Convolutional Neural Network Quantification of Gleason Pattern 4 and Association With Biochemical Recurrence in Intermediate-Grade Prostate Tumors.

Differential classification of prostate cancer grade group (GG) 2 and 3 tumors remains challenging, likely because of the subjective quantification of the percentage of Gleason pattern 4 (%GP4). Artificial intelligence assessment of %GP4 may improve its accuracy and reproducibility and provide information for prognosis prediction. To investigate this potential, a convolutional neural network (CNN) model was trained to objectively identify and quantify Gleason pattern (GP) 3 and 4 areas, estimate %GP4, and assess whether CNN-predicted %GP4 is associated with biochemical recurrence (BCR) risk in intermediate-risk GG 2 and 3 tumors. The study was conducted in a radical prostatectomy cohort (1999-2012) of African American men from the Henry Ford Health System (Detroit, Michigan). A CNN model that could discriminate 4 tissue types (stroma, benign glands, GP3 glands, and GP4 glands) was developed using histopathologic images containing GG 1 (n = 45) and 4 (n = 20) tumor foci. The CNN model was applied to GG 2 (n = 153) and 3 (n = 62) tumors for %GP4 estimation, and Cox proportional hazard modeling was used to assess the association of %GP4 and BCR, accounting for other clinicopathologic features including GG. The CNN model achieved an overall accuracy of 86% in distinguishing the 4 tissue types. Furthermore, CNN-predicted %GP4 was significantly higher in GG 3 than in GG 2 tumors (P = 7.2 × 10-11). %GP4 was associated with an increased risk of BCR (adjusted hazard ratio, 1.09 per 10% increase in %GP4; P = .010) in GG 2 and 3 tumors. Within GG 2 tumors specifically, %GP4 was more strongly associated with BCR (adjusted hazard ratio, 1.12; P = .006). Our findings demonstrate the feasibility of CNN-predicted %GP4 estimation, which is associated with BCR risk. This objective approach could be added to the standard pathologic assessment for patients with GG 2 and 3 tumors and act as a surrogate for specialist genitourinary pathologist evaluation when such consultation is not available.

Survival Outcomes in Women with Unilateral, Triple-Negative, Breast Cancer Correlated with Contralateral Prophylactic Mastectomy.

BACKGROUND: Despite increased utilization of contralateral prophylactic mastectomy (CPM), there is insufficient evidence that it improves survival in average-risk women with unilateral breast cancer. CPM may be of heightened interest to patients with triple negative breast cancer (TNBC) because these patients are more likely to have BRCA1 mutation-associated disease and are not candidates for the chemoprevention benefits of adjuvant endocrine therapy. METHODS: Survival and recurrence outcomes were evaluated for all TNBC patients from a multi-institutional database (1999-2018) at two academic cancer programs in two metropolitan cities of the Northeast and Midwest. Median follow-up time was 3.7 years. RESULTS: Seven hundred and nighty six TNBC patients were evaluated and 15.45% underwent CPM. Women undergoing CPM were more likely to be white (p < 0.001), younger (p < 0.001), and underwent genetic testing (p < 0.001). A borderline survival benefit was observed for TNBC patients undergoing CPM (5-year overall survival 95.1% vs. 85.0%; p = 0.05). There was no difference in survival when BRCA mutation carriers were excluded (5-year overall survival 94.1% vs. 85.2%; p = 0.12). For BRCA mutation carriers, a numeric trend was observed for improved survival for patients undergoing CPM (5-year overall survival 97.2% vs. 84.1%; p = 0.35). Among patients not undergoing CPM, the rate of developing a new primary breast cancer was 2.2% (15/673). Among these 15 patients, 20% (3/15) were known BRCA mutation carriers. CONCLUSIONS: Our data demonstrate no survival benefit for TNBC patients without BRCA1/2 mutations undergoing CPM.

Benefit of adjuvant chemotherapy in node-negative T1a versus T1b and T1c triple-negative breast cancer.

