Development of a network visualization and analysis system for malignant tumors based on transcriptome data

Shiny technology has developed rapidly in recent years, as an R package for developing interactive app, through which we can package the written R code into a web app, which can not only save user time, but also accelerate the development of the speed of user-end communication, analyze the transcriptome data of related malignant tumors, and construct a ceRNA network diagram of desired malignant tumors. The code utilizing shiny technology package can facilitate users to map the ceRNA network associated with malignant tumors only through screen operation, significantly improving the efficiency and accuracy of clinical decision support in primary hospitals

Effects of different transcranial magnetic stimulations on neuropathic pain after spinal cord injury

IntroductionRepetitive transcranial magnetic stimulation (rTMS) is an effective non-invasive cortical stimulation technique in the treatment of neuropathic pain. As a new rTMS technique, intermittent theta burst stimulation (iTBS) is also effective at relieving pain. We aimed to establish the pain-relieving effectiveness of different modalities on neuropathic pain. The study was conducted in individuals with spinal cord injury (SCI) and different modalities of rTMS.MethodsThirty-seven individuals with SCI were randomly allocated to three groups, in which the “iTBS” group received iTBS, the “rTMS” group received 10 Hz rTMS, and the “iTBS + rTMS” group received iTBS and 10 Hz rTMS successively of the primary motor cortex 5 days a week for 4 weeks, and they all underwent the full procedures. The primary outcome measure was change in the visual analog scale (VAS), and the secondary outcomes were measured using the Hamilton Rating Scale for Depression (HAM-D) and the Pittsburgh Sleep Quality Index (PSQI). All the outcomes were evaluated at 1 day before stimulation (baseline), 1 day after the first week of stimulation (S1), and 1 day after the last stimulation (S2).ResultsThe VAS scores showed significant pain improvement after 4 weeks of stimulation (p = 0.0396, p = 0.0396, and p = 0.0309, respectively) but not after 1 week of stimulation. HAM-D scores declined, but the decreases were not significant until 4 weeks later (p = 0.0444, p = 0.0315, and p = 0.0447, respectively). PSQI scores were also significantly decreased after 4 weeks of stimulation (p = 0.0446, p = 0.0244, and p = 0.0088, respectively). Comparing the three modalities, VAS, HAM-D, and PSQI scores at S1 showed no differences, and, at S2, VAS scores showed significant differences (p = 0.0120; multiple comparisons showed significant differences between iTBS and iTBS + rTMS, p = 0.0091), while the HAM-D and PSQI scores showed no differences.DiscussionThe primary and secondary outcomes all showed significant improvement, indicating that the three different modalities were all effective at relieving the pain. However, not all the three stimulations were of same effectiveness after treatment; there were statistical differences in the treatment of neuropathic pain between iTBS as a priming stimulus and as a single procedure

Design and implementation of cancer patient survival prediction model based on ensemble learning method

To study the impact of genomics on cancer diseases, bioinformatics and integrated learning methods are used to conduct survival analysis on colon cancer and rectal cancer data in the cancer gene map database. Firstly, according to the significant expression and stability test, the long-chain non-coding RNA in the transcriptome that has a significant impact on clinical prognosis survival analysis was initially screened. Then use a random forest ensemble learning algorithm to train it to get a preliminary model. Finally, based on the optimized random survival forest model, the Cox regression model was once again integrated, and the risk values of the two were integrated. The RCCT (Random-Cox Combined to Survival) method was proposed to provide clinical decision-makers certain reference values