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Long non-coding RNAs (lncRNAs) which are usually thought to have no protein coding ability, are widely involved in cell proliferation, signal transduction and other biological activities. However, recent studies have suggested that short open reading frames (sORFs) of some lncRNAs can encode small functional peptides (micropeptides). These micropeptides appear to play important roles in calcium homeostasis, embryonic development and tumorigenesis, suggesting their potential as therapeutic targets and diagnostic biomarkers. Currently, bioinformatic tools as well as experimental methods such as ribosome mapping and in vitro translation are applied to predict the coding potential of lncRNAs. Furthermore, mass spectrometry, specific antibodies and epitope tags are used for validating the expression of micropeptides. Here, we review the physiological and pathological functions of recently identified micropeptides as well as research strategies for predicting the coding potential of lncRNAs to facilitate the further research of lncRNA encoded micropeptides.
Assuntos
Feminino , Gravidez , Humanos , RNA Longo não Codificante/genética , Projetos de Pesquisa , Anticorpos , Carcinogênese , MicropeptídeosRESUMO
In recent years,the application of machine learning in the field of radiotherapy has been gradually increased along with the development of big data and artificial intelligence technology.Through the training of previous plans,machine learning can predict the results of plan quality and dose verification.It can also predict the multi-leaf collimator (MLC) positioning error and linear accelerator performance.In addition,machine learning can be applied in the quality assurance of intensity-modulated radiotherapy to improve the quality and efficiency of treatment plan and implementation,increase the benefits to the patients and reduce the risk.However,there are many problems,such as difficulty in the selection,extraction and calculation of characteristic value,requirement for large training sample size and insufficient prediction accuracy,which impede its clinical translation and application.In this article,research progress on the application of machine learning in the quality assurance of IMRT was reviewed.
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Objective To investigate the application value of volumetric modulated arc therapy (VMAT) combined with zoledronic acid in treatment of vertebral metastases. Methods A total of 58 patients with vertebral metastasis in Qingdao Central Hospital from January 2015 to June 2017 were extracted according to simple random sampling method. The data of 58 patients were analyzed retrospectively, and they were divided into 2 groups according to the different radiotherapy technology. The study group (28 cases) received VMAT combined with zoledronic acid treatment, and the control group (30 cases) received general radiotherapy combined with zoledronic acid treatment. The efficacy and tissue damage of the patients in both groups were compared according to the evaluation criterion including the local control rate, pain relief rate, local progression-free survival rate and exposure of the spinal cord. Results The local control rates were 92.9 ％ (26/28) and 73.3 ％ (22/30) respectively in the study group and the control group (χ2= 3.869, P=0.049). The pain relief rates were 96.4 ％ (27/28) and 76.7 ％ (23/30) respectively (χ2= 4.757, P= 0.029). The local progression-free survival rates of 3-month were 85.7 ％ (24/28), 60.0 ％ (18/30) respectively (χ2=4.794, P= 0.029) and the local progression-free survival rates of 6-month were 78.6 ％ (22/28), 50.0 ％(15/30) in the study group and the control group (χ2=5.119, P=0.024). The exposure of the spinal cord was (29.80±0.88) Gy and (39.90±0.20) Gy, respectively (t= -61.083, P= 0.000). Conclusion VMAT combined with zoledronic acid can improve the local control rate, pain relief rate, local progression-free survival rate, and reduce the exposure of the spinal cord.
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Machine learning has developed rapidly in recent years.Using machine learning to predict the radiotherapy outcomes and complications can more accurately evaluate the patients' conditions and take appropriate treatment measures as soon as possible.The non-dose and dose related factors generated during radiotherapy are filtered and input into the algorithm model,then corresponding prediction result can be obtained.There are many algorithm models to predict survival rate,tumor control rate and radiotherapy complications,and the predicted result are more accurate now.However,the algorithm model also has various problems,and it needs constant exploration and improvement.
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Objective To study the cause,prevention and treatment of postpancreaticoduodenectomy hemorrhage.Method The clinical data of 422 patients who underwent PD in our hospital between January 2000 and January 2012 were retrospective analyzed.Results The incidence of postoperative hemorrhage was 8.1％ (34/422),and the mortality was 20.6％ (7/34).Early and delayed hemorrhage occurred in 19 and 15 patients,respectively.Intra-abdominal and gastrointestinal hemorrhage occurred in 20 and 14 patients,respectively.For the 19 patients who underwent reoperation,the mortality was 20.6％ (7/34).When compared with the delayed hemorrhage group,the mortality of the early hemorrhage group was significantly lower (P＜0.05).Conclusions Meticulous operation and reliable hemostasis during operation and prevention of pancreatic fistula,biliary fistula and peritoneal fluid collection after operation are the key points in reducing postoperative hemorrhage.A timely and decisive reoperation is important to manage postoperative hemorrhage.