Using big data to analyze the vaccination status of children with congenital heart disease in Yinzhou District, China.

Congenital heart disease (CHD) represents a significant population warranting particular attention concerning vaccination coverage. To comprehend the vaccination status of CHD within Yinzhou District, Ningbo City, China, and to facilitate the formulation of preventive, control, and immunization strategies against vaccine-preventable diseases in children with congenital heart conditions. Using the China Yinzhou Electronic Health Record Study (CHERRY) database, we analyzed the vaccination coverage of children with CHD born between January 1, 2016 and September 20, 2021, and analyzed the influencing factors associated with the level of vaccination coverage. This study involved 762 children diagnosed with CHD at the age of 12 months, revealing that 86.74% of these children had received at least one dose of the National Immunization Program (NIP) vaccines. The coverage for non-NIP vaccines, such as the rotavirus vaccine, influenza vaccine, Influenza Haemophilus influenzae Type b (Hib) Conjugate Vaccine, 13-valent pneumococcal conjugate vaccine (PCV13), and inactivated enterovirus type 71 vaccine (EV71), stood at 27.30%, 7.74%, 63.25%, 33.76%, and 34.51%, respectively. The completion coverage for the entire vaccination schedule were 27.30%, 5.51%, 55.77%, 34.25%, and 25.59%, respectively. There was a statistically significant correlation between vaccination coverage in classification of diagnostic medical institutions and the types of diagnosed diseases. Compared to their typically developing counterparts, 12-month-old children afflicted with CHD exhibit a slightly diminished vaccination coverage, alongside a discernible inclination toward delayed vaccination. Notably, the determination to undergo vaccinations seems predominantly influenced by the classification of diagnostic medical institutions. In practical terms, proactive measures involving early diagnosis, comprehensive health assessments, and timely interventions ought to be implemented to enhance vaccination rates while prioritizing safety.

Factors Influencing Postoperative Prognosis in Patients with Hypopharyngeal and Laryngeal Carcinoma.

OBJECTIVES: Despite the increasingly modern surgical techniques in the oncology field, the factors that influence postoperative prognosis in patients with hypopharyngeal and laryngeal carcinoma (HLC) remain unclear. The study aimed to evaluate the factors influencing the prognosis of HLC patients with pathological diagnosis of squamous cell carcinoma, and the findings are intended to direct follow-up management strategies. METHODS: A retrospective cohort study was performed. The study population included 407 postoperative patients with HLC from 2011 to 2015. Univariate and multivariate analyses were used to examine the prognostic factors identified. RESULTS: Based on univariate analysis results, smoking and alcohol history, tumor differentiation, preoperative radiotherapy, primary tumor sites, flap reconstruction, lymph node invasion (LNI), and preoperative albumin levels (PAL) significantly affects the prognosis of HLC patients (P < .05). Meanwhile, multivariate analysis revealed that smoking pack-year (OR = 1.002, 95% CI = 1.001 ∼ 1.003), primary tumor sites (OR = 6.241, 95% CI = 1.715 ∼ 18.433), LNI (OR = 2.869, 95% CI = 1.095 ∼ 8.743), and PAL (OR = .020, 95% CI = .004 ∼ 0.104) were associated with complications. Tumor differentiation (OR = 0.650, 95% CI = .383 ∼ 0.855), primary tumor sites (OR = 12.392, 95% CI = 3.290 ∼ 26.679), LNI (OR = 16.323, 95% CI = 2.726 ∼ 47.729), preoperative radiotherapy (OR = 9.300, 95% CI = 3.182 ∼ 27.181), and PAL (OR = .321, 95% CI = .141 ∼ .732) were associated with overall survival rates. CONCLUSION: Smoking and alcohol history, tumor differentiation, LNI, primary tumor sites, flap reconstruction, PAL, and preoperative radiotherapy are crucial factors that influence the postoperative prognosis of patients with HLC. In addition, a monogram of five factors was established to predict the survival rates of HLC patients.