PURPOSE: National comprehensive cancer network guidelines recommend delivery of adjuvant chemotherapy in node-negative triple-negative breast cancer (TNBC) if the tumor is > 1 cm and consideration of adjuvant chemotherapy for T1b but not T1a disease. These recommendations are based upon sparse data on the role of adjuvant chemotherapy in T1a and T1b node-negative TNBC. Our objective was to clarify the benefits of chemotherapy for patients with T1N0 TNBC, stratified by tumor size. METHODS: We performed a retrospective analysis of survival outcomes of TNBC patients at two academic institutions in the United States from 1999 to 2018. Primary tumor size, histology, and nodal status were based upon surgical pathology. The Kaplan-Meier plot and 5-year unadjusted survival probability were evaluated. RESULTS: Among 282 T1N0 TNBC cases, the status of adjuvant chemotherapy was known for 258. Mean follow-up was 5.3 years. Adjuvant chemotherapy was delivered to 30.5% of T1a, 64.7% T1b, and 83.9% T1c (p < 0.0001). On multivariable analysis, factors associated with delivery of adjuvant chemotherapy were tumor size and grade 3 disease. Improved overall survival was associated with use of chemotherapy in patients with T1c disease (93.2% vs. 75.2% p = 0.008) but not T1a (100% vs. 100% p = 0.3778) or T1b (100% vs. 95.8% p = 0.2362) disease. CONCLUSION: Our data support current guidelines indicating benefit from adjuvant chemotherapy in node-negative TNBC associated with T1c tumors but excellent outcomes were observed in the cases of T1a and T1b disease, regardless of whether adjuvant chemotherapy was delivered.

Retrospective motion correction for preclinical/clinical magnetic resonance imaging based on a conditional generative adversarial network with entropy loss.

Multishot scan magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving the image quality in MRI. This work proposes and validates a new end-to-end motion-correction method for the multishot sequence that incorporates a conditional generative adversarial network with minimum entropy (cGANME) of MR images. The cGANME contains an encoder-decoder generator to obtain motion-corrected images and a PatchGAN discriminator to classify the image as either real (motion-free) or fake (motion-corrected). The entropy of the images is set as one loss item in the cGAN's loss as the entropy increases monotonically with the motion artifacts. An ablation experiment of the different weights of entropy loss was performed to evaluate the function of entropy loss. The preclinical dataset was acquired with a fast spin echo pulse sequence on a 7.0-T scanner. After the simulation, we had 10,080/2880/1440 slices for training, testing, and validating, respectively. The clinical dataset was downloaded from the Human Connection Project website, and 11,300/3500/2000 slices were used for training, testing, and validating after simulation, respectively. Extensive experiments for different motion patterns, motion levels, and protocol parameters demonstrate that cGANME outperforms traditional and some state-of-the-art, deep learning-based methods. In addition, we tested cGANME on in vivo awake rats and mitigated the motion artifacts, indicating that the model has some generalizability.

Measuring case severity: a novel tool for benchmarking and clinical documentation improvement.

