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Abstract Objective To investigate the status quo of nursing practices, job burnout and social support among private hospitals in Dazhou, Sichuan Province, and perform correlational analysis. Methods A cross-sectional study was conducted. A total of 244 nurses were surveyed via convenience sampling from 7 private hospitals, which were randomly selected from Dazhou city. A self-designed demographic sociology questionnaire was applied, and the Chinese versions of the Maslach Burnout Scale and the Social Support scale administered from September to November 2022. Results The investigated nurses were relatively young (26.84 ± 4.90 years), had a short duration of service, a low academic background, a low professional title and a low income; however, they were highly satisfied with their working environment and had a low level of burnout, and their level of personal achievement was low. Overtime work, education level and number of children had significant impacts on the level of nurse burnout. The level of social support was high, and various dimensions were negatively correlated with the emotional exhaustion and depersonalization dimensions of burnout (r=-0.438, P

Abstract Background Cognitive dysfunction caused by infection frequently emerges as a complication in sepsis survivor patients. However, a comprehensive understanding of its pathogenesis remains elusive. Methods In our in vivo experiments, an animal model of endotoxemia was employed, utilizing the Novel Object Recognition Test and Morris Water Maze Test to assess cognitive function. Various techniques, including immunofluorescent staining, Western blotting, blood‒brain barrier permeability assessment, Limulus Amebocyte Lysate (LAL) assay, and Proximity-ligation assay, were employed to identify brain pathological injury and neuroinflammation. To discern the role of Caspase-11 (Casp11) in hematopoietic or non-hematopoietic cells in endotoxemia-induced cognitive decline, bone marrow chimeras were generated through bone marrow transplantation (BMT) using wild-type (WT) and Casp11-deficient mice. In vitro studies involved treating BV2 cells with E. coli-derived outer membrane vesicles to mimic in vivo conditions. Results Our findings indicate that the deficiency of Casp11-GSDMD signaling pathways reverses infection-induced cognitive dysfunction. Moreover, cognitive dysfunction can be ameliorated by blocking the IL-1 effect. Mechanistically, the absence of Casp11 signaling significantly mitigated blood‒brain barrier leakage, microglial activation, and synaptic damage in the hippocampal CA3 region, ultimately leading to improved cognitive function. Conclusion This study unveils the crucial contribution of Casp11 and GSDMD to cognitive impairments and spatial memory loss in a murine sepsis model. Targeting Casp11 signaling emerges as a promising strategy for preventing or treating cognitive dysfunction in patients with severe infections.

Pien Tze Huang (PZH), a class-1 nationally protected traditional Chinese medicine (TCM), has been used to treat liver diseases such as hepatitis; however, the effect of PZH on the progression of sepsis is unknown. Here, we reported that PZH attenuated lipopolysaccharide (LPS)-induced sepsis in mice and reduced LPS-induced production of proinflammatory cytokines in macrophages by inhibiting the activation of mitogen-activated protein kinase (MAPK) and nuclear factor-kappa B (NF-κB) signalling. Mechanistically, PZH stimulated signal transducer and activator of transcription 3 (STAT3) phosphorylation to induce the expression of A20, which could inhibit the activation of NF-κB and MAPK signalling. Knockdown of the bile acid (BA) receptor G protein-coupled bile acid receptor 1 (TGR5) in macrophages abolished the effects of PZH on STAT3 phosphorylation and A20 induction, as well as the LPS-induced inflammatory response, suggesting that BAs in PZH may mediate its anti-inflammatory effects by activating TGR5. Consistently, deprivation of BAs in PZH by cholestyramine resin reduced the effects of PZH on the expression of phosphorylated-STAT3 and A20, the activation of NF-κB and MAPK signalling, and the production of proinflammatory cytokines, whereas the addition of BAs to cholestyramine resin-treated PZH partially restored the inhibitory effects on the production of proinflammatory cytokines. Overall, our study identifies BAs as the effective components in PZH that activate TGR5-STAT3-A20 signalling to ameliorate LPS-induced sepsis.

