Advances in systemic immune inflammatory indices in non-small cell lung cancer: A review.

Lung cancer is one of the most prevalent cancers globally, with non-small cell lung cancers constituting the majority. These cancers have a high incidence and mortality rate. In recent years, a growing body of research has demonstrated the intricate link between inflammation and cancer, highlighting that inflammation and cancer are inextricably linked and that inflammation plays a pivotal role in cancer development, progression, and prognosis of cancer. The Systemic Immunoinflammatory Index (SII), comprising neutrophil, lymphocyte, and platelet counts, is a more comprehensive indicator of the host's systemic inflammation and immune status than a single inflammatory index. It is widely used in clinical practice due to its cost-effectiveness, simplicity, noninvasiveness, and ease of acquisition. This paper reviews the impact of SII on the development, progression, and prognosis of non-small cell lung cancer.

Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning.

BACKGROUND: Computer-aided tongue and face diagnosis technology can make Traditional Chinese Medicine (TCM) more standardized, objective and quantified. However, many tongue images collected by the instrument may not meet the standard in clinical applications, which affects the subsequent quantitative analysis. The common tongue diagnosis instrument cannot determine whether the patient has fully extended the tongue or collected the face. OBJECTIVE: This paper proposes an image quality control algorithm based on deep learning to verify the eligibility of TCM tongue diagnosis images. METHODS: We firstly gathered enough images and categorized them into five states. Secondly, we preprocessed the training images. Thirdly, we built a ResNet34 model and trained it by the transfer learning method. Finally, we input the test images into the trained model and automatically filter out unqualified images and point out the reasons. RESULTS: Experimental results show that the model's quality control accuracy rate of the test dataset is as high as 97.06%. Our methods have the strong discriminative power of the learned representation. Compared with previous studies, it can guarantee subsequent tongue image processing. CONCLUSIONS: Our methods can guarantee the subsequent quantitative analysis of tongue shape, tongue state, tongue spirit, and facial complexion.

Application of visual transformer in renal image analysis.

Deep Self-Attention Network (Transformer) is an encoder-decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis.

Rice straw returning enhances cadmium activation by accelerating iron cycling thus hydroxyl radical production in paddy soils during drainage.

Straw returning is widely found elevating the bioavailability of cadmium (Cd) in paddy soils with unclear biogeochemical mechanisms. Here, a series of microcosm incubation experiments were conducted and spectroscopic and microscopic analyses were employed. The results showed that returning rice straw (RS) efficiently increased amorphous Fe and low crystalline Fe (II) to promote the production of hydroxyl radicals (OH) thus Cd availability in paddy soils during drainage. On the whole, RS increased OH and extractable Cd by 0.2-1.4 and 0.1-3.3 times, respectively. While the addition of RS effectively improved the oxidation rate of structural Fe (II) mineral (i.e., FeS) to enhance soil Cd activation (up to 38.5 %) induced by the increased OH (up to 69.2 %). Additionally, the existence of CO32- significantly increased the efficiency level on OH production and Cd activation, which was attributed to the improved reactivity of Fe (II) by CO32- in paddy soils. Conclusively, this study emphasizes risks of activating soil Cd induced by RS returning-derived OH, providing a new insight into evaluating the safety of straw recycling.

Tissue-derived exosome proteomics identifies promising diagnostic biomarkers for esophageal cancer.