BACKGROUND: Severity of illness (SOI) is an All Patients Refined Diagnosis Related Groups (APR DRG) modifier based on comorbidity capture. Tracking SOI helps hospitals improve performance and resource distribution. Furthermore, benchmarking SOI plays a key role in Quality Improvement (QI) efforts such as Clinical Documentation Improvement (CDI) programs. The current SOI system highly relies on the 3 M APR DRG grouper that is updated annually, making it difficult to track severity longitudinally and benchmark against hospitals with different patient populations. Here, we describe an alternative SOI scoring system that is grouper-independent and that can be tracked longitudinally. METHODS: Admission data for 2019-2020 U.S. News and World Report Honor Roll facilities were downloaded from the Vizient Clinical Database and split into training and testing datasets. Elixhauser comorbidities, body systems developed from the Healthcare Cost and Utilization Project (HCUP), and ICD-10-CM complication and comorbidity (CC/MCC) indicators were selected as the predictors for orthogonal polynomial regression models to predict patients' admission and discharge SOI. Receiver operating characteristic (ROC) and Precision-Recall (PR) analysis, and prediction accuracy were used to evaluate model performance. RESULTS: In the training dataset, the full model including both Elixhauser comorbidities and body system CC/MCC indicators had the highest ROC AUC, PR AUC and predication accuracy for both admission (ROC AUC: 92.9%; PR AUC: 91.0%; prediction accuracy: 85.4%) and discharge SOI (ROC AUC: 93.6%; PR AUC: 92.8%; prediction accuracy: 86.2%). The model including only body system CC/MCC indicators had similar performance for admission (ROC AUC: 92.4%; PR AUC: 90.4%; prediction accuracy: 84.8%) and discharge SOI (ROC AUC: 93.1%; PR AUC: 92.2%; prediction accuracy: 85.6%) as the full model. The model including only Elixhauser comorbidities exhibited the lowest performance. Similarly, in the validation dataset, the prediction accuracy was 86.2% for the full model, 85.6% for the body system model, and 79.3% for the comorbidity model. With fewer variables and less model complexity, the body system model was more efficient and was determined to be the optimal model. The probabilities generated from this model, named J_Score and J_Score_POA, successfully measured SOI and had practical applications in assessment of CDI performance. CONCLUSIONS: The J_Scores generated from the body system model have significant value in evaluating admission and discharge severity of illness. We believe that this new scoring system will provide a useful tool for healthcare institutions to benchmark patients' illness severity and augment Quality Improvement (QI) efforts.

Amelioration of Maternal Immune Activation-Induced Autism Relevant Behaviors by Gut Commensal Parabacteroides goldsteinii.

Autism spectrum disorder (ASD) is characterized by cognitive inflexibility and social deficits. Probiotics have been demonstrated to play a promising role in managing the severity of ASD. However, there are no effective probiotics for clinical use. Identifying new probiotic strains for ameliorating ASD is therefore essential. Using the maternal immune activation (MIA)-based offspring ASD-like mouse model, a probiotic-based intervention strategy was examined in female mice. The gut commensal microbe Parabacteroides goldsteinii MTS01, which was previously demonstrated to exert multiple beneficial effects on chronic inflammation-related-diseases, was evaluated. Prenatal lipopolysaccharide (LPS) exposure induced leaky gut-related inflammatory phenotypes in the colon, increased LPS activity in sera, and induced autistic-like behaviors in offspring mice. By contrast, P. goldsteinii MTS01 treatment significantly reduced intestinal and systemic inflammation and ameliorated disease development. Transcriptomic analyses of MIA offspring indicated that in the intestine, P. goldsteinii MTS01 enhanced neuropeptide-related signaling and suppressed aberrant cell proliferation and inflammatory responses. In the hippocampus, P. goldsteinii MTS01 increased ribosomal/mitochondrial and antioxidant activities and decreased glutamate receptor signaling. Together, significant ameliorative effects of P. goldsteinii MTS01 on ASD relevant behaviors in MIA offspring were identified. Therefore, P. goldsteinii MTS01 could be developed as a next-generation probiotic for ameliorating ASD.

Robust water-fat separation based on deep learning model exploring multi-echo nature of mGRE.

