Association of Interleukin-17A and Interleukin-17F Gene Polymorphisms with Atopic Dermatitis in Chinese Children.

Background: Atopic dermatitis (AD) is a chronic, recurrent inflammatory disease associated with an unbalanced immune response in the upper layers of the skin tissue, mostly starting in childhood. As important factors in gene expression regulation, polymorphisms in interleukin (IL)-17A and IL-17F may be associated with the susceptibility and severity of AD. Methods: Blood samples and clinical information were obtained from 132 patients with AD and 100 healthy children. Using multiplex polymerase chain reaction and next-generation sequencing, five potential single-nucleotide polymorphisms (SNPs) of IL-17A and IL-17F were genotyped in all participants. The relationship between SNPs and susceptibility to or severity of AD was examined by analyzing haplotypes and genetic models. Results: The IL-17A rs3819025 polymorphism was substantially associated with higher AD risk in both the allele model (p = 0.03; odds ratio [OR] = 1.76; confidence interval [CI]: 1.05-2.95) and the dominant model (p = 0.04, OR = 1.85; CI: 1.03-3.33). There was no correlation between AD susceptibility and the IL-17A (rs2275913 and rs4711998) or IL-17F (rs763780 and rs12203736) SNPs (all p > 0.05). Additionally, the five IL-17A and IL-17F SNPs did not significantly differ across the mild-to-moderate and severe subgroups (all p > 0.05). Conclusions: The IL-17A/rs3819025 polymorphism was linked to the development of AD, whereas the IL-17F polymorphism was unrelated to the susceptibility to and severity of AD. The IL-17A polymorphism may provide valuable information to speculate on the susceptibility to AD in Chinese Han children.

Discovering pathway biomarkers of hepatocellular carcinoma occurrence and development by dynamic network entropy analysis.

OBJECTIVE: Gene expression profiling techniques measure the transcription of thousands of genes in a parallel manner. With more and more hepatocellular carcinoma (HCC) transcriptomic data becoming available, the high-throughput data provides an unprecedented opportunity to discover HCC diagnostic biomarkers. In this work, we propose a bioinformatics method based on dynamic network entropy analysis, called DNEA, to identify potential pathway biomarkers for HCC occurrence and development by integrating transcriptome and interactome. METHODS: We firstly collect the pathways documented in different knowledge-bases and then impose the genome-wide human transcriptomic data of multistage cancerous tissues during the development and progression of HCC. After linking the gene sets of pathways into individual connected networks, we map the corresponding gene expression information onto these pathways. The dynamic network entropy of individual pathways is calculated to evaluate its activities and dysfunctionalities during the disease occurrence and development. We use the overall significant difference in the entropic dynamics during the time course to prioritize distinctive pathways during disease progression. Then machine learning classification methods are employed to screen out pathway biomarkers with the classification ability to distinguish different-stage samples of HCC progression. RESULTS: Pathway biomarkers discovered based on DNEA demonstrate good classification performance in measuring HCC progression. The classification accuracy is as follows: DNA replication pathway (mean AUC = 0.82, 20 genes) from KEGG, FMLP pathway (mean AUC = 0.84, 14 genes) from BioCarta, and downstream signaling of activated FGFR pathway (mean AUC = 0.80, 15 genes) from Reactome. At the same time, previous studies have shown that these genes and pathways screened are closely related to the occurrence and development of HCC in terms of oncogenesis dysfunctions. CONCLUSIONS: Our method for cancer biomarker discovery based on dynamic network entropy analysis is effective and efficient in identifying pathway biomarkers related to the progression of complex diseases.

Alliance chain-based simulation on a new clinical research data pricing model.

Background: Multicenter clinical research faces many challenges, including how to quantitatively evaluate the data contribution of each research center. However, few data pricing model meets the requirements to the scenario. Thus, a suitable mechanism to measure the data value for clinical research is required. Methods: Extensive documents were acquired and analyzed, including a rare disease list from the National Health Commission, data structures of the electronic medical records (EMR) system, diagnosis-related groups (DRGs) regulations from the Health Commission of Zhejiang Province, and the Clinical Service Price List of Zhejiang Province. Nine senior experts were invited as consultants from hospital and enterprises with professional field of clinical research, data governance, and health economics. After brainstorming and expert evaluation, seven data attributes were identified as the main factors affecting the value of medical data. Different weights were assigned for each attribute based on its influence on data value. Each attribute was quantized to an index based on proposed algorithms. The data value models for chronic diseases and other diseases were distinguished given the different sensitivity of data timeliness. A simulation system using blockchain and federated learning techniques was constructed to verify the data pricing model in the scenario of clinical research. Results: A comprehensive clinical data pricing model is proposed and the simulation of three research centers with 50 million real clinical data entries was conducted to verify its effectiveness. It demonstrates that the proposed model can compute medical data value quantitatively. Conclusions: Quantitative evaluation of the value of medical data for multicenter clinical research based on the proposed data pricing model works well in simulation. This model will be improved by real-world applications in the near future.

