Immune landscape of hepatocellular carcinoma tumor microenvironment identifies a prognostic relevant model.

Background: Various studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression. Methods: We integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients. Results: To describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration. Conclusions: Taken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

Secondary correction of nasal deformities in cleft lip patients using acellular dermal matrix grafting on the nasal tip with open rhinoplasty.

Secondary nasal deformities in patients with unilateral cleft lip represent surgical challenges. Open rhinoplasty involving repositioning of the lower lateral cartilage has been shown to be a suitable technique for patients with cleft lip and nose deformities. This study aimed to explore a particular method of rhinoplasty and to assess the aesthetic outcomes for secondary unilateral cleft lip and nose deformities following its use. Fifty-seven patients treated for secondary unilateral cleft lip nasal deformities from January 2012 to December 2018 were enrolled in the study. Open rhinoplasty combined with a reverse-U incision and acellular dermal matrix grafting on the nasal tip was performed in all patients by the same surgeon. In our follow-up study we evaluated the results by measuring angles on photographs and scoring the appearance before and after operation. Data were statistically analysed using the t test. Appearances were improved in all patients. Both the alar base-nasal tip-columellar base angle and the nostril axis angles were smaller postoperatively (p < 0.001). According to the outcome scores, most patients (53/57) agreed that there was an obvious improvement in the appearance of their noses following surgery, and overall they were satisfied with the results of the revision procedure. A distinct improvement in nasal appearance can be achieved with this rhinoplasty. Our surgical method is effective and reliable in patients with secondary unilateral cleft lip and nose deformities, and is worth promoting in the clinic.

FIT-based risk-stratification model effectively screens colorectal neoplasia and early-onset colorectal cancer in Chinese population: a nationwide multicenter prospective study.

No fully validated risk-stratification strategies have been established in China where colonoscopies resources are limited. We aimed to develop and validate a fecal immunochemical test (FIT)-based risk-stratification model for colorectal neoplasia (CN); 10,164 individuals were recruited from 175 centers nationwide and were randomly allocated to the derivation (n = 6776) or validation cohort (n = 3388). Multivariate logistic analyses were performed to develop the National Colorectal Polyp Care (NCPC) score, which formed the risk-stratification model along with FIT. The NCPC score was developed from eight independent predicting factors and divided into three levels: low risk (LR 0-14), intermediate risk (IR 15-17), and high risk (HR 18-28). Individuals with IR or HR of NCPC score or FIT+ were classified as increased-risk individuals in the risk-stratification model and were recommended for colonoscopy. The IR/HR of NCPC score showed a higher prevalence of CNs (21.8%/32.8% vs. 11.0%, P < 0.001) and ACNs (4.3%/9.2% vs. 2.0%, P < 0.001) than LR, which was also confirmed in the validation cohort. Similar relative risks and predictive performances were demonstrated between non-specific gastrointestinal symptoms (NSGS) and asymptomatic cohort. The risk-stratification model identified 73.5% CN, 82.6% ACN, and 93.6% CRC when guiding 52.7% individuals to receive colonoscopy and identified 55.8% early-onset ACNs and 72.7% early-onset CRCs with only 25.6% young individuals receiving colonoscopy. The risk-stratification model showed a good risk-stratification ability for CN and early-onset CRCs in Chinese population, including individuals with NSGS and young age.

Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers.

Visual reasoning is critical in many complex visual tasks in medicine such as radiology or pathology. It is challenging to explicitly explain reasoning processes due to the dynamic nature of real-time human cognition. A deeper understanding of such reasoning processes is necessary for improving diagnostic accuracy and computational tools. Most computational analysis methods for visual attention utilize black-box algorithms which lack explainability and are therefore limited in understanding the visual reasoning processes. In this paper, we propose a computational method to quantify and dissect visual reasoning. The method characterizes spatial and temporal features and identifies common and contrast visual reasoning patterns to extract significant gaze activities. The visual reasoning patterns are explainable and can be compared among different groups to discover strategy differences. Experiments with radiographers of varied levels of expertise on 10 levels of visual tasks were conducted. Our empirical observations show that the method can capture the temporal and spatial features of human visual attention and distinguish expertise level. The extracted patterns are further examined and interpreted to showcase key differences between expertise levels in the visual reasoning processes. By revealing task-related reasoning processes, this method demonstrates potential for explaining human visual understanding.

A neotropical Miocene pollen database employing image-based search and semantic modeling.

PREMISE OF THE STUDY: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • METHODS: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • RESULTS: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • DISCUSSION: Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.