PURPOSE: To design a new deep learning network for fast and accurate water-fat separation by exploring the correlations between multiple echoes in multi-echo gradient-recalled echo (mGRE) sequence and evaluate the generalization capabilities of the network for different echo times, field inhomogeneities, and imaging regions. METHODS: A new multi-echo bidirectional convolutional residual network (MEBCRN) was designed to separate water and fat images in a fast and accurate manner for the mGRE data. This new MEBCRN network contains 2 main modules, the first 1 is the feature extraction module, which learns the correlations between consecutive echoes, and the other one is the water-fat separation module that processes the feature information extracted from the feature extraction module. The multi-layer feature fusion (MLFF) mechanism and residual structure were adopted in the water-fat separation module to increase separation accuracy and robustness. Moreover, we trained the network using in vivo abdomen images and tested it on the abdomen, knee, and wrist images. RESULTS: The results showed that the proposed network could separate water and fat images accurately. The comparison of the proposed network and other deep learning methods shows the advantage in both quantitative metrics and robustness for different TEs, field inhomogeneities, and images acquired for various imaging regions. CONCLUSION: The proposed network could learn the correlations between consecutive echoes and separate water and fat images effectively. The deep learning method has certain generalization capabilities for TEs and field inhomogeneity. Although the network was trained only in vivo abdomen images, it could be applied for different imaging regions.

Outcome of African-American compared to White-American patients with early-stage breast cancer, stratified by phenotype.

BACKGROUND: Breast cancer mortality rates are 39% higher in the African-American (AA) women compared to White-American (WA) women despite the advances in overall breast cancer screening and treatments. Several studies have undertaken to identify the factors leading to this disparity in United States with possible effects of lower socioeconomic status and underlying aggressive biology. METHODS: A retrospective analysis was done using a prospectively maintained database of a metropolitan health system. Patients were selected based on diagnosis of early-stage breast cancer between 10/1998 and 02/2017, and included women over age of 18 with clinically node-negative disease. Patients were then stratified by phenotype confirmed by pathology and patient-identified race. RESULTS: A total of 2,298 women were identified in the cohort with 39% AA and 61% WA women. The overall mean age at the time of diagnosis for AA women was slightly younger at 60 years compared to 62 years for WA women (p = 0.003). Follow-up time was longer for the WA women at 95 months vs. 86 months in AA women. The overall 5-year survival was analyzed for the entire cohort, with the lowest survival occurring in patients with triple-negative breast cancer (TNBC). Phenotype distribution revealed a higher incidence of TNBC in AA women compared to WA women (AA 16% vs. WA 10%; p < 0.0001). AA women also had higher incidence of HER2 positive cancers (AA 16.8% vs. WA 15.3%; p < 0.0001). WA women had a significantly higher distribution of Non-TNBC/HER2-negative phenotype (AA 55% vs. WA 65%; p < 0.0001). Furthermore, a subgroup analysis was done for a sentinel lymph node (SLN) negative cohort that showed higher rates of grade 3 tumors in AA (AA 35% vs. WA 23%; p < 0.0001); and higher rates of grade 1 and grade 2 tumors in WA (30% vs. 21% and 44% vs. 40%). Despite higher grade tumors in AA women, five-year overall survival outcomes in SLN-negative cohort did not differ between AA and WA women when stratifying based on tumor subtype. CONCLUSION: Breast cancer survival disparities in AA and WA women with SLN-negative breast cancer are diminished when evaluated at early-stage cancers defined by SLN-negative tumors. Our evaluation suggests that when diagnosed early, phenotype does not contribute to racial survival outcomes. The lower survival rate in AA women with breast cancer may be attributed to later stage biology between the two races, or underlying socioeconomic disparities.

The interplay of growth differentiation factor 15 (GDF15) expression and M2 macrophages during prostate carcinogenesis.

