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Acute lung injury is a common complication of sepsis and characterized by a high mortality rate during hospitalization. Proanthocyanidin, which are abundant compounds found in various plants, have shown promising effects in preventing chronic diseases, and their oligomers have attracted attention for their high bioavailability and strong antioxidant activity. In recent years, there have been suggestions that oligomeric proanthocyanidin may possess lung-protective properties, however, the specific mechanisms involved have not been fully elucidated. This study reveals that the therapeutic efficacy of oligomeric proanthocyanidin in mitigating lung injury during sepsis, achieved through the reduction of neutrophil extracellular traps accumulation and the attenuation of inflammation, can be attributed to the capacity to safeguard the integrity of the intestinal mucus layer and the intestinal barrier. This protective action results in a decrease in endotoxin entry into the bloodstream and bacterial translocation, both of which can trigger neutrophil extracellular traps causing further lung injury.

In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.

Programmed death ligand 1 (PD-L1) strongly inhibits T cell activation, thereby aiding tumors in escaping the immune response. PD-L1 inhibitors have proven to be effective in the treatment of different types of cancer, including non-small cell lung cancer (NSCLC). Yet, the knowledge regarding the biological function of tumor-intrinsic PD-L1 in lung cancer remains obscure. In our study, we set the goal of determining the function of PD-L1 using overexpression and knockdown strategies. PD-L1 silencing resulted in decreased migratory and invasive ability of tumor cells, together with attenuated colony-forming capacity. Ectopic expression of PD-L1 showed the opposite effects, along with increased activities of MAPK and Wnt/β-catenin pathways, and the upregulation of Wnt/β-catenin target genes. Additionally, overexpression of PD-L1 was associated with dysregulated cellular and exosomal miRNAs involved in tumor progression and metastasis. In primary lung tumors, immunohistochemistry revealed that both PD1 and PD-L1 were highly expressed in squamous cell carcinoma (SCC) compared to adenocarcinoma (p = 0.045 and p = 0.036, respectively). In SCC, PD1 expression was significantly associated with tumor grading (p = 0.016). Taken together, our data suggest that PD-L1 may exert an oncogenic function in NSCLC through activating Wnt/β-catenin signaling, and may act as a potential diagnostic marker for lung SCC.

Immune checkpoint inhibitor (ICI) treatment is considered as an innovative approach for cancers. Since not every patient responded well to ICI therapy, it is imperative to screen out novel signatures to predict prognosis. Based on 407 gastric cancer (GC) samples retrieved from The Cancer Genome Atlas (TCGA), 36 immune-related hub genes were identified by weighted gene co-expression network analysis (WGCNA), and eight of them (RNASE2, CGB5, INHBE, DUSP1, APOA1, CD36, PTGER3, CTLA4) were used to formulate the Cox regression model. The obtained risk score was proven to be significantly correlated with overall survival (OS), consistent with the consequence of the Gene Expression Omnibus (GEO) cohort (n = 433). Then, the relationship between the risk score and clinical, molecular and immune characteristics was further investigated. Results showed that the low-risk subgroup exhibited higher mutation rate, more M1 macrophages, CD8+ and CD4+ T cells infiltrating, more active MHC-I, and bias to “IFN-γ Dominant” immune type, which is consistent with our current understanding of tumor prognostic risk. Furthermore, it is suggested that our model can accurately predict 1-, 2-, and 3-year OS of GC patients, and that it was superior to other canonical models, such as TIDE and TIS. Thus, these eight genes are probably considered as potential signatures to predict prognosis and to distinguish patient benefit from ICI, serving as a guiding individualized immunotherapy.