BackgroundNurses are the largest occupational group in the health field, with inestimable value in realizing universal health coverage, and nurses’ physical and mental health has become an ordinary global reality. Compared with explicit absence, nurses’ presenteeism has a more lasting impact and significant harm and loss. It has become an essential factor affecting nurses’ physical and mental health, declining quality of healthcare services, and elevated healthcare-related risks. There is a lack of research exploring whether occupational coping self-efficacy influences nurses’ presenteeism behavior, especially in less-developed regions of China.ObjectiveThis study aimed to investigate the current status of ICU nurses’ occupational coping self-efficacy and presenteeism in public hospitals in western China and to explore the impact of ICU nurses’ occupational coping self-efficacy on presenteeism.MethodsA cross-sectional research design selected 722 ICU nurses in western China from January to February 2023 as survey respondents. A general information questionnaire, Occupational Coping Self-Efficacy Scale (OCSE-N), and Stanford Presenteeism Scale (SPS-6) were used. SPSS 21.0 software was used for statistical analysis. Pearson correlation analysis and multivariate hierarchical regression were used to explore the influence of ICU nurses’ occupational coping self-efficacy on presenteeism.ResultsA total of 722 ICU nurses completed the questionnaire. The OCSE-N score of ICU nurses was (22.24 ± 6.15), and the SPS-6 score was (16.83 ± 4.24). The high presenteeism was 67.23%. Correlation analysis showed that in ICU nurses, OCSE-N total score was negatively correlated with SPS-6 total score (r = −0.421, p

ObjectiveRelative to explicit absenteeism, nurses' presenteeism has a more lasting impact and is more harmful and costly. This study aimed to explore the relationship between work-family conflict, perceived social support, and presenteeism and whether perceived social support mediates the relationship between work-family conflict and presenteeism among ICU nurses working on shifts in Chinese public hospitals.Materials and methodsA cross-sectional research design was conducted from January to April 2023 in Sichuan Province, China. A total of 609 valid questionnaires were collected. The questionnaires contained information on demographic characteristics, the Work-Family Conflict (WFC) scale, the Perceived Social Support Scale (PSSS), and Stanford Presenteeism Scale-6 (SPS-6). Multiple stratified regression was used to explore the mediating role of perceived social support between work-family conflict and presenteeism. The mediating effect of perceived social support in work-family conflict and presenteeism was tested by Model 4 in the PROCESS 4.1 macro program in SPSS.ResultsA total of 609 nurses were included in this study, and the mean presenteeism score for ICU nurses working on shifts was 16.01 ± 4.293 (Mean ± SD), with high presenteeism accounting for 58.46%. After controlling for sociodemographic characteristic variables, work-family conflict was positively associated with presenteeism, explaining 7.7% of the variance. High perceived social support was related to low presenteeism, explaining 11.5% of the variance. Perceived social support mediated the association between work-family conflict and presenteeism among ICU nurses working on shifts.ConclusionsChinese shift-work ICU nurses' high presenteeism scores deserve managers' attention. Work-family conflict is a significant predictor of nurses' presenteeism. Perceived social support is essential in improving nurses' work-family conflict and mediates the relationship between work-family conflict and presenteeism. Improving social support can reduce the impact of work-family conflict on presenteeism among nurses working shifts.

P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.

BackgroundPresenteeism is a significant global public health problem, and nurses are a high-prevalence group of presenteeism, affecting not only nurses’ physical and mental health, work efficiency, and quality of work but even poses a serious threat to patient safety.ObjectiveThe categorization of presenteeism among ICU nurses is unclear. Our research aims to explore the subtypes of presenteeism among ICU nurses based on latent profile analysis, analyze the influencing factors of different subtypes, and provide a reference basis for developing targeted interventions to reduce the presenteeism rate.MethodsFrom January to February 2023, 509 ICU nurses in Sichuan Province, China, were selected as respondents and surveyed using the general information questionnaire, the presenteeism scale, and the perceived social support scale. Potential categories of presenteeism among ICU nurses were explored using potential profile analysis, and factors influencing the potential types of presenteeism among ICU nurses were investigated using the chi-square test and multivariate logistic regression analysis.ResultsThe best model was suggested to consist of three profiles: low presenteeism-normal coping group (18.3%), moderate presenteeism group (47.9%), and high presenteeism-work limitation group (33.8%). Multiple logistic regression results showed that secondary hospitals (OR = 0.116, p = 0.015), good physical health (OR = 0.084, p = 0.023), general physical health (OR = 0.016, p = 0.037), ICU human resource allocation = 1:2.5 to 3 (OR = 0.315, p = 0.007), and higher social support scores (OR = 0.975, p = 0.047) were more likely to be grouped into low presenteeism-normal coping group; married with no children (OR = 24.554, p = 0.005) were more likely to be grouped into moderate presenteeism group; and having experienced workplace violence in the past year (OR = 1.182, p = 0.049) were more likely to be grouped into high presenteeism-work limitation group.ConclusionThere is group heterogeneity in the presenteeism of ICU nurses, and nursing managers should develop targeted interventions to reduce the presenteeism rate of ICU nurses according to the characteristics and influencing factors of each type of presenteeism of ICU nurses.