M2 (tumor-supportive) macrophages may upregulate growth differentiation factor 15 (GDF15), which is highly expressed in prostate tumors, but the combined utility of these markers as prognostic biomarkers are unclear. We retrospectively studied 90 prostate cancer cases that underwent radical prostatectomy as their primary treatment and were followed for biochemical recurrence (BCR). These cases also had a benign prostate biopsy at least 1 year or more before their prostate cancer surgery. Using computer algorithms to analyze digitalized immunohistochemically stained slides, GDF15 expression and the presence of M2 macrophages based on the relative density of CD204- and CD68-positive macrophages were measured in prostate: (i) benign biopsy, (ii) cancer and (iii) tumor-adjacent benign (TAB) tissue. Both M2 macrophages (P = 0.0004) and GDF15 (P < 0.0001) showed significant inter-region expression differences. Based on a Cox proportional hazards model, GDF15 expression was not associated with BCR but, in men where GDF15 expression differences between cancer and TAB were highest, the risk of BCR was significantly reduced (hazard ratio = 0.26; 95% confidence interval = 0.09-0.94). In addition, cases with high levels of M2 macrophages in prostate cancer had almost a 5-fold increased risk of BCR (P = 0.01). Expression of GDF15 in prostate TAB was associated with M2 macrophage levels in both prostate cancer and TAB and appeared to moderate M2-macrophage-associated BCR risk. In summary, the relationship of GDF15 expression and CD204-positive M2 macrophage levels is different in a prostate tumor environment compared with an earlier benign biopsy and, collectively, these markers may predict aggressive disease.

Extended methods for gene-environment-wide interaction scans in studies of admixed individuals with varying degrees of relationships.

The etiology of many complex diseases involves both environmental exposures and inherited genetic predisposition as well as interactions between them. Gene-environment-wide interaction studies (GEWIS) provide a means to identify the interactions between genetic variation and environmental exposures that underlie disease risk. However, current GEWIS methods lack the capability to adjust for the potentially complex correlations in studies with varying degrees of relationships (both known and unknown) among individuals in admixed populations. We developed novel generalized estimating equation (GEE) based methods-GEE-adaptive and GEE-joint-to account for phenotypic correlations due to kinship while accounting for covariates, including, measures of genome-wide ancestry. In simulation studies of admixed individuals, both methods controlled family-wise error rates, an advantage over the case-only approach. They demonstrated higher power than traditional case-control methods across a wide range of underlying alternative hypotheses, especially where both marginal and interaction effects were present. We applied the proposed method to conduct a GEWIS of a known sarcoidosis risk factor (insecticide exposure) and risk of sarcoidosis in African Americans and identified two novel loci with suggestive evidence of G × E interaction.

Hereditary Susceptibility for Triple Negative Breast Cancer Associated With Western Sub-Saharan African Ancestry: Results From an International Surgical Breast Cancer Collaborative.

OBJECTIVE: To investigate subtype-specific risk of germline alleles associated with triple negative breast cancer (TNBC) in African ancestry populations. BACKGROUND: Breast cancer (BC) mortality is higher in African American (AA) compared to White American (WA) women; this disparity is partly explained by 2-fold higher TNBC incidence. METHODS: We used a surgically maintained biospecimen cohort of 2884 BC cases. Subsets of the total (760 AA; 962 WA; 910 West African/Ghanaian; 252 East African/Ethiopian) were analyzed for genotypes of candidate alleles. A subset of 417 healthy controls were also genotyped, to measure associations with overall BC risk and TNBC. RESULTS: TNBC frequency was highest in Ghanaian and AA cases (49% and 44% respectively; P < 0.0001) and lowest in Ethiopian and WA cases (17% and 24% respectively; P < 0.0001). TNBC cases had higher West African ancestry than non-TNBC (P < 0.0001). Frequency of the Duffy-null allele (rs2814778; an African ancestral variant adopted under selective pressure as protection against malaria) was associated with TNBC-specific risk (P < 0.0001), quantified West African Ancestry (P < 0.0001) and was more common in AA, Ghanaians, and TNBC cases. Additionally, rs4849887 was significantly associated with overall BC risk, and both rs2363956 and rs13000023 were associated with TNBC-specific risk, although none as strongly as the Duffy-null variant. CONCLUSIONS: West African ancestry is strongly correlated with TNBC status, as well as germline variants related to BC risk. The Duffy-null allele was associated with TNBC risk in our cohort.

Distribution and Short-term Prognostic Value of the 21-gene recurrence score in African American compared to White American breast cancer patients.

