MiR-125b-5p/STAT3 Axis Regulates Drug Resistance in Osteosarcoma Cells by Acting on ABC Transporters.

Background: The poor prognosis of the highly malignant tumor osteosarcoma stems from its drug resistance and therefore exploring its resistance mechanisms will help us identify more effective treatment options. However, the effects of miR-125b-5p on drug resistance in osteosarcoma cells are still unclear. Methods: To study the effects of miR-125b-5p on drug resistance in osteosarcoma cells. Osteosarcoma-resistant miR-125b-5p was obtained from the databases GeneCards and g:Profiler. CCK8, western blot, and transwell were applied for the detection of the miR-125b-5p effects on proliferation, migration, invasion, apoptosis, and drug resistance in osteosarcoma. Bioinformatics is aimed at demonstrating the targeting factor miR-125b-5p, performing protein interaction enrichment analysis by Metascape, and finally validating by binding sites. Results: Upregulation of miR-125b-5p restrains proliferation, migration, and invasion of osteosarcoma and promotes apoptosis. In addition, miR-125b-5p can restore drug sensitivity in drug-resistant osteosarcoma. miR-125-5p restrains the signal transducer and inhibits the transcription 3 (STAT3) expression activator via targeting its 3'-UTR. STAT3 affects drug-resistant osteosarcoma to regulate the ABC transporter. Conclusion: miR-125b-5p/STAT3 axis mediates the drug resistance of osteosarcoma by acting on ABC transporter.

The incidence and severity of the cold intolerance after reverse homodigital dorsoradial flap for coverage of thumb tissue defect.

BACKGROUND: The reverse homodigital dorsoradial flap (RHDF) has been an optional treatment for thumb soft tissue defects. The current study aims to investigate the incidence and severity of cold intolerance after the use of the RHDF for thumb soft tissue reconstruction. METHODS: 49 patients with thumb soft-tissue defects treated with RHDF from 2010 to 2018 were included with the mean follow-up time of 36 (range, 14-61) months. The assessment of sensory recovery in the flap, the Cold Intolerance Symptom Severity (CISS) questionnaire, symptoms triggering temperature grade, and natural history of the symptoms were included in the final follow-up. RESULTS: 35 out of 49 patients (71.4%) reported cold intolerance. The mean CISS score of 35 cases was 28 (range 3-72) points. The mean age of the intolerance group was significantly older than that of the non-cold intolerance group. The incidence of cold intolerance in the dorsum was significantly lower than in the pulp and tip. