Hotspot Analysis of Sepsis Literature

Biclustering data analysis: a comprehensive survey

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

Berberine Alleviates Gastroesophageal Reflux-Induced Airway Hyperresponsiveness in a Transient Receptor Potential A1-Dependent Manner

BACKGROUND

To investigate the beneficial effect of berberine on gastroesophageal reflux-induced airway hyperresponsiveness (GERAHR) and explore the underlying mechanism.

METHODS

Coword cluster analysis and strategic coordinates were used to identify hotspots for GERAHR research, and an online tool (STRING, https://string-db.org/) was used to predict the potential relationships between proteins. Guinea pigs with chemically induced GERAHR received PBS or different berberine-based treatments to evaluate the therapeutic effect of berberine and characterize the underlying mechanism. Airway responsiveness was assessed using a plethysmography system, and protein expression was evaluated by western blotting, immunohistochemical staining, and quantitative PCR analysis.

RESULTS

Bioinformatics analyses revealed that TRP channels are hotspots of GERAHR research, and TRPA1 is related to the proinflammatory neuropeptide substance P (SP). Berberine, especially at the middle dose tested (MB, 150 mg/kg), significantly improved lung function, suppressed inflammatory cell infiltration, and protected inflammation-driven tissue damage in the lung, trachea, esophagus, and nerve tissues in GERAHR guinea pigs. MB reduced the expression of TRPA1, SP, and tumor necrosis factor-alpha (TNF-α) in evaluated organs and tissues. Meanwhile, the MB-mediated protective effects were attenuated by simultaneous TRPA1 activation.

CONCLUSIONS

Mechanistically, berberine was found to suppress GERAHR-induced upregulation of TRPA1, SP, and TNF-α in many tissues. Our study has highlighted the potential therapeutic value of berberine for the treatment of GERAHR.

Bufalin: A Systematic Review of Research Hotspots and Antitumor Mechanisms by Text Mining and Bioinformatics

Bufalin is an anticancer drug extract from traditional Chinese medicine. Several articles about bufalin have been published. However, the literature on bufalin has not yet been systematically studied. This study aimed to identify the study status and knowledge structures of bufalin and to summarize the antitumor mechanism. Data were retrieved and downloaded from the PubMed database. The softwares of BICOMB, gCLUTO, Ucinet 6.0, and NetDraw2.084 were used to analyze these publications. The bufalin related genes were recognized and tagged by ABNER software. Then these BF-related genes were performed by Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, and protein-protein interaction (PPI) network analysis. A total of 474 papers met the search criteria from 2000 to 2019. By biclustering clustering analysis, the 50 high-frequency main MeSH terms/subheadings were classified into 5 clusters. The clusters of drug therapy and the mechanism of bufalin were hotspot topics. A total of 50 genes were identified as BF-related genes. PPI network analysis showed that inducing apoptosis was the main effect of bufalin, and apoptosis-related gene Caspase 3 was the most reported by people. Bufalin could inhibit the proliferation, invasion, and metastasis of cancer cells through multiple signaling pathways, such as PI3K/AKT, Hedgehog, MAPK/JNK, Wnt/[Formula: see text]-catenin, TGF-[Formula: see text]/Smad, Integrin signaling pathway, and NF-KB signaling pathway via KEGG analysis. Through the quantitative analysis of bufalin literature, we revealed the research status and hot spots in this field and provided some guidance for further research.

Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018

BACKGROUND Pediatric liver transplantation is used to treat children with end-stage liver disease. This study explored the research hotspots and bibliometric characteristics of pediatric liver transplantation through a variety of bibliometric analysis software. We conducted hotspot analysis to help determine important directions for future scientific research. MATERIAL AND METHODS The study samples were articles related to pediatric liver transplantation published in PubMed in the past 5 years. The high-frequency keywords are extracted by BICOMB software, and then a binary matrix and a common word matrix were constructed. Gcluto software was used to perform double-clustering and visual analysis on high-frequency words, and then we obtained hot area classification. Strategic coordinates are constructed using Excel. Citespace and VOSviewer software are used for further analysis and bibliometric data visualization. RESULTS A total of 36 high-frequency words were found in the 4118 studies. A peak map was drawn through double-cluster analysis. Biclustering analysis was used to calculate the concentricity and density of each hotspot. We obtained the top 10 countries/regions engaged in pediatric liver transplantation research. VOSviewer was used to visualize the co-author map. CONCLUSIONS We found 5 clusters and 7 aspects for pediatric liver transplantation. Additionally, calculation results showed that post-transplant lymphoproliferative disorder in pediatric patients and outcomes of multivisceral transplantation seem very promising. This conclusion is of great value for future exploratory research.

Value of early diagnosis of sepsis complicated with acute kidney injury by renal contrast-enhanced ultrasound

BACKGROUND

The incidence of acute kidney injury (AKI) in patients with sepsis is high, and the prognosis of patients with septic AKI is poor. The early diagnosis and treatment of septic AKI is of great significance in improving the prognosis of patients with sepsis.

AIM

To explore the value of contrast-enhanced ultrasound (CEUS), serum creatinine (Scr), and other indicators in the early diagnosis of septic AKI.

METHODS

Ninety patients with sepsis during hospitalization at Tongji Hospital of Tongji University were recruited as subjects. Each patient was recorded with relevant basic data, clinical indicators, and CEUS results. The patients were divided into AKI group and non-AKI group according to the results of renal function diagnosis after 48 h. On the 7th day, the renal function of the non-AKI group was re-evaluated and the patients were further divided into AKI subgroup and non-AKI subgroup. The differences of the indicators in different groups were compared, and the diagnostic value of each indicator and their combination for septic AKI was analyzed.

RESULTS

Systemic inflammatory response score (2.58 ± 0.75), blood lactic acid (3.01 ± 1.33 mmol/L), Scr (141.82 ± 27.19 μmol/L), blood urea nitrogen (4.41 ± 0.81mmol/L), and rise time (10.23 ± 2.63 s) in the AKI group were higher than those in the non-AKI group. Peak intensity (PI) (10.78 ± 3.98 dB) and wash in slope (WIS) (1.07 ± 0.53 dB/s) were lower than those in the non-AKI group. The differences were statistically significant (P < 0.05). The PI (12.83 ± 3.77 dB) and WIS (1.22 ± 0.68 dB/s) in the AKI subgroup were lower than those in the non-AKI subgroup, and the differences were statistically significant (P < 0.05). The area under curve (AUC) of Scr for the diagnosis of septic AKI was 0.825 with a sensitivity of 56.76% and a specificity of 100%. The AUCs of WIS and PI (0.928 and 0.912) were higher than those of Scr. Their sensitivities were 100%, but the specificities were 71.70% and 75.47%. The AUC of the combination of three indicators for the diagnosis of septic AKI was 0.943, which was significantly higher than the AUC diagnosed by each single indicator. The sensitivity was 94.59%, and the specificity was 81.13%.

CONCLUSION

The combination of Scr, PI, and WIS can improve the diagnostic accuracy of septic AKI. PI and WIS are expected to predict the occurrence of early septic AKI.