We evaluated 328 patients (34.8% African American [AA]; 65.2% White American [WA]) with hormone receptor-positive, HER2/neu-negative breast cancer. Mean age (60 years); mean tumor size (1.6 and 1.7 cm for AA and WA, respectively) were similar, and mean BMI was higher for AA (33 vs 29.8; P = 0.001). Recurrence score (RS) distribution was similar- 8.3% AA and 5.9% WA with high RS (≥31). No significant differences were observed in delivery of chemotherapy stratified by score. With median follow-up 27.2 months for AA and 33.4 months for WA, distant recurrence occurred in 1.0% and 1.6%, respectively (P = 1). Our results suggest comparable RS utility in AA and WA patients.

Utilization of the 21-Gene Recurrence Score in a Diverse Breast Cancer Patient Population: Development of a Clinicopathologic Model to Predict High-Risk Scores and Response to Neoadjuvant Chemotherapy.

INTRODUCTION: The 21-gene expression profile [Oncotype DX Recurrence Score (RS)] stratifies benefit from adjuvant chemotherapy in hormone receptor (HR)-positive, HER2/neu-negative, node-negative breast cancer. It is not routinely applied to predict neoadjuvant chemotherapy (NACT) response; data in diverse patient populations also are limited. We developed a statistical model based on standard clinicopathologic features to identify high-risk cases (RS > 30) and then evaluated ability of predicted high RS to predict for NACT downstaging. METHODS: Primary surgery patients with Oncotype DX RS testing 2012-2016 were identified from a prospectively-maintained database. A RS predictive model was created and applied to a dataset of comparable NACT patients. Response was defined as tumor size decrease ≥ 1 cm. RESULTS: Of 394 primary surgery patients-60.4% white American; 31.0% African American-RS distribution was similar for both groups. No single feature reliably identified high RS patients; however, a model accounting for age, HR expression, proliferative index (MIB1/Ki67), histology, and tumor size was generated, with receiver operator area under the curve 0.909. Fifty-six NACT patients were identified (25 African American). Of 21 cases with all relevant clinicopathology, 14 responded to NACT and the model generated high-risk RS in 14 (100%); conversely, of 16 cases generating high-risk RS, only 2 did not respond. CONCLUSIONS: Predictive modelling can identify high RS patients; this model also can identify patients likely to experience primary tumor downstaging with NACT. Until this model is validated in other datasets, we recommend that Oncotype-eligible patients undergo primary surgery with decisions regarding chemotherapy made in the adjuvant setting.

A comparison of the immunological potency of Burkholderia lipopolysaccharides in endotoxemic BALB/c mice.

Lipopolysaccharide is one of the virulence factors of the soil-borne pathogens Burkholderia pseudomallei, B. thailandensis, B. cenocepacia and B. multivorans, which cause septic melioidosis (often in B. pseudomallei infections but rarely in B. thailandensis infections) or cepacia syndromes (commonly in B. cenocepacia infections but rarely in B. multivorans infections). The inflammatory responses in Burkholderia LPS-induced endotoxemia were evaluated in this study. Prior to induction, the conserved structures and functions of each purified LPS were determined using electrophoretic phenotypes, the ratios of 3-hydroxytetradecanoic to 3-hydroxyhexadecanoic acid and endotoxin units. In an in vitro assay, cytokine expression of myeloid differentiation primary response gene 88 and Toll/IL-1 receptor domain containing adapter-inducing INF-ß-dependent signaling-dependent signaling differed when stimulated by different LPS. Endotoxemia was induced in mice by s.c. injection as evidenced by increasing serum concentrations of 3-hydroxytetradecanoic acid and the septic prognostic markers CD62E and ICAM-1. During endotoxemia, splenic CD11b+ I-A+ , CD11b+ CD80+ , CD11b+ CD86+ and CD11b+ CD11c+ subpopulations increased. After induction with B. pseudomallei LPS, there were significant increases in splenic CD49b NK cells and CD14 macrophages. The inflamed CD11b+ CCR2+ , CD11b+ CD31+ , CD11b+ CD14+ , resident CD11b+ CX3 CR1+ and progenitor CD11b+ CD34+ cells showed delayed increases in bone marrow. B. multivorans LPS was the most potent inducer of serum cytokines and chemokines, whereas B. cenocepacia LPS induced relatively low concentrations of the chemokines MIP-1α and MIP-1ß. Endotoxin activities did not correlate with the virulence of Burkholderia strains. Thus factors other than LPS and/or other mechanisms of low activity LPS must mediate the pathogenicity of highly virulent Burkholderia strains.