Background: Societal changes during the COVID-19 pandemic may affect childrens health behaviors and exacerbate disparities. This study aimed to describe childrens health behaviors during the COVID-19 pandemic, how they vary by sociodemographic characteristics, and the extent to which parent coping strategies mitigate the impact of pandemic-related financial strain on these behaviors. Methods: This study used pooled data from 50 cohorts in the Environmental influences on Child Health Outcomes Program. Children or parent proxies reported sociodemographic characteristics, health behaviors, and parent coping strategies. Results: Of 3315 children aged 3-17 years, 49% were female and 57% were non-Hispanic white. Children of parents who reported food access as a source of stress were 35% less likely to engage in a higher level of physical activity. Children of parents who changed their work schedule to care for their children had 82 fewer min/day of screen time and 13 more min/day of sleep compared with children of parents who maintained their schedule. Parents changing their work schedule were also associated with a 31% lower odds of the child consuming sugar-sweetened beverages. Conclusions: Parents experiencing pandemic-related financial strain may need additional support to promote healthy behaviors. Understanding how changes in parent work schedules support shorter screen time and longer sleep duration can inform future interventions.

BackgroundPerinatal quality improvement lacks valid tools to measure adverse hospital experiences disproportionately impacting Black mothers and birthing people. Measuring and mitigating harm requires using a framework that centers the lived experiences of Black birthing people in evaluating inequitable care, namely, obstetric racism. We sought to develop a valid patient-reported experience measure (PREM) of Obstetric Racism© in hospital-based intrapartum care designed for, by, and with Black women as patient, community, and content experts.MethodsPROMIS© instrument development standards adapted with cultural rigor methodology. Phase 1 included item pool generation, modified Delphi method, and cognitive interviews. Phase 2 evaluated the item pool using factor analysis and item response theory.ResultsItems were identified or written to cover 7 previously identified theoretical domains. 806 Black mothers and birthing people completed the pilot test. Factor analysis concluded a 3 factor structure with good fit indices (CFI = 0.931-0.977, RMSEA = 0.087-0.10, R2  > .3, residual correlation

The eighth member of Upper Triassic Yanchang Formation (Chang 8) is the major oil reservoir unit in Jiyuan oil field, though with the high potential for oil exploration. The Chang 8 sandstones are characterized with low porosity, low permeability and strong microscopic heterogeneities due to the complex deep-burial diagenetic history. Detailed petrological studies by thin section, X-ray diffraction, scanning electron microscopy, core analysis have been used to investigate the lithogology characteristics, diagenesis, diagenetic minerals and their coupling impacts on reservoir property. The results show that Chang 8 sandstones comprise fine to mediumgrained subarkoses, feldspathic litharenites. The pore systems are dominated by remaining primary intergranular pores, secondary dissolution porosity and micropores. Then, five diagenetic facies were divided in Chang 8 sandstones based on the type and degree of diagenesis, diagenetic minerals assemblages and their coupling effects on the reservoir quality. They consist of grain-coating chlorite weak dissolution facies, unstable component dissolution facies, tight compaction facies, clay minerals filling facies and carbonate cementation facies. The well logging response characteristics of various diagenetic facies are summarized on Gamma Ray (GR), Density Logging (DEN), Acoustic (AC), Compensated Neutron Logging (CNL), and True Formation Resistivity (RT) by translating diagenetic facies to well log responses, the diagenetic facies were defined by a set of log responses, and porosity, permeability ranges for each diagenetic facies were determined from core analyses. Well log data of Luo 13 and Chi 212 are processed to evaluate the accuracy of the predictive model. The diagenetic facies are predicted on the vertical profile based on the generated model. Predicted distribution of diagenetic facies precisely coincide with the microscopic observations, and diagenetic facies in Chang 8 sandstones are generally locally distributed. Tight compaction and carbonate cementation diagenetic facies mainly correspond to the non-reservoir and dry layers of Chang 8 sandstones, layers with higher oil potentials are mainly developed in the grain-coating chlorite weak dissolution and unstable component dissolution diagenetic facies. By translating diagenetic facies to well log responses, diagenetic facies and reservoir properties of intervals that lack core control could be predicted with the same or similar log responses.

