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AimThe aim of this study was to evaluate the effect of the Topping-off technique in preventing the aggravation of degeneration caused by adjacent segment fusion.MethodsClinical parameters of patients who underwent L5-S1 posterior lumbar interbody fusion + interspinous process at L4-L5 (PLIF + ISP) with the Wallis system (Topping-off group) were compared retrospectively with those of patients who underwent solely PLIF. Pre- and post-operative x-ray measurements, visual analogue scale (VAS) scores, and Japanese Orthopaedic Association (JOA) scores were assessed in all subjects. Normal L1-S1 lumbosacral finite element models were established in accordance with the two types of surgery in our study, respectively. Virtual loading was added to assess the motility, disc pressure, and facet joint stress of L4-L5.ResultsThere were 22 and 23 valid cases included in the Topping-off and PLIF groups. No degeneration was observed in either group. Both VAS and JOA scores improved significantly post-operatively (P < 0.01). The intervertebral angle and lumbar lordosis of L4-L5 were both significantly increased (t = −2.89 and −2.68, P < 0.05 in the Topping-off group and t = −2.25 and −2.15, P < 0.05 in the PLIF group). In the Topping-off group, x-ray in dynamic position showed no significant difference in the angulation or distance of the anterior movement of the L4-L5 segment. The angle of hyper-extension and distance of the posterior movement of L4 were significantly decreased. In the PLIF group, both hyper-flexion and hyper-extension and posterior movement were increased significantly. In finite element analysis, displacement of the L4 vertebral body, pressure of the annulus fibrosus and nucleus pulposus, and stress of the bilateral facet joint were less in the Topping-off group under loads of anterior flexion and posterior extension. Facet joint stress on the left side of the L4-L5 segment was also less in the Topping-off group under left flexion loads.ConclusionShort-term efficacy and safety between Topping-off and PLIF were similar, whilst the Topping-off technique could restrict the hyper-extension movement of adjacent segments, prevent back and forth movement of proximal vertebrae, and decrease loads of intervertebral disc and facet joints.

Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by training on 464,105 images from 12,179 patients and test its performance on 2263 patients from three independent datasets. At the patient-level, CRCNet achieves an area under the precision-recall curve (AUPRC) of 0.882 (95% CI: 0.828–0.931), 0.874 (0.820–0.926) and 0.867 (0.795–0.923). CRCNet exceeds average endoscopists performance on recall rate across two test sets (91.3% versus 83.8%; two-sided t-test, p < 0.001 and 96.5% versus 90.3%; p = 0.006) and precision for one test set (93.7% versus 83.8%; p = 0.02), while obtains comparable recall rate on one test set and precision on the other two. At the image-level, CRCNet achieves an AUPRC of 0.990 (0.987–0.993), 0.991 (0.987–0.995), and 0.997 (0.995–0.999). Our study warrants further investigation of CRCNet by prospective clinical trials.

MicroRNAs (miRNAs) are involved in the progression of many cancers through largely unelucidated mechanisms. The results of our present study identified a gene cluster, miR‐221/222, that is constitutively upregulated in serum exosome samples of patients with colorectal carcinoma (CRC) with liver metastasis (LM); this upregulation predicts a poor overall survival rate. Using an in vitro cell coculture model, we demonstrated that CRC exosomes harboring miR‐221/222 activate liver hepatocyte growth factor (HGF) by suppressing SPINT1 expression. Importantly, miR‐221/222 plays a key role in forming a favorable premetastatic niche (PMN) that leads to the aggressive nature of CRC, which was further shown through in vivo studies. Overall, our results show that exosomal miR‐221/222 promotes CRC progression and may serve as a novel prognostic marker and therapeutic target for CRC with LM.