Esophageal cancer (EC) is a fatal digestive disease with a poor prognosis and frequent lymphatic metastases. Nevertheless, reliable biomarkers for EC diagnosis are currently unavailable. Accordingly, we have performed a comparative proteomics analysis on cancer and paracancer tissue-derived exosomes from eight pairs of EC patients using label-free quantification proteomics profiling and have analyzed the differentially expressed proteins through bioinformatics. Furthermore, nano-flow cytometry (NanoFCM) was used to validate the candidate proteins from plasma-derived exosomes in 122 EC patients. Of the 803 differentially expressed proteins discovered in cancer and paracancer tissue-derived exosomes, 686 were up-regulated and 117 were down-regulated. Intercellular adhesion molecule-1 (CD54) was identified as an up-regulated candidate for further investigation, and its high expression in cancer tissues of EC patients was validated using immunohistochemistry, real-time quantitative PCR (RT-qPCR), and western blot analyses. In addition, plasma-derived exosome NanoFCM data from 122 EC patients concurred with our proteomic analysis. The receiver operating characteristic (ROC) analysis demonstrated that the AUC, sensitivity, and specificity values for CD54 were 0.702, 66.13%, and 71.31%, respectively, for EC diagnosis. Small interference (si)RNA was employed to silence the CD54 gene in EC cells. A series of assays, including cell counting kit-8, adhesion, wound healing, and Matrigel invasion, were performed to investigate EC viability, adhesive, migratory, and invasive abilities, respectively. The results showed that CD54 promoted EC proliferation, migration, and invasion. Collectively, tissue-derived exosomal proteomics strongly demonstrates that CD54 is a promising biomarker for EC diagnosis and a key molecule for EC development.

Research trends in cardiovascular tissue engineering from 1992 to 2022: a bibliometric analysis.

Background: Cardiovascular tissue engineering (CTE) is a promising technique to treat incurable cardiovascular diseases, such as myocardial infarction and ischemic cardiomyopathy. Plenty of studies related to CTE have been published in the last 30 years. However, an analysis of the research status, trends, and potential directions in this field is still lacking. The present study applies a bibliometric analysis to reveal CTE research trends and potential directions. Methods: On 5 August 2022, research articles and review papers on CTE were searched from the Web of Science Core Collection with inclusion and exclusion criteria. Publication trends, research directions, and visual maps in this field were obtained using Excel (Microsoft 2009), VOSviewer, and Citespace software. Results: A total of 2,273 documents from 1992 to 2022 were included in the final analysis. Publications on CTE showed an upward trend from 1992 [number of publications (Np):1] to 2021 (Np:165). The United States (Np: 916, number of citations: 152,377, H-index: 124) contributed the most publications and citations in this field. Research on CTE has a wide distribution of disciplines, led by engineering (Np: 788, number of citations: 40,563, H-index: 105). "Functional maturation" [red cluster, average published year (APY): 2018.63, 30 times], "cell-derived cardiomyocytes" (red cluster, APY: 2018.43, 46 times), "composite scaffolds" (green cluster, APY: 2018.54, 41 times), and "maturation" (red cluster, APY: 2018.17, 84 times) are the main emerging keywords in this area. Conclusion: Research on CTE is a hot research topic. The United States is a dominant player in CTE research. Interdisciplinary collaboration has played a critical role in the progress of CTE. Studies on functional maturation and the development of novel biologically relevant materials and related applications will be the potential research directions in this field.

Imaging and pathological characteristics, treatment, and prognosis of pulmonary sequestration-A retrospective study of 13 cases.

OBJECTIVE: This study aimed to summarize and analyze the characteristics of pulmonary sequestration to improve our understanding of this disease. METHODS: Between January 2019 and April 2023, the clinical data of 13 patients with pulmonary sequestration underwent surgical treatment at the First Affiliated Hospital of Gannan Medical University. RESULTS: The male-to-female ratio was 4:9, the age was 0.5 to 60 years, and the average age was 38 ± 19 years. There were 10 and 3 cases of intralobar and extralobar pulmonary sequestration, respectively. Chest enhanced computed tomography (CT) and three-dimensional vascular reconstruction showed that the abnormal blood vessels were derived from the descending thoracic aorta in nine cases and from other blood vessels in four cases. Three patients underwent thoracoscopic lobectomy, two underwent thoracoscopic segmentectomy, and eight underwent thoracoscopic wedge resection. All the patients successfully completed the surgery and were discharged postoperatively. CONCLUSIONS: Some patients with pulmonary sequestration exhibit no obvious symptoms. Patients with clinical symptoms are easily confused for pneumonia, bronchial cysts, lung abscesses, and lung tumors; therefore, patients with pulmonary sequestration are prone to missed diagnosis and misdiagnosis. Currently, enhanced chest CT combined with three-dimensional vascular reconstruction can accurately show the course, branches, and relationship with the mass of the feeding artery. Routine pathological examination is helpful to further clarify the diagnosis of pulmonary sequestration. Minimally invasive thoracoscopic surgery is the preferred treatment for patients with pulmonary sequestration. Surgical resection is safe and feasible, and satisfactory results are typically obtained.

