An Experimental Study on the Frictional Behavior of Ultrathin Metal Sheets at Elevated Temperatures.

Hot forming is an effective approach for improving the formability of ultrathin metal sheets, such as those made of stainless steel and pure titanium. However, the increased friction coefficient between the tool and the high-temperature metal sheet negatively affects material flow during hot forming, potentially resulting in severe local thinning or even cracking. This study explores the frictional behavior of 0.1 mm thick ferritic stainless steel (FSS) and commercially pure titanium (CP-Ti) sheets at elevated temperatures. A friction testing apparatus was developed to measure the friction coefficients of these metal sheets from room temperature (25 °C) up to 600 °C. The friction coefficient of the FSS sheet increased monotonically with temperature, whereas that of the CP-Ti sheet first increased and then decreased. Post-friction testing microscopic examination demonstrated that built-up edges formed on the surfaces of the friction blocks when rubbed against the stainless steel, contributing to the higher friction coefficients. This study provides a foundation for understanding frictional behavior during the hot forming of ultrathin metal sheets.

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a type of cancer and has some of the highest rates of both incidence and mortality globally. Developing accurate models for survival prediction provides a basis clinical judgment and decision making, improving the survival status of ESCC patients. Although many predictive models have been developed, there is still lack of highly accurate survival prediction models for ESCC patients. This study proposes a novel survival prediction model for ESCC patients based on principal component analysis (PCA) and least-squares support vector machine (LSSVM) optimized by an improved dragonfly algorithm with hybrid strategy (HSIDA). The original 17 blood indicators are condensed into five new variables by PCA, reducing data dimensionality and redundancy. An improved dragonfly algorithm based on hybrid strategy is proposed, which addresses the limitations of dragonfly algorithm, such as slow convergence, low search accuracy and insufficient vitality of late search. The proposed HSIDA is used to optimize the regularization parameter and kernel parameter of LSSVM, improving the prediction accuracy of the model. The proposed model is validated on the dataset of 400 patients with ESCC in the clinical database of First Affiliated Hospital of Zhengzhou University and the State Key Laboratory of Esophageal Cancer Prevention and Control of Henan Province. The experiment results demonstrate that the proposed HSIDA-LSSVM has the best prediction performance than LSSVM, HSIDA-BP, IPSO-LSSVM, COA-LSSVM and IBA-LSSVM. The proposed model achieves the accuracy of 96.25%, sensitivity of 95.12%, specificity of 97.44%, precision of 97.50%, and F1 score of 96.30%.

Efficient Passive Daytime Radiative Cooling by Hierarchically Designed Films Integrating Robust Durability.

Surfaces with efficient passive daytime radiative cooling (PDRC) are underpinned by maximizing both solar reflection and thermal radiation to the outer space at no additional energy cost. Despite notable progress, their practical applications are of great challenge due to their complicated fabrication processes, easy contamination and damage, and high costs. Herein, we fabricate a hierarchically designed passive daytime radiative cooling film (HPRF) comprising cost-effective Al2O3 particles and poly(dimethylsiloxane) (PDMS) via a simple phase separation method. The designed film possesses a high solar spectrum reflectance of ∼0.96 and a mid-infrared emittance of ∼0.95, achieving a ∼12.4 °C subambient cooling under direct solar irradiation. This excellent PDRC is due to the efficient Mie scattering of sunlight by hierarchical micro-/nanostructures and selected molecular vibrations of PDMS combined with the phonon polariton resonance of Al2O3 particles, respectively. Moreover, the designed HPRF is accompanied with robust durability endowed by superior self-cleaning, flexibility, and anti-ultraviolet radiation that can present substantial application promises of thermal management in various electronic devices and wearable products.

An electrothermal platform for active droplet manipulation.