IntroductionThis study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia.MethodsA total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison.ResultsThe optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model’s predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics.DiscussionThe constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies.

Background and purposePatients with asymptomatic carotid stenosis, even without stroke, are at high risk for cognitive impairment, and the neuroanatomical basis remains unclear. Using a novel edge-centric structural connectivity (eSC) analysis from individualized single-subject cortical thickness networks, we aimed to examine eSC and network measures in severe (> 70%) asymptomatic carotid stenosis (SACS).MethodsTwenty-four SACS patients and 24 demographically- and comorbidities-matched controls were included, and structural MRI and multidomain cognitive data were acquired. Individual eSC was estimated via the Manhattan distances of pairwise cortical thickness histograms.ResultsIn the eSC analysis, SACS patients showed longer interhemispheric but shorter intrahemispheric Manhattan distances seeding from left lateral temporal regions; in network analysis the SACS patients had a decreased system segregation paralleling with white matter hyperintensity burden and recall memory. Further network-based statistic analysis identified several eSC and subgraph features centred around the Perisylvian regions that predicted silent lesion load and cognitive tests.ConclusionWe conclude that SACS exhibits abnormal eSC and a less-optimized trade-off between physical cost and network segregation, providing a reference and perspective for identifying high-risk individuals.

Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC.Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance.Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42).Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.

Cropland soil organic carbon (SOC) is crucial for global food security and mitigating the greenhouse effect. Accurate SOC prediction using hyperspectral data is essential for dynamic monitoring of soil carbon pools in croplands. However, effective methods to reduce hyperspectral data dimensionality and integrate it with suitable regression algorithms for reliable prediction models are poorly understood. In this study, we analyzed 108 soil samples from Changting County, Fujian Province, China. Our objective was to evaluate the performance of various combinations of six feature selection methods and four regression algorithms for SOC prediction. Our findings are as follows: the combination of the Successive Projections Algorithm (SPA) and Partial Least Squares (PLS) yielded the most favorable results, with R2 (0.61), RMSE (1.77 g/kg), and MAE (1.48 g/kg). Moreover, we determined the relative importance of variables, with the following ranking: 696 nm > 892 nm > 783 nm > 1641 nm > 1436 nm > 396 nm > 392 nm > 2239 nm > 2129 nm. Notably, 696 nm exhibited the highest importance in the SPA-PLS model, with the Variable Importance in Projection (VIP) value of 1.22. This study provides profound insights into feature selection methods and regression algorithms for SOC prediction, highlighting the superiority of SPA-PLS as the optimal combination.

Double-stranded RNA dependent kinase R (PKR) is originally identified as an intracellular sensor of viral infection, but its role in bacterial infection remains largely unknown. Here we report that PKR was an important regulator of antibacterial immunity in sepsis. Genetic deletion of PKR or pharmacological inhibition of its kinase activity markedly increased bacterial loads, organ injury, and mortality in polymicrobial infection induced by cecal ligation and puncture (CLP). In contrast, PKR deficiency or inhibition did not affect bacterial loads, organ injury, or mortality when mice were systemically challenged with Escherichia coli, an abundant microbe in the gastrointestinal tract. PKR deficiency or inhibition markedly decreased the release of interleukin (IL)-1β after CLP. Defect in IL-1 signaling phenocopied PKR deficiency or inhibition in CLP-induced bacterial sepsis. Taken together, these findings identified a critical role of the PKR signaling pathway in antibacterial immunity.