TAK-242 protects against oxygen-glucose deprivation and reoxygenation-induced injury in brain microvascular endothelial cells and alters the expression pattern of lncRNAs.

Background: Deep hypothermic circulatory arrest (DHCA) is a technique used during the surgical treatment of aneurysms of the thoracic aorta in adult patients, and complex congenital heart disease in neonates. And brain microvascular endothelial cells (BMECs) are essential components of the cerebrovascular network and participate in maintaining the blood-brain barrier (BBB) and brain function. In our previous study, we found that oxygen-glucose deprivation and reoxygenation (OGD/R) activated Toll-like receptor 4 (TLR4) signaling in BMECs, and induced pyroptosis and inflammation. In this study, we further investigated the potential mechanism of ethyl(6R)-6-[N-(2-Chloro-4-fluorophenyl) sulfamoyl] cyclohex-1-ene-1-carboxylate (TAK-242) on BMECs under OGD/R, as in patients with sepsis, the TAK-242 was tested in clinical trials. Methods: To confirm the function of TAK-242 on BMECs under OGD/R, cell viability, inflammatory factors, inflammation-associated pyroptosis, and nuclear factor-κB (NF-κB) signaling were determined using Cell Counting Kit-8 (CCK-8) assay, enzyme-linked immunosorbent assay (ELISA), and western blotting, respectively. To investigate the lncRNAs associated with TLR4 during OGD/R, long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) expression patterns were profiled with RNA deep sequencing. Moreover, to confirm whether lncRNA-encoded short peptides, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used. Results: Relative control group, OGD/R inhibited the cell viability, increased the section of inflammatory factors secretion, including IL-1ß, IL-6, and TNF-α, and promoted the pathways of TLR4/NLRP3/Caspase-1 and TLR4/NF-κB. However, TAK-242 + OGD/R group promoted OGD/R cell viability, decreased OGD/R-induced inflammatory factors secretion, and inhibited the pathways of TLR4/NLRP3/Caspase-1 and TLR4/NF-κB. In addition, AABR07000411.1, AABR070006957.1, and AABR070008256.1 were decreased in OGD/R cells compared with controls, but TAK-242 restored their expression under OGD/R condition. AABR07000473.1, AC130862.4, and LOC10254972.6 were induced by OGD/R, but were suppressed in TAK-242 + OGD/R cells compared with OGD/R. Moreover, AABR07049961.1, AC127076.2, AABR07066020.1, and AABR07025303.1-encoded short peptides were dysregulated in OGD/R cells, and TAK-242 attenuated the dysregulation of AABR07049961.1, AC127076.2, and AABR07066020.1-encoded short peptides. Conclusions: TAK-242 alters the expression pattern of lncRNAs in OGD/R cells, and differently expressed lncRNAs may exert a protective effect against OGD/R injury through a mechanism of competing endogenous RNA (ceRNA) and encoding short peptides. These findings maybe provide a new theory basis for the treatment of DHCA.

Role of exosomal noncoding RNA in esophageal carcinoma.