The smart control of droplet transport through surface structures and external fields provides exciting opportunities in engineering fields of phase change heat transfer, biomedical chips, and energy harvesting. Here we report the wedge-shaped slippery lubricant-infused porous surface (WS-SLIPS) as an electrothermal platform for active droplet manipulation. WS-SLIPS is fabricated by infusing a wedge-shaped superhydrophobic aluminum plate with phase-changeable paraffin. While the surface wettability of WS-SLIPS can be readily and reversibly switched by the freezing-melting cycle of paraffin, the curvature gradient of the wedge-shaped substrate automatically induces an uneven Laplace pressure inside the droplet, endowing WS-SLIPS the ability to directionally transport droplets without any extra energy input. We demonstrate that WS-SLIPS features spontaneous and controllable droplet transport capability to initiate, brake, lock, and resume the directional motion of various liquid droplets including water, saturated NaCl solution, ethanol solution, and glycerol, under the control of preset DC voltage (∼12 V). In addition, the WS-SLIPS can automatically repair surface scratches or indents when heated and retain the full liquid-manipulating capability afterward. The versatile and robust droplet manipulation platform of WS-SLIPS can be further used in practical scenarios such as laboratory-on-a-chip settings, chemical analysis and microfluidic reactors, paving a new path to develop advanced interface for multifunctional droplet transport.

Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study.

Background: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC). Methods: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362. Findings: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers. Interpretation: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG). Funding: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).

Chitosan based photothermal scaffold fighting against bone tumor-related complications: Recurrence, infection, and defects.

After bone tumor resection, the severe complications including cancer recurrence, infection and extensive bone loss are still a challenge. To address this problem, a chitosan/hydroxypropyltrimethyl ammonium chloride chitosan/hydroxyapatite/black phosphorus (CS/HC/HA/BP) hybrid photothermal scaffold with a multistage photothermal strategy was developed. HC-stabilized BP endowed the scaffold with simultaneous antitumor/antibacterial properties under photothermal stimulation of <50 °C. Subsequently, excellent osteogenesis could be achieved with mild hyperthermia stimulation (∼42 °C) through up-regulating the expressions of heat shock proteins. Under NIR irradiation, the scaffold could eliminate 95 % of osteosarcoma cells as well as 97 % of E. coli and 92 % of S. aureus. The osteogenic gene expressions of ALP, COL 1A1, and OCN in photothermal group were 1.64, 1.31 and 1.27 folds higher than that of non-photothermal group in vivo, respectively. Therefore, the obtained scaffold synergized with multistage photothermal strategy was effective and a reference for the treatment of other complex diseases.

High HDAC5 expression correlates with a poor prognosis and the tumor immune microenvironment in gastric cancer.

Background: Gastric cancer (GC) is one of the most common malignant tumors worldwide and has a poor prognosis. Previous studies have confirmed differential histone deacetylase 5 (HDAC5) expression in various common tumors. HDAC5 is also associated with prognosis and plays a role in cancer cell proliferation, invasion, and metastasis, as well as the tumor immune microenvironment (TIME). However, HDAC5 in GC is not well understood. The aims of study were to investigate the HDAC5 expression correlates with prognosis and the TIME in GC. Methods: A total of 355 tumor tissues and 300 matched paracancerous tissues were collected from GC patients who underwent radical surgery. The correlation between clinicopathological characteristics, immune-related factors and HDAC5 expression were analyzed. Univariate and multivariate Cox regression analyses were used to confirm the independent factors affecting the prognosis of GC. Survival curves were plotted using the Kaplan-Meier method. Furthermore, the stomach adenocarcinoma (STAD) dataset was downloaded from The Cancer Genome Atlas (TCGA). The expression levels of HDAC5 were defined as high or low using the gene set variance analysis (GSVA) package. Identification of differential immune infiltrating cells was performed by single sample gene set enrichment analysis (ssGSEA). Results: The positive expression rate of HDAC5 was higher in tumor tissues than in paracancerous tissues (38.87% vs. 14.67%, P<0.001). Univariate and multivariate Cox analyses showed that HDAC5 was an independent factor affecting the prognosis of GC. The HDAC5 expression levels were correlated with age (P=0.046), smoking history (P=0.001), Lauren type (P=0.042), and pM stage (P=0.012). Furthermore, these levels were correlated with CD3+ T cells (P<0.001), CD4+ T cells (P<0.001), CD8+ T cells (P<0.001) and PD-L1 (P=0.001). Further analysis of patients in TCGA cohort confirmed the association between HDAC5 and activated CD4 T cells, activated CD8 T cells, and other immune infiltrating cells. Conclusions: HDAC5 is highly expressed in tumor tissues and is an independent factor affecting the prognosis of GC. Additionally, HDAC5 can regulate the TIME of GC and is a potential target for immunotherapy.

High CHAF1A Expression Levels Are Positively-Correlated with PD-L1 Expression and Indicate Poor Prognosis in Gastric Cancer.

Objective: The aim of this study was to analyze the association between the expression of chromatin assembly factor 1 subunit A (CHAF1A) in gastric cancer (GC) and clinicopathological features, disease prognosis, and expression of programmed cell death-ligand 1 (PD-L1). Material and Methods. A total of 140 GC tissue specimens were collected between January 2013 and December 2017. CHAF1A expression in GC and paracancerous tissues was determined. Then, the associations between CHAF1A expression level in the collected tissues and clinicopathological features as well as PD-L1 expression level were investigated. Cox regression analyses were carried out to determine whether CHAF1A is an independent prognostic factor for GC. Finally, the association between CHAF1A expression levels and survival of the GC patients was investigated. Results: A significantly higher level of CHAF1A expression in GC tissues was found compared to that in paracancerous tissues (p=0.042). CHAF1A expression level in GC tissues was found to be strongly associated with family history (p=0.005), smoking history (p=0.016), T stage (p=0.001), tumor marker AFP (p=0.017), tumor marker CEA (p=0.027), and PD-L1 expression (p=0.029). CHAF1A expression was also found to be positively correlated to PD-L1 expression (p=0.012). Moreover, high CHAF1A expression levels were found to lead to poor prognosis (p=0.019). Univariate and multivariate analyses all showed that CHAF1A was an independent poorer prognostic factor for gastric cancer (p=0.021, HR = 1.175, 95% CI: 1.090-2.890 for univariate analyses; p=0.014, HR = 2.191, 95% CI:1.170-4.105 for multivariate analyses). A high level of CHAF1A expression was thus found to be an independent risk factor for GC prognosis. Conclusion: High CHAF1A expression is associated with poor GC prognosis and positively correlated to PD-L1 expression. Thus, CHAF1A expression level may be used as a novel biomarker for GC diagnosis.

SFRP4 Is a Potential Biomarker for the Prognosis and Immunotherapy for Gastric Cancer.

Purpose: Secreted frizzled-related protein 4 (SFRP4) is a member of the SFRP family, which functions as either a tumor suppressor or a prooncogenic factor in distinct tumor types. Our research aimed to explore the expression of SFRP4 in gastric cancer, its prognostic significance, and its relationship with immune cell infiltration. Materials and Methods: Gastric cancer and paracancerous tissue specimens from surgically resected gastric cancer patients were collected to construct tissue microarrays, and immunohistochemistry was used to detect the expression of SFRP4, PD-L1, CD3+ T, CD4+ T, and CD8+ T in these microarrays. The differential expression of SFRP4 and its relationship with the immune microenvironment were evaluated using the TIMER and TISIDB databases. Finally, patient survival was assessed. Results: SFRP4 expression was elevated in gastric cancer tissues and linked to a poor prognosis (P=0.021). The 5-year survival rate for patients with high SFRP4 expression was only 39.81% but reached 60.02% for patients with low SFRP4 expression. Increased SFRP4 expression correlated with high CD8+ T-cell infiltration (P=0.015) and positive PD-L1 expression (P=0.036). High SFRP4 expression was an independent predictor of overall survival (P=0.024 in univariable analysis, P=0.011 in multivariable analysis). Using online databases, we found that SFRP4 expression was higher in gastric cancer tissues and substantially was associated with the immune microenvironment. Conclusion: SFRP4 is an oncogenic driver that can predict patient survival time in gastric cancer, as well as an important immune-related factor. SFRP4 may be important for guiding immunotherapy in gastric cancer patients.

One-Step Fabrication of Hot-Water-Repellent Surfaces.

Hot-water repellency is of great challenge on traditional superhydrophobic surfaces due to the condensation of tiny droplets within the cavities of surface textures, which builds liquid bridges to connect the substrate and hot water and thus destroys the surface water-repellence performance. For the unique structural features and scales, current approaches to fabricate surfaces with hot-water repellency are always complicated and modified by fluorocarbon. Here, we propose a facile and fluorine-free one-step vapor-deposition method for fabricating excellent hot-water-repellent surfaces, which at room temperature even repel water droplets of temperature up to 90 °C as well as other normal-temperature droplets with surface tension higher than 48.4 mN/m. We show that whether the unique hot-water repellency is achieved depends on a trade-off between the solid-liquid contact time and hot-vapor condensation time, which determines the probability of formation of liquid bridges between the substrate and hot-water. Moreover, the designed surfaces exhibit excellent self-cleaning performance in some specific situations, such as oil medium, hot water and condensation environments. We envision that this facile and fluorine-free strategy for fabricating excellent hot-water-repellent surfaces could be valuable in popularizing their practical applications.