Spatial-temporal clustering analysis of influenza incidence in Yinzhou District from 2017 to 2021 / 预防医学

Objective@#To investigate the spatio-temporal clustering characteristics of influenza in Yinzhou District, Ningbo City, Zhejiang Province from 2017 to 2021, so as to provide insights into prevention and control of influenza. Methods Data of influenza in Yinzhou District from 2017 to 2021 were collected from the Chinese Disease Prevention and Control Information System. The software ArcGIS 10.8 was employed for spatial autocorrelation analysis, and SaTScan 10.1 was employed for spatio-temporal scanning to analyze the temporal and spatial clustering characteristics of influenza incidence in Yinzhou District. @*Methods@#Data of influenza in Yinzhou District from 2017 to 2021 were collected from the Chinese Disease Prevention and Control Information System. The software ArcGIS 10.8 was employed for spatial autocorrelation analysis, and SaTScan 10.1 was employed for spatio-temporal scanning to analyze the temporal and spatial clustering characteristics of influenza incidence in Yinzhou District.@*Results@#Totally 60 543 influenza cases were reported in Yinzhou District from 2017 to 2021, with an incidence of 0.76%. The incidence of influenza peaked in December 2019 (9.35%) and January 2020 (9.28%) during the period between 2017 and 2021. Spatial autocorrelation analysis showed that there was a positive spatial correlation of influenza incidence in Yinzhou District from 2018 to 2021 (all P<0.05), and a high clustering in 2019 and 2021. Zhonghe Street showed a low-high clustering from 2017 to 2020; Jiangshan Town showed a low-high clustering in 2017 and 2020, and a high-high clustering in 2019 and 2021; Shounan Street showed a high-high clustering from 2018 to 2020; Yunlong Street showed a high-high clustering in 2021. Spatio-temporal scanning analysis showed that the class Ⅰ clusters were located in the central region which centered in Dongqianhu Town, with aggregation time in August 2017, in the northwest region with aggregation time in December and January from 2018 to 2020, and in the west region with aggregation time in August 2021.@* Conclusion @#The incidence of influenza in Yinzhou District from 2017 to 2021 showed a spatio-temporal clustering in the northwestern region in winter and summer.

Prognostic signatures and potential pathogenesis of eRNAs-related genes in colon adenocarcinoma.

Enhancer RNAs (eRNAs) are a subclass of long noncoding RNAs (lncRNAs) that have a wide effect in human tumors. However, the systematic analysis of potential functions of eRNAs-related genes (eRGs) in colon cancer (CC) remains unexplored. In this study, a total of 8231 eRGs including 6236 protein-coding genes and 1995 lncRNAs were identified in CC based on the multiple resources. These eRGs showed higher expression level and stability compared to other genes. What's more, the functions of these eRGs were closely related to cancer. Then a prognostic prediction model with 12 eRGs signatures were obtained for colon adenocarcinoma (COAD) patients. ROC curves showed the AUCs were 0.81, 0.77, and 0.78 for 1-, 3-, and 5-year survival prediction, respectively. And the prognostic model also manifested good performance in the validation datasets. Besides, the expression levels of two prognostic signatures, TMEM220 and LRRN2, were verified to be significantly lower in CC tissues than in adjacent noncancerous tissues (p < .05). Finally, the distinct molecular features were characterized between the high- and low-risk group through multiomics analysis including DNA mutation and methylation. Our results show eRGs signatures based prognostic model has high accuracy and may provide innovative biomarkers in COAD.

Regulatory Network Analysis of Mutated Genes Based on Multi-Omics Data Reveals the Exclusive Features in Tumor Immune Microenvironment Between Left-Sided and Right-Sided Colon Cancer.

Left-sided colon cancer (LCC) and right-sided colon cancer (RCC) have distinct characteristics in tumor immune microenvironment (TIME). Although existing studies have shown a strong association between gene mutations and TIME, whether the regulatory mechanisms between gene mutations and TIME are different between RCC and LCC is still unclear. In this study, we showed the fractions of CD8+ T cells were higher while those of regulatory T cells were lower in RCC. Besides, a stronger association between gene mutations and TIME was observed in RCC. Specifically, using multi-omics data, we demonstrated the mutations of most top mutated genes (TMGs) including BRAF, PCLO, MUC16, LRP2, ANK3, KMT2D, RYR2 made great contributions to elevated fraction of immune cells by up-regulating immune-related genes directly or indirectly through miRNA and DNA methylation, whereas the effects of APC, TP53 and KRAS mutations on TIME were reversed in RCC. Remarkably, we found the expression levels of several immune checkpoint molecules such as PD-1 and LAG3 were correlated with corresponding DNA methylation levels, which were associated with the mutations of TMGs in RCC. In contrast, the associations between gene mutations and TIME were less significant in LCC. Besides, survival analyses showed APC mutation had adverse impact on immunotherapy while patients with BRAF mutation were more suitable for immunotherapy in colon cancer. We hope that our results will provide a deeper insight into the sophisticated mechanism underlying the regulation between mutations and TIME, and thus boost the discovery of differential immunotherapeutic strategies for RCC and LCC.