Abstract Background With the advance of antibiotics and life support therapy, the mortality of sepsis has been decreasing in recent years. However, the incidence of sepsis-associated encephalopathy (SAE), a common complication of sepsis, is still high. There are few effective therapies to treat clinical SAE. We previously found that ethyl pyruvate (EP), a metabolite derivative, is able to effectively inhibit the NLRP3 inflammasome activation. Administration of ethyl pyruvate protects mice against polymicrobial sepsis in cecal ligation and puncture (CLP) model. The aim of present study is to investigate if ethyl pyruvate is able to attenuate SAE. Methods After CLP, C57BL/6 mice were intraperitoneally or intrathecally injected with saline or ethyl pyruvate using the sham-operated mice as control. New Object Recognition (NOR) and Morris Water Maze (MWM) were conducted to determine the cognitive function. Brain pathology was assessed via immunohistochemistry. To investigate the mechanisms by which ethyl pyruvate prevent SAE, the activation of NLRP3 in the hippocampus and the microglia were determined using western blotting, and cognitive function, microglia activation, and neurogenesis were assessed using WT, Nlrp3 −/− and Asc −/− mice in the sublethal CLP model. In addition, Nlrp3 −/− and Asc −/− mice treated with saline or ethyl pyruvate were subjected to CLP. Results Ethyl pyruvate treatment significantly attenuated CLP-induced cognitive decline, microglia activation, and impaired neurogenesis. In addition, EP significantly decreased the NLRP3 level in the hippocampus of the CLP mice, and inhibited the cleavage of IL-1β induced by NLRP3 inflammsome in microglia. NLRP3 and ASC deficiency demonstrated similar protective effects against SAE. Nlrp3 −/− and Asc −/− mice significantly improved cognitive function and brain pathology when compared with WT mice in the CLP models. Moreover, ethyl pyruvate did not have additional effects against SAE in Nlrp3 −/− and Asc −/− mice. Conclusion The results demonstrated that ethyl pyruvate confers protection against SAE through inhibiting the NLRP3 inflammasome.

ObjectiveBy virtue of largely disparate clinical outcomes of prostate cancer (PCA), there is a pressing need to search for useful biomarkers for PCA prognosis. Cell-free DNA (cfDNA) is a promising biomarker for detecting, monitoring, and predicting survival of prostate cancer (PCA). However, the utility of total cfDNA quantitation in PCA in clinical setting remains elusive. Here, we performed a thorough meta-analysis to assess the prognostic value of cfDNA concentration for patients with PCA. In addition, we tested the possibility of the combination of PSA and cfDNA test results to improve the prediction power in PCA prognosis.Method and MaterialsMore than six databases, including PubMed, Web of Science, Medline, PMC, EMBASE and the Cochrane Library were searched. Results yielded all eligible articles from the date of inception to June 30, 2020. Continuous, diagnostic, and prognostic variables in cfDNA in PCA were included in the meta-analysis by STATA.ResultsA total of 23 articles were enrolled in our meta-analysis: 69.6% (16/23) were related to diagnosis, and 56.5% (13/23) were related to prognosis. The pooled concentration of cfDNA in PCA patients was significantly higher than in the control group (SMD = 0.89, 95%CI = 0.53, 1.26), mirroring results for the prostate-specific antigen (PSA). For the detection test variables, the SROC with 95%CI was 0.87 (0.84–0.90) for cfDNA concentration. In terms of prognostic variables, the concentrations of cfDNA were significantly related with progression-free survival (PFS, logHR = 0.84 (95%CI0.39, 1.28) and overall survival [OS, log HR = 0.60 (95%CI0.29, 0.90)]. Lastly, the test showed no significant publication bias in the present meta-analysis, excluding the diagnostic meta-analysis.ConclusionsThe concentration of cell-free DNA is high in the prostate cancer patients. The present study substantiates the prognostic value of the cfDNA concentration. High concentration cfDNA correlates with poor disease outcome of CRPC. The study cohort with large sample size is needed to evaluate the prognosis value of cfDNA in the future. We also emphasized that combination of PSA and cf DNA quantitation is important in future large individual meta study.

