and disease prediction of NDRV in Shandong.

The role of duck LGP2 in innate immune response of host anti-tembusu virus.

Laboratory of Genetics and Physiology 2 (LGP2), along with Retinoic Acid Induced Gene-I (RIG-I) and Melanoma Differentiation Associated Gene 5, are members of the retinoic acid-inducible gene-I-like receptors (RLRs) in pattern recognition receptors, playing an important role in the host's innate immunity. Due to lacking a caspase activation and recruitment domain, LGP2 is controversially regarded as a positive or negative regulator in the antiviral response. This study aimed to explore how duck LGP2 (duLGP2) participates in duck innate immunity and its role in countering the duck Tembusu virus (DTMUV). In duck embryo fibroblast cells, the overexpression of duLGP2 significantly reduced the cell's antiviral capacity by inhibiting type I interferon (IFN) production and the expression of downstream IFN-stimulated genes. Conversely, duLGP2 knockdown had the opposite effect. For the first time, we introduced the LGP2 gene fragment into duck embryos using a lentiviral vector to ensure persistent expression and generated gene-edited ducks with LGP2 overexpression. We demonstrated that duLGP2 facilitates DTMUV replication in both in vitro and in vivo experiments, leading to robust inflammatory and antiviral responses. Interestingly, the repressive effects of duLGP2 on type I IFN production were only observed in the early stage of DTMUV infection, with type I IFN responses becoming enhanced as the viral load increased. These results indicate that duLGP2 acts as a negative regulator during the resting state and early stages of DTMUV infection. This study provides a theoretical basis for further research on duck RLRs and developing new anti-DTMUV drugs or vaccine adjuvants.

Role of clinical data and multidetector computed tomography findings in acute superior mesenteric artery embolism.

BACKGROUND: Superior mesenteric artery embolism (SMAE) has acute onset and fast progression, which seriously threatens the life of patients. Multidetector computed tomography (MDCT) is one of the most important diagnostic methods for SMAE, which plays an important role in the diagnosis and prognosis of SMAE. AIM: To evaluate the value of combined clinical data and MDCT findings in the diagnosis of acute SMAE and predict the risk factors for SMAE-related death. METHODS: Data from 53 SMAE patients who received abdominal MDCT multi-phase enhancement and superior mesenteric artery digital subtraction angiography examinations were collected. Univariate cox regression and multivariate cox model were used to analyze the correlation between death risk and clinical and computed tomography features in SMAE patients. RESULTS: Univariate Cox regression model showed that intestinal wall thinning, intestinal wall pneumatosis, blood lactate > 2.1 mmol/L and blood pH < 7.35 increased the risk of death in patients with SMAE. After adjusting for age, sex, embolic involvement length and embolic distribution region, multivariate Cox regression model I showed that blood lactate > 2.1 mmol/L (HR = 5.26, 95%CI: 1.04-26.69, P = 0.045) and intestinal wall thinning (HR = 9.40, 95%CI: 1.05-83.46, P = 0.044) were significantly increases the risk of death in patients with SMAE. CONCLUSION: For patients with SAME, increased blood lactate and intestinal wall thinning are the risk factors for death; hence, close monitoring may reduce the mortality rate. Clinical observation combined with MDCT signs can significantly improve SMAE diagnosis.

Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.

Background: Computed tomography (CT) plays an essential role in classifying stroke, quantifying penumbra size and supporting stroke-relevant radiomics studies. However, it is difficult to acquire standard, accurate and repeatable images during follow-up. Therefore, we invented an intelligent CT to evaluate stroke during the entire follow-up. Methods: We deployed a region proposal network (RPN) and V-Net to endow traditional CT with intelligence. Specifically, facial detection was accomplished by identifying adjacent jaw positions through training and testing an RPN on 76,382 human faces using a preinstalled 2-dimensional camera; two regions of interest (ROIs) were segmented by V-Net on another training set with 295 subjects, and the moving distance of scanning couch was calculated based on a pre-generated calibration table. Multiple cohorts including 1,124 patients were used for performance validation under three clinical scenarios. Results: Cranial Automatic Planbox Imaging Towards AmeLiorating neuroscience (CAPITAL)-CT was invented. RPN model had an error distance of 4.46 ± 0.02 pixels with a success rate of 98.7% in the training set and 100% with 2.23 ± 0.10 pixels in the testing set. V-Net-derived segmentation maintained a clinically tolerable distance error, within 3 mm on average, and all lines presented with a tolerable angle error, within 3° on average in all boundaries. Real-time, accurate, and repeatable automatic scanning was accomplished with and a lower radiation exposure dose (all P < 0.001). Conclusions: CAPITAL-CT generated standard and reproducible images that could simplify the work of radiologists, which would be of great help in the follow-up of stroke patients and in multifield research in neuroscience.