An improved joint non-negative matrix factorization for identifying surgical treatment timing of neonatal necrotizing enterocolitis.

Neonatal necrotizing enterocolitis is a severe neonatal intestinal disease. Timely identification of surgical indications is essential for newborns in order to seek the best time for treatment and improve prognosis. This paper attempts to establish an algorithm model based on multimodal clinical data to determine the features of surgical indications and construct an auxiliary diagnosis model. The proposed algorithm adds hypergraph constraints on the two modal data based on Joint Nonnegative Matrix Factorization (JNMF), aiming to mine the higher-order correlations of the two data features. In addition, the adjacency matrix of the two kinds of data is used as a network regularization constraint to prevent overfitting. Orthogonal and L1-norm regulations were introduced to avoid feature redundancy and perform feature selection, respectively, and confirmed 14 clinical features. Finally, we used three classifiers, random forest, support vector machine, and logistic regression, to perform binary classification of patients requiring surgery. The results show that when the features selected by the proposed algorithm model are classified by random forest, the area under the ROC curve is 0.8, which has high prediction accuracy.

Detection of Diseases Using Machine Learning Image Recognition Technology in Artificial Intelligence.

With the continuous development and improvement of artificial intelligence technology, machine learning technology has also been extensively developed, which has promoted the development of computer vision, image processing, natural language processing, and other fields. Purpose. This article aims to apply the image processing technology based on machine learning in the detection of childhood diseases and propose the application of image processing technology to the detection of childhood diseases. This article introduces machine learning, image recognition technology, and related algorithms in detail and experiments on image recognition technology based on machine learning. The experimental results show that image recognition technology based on machine learning can well identify white blood cells that are difficult to distinguish with the naked eye, with a recognition rate of up to 90%. Applying image recognition technology based on machine learning in disease diagnosis has greatly improved the level of medical diagnosis.

A Deep-Learning Aided Diagnostic System in Assessing Developmental Dysplasia of the Hip on Pediatric Pelvic Radiographs.

Background: Developmental dysplasia of the hip (DDH) is a common orthopedic disease in children. In clinical surgery, it is essential to quickly and accurately locate the exact position of the lesion, and there are still some controversies relating to DDH status. We adopt artificial intelligence (AI) to solve the above problems. Methods: In this paper, automatic DDH measurements and classifications were achieved using a three-stage pipeline. In the first stage, we used Mask-RCNN to detect the local features of the image and segment the bony pelvis, including the ilium, pubis, ischium, and femoral heads. For the second stage, local image patches focused on semantically related areas for DDH landmarks were extracted by high-resolution network (HRNet). In the third stage, some radiographic results are obtained. In the above process, we used 1,265 patient x-ray samples as the training set and 133 samples from two other medical institutions as the verification set. The results of AI were compared with three orthopedic surgeons for reliability and time consumption. Results: AI-aided diagnostic system's Tönnis and International Hip Dysplasia Institute (IHDI) classification accuracies for both hips ranged from 0.86 to 0.95. The measurements of numerical indices showed that there was no statistically significant difference between surgeons and AI. Tönnis and IHDI indicators were similar across the AI system, intermediate surgeon, and junior surgeon. Among some objective interpretation indicators, such as acetabular index and CE angle, there were good stability and consistency among the four observers. Intraclass consistency of acetabular index and CE angle among surgeons was 0.79-0.98, while AI was 1.00. The measurement time required by AI was significantly less than that of the doctors. Conclusion: The AI-aided diagnosis system can quickly and automatically measure important parameters and improve the quality of clinical diagnosis and screening referral process with a convenient and efficient way.

A new method for excavating feature lncRNA in lung adenocarcinoma based on pathway crosstalk analysis.

Recent theoretical and experimental studies indicate that long-chain noncoding RNAs (lncRNAs) are essential for the growth and differentiation of cells and the occurrence and development of tumors in epigenetics, but the regulation of lncRNA on gene expression, transcriptional activation, and transcriptional interference in diseases is still unclear. There is an urgent need for effective methods to discover significant lncRNAs with their functions on gene regulatory mechanisms. For this purpose, a new method of extracting significant lncRNA based on pathway crosstalk and dysfunction caused by the differentially expressed genes in lung adenocarcinoma (LUAD) was proposed. The pathway analysis method based on global influence (PAGI) was first applied to find the feature genes that play an important role in the crosstalks of disease-related pathways. Then to explore the hub lncRNAs, the weighted gene coexpression network analysis (WGCNA) was used to construct coexpression models of the feature genes and lncRNAs. The experiment results showed that 64 out of the 322 hub lncRNAs were closely related to the clinical features of patients with LUAD. Among them, nine lncRNAs (UCA1, LINC00857, PVT1, PCAT6, LINC00460, LINC00319, AP000553.1, AP000439.2, and AP005233.2) were identified to be tightly correlated with non-small-cell lung cancer (NSCLC) pathways. In summary, we offer an effective way to extract significant lncRNA by dysfunctional pathway crosstalk in LUAD which allows the selected lncRNAs with more biologically interpreted and reproducible results. This method can be applied to other diseases and provide useful information for understanding the pathogenesis of human cancer.