ObjectiveTumor-infiltrating immune cells might add a predictive value for the prognostic stratification of patients with pancreatic ductal adenocarcinoma (PDAC) and chemotherapy response. We aimed to develop a prognostic model based on the tumor-infiltrating immune cell signature to improve the prediction of survival and chemotherapy benefits of patients with PDAC.MethodsThe abundance of tumor-infiltrating immune cells for 661 patients with PDAC from four different cohorts with survival data was collected in the training cohorts. Cox regression analysis and meta-analysis of immune cells were conducted to generate the tumor immune cell score (TICS) for prognostic stratification. Other two independent cohorts including 188 patients were then used to validate the model. Those patients who underwent chemotherapy were used to further analyze the value of TICS for predicting the chemotherapy response. Furthermore, the difference in the somatic mutations and immune-related molecules between the TICS subgroups was analyzed.Results6 out of 28 immune cells were found to be significantly associated with PDAC prognosis in the training cohorts (all P < 0.05). The developed TICS could significantly predict the PDAC survival and chemotherapy benefit both in the training and the external validation cohorts (log-rank test, P < 0.05). Significant differences were found in different TICS subgroups in terms of the immune characteristics, checkpoint genes, and tumor mutational burden. Functional and pathway analyses further proved that the TICS was significantly related to the tumor immunity response in patients with PDAC.ConclusionTICS might be used to predict PDAC patients with a better survival and greater chemotherapy benefit.

Abstract Background SALL1 is a multi-zinc finger transcription factor that regulates organogenesis and stem cell development, but the role of SALL1 in tumor biology and tumorigenesis remains largely unknown. Methods We analyzed SALL1 expression levels in human and murine breast cancer cells as well as cancer tissues from different types of breast cancer patients. Using both in vitro co-culture system and in vivo breast tumor models, we investigated how SALL1 expression in breast cancer cells affects tumor cell growth and proliferation, metastasis, and cell fate. Using the gain-of function and loss-of-function strategies, we dissected the molecular mechanism responsible for SALL1 tumor suppressor functions. Results We demonstrated that SALL1 functions as a tumor suppressor in breast cancer, which is significantly down-regulated in the basal like breast cancer and in estrogen receptor (ER), progesterone receptor (PR) and epidermal growth factor receptor 2 (HER2) triple negative breast cancer patients. SALL1 expression in human and murine breast cancer cells inhibited cancer cell growth and proliferation, metastasis, and promoted cell cycle arrest. Knockdown of SALL1 in breast cancer cells promoted cancer cell growth, proliferation, and colony formation. Our studies revealed that tumor suppression was mediated by recruitment of the Nucleosome Remodeling and Deacetylase (NuRD) complex by SALL1, which promoted cancer cell senescence. We further demonstrated that the mechanism of inhibition of breast cancer cell growth and invasion by SALL1-NuRD depends on the p38 MAPK, ERK1/2, and mTOR signaling pathways. Conclusion Our studies indicate that the developmental control gene SALL1 plays a critical role in tumor suppression by recruiting the NuRD complex and thereby inducing cell senescence in breast cancer cells.

The objective was to investigate the influence of audiovisual training on horizontal sound localization and the underlying neurological mechanisms using a combination of psychoacoustic and electrophysiological (i.e., event-related potential, ERP) measurements on sound localization. Audiovisual stimuli were used in the training group, whilst the control group was trained using auditory stimuli only. Training sessions were undertaken once per day for three consecutive days. Sound localization accuracy was evaluated daily after training, using psychoacoustic tests. ERP responses were measured on the first and last day of tasks. Sound localization was significantly improved in the audiovisual training group when compared to the control group. Moreover, a significantly greater reduction in front-back confusion ratio for both trained and untrained angles was found between pre- and post-test in the audiovisual training group. ERP measurement showed a decrease in N1 amplitude and an increase in P2 amplitude in both groups. However, changes in late components were only found in the audiovisual training group, with an increase in P400 amplitude and decrease in N500 amplitude. These results suggest that the interactive effect of audiovisual localization training is likely to be mediated at a relatively late cognitive processing stage.

Since head-related transfer functions (HRTFs) represent the interactions between sounds and physiological structures of listeners, anthropometric parameters represent a straightforward way to customize (or predict) individualized HRTFs. This paper proposes a hybrid algorithm for predicting median-plane individualized HRTFs using anthropometric parameters. The proposed hybrid algorithm consists of three parts: decomposition of HRTFs; selection of key anthropometric parameters; and establishing a prediction formula. Firstly, an independent component analysis (ICA) is applied to median-plane HRTFs from multiple subjects to obtain independent components and subject-dependent weight coefficients. Then, a factor analysis is used to select key anthropometric parameters relevant to HRTFs. Finally, a regression formula that connects ICA weight coefficients to key anthropometric parameters is established by a multiple linear regression. Further, the effectiveness of the proposed hybrid algorithm is verified by an objective evaluation via spectral distortion and a subjective localization experiment. The results show that, when compared with generic Knowles Electronics Manikin for Acoustic Research (KEMAR) HRTFs, the spectral characteristics of the predicted HRTFs are closer to those of the individualized HRTFs. Moreover, the predicted HRTFs can alleviate front−back and up−down confusion and improve the accuracy of localization for most subjects.