Corrigendum: Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.

[This corrects the article DOI: 10.3389/fneur.2022.755492.].

Epigenetic age acceleration of early stage hepatocellular carcinoma tightly associated with hepatitis B virus load, immunoactivation, and improved survival.

Properly stratifying high-risk individuals with early stage hepatocellular carcinoma (HCC) is essential to identify patients in which the potentially therapies can be offered. To this context, we systematically investigated the prognostic value of epigenetic clock with early stage HCC as well as the association with other molecular characteristics.We computed DNA methylation (DNAm) age of 256 early stage HCC patients and 50 normal samples from TCGA by Horvath clock model. The characteristics of two DNAm age subgroups were differentiated regarding HBV expression, pathway activity, epigenomic, and genomic alteration. Cox regression and restricted cubic spline (RCS) analysis were utilized to evaluate the prognostic value of epigenetic acceleration.DNAm age was significantly associated with chronological age in normal tissue but largely disrupted in tumors (P< .001), and showed significant negative correlation with HBV expression (P< .05). We identified two DNAm age groups (DNAmAge-ACC and DNAmAge-DEC), and the former presented with an immunoactive phenotype (all FDRs<0.05 in enrichment analysis), CpG island hypermethylation (P< .001), and lower mutation burden (P= .018). Every 10-year increase in DNAm age was associated with a 18% decrease in fatality after adjustment for major clinical variables; DNAmAge-ACC had 50% lower mortality risk than DNAmAge-DEC (HR: 0.50, 95% CI: 0.27-0.94, P= .03). RCS revealed the fatality risk significantly decreased as epigenetic age accelerated (P = .04). Conclusions.In summary, we highlighted the prognostic value of epigenetic age acceleration for early stage HCC; better prognosis, relatively lower HBV load, and higher enrichment of immune signatures were tightly associated with epigenetic age accelerated tumors.

[Discussion and prospect of infusion of NK cells in the treatment of SARS-CoV-2 infection].

This article aims at investigating the feasibility and potential of natural killer (NK) cells to treat corona virus disease 2019 (COVID-19) caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). Through retrospective analysis of existing anti-virus researches and clinical application results of NK cells, and based on the characteristics and properties of this cell, we designed a plan for NK cells to treat COVID-19. As an important part of the immune system, NK cells have clear anti-viral ability. In addition, it also plays the role in immune defense, immune surveillance and immune regulation. Many clinical applications have proved the safety and effectiveness of NK cell application. Currently COVID-19 is highly contagious and harmful to people, and easy to cause irreversible lung damage. Although quarantine can effectively prevent and control viral infections, there is no effective drug treatment available. To avoid further greater losses, it is tremendously necessary to investigate an effective treatment plan.

Epigenetic age acceleration of cervical squamous cell carcinoma converged to human papillomavirus 16/18 expression, immunoactivation, and favourable prognosis.