CD3D Is an Independent Prognostic Factor and Correlates With Immune Infiltration in Gastric Cancer.

The protein encoded by CD3D is part of the T-cell receptor/CD3 complex (TCR/CD3 complex) and is involved in T-cell development and signal transduction. Previous studies have shown that CD3D is associated with prognosis and treatment response in breast, colorectal, and liver cancer. However, the expression and clinical significance of CD3D in gastric cancer are not clear. In this study, we collected 488 gastric cancer tissues and 430 paired adjacent tissues to perform tissue microarrays (TMAs). Then, immunohistochemical staining of CD3D, CD3, CD4, CD8 and PD-L1 was conducted to investigate the expression of CD3D in gastric cancer and the correlation between the expression of CD3D and tumor infiltrating lymphocytes (TILs) and PD-L1. The results showed that CD3D was highly expressed in gastric cancer tissues compared with paracancerous tissues (P<0.000). Univariate and multivariate analyses showed that CD3D was an independent good prognostic factor for gastric cancer (P=0.004, HR=0.677, 95%CI: 0.510-0.898 for univariate analyses; P=0.046, HR=0.687, 95%CI: 0.474-0.994 for multivariate analyses). In addition, CD3D was negatively correlated with the tumor location, Borrmann type and distant metastasis (P=0.012 for tumor location; P=0.007 for Borrmann type; P=0.027 for distant metastasis). In addition, the expression of CD3D was highly positively correlated with the expression of CD3, CD4, CD8, and PD-L1, and the combination of CD3D with CD3, CD4, CD8 and PD-L1 predicted the best prognosis (P=0.043). In summary, CD3D may play an important regulatory role in the tumor immune microenvironment of gastric cancer and may serve as a potential indicator of prognosis and immunotherapy response.

QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research.

BACKGROUND: While doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process requires information technology (IT) skills, which is difficult for most doctors in China. METHODS: In this paper, we build a novel tool QAnalysis, where doctors enter their analytic requirements in their natural language and then the tool returns charts and tables to the doctors. For a given question from a user, we first segment the sentence, and then we use grammar parser to analyze the structure of the sentence. After linking the segmentations to concepts and predicates in knowledge graphs, we convert the question into a set of triples connected with different kinds of operators. These triples are converted to queries in Cypher, the query language for Neo4j. Finally, the query is executed on Neo4j, and the results shown in terms of tables and charts are returned to the user. RESULTS: The tool supports top 50 questions we gathered from two hospital departments with the Delphi method. We also gathered 161 questions from clinical research papers with statistical requirements on EMR data. Experimental results show that our tool can directly cover 78.20% of these statistical questions and the precision is as high as 96.36%. Such extension is easy to achieve with the help of knowledge-graph technology we have adopted. The recorded demo can be accessed from https://github.com/NLP-BigDataLab/QAnalysis-project . CONCLUSION: Our tool shows great flexibility in processing different kinds of statistic questions, which provides a convenient way for doctors to get statistical results directly in natural language.

Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition.

Clinical named entity recognition aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research. In recent years, deep neural networks have achieved significant success in named entity recognition and many other natural language processing tasks. Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets. However, these data-driven methods typically lack the capability of processing rare or unseen entities. Previous statistical methods and feature engineering practice have demonstrated that human knowledge can provide valuable information for handling rare and unseen cases. In this paper, we propose a new model which combines data-driven deep learning approaches and knowledge-driven dictionary approaches. Specifically, we incorporate dictionaries into deep neural networks. In addition, two different architectures that extend the bi-directional long short-term memory neural network and five different feature representation schemes are also proposed to handle the task. Computational results on the CCKS-2017 Task 2 benchmark dataset show that the proposed method achieves the highly competitive performance compared with the state-of-the-art deep learning methods.