Esophageal cancer is a common malignant tumor with a high degree of malignancy. Understanding its pathogenesis and identifying early diagnostic biomarkers can significantly improve the prognosis of esophageal cancer patients. Exosomes are small double-membrane vesicles found in various body fluids containing various components (DNA, RNA, and proteins) that mediate intercellular signal communication. Non-coding RNAs are a class of gene transcription products that encode polypeptide functions and are widely detected in exosomes. There is growing evidence that exosomal non-coding RNAs are involved in cancer growth, metastasis and angiogenesis, and can also be used as diagnostic and prognostic markers. This article reviews the recent progress in exosomal non-coding RNAs in esophageal cancer, including research progress, diagnostic value, proliferation, migration, invasion, and drug resistance, provide new ideas for the precise treatment of esophageal cancer.

Role of exosomes in non-small cell lung cancer and EGFR-mutated lung cancer.

As an important mediator of information transfer between cells, exosomes play a unique role in regulating tumor growth, supporting vascular proliferation, tumor invasion, and metastasis. Exosomes are widely present in various body fluids, and therefore they can be used as a potential tool for non-invasive liquid biopsy. The present study reviews the role of exosomes in liquid biopsy, tumor microenvironment formation, and epithelial-mesenchymal transition in non-small cell lung cancer (NSCLC). By targeting epidermal growth factor receptor (EGFR) therapy as a first-line treatment for patients with NSCLC, this study also briefly describes the occurrence of EGRF+ exosomes and the role of exosomes and their contents in non-invasive detection and potential therapeutic targets in EGFR-mutated lung cancer.

Rapid identification of high and low cadmium (Cd) accumulating rice cultivars using machine learning models with molecular markers and soil Cd levels as input data.

Excessive accumulation of cadmium (Cd) in rice grains threatens food safety and human health. Growing low Cd accumulating rice cultivars is an effective approach to produce low-Cd rice. However, field screening of low-Cd rice cultivars is laborious, time-consuming, and subjected to the influence of environment × genotype interactions. In the present study, we investigated whether machine learning-based methods incorporating genotype and soil Cd concentration can identify high and low-Cd accumulating rice cultivars. One hundred and sixty-seven locally adapted high-yielding rice cultivars were grown in three fields with different soil Cd levels and genotyped using four molecular markers related to grain Cd accumulation. We identified sixteen cultivars as stable low-Cd accumulators with grain Cd concentrations below the 0.2 mg kg-1 food safety limit in all three paddy fields. In addition, we developed eight machine learning-based models to predict low- and high-Cd accumulating rice cultivars with genotypes and soil Cd levels as input data. The optimized model classifies low- or high-Cd cultivars (i.e., the grain Cd concentration below or above 0.2 mg kg-1) with an overall accuracy of 76%. These results indicate that machine learning-based classification models constructed with molecular markers and soil Cd levels can quickly and accurately identify the high- and low-Cd accumulating rice cultivars.

Oncogenic zinc finger protein ZNF687 accelerates lung adenocarcinoma cell proliferation and tumor progression by activating the PI3K/AKT signaling pathway.

BACKGROUND: Zinc finger protein 687 (ZNF687) has previously been discovered as a potential oncogene in individuals with giant cell tumors of the bone, acute myeloid leukemia, and hepatocellular carcinoma. However, its role and mechanism in lung adenocarcinoma (LUAD) remain unclear. METHODS: In LUAD cells, tumor, and matched adjacent tissue specimens, quantitative real-time RT- polymerase chain reaction (qRT-PCR), western blotting analyses, and immunohistochemistry staining (IHC) were conducted. Cell counting kit-8 (CCK8) assay, clonogenicity analysis, flow cytometry, and transwell assays were utilized to detect ZNF687 overexpression and knockdown impacts on cell growth, colony formation, cell cycle, migration, and invasion. Bioinformatic studies, qRT-PCR and western blotting studies were employed to validate the underlying mechanisms and signaling pathways implicated in the oncogenic effect of ZNF687. RESULTS: This study demonstrated that ZNF687 expression was elevated in LUAD cells and tissues. Individuals with upregulated ZNF687 had a poorer prognosis than those with downregulatedZNF687 (p < 0.001). ZNF687 overexpression enhanced LUAD growth, migration, invasion and colony formation, and the cell cycle G1-S transition; additionally, it promoted the epithelial-mesenchymal transition (EMT). In contrast, knocking down ZNF687 showed to have the opposite impact. Moreover, these effects were associated with the activity of the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling mechanism. CONCLUSION: ZNF687 was upregulated in LUAD, and high ZNF687 expression levels are associated with poor prognoses. The activation of the PI3K/AKT signaling pathway by upregulated ZNF687 increased the proliferation of LUAD cells and tumor progression. ZNF687 may be a beneficial predictive marker and a therapeutic target in LUAD.