Improvement in T helper 1-related immune responses in BALB/c mice immunized with an HIV-1 gag plasmid combined with a chimeric plasmid encoding interleukin-18 and flagellin.

Both flagellin (fliC) and IL-18 (INF-γ-inducing factor) have been developed as adjuvants for improving immunogenicity in DNA-vaccinated hosts. An HIV-1 gag plasmid encodes a protein harboring broad epitopes for cytotoxic T-lymphocytes. In this study, the immunogenicity of BALB/c mice immunized with an HIV-1 gag plasmid (pVAX/gag) combined with a chimeric plasmid encoding IL-18 fused to flagellin (pcDNA3/IL-18_fliC) or a single plasmid encoding IL-18 (pcDNA3/IL-18) and/or flagellin (pcDNA3/fliC) was assessed. Through in vitro transcription and translation, it was demonstrated that both mRNA and protein were appropriately expressed by each construct. The IL-18 and flagellin fusion protein, which could be detected in supernatants from transfected cells, was effective in inducing IFN-γ by lymphocytes. Following i.m. immunization, expressions of flagellin or IL-18 were detected in muscle cells by immunohistochemistry analysis from 72 hr. At 12 weeks post-immunization, both gag-specific IgG in sera and spleen cell proliferation were high in all murine groups. However, the IgG2a/IgG1 ratio, Th1 cytokine (IL-2 and IFN-γ) production and proportion of gag-specific CD3(+) CD8(+) IFN-γ-secreting cells were significantly higher in the murine group co-immunized with pVAX/gag plasmid and pcDNA3/IL-18_fliC than in the mice immunized with pVAX/gag plasmid combined with either pcDNA3/fliC or pcDNA3/IL-18 plasmid or both. These findings suggest that a chimeric plasmid encoding IL-18 fused to flagellin can be used as an adjuvant-like plasmid to improve the Th1 immune response, particularly for induction of CD3(+) CD8(+) IFN-γ-secreting cells in gag plasmid-vaccinated mice.

The regulation of sepiolite surface free energy and its impact on the thermal insulation property of coating.

Surface modification is used to regulate surface free energy of sepiolite with 3-glycidoxypropyltrimethoxysilanes (3-GPTMS), 3-methacryloxypropyltrimethoxysilanes (3-MAPTMS) and 3-mercaptopropyltrimethoxysilane (3-MPTMS). Through characterization by Fourier transform infrared spectroscopy, surface free energy, zeta potential and sedimentation measurements and infrared emissivity, it is found that the surface free energy of 3-MPTMS modified sepiolite decreases to 31.72 mJ/m2 and the percentage of polar component increases to 89.75%, thus leading to that the infrared emissivity of 3-MPTMS modified sepiolite increase to be higher than 0.8 and the dispersion of sepiolite has been improved. The excellent thermal insulation property of coating is prepared with 10% additive amount of 3-MPTMS modified sepiolite and the temperature difference between upper and lower box of modified sepiolite coatings is 10 degrees C which is higher than the untreated sepiolite.

Preparation and performance of thermal insulation energy saving coating materials for exterior wall.