The majority of the epigenomic reports in hepatocellular carcinoma have focused on identifying novel differentially methylated drivers or passengers of the oncogenic process. Few reports have considered the technologies in place for clinical translation of newly identified biomarkers. The aim of this study was to identify epigenomic technologies that need only a small number of samples to discriminate HCC from non-HCC tissue, a basic requirement for biomarker development trials. To assess that potential, we used quantitative Methylation Specific PCR, oligonucleotide tiling arrays, and Methylation BeadChip assays. Concurrent global DNA hypomethylation, gene-specific hypermethylation, and chromatin alterations were observed as a hallmark of HCC. A global loss of promoter methylation was observed in HCC with the Illumina BeadChip assays and the Nimblegen oligonucleotide arrays. HCC samples had lower median methylation peak scores and a reduced number of significant promoter-wide methylated probes. Promoter hypermethylation of RASSF1A, SSBP2, and B4GALT1 quantified by qMSP had a sensitivity ranging from 38% to 52%, a specificity of 100%, and an AUC from 0.58 to 0.75. A panel combining these genes with HCC risk factors had a sensitivity of 87%, a specificity of 100%, and an AUC of 0.91.

Schistosoma haematobium is associated with chronic bladder damage and may subsequently induce bladder cancer in humans, thus posing a serious threat where the parasite is endemic. Here we evaluated aberrant promoter DNA methylation as a potential biomarker to detect severe bladder damage that is associated with schistosomiasis by analyzing urine specimens.A quantitative methylation-specific PCR (QMSP) assay was used to examine the methylation status of seven genes (RASSF1A, RARβ2, RUNX3, TIMP3, MGMT, P16, ARF) in 57 urine samples obtained from volunteers that include infected and uninfected by S. haematobium from an endemic region. The Fishers Exact Test and Logistic Regression analysis were used to evaluate the methylation status with bladder damage (as assessed by ultrasound examination) in subjects with S. haematobium infection.RASSF1A and TIMP3 were significant to predict severe bladder damage both in univariate (p = 0.015 and 0.023 respectively) and in multivariate (p = 0.022 and 0.032 respectively) logistic regression analysis. Area under the receiver operator characteristic curves (AUC-ROC) for RASSF1A and TIMP3 to predict severe bladder damage were 67.84% and 63.73% respectively. The combined model, which used both RASSF1A and TIMP3 promoter methylation, resulted in significant increase in AUC-ROC compared to that of TIMP3 (77.55% vs. 63.73%.29; p = 0.023).In this pilot study, we showed that aberrant promoter methylation of RASSF1A and TIMP3 are present in urine sediments of patients with severe bladder damage associated with S. haematobium infection and that may be used to develop non-invasive biomarker of S. haematobium exposure and early molecular risk assessmentof neoplastic transformation.

Porcine epidemic diarrhea virus (PEDV), a member of Alphacoronavirus, has caused huge economic losses for the global pork industry recently. The spike (S) protein mediates PEDV entry into host cells. Herein, we investigated the interactions between the S protein and its receptor porcine aminopeptidase N (pAPN) or co-receptor sugars. The C-terminal domain (CTD) of the S1 domain is bound to pAPN. The prototype strain demonstrated similar receptor-binding activity compared with the variant field isolate. Three loops at the tips of the β-barrel domains did not play crucial roles in the PEDV S-pAPN association, indicating that PEDV conforms to a different receptor recognition model compared with transmissible gastroenteritis virus (TGEV), porcine respiratory CoV (PRCV), and human coronavirus NL63 (HCoV-NL63). The N-terminal domain (NTD) of the PEDV S1 domain could bind sugar, a possible co-receptor for PEDV. The prototype strain exhibited weaker sugar-binding activity compared with the variant field isolate. Strategies targeting the receptor binding domain (RBD) may be helpful for developing vaccines or antiviral drugs for PEDV. Understanding the differences in receptor binding between the prototype and the variant strains may provide insight into PEDV pathogenesis.