The Effects of High-volume Hemofiltration by Different Ultrasound Directing on Extra Vascular Lung Water Index in Patients with Septic Shock.

BACKGROUND: We explored the effects of high-volume hemofiltration(HVHF) by different ultrasound directing on the plasma N-terminal pro-B-type natriuretic peptide(NT-Pro-BNP), extra vascular lung water index (EVLWI), liquid net balance quantity and prognosis in patients with septic shock. METHODS: Overall, 107 intensive patients with septic shock were enrolled by retrospective analysis from Department of Intensive Care Unit (ICU) of the Shandong Provincial Hospital affiliated to Shandong University from 2014-2017. According to HVHF by different ultrasound directing, all the patients were divided into two groups ((ultrasonic cardiac output monitor (USCOM), group A, n=51cases)) and ((critical bedside ultrasound (CBU), group B, n=56cases)). RESULTS: The value of CI in group A had a significant positive correlation with the value of PCCI by the PiCCO2 monitoring (P<0.05). The lung ultrasound water B lines in group B also had a significant positive correlation with the value of EVLWI by the PiCCO2 monitoring. The cumulative liquid net balance quantity in group B had a more significant elevation than group A after treatment 7th d. The level of EVLWI after treatment 48 h and 72 h, the level of plasma NT-Pro-BNP, the levels of P(A-a)DO2,OI and blood lactic after treatment 72 h, and the APACHE II scores and SOFA scores after treatment 7thd were reduced more significantly in group B than group A (P<0.001). The mortality at 28th day had a more significant decrease in group B than group A. CONCLUSION: It could decrease the level of NT-Pro-BNP, EVLWI, P(A-a)DO2, which then improves pulmonary oxygenation. Consequently, it decreased the APACHE II and SOFA scores and improved the 28th survival rate of patients.

[The effects of joint administration of 6% hydroxyethyl starch 130/0.4 and high-volume hemofiltration on patients with acute lung injury and acute kidney injury].

OBJECTIVE: To evaluate the effects of administration of 6% hydroxyethyl starch (6% HES 130/0.4, voluven) in combination with high volume hemofiltration (HVHF) in patients with ALI and AKI. METHODS: One hundred and eight patients with acute lung injury (ALI) and acute kidney injury (AKI) were enrolled from Department of Intensive Care Unit (ICU) of the provincial Hospital Affiliated to Shandong University between August 2006 and May 2011. The patients were randomly divided into two groups A (n = 68) and B (n = 40) to receive voluven (i.v., for volume resuscitation) and voluven+HVHF for 72 hours. The arterial blood lactate concentration (Lac), high sensitivity C-reactive protein (hs-CRP) serum concentration, pulmonary function index alveolar-arterial oxygen pressure difference [P(A-a)DO2] and oxygenation index (OI), as well as kidney function index serum cystatin C (Cyst C) and serum creatinine clearance rate (CCr) were measured at the time of admission and 72 hours after the treatment for statistical analysis. RESULTS: In comparison with group A, group B had significantly (all P < 0.01) lower mean value in the level of arterial Lac (mmol/L: 1.7 ± 0.7 vs. 2.7 ± 1.5), serum hs-CRP (mg/L: 35.8 ± 18.8 vs. 99.5 ± 20.4), P(A-a)DO2 (mm Hg, 1 mm Hg=0.133 kPa: 115.5 ± 23.1 vs. 155.4 ± 27.4), Cyst C (mg/L: 2.06 ± 1.12 vs. 3.95 ± 2.06) and significantly higher (both P < 0.01) mean value of OI (mm Hg: 295.2 ± 38.8 vs. 239.5 ± 32.7) and CCr (ml/min: 108.71 ± 31.33 vs. 90.21 ± 30.35) 72 hours after treatment. The mortality rate of group B was significantly lower than group A [10.00%(4/40) vs. 29.41%(20/68), P < 0.05] 7 days after the admission. CONCLUSION: 6% HES 130/0.4 in combination with HVHF could improve the lung and kidney function of the patients with ALI and AKI, prevent the development of multiple organ dysfunction syndrome (MODS), therefore improve the survival rate of these patients.