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Anti-PD-1 immunotherapy has been widely applied in patients with some types of lymphoma. Classical Hodgkin's lymphoma (cHL) is highly sensitive to immunotherapy, but non-Hodgkin's lymphoma (NHL) does not show a good response. Studies have indicated that haematopoietic progenitor kinase 1 (HPK1) suppresses T cells and reduces antitumour immunity. Therefore, HPK1 inhibitors may restore and elicit antitumour immune responses and are promising candidate drug targets for cancer immunotherapy. We first explored the Gene Expression Profile Interactive Analysis (GEPIA) database and predicted that HPK1 expression was increased in diffuse large B-cell lymphoma (DLBCL) and associated with Nod-like receptor protein 3 (NLRP3) expression. We investigated whether an HPK1 inhibitor could enhance the tumour response to anti-PD-1 immunotherapy in NHL and the association between HPK1 and NLRP3 expression. Employing shHPK1 and an inhibitor, we demonstrated that the HPK1 inhibitor increased anti-PD-1-mediated T-cell cytotoxicity in BJAB and WSU-DLCL2 cells cocultured with peripheral blood mononuclear cells (PBMCs). HPK1 inhibitor treatment increased PD-1, PD-L1, Bax, p53 and NK-kB expression but decreased NLRP3 expression, indicating that the HPK1 inhibitor promoted apoptosis and blocked the NLRP3 inflammasome pathway to affect anti-PD-1-mediated T-cell cytotoxicity. Moreover, the HPK1 inhibitor enhanced the efficiency of anti-PD-1 immunotherapy in vivo in a zebrafish xenograft model of NHL. In summary, this study provides evidence that an HPK1 inhibitor enhanced the tumour response to anti-PD-1 immunotherapy in NHL by promoting apoptosis and blocking the NLRP3 pathway. These findings provide a potential therapeutic option for NHL combining HPK1 inhibitor treatment and anti-PD-1 immunotherapy. [ABSTRACT FROM AUTHOR]

One of the core challenges of intelligent fault diagnosis is that the diagnosis model requires numerous labeled training datasets to achieve satisfactory performance. Generating training data using a virtual model is a potential solution for addressing such a problem, and the construction of a high-fidelity virtual model is fundamental and critical for data generation. In this study, a digital twin-assisted dynamic model updating method for fault diagnosis is thus proposed to improve the fidelity and reliability of a virtual model, which can enhance the generated data quality. First, a virtual model is established to mirror the vibration response of a physical entity using a dynamic modeling method. Second, the modeling method is validated through a frequency analysis of the generated signal. Then, based on the signal similarity indicator, a physical–virtual signal interaction method is proposed to dynamically update the virtual model in which parameter sensitivity analysis, surrogate technique, and optimization algorithm are applied to increase the efficiency during the model updating. Finally, the proposed method is successfully applied to the dynamic model updating of a single-stage helical gearbox; the virtual data generated by this model can be used for gear fault diagnosis. [ABSTRACT FROM AUTHOR]

In machinery fault diagnosis, labeled data are always difficult or even impossible to obtain. Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain, which has been widely used for cross domain fault diagnosis. However, existing methods focus on either marginal distribution adaptation (MDA) or conditional distribution adaptation (CDA). In practice, marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence. In this paper, a dynamic distribution adaptation based transfer network (DDATN) is proposed for cross domain bearing fault diagnosis. DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy (IDMMD) for dynamic distribution adaptation (DDA), which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain. The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods. [ABSTRACT FROM AUTHOR]

Motor deficits are observed in Alzheimer's disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans. [ABSTRACT FROM AUTHOR]

