Biomarkers for Prognosis in Pancreatic Neuroendocrine Tumors

2021 ◽  
Author(s):  
Mojun Zhu ◽  
Karl R. Sorenson ◽  
Rebecca Liu ◽  
Bonnie E. Gould Rothberg ◽  
Thorvardur R Halfdanarson

Pancreatic neuroendocrine tumors (PNETs) encompass a diverse group of malignancies marked by histological heterogeneity and highly variable clinical outcomes. We performed a systematic review on potential prognostic biomarkers in PNETs by searching the PubMed database. A total of 472 manuscripts were reviewed in detail and 52 multivariate studies met the inclusion criteria proposed by the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). These altogether analyzed 53 unique targets and 36 of them were statistically associated with survival.

2020 ◽  
Vol 27 (12) ◽  
pp. 1276-1287
Author(s):  
Brigida Anna Maiorano ◽  
Giovanni Schinzari ◽  
Sabrina Chiloiro ◽  
Felicia Visconti ◽  
Domenico Milardi ◽  
...  

Pancreatic neuroendocrine tumors (PanNETs) are rare tumors having usually an indolent behavior, but sometimes with unpredictable aggressiveness. PanNETs are more often non-functioning (NF), unable to produce functioning hormones, while 10-30% present as functioning (F) - PanNETs, such as insulinomas , gastrinomas , and other rare tumors. Diagnostic and prognostic markers, but also new therapeutic targets, are still lacking. Proteomics techniques represent therefore promising approaches for the future management of PanNETs. We conducted a systematic review to summarize the state of the art of proteomics in PanNETs. A total of 9 studies were included, focusing both on NF- and F-PanNETs. Indeed, proteomics is useful for the diagnosis, the prognosis and the detection of therapeutic targets. However, further studies are required. It is also warranted to standardize the analysis methods and the collection techniques, in order to validate proteins with a relevance in the personalized approach to PanNETs management.


Oncotarget ◽  
2017 ◽  
Vol 8 (21) ◽  
pp. 35368-35375 ◽  
Author(s):  
Jingfei Guo ◽  
Jianjun Zhao ◽  
Xinyu Bi ◽  
Zhiyu Li ◽  
Zhen Huang ◽  
...  

2021 ◽  
pp. 197140092110428
Author(s):  
Kun Hou ◽  
Lai Qu ◽  
Jinlu Yu

Background Giant aneurysms of the intracranial vertebral artery are very rare cerebrovascular lesions. Due to the rarity of these aneurysms, we know little about them. Methods We performed a systematic review of the English literature by searching the PubMed database. The inclusion criteria were as follows: (a) the full text was available and (b) complete clinical data were available. Results A total of 45 articles were identified, containing 53 patients (53 aneurysms). The patients were aged from 5 to 77 years (48.8 ± 20.8 years). Four patients receiving conservative treatment died. The remaining 49 patients were divided into the aneurysm removal group ( n = 17) and the aneurysm reserve group ( n = 32). The outcomes of the 49 treated cases could be obtained in 45 cases, 31 of which (68.9%, 31/45) had a Glasgow outcome scale score of 4–5. Conclusions It is still difficult to treat intracranial giant vertebral artery aneurysms, regardless of the treatment selected. Because of the malignant natural history, aggressive treatment is still advocated.


HPB ◽  
2018 ◽  
Vol 20 ◽  
pp. S562
Author(s):  
R. Latorre Fragua ◽  
A. Manuel Vazquez ◽  
C. Ramiro Pérez ◽  
C. Garcia Amador ◽  
B. Gonzalez Sierra ◽  
...  

Author(s):  
Luis Hurtado-Pardo ◽  
Javier A. Cienfuegos ◽  
Miguel Ruiz-Canela ◽  
Pablo Panadero ◽  
Alberto Benito ◽  
...  

2020 ◽  
Vol 9 (2) ◽  
pp. 332-337
Author(s):  
Haeril Amir ◽  
Sudarman Sudarman

The aim of this study was to determine the benefits of RCD on nurses themselves, this literature through identification from the Pubmed database, Science direct and online wiley, use the keywords 'Reflection' and 'Case' and 'Nursing'. The method of searching articles uses PICOT technique, Prism Flow diagram, abstraction and synthetic data. Through fulltext screening, double publication and eligibility, 455 research articles were found. The next step is to screen through the inclusion criteria and exclusion criteria so that the final result of the article found is 4 articles. Articles have a lot to explain about the benefits RCD for nurses, RCD can add to the knowledge of nurses, minimize the gap theory and practice so that errors can be resolved. Literature is also finding benefits RCD on nurses is increasing the professionalism of the work and cooperation among fellow colleagues. Implementation of the RCD environment of clinical very ber benefits


2020 ◽  
Vol 17 (2) ◽  
pp. 242-249 ◽  
Author(s):  
Bethany Forseth ◽  
Stacy D. Hunter

Background: There is limited research examining the intensity of yoga and intensity variations between different styles. The purpose of this review is to examine the intensity of yoga based on different physiologic responses both between different yoga styles and within styles of yoga. Methods: Articles were searched for on the PubMed database in early 2019. Inclusion criteria were as follows: (1) written in English, (2) cite a specific style of yoga and include whole yoga session, and (3) measure metabolic or heart rate response. Results: Ten articles were reviewed; articles reported oxygen consumption (n = 1), heart rate (n = 4), or both variables (n = 5). Yoga styles assessed included ashtanga (n = 2), Bikram (n = 3), gentle (n = 1), hatha (n = 3), Iyengar (n = 1), power (n = 1), and vinyasa (n = 1). Oxygen consumption commonly categorized yoga as a light-intensity activity, while heart rate responses classified different yoga into multiple intensities. Conclusion: This review demonstrates that large differences in intensity classifications are observed between different styles of yoga. Furthermore, metabolic and heart rate responses can be variable, leading to inconsistent intensity classifications. This is likely due to their nonlinear relationship during yoga. Thus, it is imperative that the field of yoga research works together to create a standard for reporting yoga.


Pancreatology ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 738-750 ◽  
Author(s):  
Jian-kang Zhu ◽  
Dong Wu ◽  
Jian-wei Xu ◽  
Xin Huang ◽  
Yuan-yuan Jiang ◽  
...  

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