Publications by authors named "Qiuhong Zeng"

3 Publications

  • Page 1 of 1

Effect of Osteoporosis on Adjacent Segmental Degeneration After Posterior Lumbar Interbody Fusion Under Whole Body Vibration.

World Neurosurg 2021 Aug 12;152:e700-e707. Epub 2021 Jun 12.

School of Chinese Medicine, Jinan University, Guangzhou, China. Electronic address:

Background: Adjacent segmental degeneration (ASD) is one of the common complications after posterior lumbar interbody fusion (PLIF). Both whole body vibration (WBV) and osteoporosis are important factors associated with the biomechanics of the lumbar spine. However, to the best of our knowledge, no studies have investigated the effects of osteoporosis on ASD after PLIF under WBV.

Methods: In the present study, using one normal model, one PLIF model and one PLIF with osteoporosis model of the L1-S1 segment were developed. A 5-Hz, 40-N sinusoidal vertical load was imposed on the superior surface of L1 of each model to simulate WBV, and the dynamic responses and maximal values of intradiscal pressure, shear stress on annulus fibrosus, total deformation, and disc bulge were evaluated in the L1-L2, L2-L3, L3-L4, and L5-S1 segments.

Results: At the L1-L2, L2-L3, and L3-L4 levels, the differences in the dynamic responses and maximal values in intradiscal pressure, shear stress, total deformation, and disc bulge between the PLIF and PLIF with osteoporosis models were slight. However, at the L5-S1 level, the dynamic response curves and maximal intradiscal pressure, shear stress, and disc bulge values in the PLIF with osteoporosis model were significantly lower than those in the PLIF model.

Conclusions: Osteoporosis can mitigate the development of ASD in the lower adjacent segment but has no obvious influence on the upper adjacent segments during WBV.
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http://dx.doi.org/10.1016/j.wneu.2021.06.031DOI Listing
August 2021

A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions.

Front Genet 2020 15;11:573787. Epub 2021 Jan 15.

Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.

It is meaningful to assess the risk of cancer incidence among patients with precancerous colorectal lesions. Comparing the within-sample relative expression orderings (REOs) of colorectal cancer patients measured by multiple platforms with that of normal colorectal tissues, a qualitative transcriptional signature consisting of 1,840 gene pairs was identified in the training data. Within an evaluation dataset of 16 active and 18 inactive (remissive) ulcerative colitis subjects, the median incidence risk score of colorectal carcinoma was 0.6402 in active ulcerative colitis subjects, significantly higher than that in remissive subjects (0.3114). Evaluation of two other independent datasets yielded similar results. Moreover, we found that the score significantly positively correlated with the degree of dysplasia in the case of colorectal adenomas. In the merged dataset, the median incidence risk score was 0.9027 among high-grade adenoma samples, significantly higher than that among low-grade adenomas (0.8565). In summary, the developed incidence risk score could well predict the incidence risk of precancerous colorectal lesions and has value in clinical application.
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http://dx.doi.org/10.3389/fgene.2020.573787DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7844367PMC
January 2021

A qualitative transcriptional signature for the early diagnosis of colorectal cancer.

Cancer Sci 2019 Oct 3;110(10):3225-3234. Epub 2019 Sep 3.

Department of Bioinformatics, School of Basic Medical Sciences, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China.

Currently, using biopsy specimens for the early diagnosis of colorectal cancer (CRC) is not entirely reliable due to insufficient sampling amount and inaccurate sampling location. Thus, it is necessary to develop a signature that can accurately identify patients with CRC under these clinical scenarios. Based on the relative expression orderings of genes within individual samples, we developed a qualitative transcriptional signature to discriminate CRC tissues, including CRC adjacent normal tissues from non-CRC individuals. The signature was validated using multiple microarray and RNA sequencing data from different sources. In the training data, a signature consisting of 7 gene pairs was identified. It was well validated in both biopsy and surgical resection specimens from multiple datasets measured by different platforms. For biopsy specimens, 97.6% of 42 CRC tissues and 94.5% of 163 non-CRC (normal or inflammatory bowel disease) tissues were correctly classified. For surgically resected specimens, 99.5% of 854 CRC tissues and 96.3% of 81 CRC adjacent normal tissues were correctly identified as CRC. Notably, we additionally measured 33 CRC biopsy specimens by the Affymetrix platform and 13 CRC surgical resection specimens, with different proportions of tumor epithelial cells, ranging from 40% to 100%, by the RNA sequencing platform, and all these samples were correctly identified as CRC. The signature can be used for the early diagnosis of CRC, which is also suitable for minimum biopsy specimens and inaccurately sampled specimens, and thus has potential value for clinical application.
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http://dx.doi.org/10.1111/cas.14137DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778657PMC
October 2019
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