Superabsorbent polymers (SAPs) are a kind of materials which are widely used in many fields where the absorption of various aqueous solutions is prime importance. Given the fact that a great number of SAPs available on the global market are made from the poorly degradable and expensive materials, recently, there has been increasing interest in looking for low-cost and environmental friendly solutions in SAP production industry. In this study, three types of hybrid SAPs composites were synthesized through the graft polymerization of acrylic acid (AA) into three kinds of natural polysaccharides backbone chain, separately, which are: Hydroxyethyl cellulose (HEC), Xyloglucan (XG), and Guar gum (GG) in an aqueous solution, with ammonium persulfate (APS) as the initiator, N,Nʹ-methylenebisacrylamide (MBA) as the cross-linker, and diatomite clay as an inorganic nano-filler. The FTIR and SEM characterization results suggested that three kinds of hybrid SAP composites were successfully synthesized with high porosity on the surface. The study about the capacity of water absorbent revealed that the optimized swelling ratios of the three superabsorbent composites (HEC-g-PAA/diatomite, XG-g-PAA/diatomite, and GG-g-PAA/diatomite) are 1174.85 g · g - 1 , 1057.06 g · g - 1 , and 692.83 g · g - 1 , respectively. In addition, the effect of each parameter such as polysaccharide species and the content of polysaccharide, initiator, cross-linker, diatomite and the degree of neutralization toward the water absorbency capacity of the obtained SAP were determined and discussed. [ABSTRACT FROM AUTHOR]

Marital status have been found as an independent prognostic factor for survival and spousal support could provide a survival advantage in various cancer types. However, the specific effect of marital status on survival in hepatocellular carcinoma (HCC) has not been explored in detail. In this study, we used the Surveillance, Epidemiology and End Results program to identify iagnosed with HCC between 1988 and 2007. Kaplan-Meier methods and multivariable Cox regression models were used to analyze long-term cancer-specific survival (CSS) outcomes and risk factors stratified by marital status. There were significant differences among these different marital status subgroups with regard to 5-year CSS rates (P < 0.001). Married HCC patients had a better 5 year CSS rate than those unmarried patients, and widowed patients were more likely to die of their cancer. A stratified analysis showed that widowed patients always had the lowest CSS rate across different cancer stage, age and gender subgroups. Even after adjusting for known confounders, unmarried patients were at greater risk of cancer-specific mortality. Social support aimed at this population could improve the likelihood of achieving cure. [ABSTRACT FROM AUTHOR]

Current literatures suggest a growing body of evidence highlighting the pivotal role of Immunogenic Cell Death (ICD) in multiple tumor types. Nevertheless, the potential and mechanisms of ICD in diffuse large B-cell lymphoma (DLBCL) remain inadequately studied. To address this gap, our current study aims to examine the impact of ICD on DLBCL and identify a corresponding gene signature in DLBC. Using the expression profiles of ICD-associated genes, the gene expression omnibus (GEO) samples were segregated into ICD-high and ICD-low subtypes utilizing non-negative matrix factorization clustering. Next, univariate and LASSO Cox regression analyses were employed to establish the ICD-related gene signature. Subsequently, the CIBERSORT tool, ssGSEA, and ESTIMATE algorithm were utilized to examine the association between the signature and tumor immune microenvironment of DLBC. Finally, the oncoPredict algorithm was implemented to evaluate the drug sensitivity prediction of DLBCL patients. These findings suggest that the immune microenvironment of the ICD-high group with a poor prognosis was significantly suppressed. An 8-gene ICD-related signature was identified and validated to prognosticate and evaluate the tumor immune microenvironment in DLBCL. Similarly, the high-risk group exhibited a worse prognosis compared to the low-risk group, and the immune function was considerably suppressed. Moreover, the results of oncoPredict algorithm indicated that patients in the high-risk group exhibited higher sensitivity to Cisplatin, Cytarabine, Epirubicin, Oxaliplatin, and Vincristine with low IC50. In conclusion, the present study provides novel insights into the role of ICD in DLBCL by identifying a new biomarker for the disease and may have implications for the development of immune-targeted therapies for the tumor. [ABSTRACT FROM AUTHOR]