Nano zinc oxide with a high refractive index has good thermal reflection performance, hollow glass microspheres have good thermal reflection and insulation performance, and sepiolite nanofibers with many nanostructural pores have good thermal insulation performance. The dispensability of nano zinc oxide in coating materials was improved by optimizing surface silane coupling agent modification process, leading to the good thermal reflection performance. The thermal insulation performance was improved by hollow glass microspheres and sepiolite nanofibers. On this basis, the thermal insulation coating materials were prepared by exploring the effect of amount, complex mode, and other factors of the above three kinds of functional fillers on the thermal reflection and insulation performance of coating materials. The results showed that the surface modification effect of nano zinc oxide was the best when the silane coupling agent addition was 6%. The reflection and insulation performance of the coatings were the best when the additions of modified nano zinc oxide, hollow glass microspheres, and sepiolite nanofibers were 3%, 4%, and 4%, respectively. Compared with the control coating materials, the thermal insulation effect was improved obviously, which was evaluated by the -13.5 degrees C increase of maximum temperature difference between the upper and the lower surfaces.

Channel microstructure and thermal insulation mechanism of sepiolite mineral nanofibers.

The longitudinal and cross sectional TEM images of sepiolite mineral nanofibers were prepared by cutting in the direction parallel and perpendicular to nanofibers, and the channel microstructure of sepiolite nanofibers was studied. The thermal insulation mechanism of sepiolite nanofibers was analyzed according to the diagrammatic sketch obtained from the above experimental method. The results showed that many discontinuously connected bending shape channels with about 23-26 nm in diameter existed in the center region of nanofibers, and many discontinuously connected irregular micropores and mesopores with the size of about 1-9 nm existed on the wall of nanofibers. The main reasons for the formation of channel microstructure in sepiolite nanofibers were their minerogenetic conditions and the interaction between acid and high-speed airflow in the process of nanofibers preparation, and bubbles in the hydrotherm played a significant role in the microstructure formation. The thermal insulation performance of sepiolite nanofibers could be attributed to obstructive and infrared radiative thermal insulation.

Types of Septic Cardiomyopathy: Prognosis and Influencing Factors - A Clinical Study.

Objective: To explore the prognostic outcomes associated with different types of septic cardiomyopathy and analyze the factors that exert an influence on these outcomes. Methods: The data collected within 24 hours of ICU admission included cardiac troponin I (cTnI), N-terminal pro-Brain Natriuretic Peptide (NT-proBNP); SOFA (sequential organ failure assessment) scores, and the proportion of vasopressor use. Based on echocardiographic outcomes, septic cardiomyopathy was categorized into left ventricular (LV) systolic dysfunction, LV diastolic dysfunction, and right ventricular (RV) systolic dysfunction. Differences between the mortality and survival groups, as well as between each cardiomyopathy subgroup and the non-cardiomyopathy group were compared, to explore the influencing factors of cardiomyopathy. Results: A cohort of 184 patients were included in this study, with LV diastolic dysfunction having the highest incidence rate (43.5%). The mortality group had significantly higher SOFA scores, vasopressor use, and cTnI levels compared to the survival group; the survival group had better LV diastolic function than the mortality group (p < 0.05 for all). In contrast to the non-cardiomyopathy group, each subgroup within the cardiomyopathy category exhibited elevated levels of cTnI. The subgroup with left ventricular diastolic dysfunction demonstrated a higher prevalence of advanced age, hypertension, diabetes mellitus, coronary artery disease, and an increased mortality rate; the RV systolic dysfunction subgroup had higher SOFA scores and NT-proBNP levels, and a higher mortality rate (P < 0.05 for all); the LV systolic dysfunction subgroup had a similar mortality rate (P > 0.05). Conclusion: Patients with advanced age, hypertension, diabetes mellitus, or coronary artery disease are more prone to develop LV diastolic dysfunction type of cardiomyopathy; cardiomyopathy subgroups had higher levels of cTnI. The RV systolic dysfunction cardiomyopathy subgroup had higher SOFA scores and NT-proBNP levels. The occurrence of RV systolic dysfunction in patients with sepsis significantly increased the mortality rate.