BACKGROUND: Ageing-associated molecular changes have been assumed to trigger malignant transformations and the epigenetic clock, and the DNA methylation age has been shown to be highly correlated with chronological age. However, the associations between the epigenetic clock and cervical squamous cell carcinoma (CSCC) prognosis, other molecular characteristics, and clinicopathological features have not been systematically investigated. To this end, we computed the DNA methylation (DNAm) age of 252 CSCC patients and 200 normal samples from TCGA and three external cohorts by using the Horvath clock model. We characterized the differences in human papillomavirus (HPV) 16/18 expression, pathway activity, genomic alteration, and chemosensitivity between two DNAm age subgroups. We then used Cox proportional hazards regression and restricted cubic spline (RCS) analysis to assess the prognostic value of epigenetic acceleration. RESULTS: DNAm age was significantly associated with chronological age, but it was differentiated between tumour and normal tissue (P < 0.001). Two DNAm age groups, i.e. DNAmAge-ACC and DNAmAge-DEC, were identified; the former had high expression of the E6/E7 oncoproteins of HPV16/18 (P < 0.05), an immunoactive phenotype (all FDRs < 0.05 in enrichment analysis), CpG island hypermethylation (P < 0.001), and lower mutation load (P = 0.011), including for TP53 (P = 0.002). When adjusted for chronological age and tumour stage, every 10-year increase in DNAm age was associated with a 12% decrease in fatality (HR 0.88, 95% CI 0.78-0.99, P = 0.03); DNAmAge-ACC had a 41% lower mortality risk and 47% lower progression rate than DNAmAge-DEC and was more likely to benefit from chemotherapy. RCS revealed a positive non-linear association between DNAm age and both mortality and progression risk (both, P < 0.05). CONCLUSIONS: DNAm age is an independent predictor of CSCC prognosis. Better prognosis, overexpression of HPV E6/E7 oncoproteins, and higher enrichment of immune signatures were observed in DNAmAge-ACC tumours.

Topological Reorganization of the Default Mode Network in Severe Male Obstructive Sleep Apnea.

Impaired spontaneous regional activity and altered topology of the brain network have been observed in obstructive sleep apnea (OSA). However, the mechanisms of disrupted functional connectivity (FC) and topological reorganization of the default mode network (DMN) in patients with OSA remain largely unknown. We explored whether the FC is altered within the DMN and examined topological changes occur in the DMN in patients with OSA using a graph theory analysis of resting-state functional magnetic resonance imaging data and evaluated the relationship between neuroimaging measures and clinical variables. Resting-state data were obtained from 46 male patients with untreated severe OSA and 46 male good sleepers (GSs). We specifically selected 20 DMN subregions to construct the DMN architecture. The disrupted FC and topological properties of the DMN in patients with OSA were characterized using graph theory. The OSA group showed significantly decreased FC of the anterior-posterior DMN and within the posterior DMN, and also showed increased FC within the DMN. The DMN exhibited small-world topology in both OSA and GS groups. Compared to GSs, patients with OSA showed a decreased clustering coefficient (Cp) and local efficiency, and decreased nodal centralities in the left posterior cingulate cortex and dorsal medial prefrontal cortex, and increased nodal centralities in the ventral medial prefrontal cortex and the right parahippocampal cortex. Finally, the abnormal DMN FC was significantly related to Cp, path length, global efficiency, and Montreal cognitive assessment score. OSA showed disrupted FC within the DMN, which may have contributed to the observed topological reorganization. These findings may provide further evidence of cognitive deficits in patients with OSA.

PRL-3 promotes gastric cancer peritoneal metastasis via the PI3K/AKT signaling pathway in vitro and in vivo.

The peritoneal metastasis-associated phosphatase of regenerating liver-3 (PRL-3) is upregulated in gastric cancer. The phosphatidylinositol 3-kinase (PI3K)/RAC serine/threonine-protein kinase (AKT) signaling pathway acts downstream of PRL-3 in gastric cancer. However, the exact PRL-3 signaling mechanisms are poorly understood. The present study investigated whether PRL-3 facilitates the peritoneal metastasis of gastric cancer via the PI3K/AKT pathway in vivo and in vitro. Nude mouse models of peritoneal metastasis were established using SGC7901/PRL-3 cell lines. The results confirmed that the invasion and migration abilities of SGC7901/PRL-3 cells were significantly increased in these models. Furthermore, western blotting demonstrated that the expression of p-AKT, matrix metallopeptidase-2 (MMP-2) and -9 proteins increased in SGC7901/PRL-3 cells. These effects were suppressed in SGC7901 cell lines when PI3K was inhibited by LY294002. Furthermore, tumors derived from the peritoneal injection of SGC7901/PRL-3 cells were significantly smaller when the cells were grown in the presence of LY249002, compared with cells grown in its absence. These results indicated that targeted inhibition of the PI3K/AKT signaling pathway decreased the effects of PRL-3 on metastasis in vivo. Collectively, the results of the present study indicated that PRL-3 acts via the PI3K/AKT pathway to promote peritoneal metastasis and invasion of gastric cancer cells in vitro and in vivo.