BACKGROUND: High density lipoprotein (HDL) was reported to decrease plasma glucose and promote insulin secretion in type 2 diabetes patients. This investigation was designed to determine the effects and mechanisms of HDL on glucose uptake in adipocytes and glycogen synthesis in muscle cells. METHODS AND RESULTS: Actions of HDL on glucose uptake and GLUT4 translocation were assessed with 1-[(3)H]-2-deoxyglucose and plasma membrane lawn, respectively, in 3T3-L1 adipocytes. Glycogen analysis was performed with amyloglucosidase and glucose oxidase-peroxidase methods in normal and palmitate-treated L6 cells. Small interfering RNA was used to observe role of scavenger receptor type I (SR-BI) in glucose uptake of HDL. Corresponding signaling molecules were detected by immunoblotting. HDL stimulated glucose uptake in a time- and concentration-dependent manner in 3T3-L1 adipocytes. GLUT4 translocation was significantly increased by HDL. Glycogen deposition got enhanced in L6 muscle cells paralleling with elevated glycogen synthase kinase3 (GSK3) phosphorylation. Meanwhile, increased phosphorylations of Akt-Ser473 and AMP activated protein kinase (AMPK) α were detected in 3T3-L1 adipocytes. Glucose uptake and Akt-Ser473 activation but not AMPK-α were diminished in SR-BI knock-down 3T3-L1 cells. CONCLUSIONS: HDL stimulates glucose uptake in 3T3-L1 adipocytes through enhancing GLUT4 translocation by mechanisms involving PI3K/Akt via SR-BI and AMPK signaling pathways, and increases glycogen deposition in L6 muscle cells through promoting GSK3 phosphorylation.

Abstract Background Pediatric self-report should be considered the standard for measuring patient reported outcomes (PRO) among children. However, circumstances exist when the child is too young, cognitively impaired, or too ill to complete a PRO instrument and a proxy-report is needed. This paper describes the development process including the proxy cognitive interviews and large-field-test survey methods and sample characteristics employed to produce item parameters for the Patient Reported Outcomes Measurement Information System (PROMIS) pediatric proxy-report item banks. Methods The PROMIS pediatric self-report items were converted into proxy-report items before undergoing cognitive interviews. These items covered six domains (physical function, emotional distress, social peer relationships, fatigue, pain interference, and asthma impact). Caregivers (n = 25) of children ages of 5 and 17 years provided qualitative feedback on proxy-report items to assess any major issues with these items. From May 2008 to March 2009, the large-scale survey enrolled children ages 8-17 years to complete the self-report version and caregivers to complete the proxy-report version of the survey (n = 1548 dyads). Caregivers of children ages 5 to 7 years completed the proxy report survey (n = 432). In addition, caregivers completed other proxy instruments, PedsQL™ 4.0 Generic Core Scales Parent Proxy-Report version, PedsQL™ Asthma Module Parent Proxy-Report version, and KIDSCREEN Parent-Proxy-52. Results Item content was well understood by proxies and did not require item revisions but some proxies clearly noted that determining an answer on behalf of their child was difficult for some items. Dyads and caregivers of children ages 5-17 years old were enrolled in the large-scale testing. The majority were female (85%), married (70%), Caucasian (64%) and had at least a high school education (94%). Approximately 50% had children with a chronic health condition, primarily asthma, which was diagnosed or treated within 6 months prior to the interview. The PROMIS proxy sample scored similar or better on the other proxy instruments compared to normative samples. Conclusions The initial calibration data was provided by a diverse set of caregivers of children with a variety of common chronic illnesses and racial/ethnic backgrounds. The PROMIS pediatric proxy-report item banks include physical function (mobility n = 23; upper extremity n = 29), emotional distress (anxiety n = 15; depressive symptoms n = 14; anger n = 5), social peer relationships (n = 15), fatigue (n = 34), pain interference (n = 13), and asthma impact (n = 17).

Abstract Background Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. Results We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles. Conclusion The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at http://poinet.bioinformatics.tw/.

Abstract Background The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. Results Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO http://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. Conclusion This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.