In silico Method in CRISPR/Cas System: An Expedite and Powerful Booster.

The CRISPR/Cas system has stood in the center of attention in the last few years as a revolutionary gene editing tool with a wide application to investigate gene functions. However, the labor-intensive workflow requires a sophisticated pre-experimental and post-experimental analysis, thus becoming one of the hindrances for the further popularization of practical applications. Recently, the increasing emergence and advancement of the in silico methods play a formidable role to support and boost experimental work. However, various tools based on distinctive design principles and frameworks harbor unique characteristics that are likely to confuse users about how to choose the most appropriate one for their purpose. In this review, we will present a comprehensive overview and comparisons on the in silico methods from the aspects of CRISPR/Cas system identification, guide RNA design, and post-experimental assistance. Furthermore, we establish the hypotheses in light of the new trends around the technical optimization and hope to provide significant clues for future tools development.

Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD.

Lung cancer has been the focus of attention for many researchers in recent years for the leading contribution to cancer-related death worldwide, in which lung adenocarcinoma (LUAD) is the most common histological type. However, the potential mechanism behind LUAD initiation and progression remains unclear. Aiming to dissect the tumor microenvironment of LUAD and to discover more informative prognosis signatures, we investigated the immune-related differences in three types of genetic or epigenetic characteristics (expression status, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and both the immune-related metabolic and neural systems by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct the prognostic prediction model. For the prognostic predictions on the independent test set, the performance of the trained models (average concordance index [C-index] = 0.839) is satisfied, with average 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.796, 0.786, and 0.777. Finally, the overall model was constructed based on all samples, which comprised 27 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.861), 3-year (AUC = 0.850), and 5-year (AUC = 0.916) survival predictions.

Function and regulation annotation of up-regulated long non-coding RNA LINC01234 in gastric cancer.

BACKGROUND: Accumulated evidences indicate that long non-coding RNAs (lncRNAs) participate in many biological mechanisms. Moreover, it acts as an essential regulator in various human diseases such as gastric cancer (GC). Nevertheless, the comprehensive regulatory roles and clinical significance of most lncRNAs in GC are not fully understood. METHODS: In this research, our aim was to investigate the underlying mechanism of lncRNA LINC01234 in GC. Firstly, the usage of qRT-PCR helped to establish expression pattern of LINC01234 in GC tissues. Following this, appropriate statistical tests were applied to analyze the relation between expression level and clinicopathological factors. Ultimately, potential functions and regulatory network of LINC01234 were concluded via GSEA and a series of bioinformatics tools or databases, respectively. RESULTS: Consequently, at the end of research we found LINC01234 is up-regulated in GC tissues in comparison with adjacent normal tissues. Furthermore, its expression level is correlated with differentiation of patients with GC. It is also important to highlight bioinformatics analysis revealed that LINC01234 is involved in cancer-associated pathways such as cell cycle and mismatch repair. Also, regulatory network of LINC01234 presented a probability in the involvement of tumorigenesis through regulating cancer-associated genes. CONCLUSION: Overall, our results suggested that LINC01234 may play a crucial role in GC.

Designing a general method for predicting the regulatory relationships between long noncoding RNAs and protein-coding genes based on multi-omics characteristics.

MOTIVATION: Long noncoding RNA (lncRNA) has been verified to interact with other biomolecules especially protein-coding genes (PCGs), thus playing essential regulatory roles in life activities and disease development. However, the inner mechanisms of most lncRNA-PCG relationships are still unclear. Our study investigated the characteristics of true lncRNA-PCG relationships and constructed a novel predictor with machine learning algorithms. RESULTS: We obtained the 307 true lncRNA-PCG pairs from database and found that there are significant differences in multiple characteristics between true and random lncRNA-PCG sets. Besides, 3-fold cross-validation and prediction results on independent test sets show the great AUC values of LR, SVM and RF, among which RF has the best performance with average AUC 0.818 for cross-validation, 0.823 and 0.853 for two independent test sets, respectively. In case study, some candidate lncRNA-PCG relationships in colorectal cancer were found and HOTAIR-COMP interaction was specially exemplified. The proportion of the reported pairs in the predicted positive results was significantly higher than that in negative results (P < 0.05). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.