A combined deformable model and medical transformer algorithm for medical image segmentation.

Deep learning-based segmentation models usually require substantial data, and the model usually suffers from poor generalization due to the lack of training data and inefficient network structure. We proposed to combine the deformable model and medical transformer neural network on the image segmentation task to alleviate the aforementioned problems. The proposed method first employs a statistical shape model to generate simulated contours of the target object, and then the thin plate spline is applied to create a realistic texture. Finally, a medical transformer network was constructed to segment three types of medical images, including prostate MR image, heart US image, and tongue color images. The segmentation accuracy of the three tasks achieved 89.97%, 91.90%, and 94.25%, respectively. The experimental results show that the proposed method improves medical image segmentation performance.

Transcription factor Nrf2 binds to circRNAPIBF1 to regulate SOD2 in lung adenocarcinoma progression.

Emerging evidence indicates that circular RNAs (circRNAs) play important roles in disease development, especially in cancers. Analysis of circRNA expression microarrays from the Gene Expression Omnibus database revealed that circPIBF1 was highly upregulated in lung adenocarcinoma (LUAD). The main aim of this study was to probe the function of circPIBF1 in pyroptosis of LUAD cells and the signal transduction pathways involved. CircPIBF1 was significantly overexpressed in LUAD and was related to the dismal prognosis of patients with LUAD. CircPIBF1 could bind to nuclear factor erythroid 2-related factor 2 (Nrf2), which further promoted the expression of superoxide dismutase 2 (SOD2). In addition, Nrf2 was also observed to recruit histone acetyltransferase E1A binding protein p300 (EP300) to enhance H3K27ac modification of SOD2, thus modulating the Nrf2-Keap1 signaling pathway. Moreover, we found that knockdown of circPIBF1 significantly suppressed the expression of SOD2 in cells and LUAD cell growth, while enhanced the expression of pyroptosis-related factors, which were further reversed by overexpression of SOD2 or EP300. Collectively, our findings suggest a direct involvement of circPIBF1 in pyroptosis-related LUAD carcinogenesis and implicate a role of Nrf2/EP300/SOD2 signaling in this process.

DAN-PD: Domain adaptive network with parallel decoder for polyp segmentation.

Endoscopy is essential for polyp diagnosis and prevention of colorectal cancer. Many deep learning methods have been proposed to perform automatic semantic segmentation of polyps in endoscopic images. However, labeled training images are always scarce, and the styles of endoscopic images from different medical centers vary greatly. The annotation of medical images requires much effort, and how to make more efficient utilization of the existing labeled data is becoming an increasingly critical issue. Considering the characteristics of polyp segmentation tasks and the need for generalization, we proposed a novel method named DAN-PD based on the Vision Transformer. Moreover, we devised the Teacher Parallel Encoder (TPE) and the Domain-Aware Parallel Decoder (DAPD) for the model. Our design innovatively introduces Unsupervised Domain Adaptation (UDA) methods and adversarial learning strategies to the polyp segmentation task. We conducted four transfer learning experiments with three public polyp image datasets to examine the model's performance. The results shows that our proposed method is ahead of other methods in all experiments and reaches the state-of-the-art level.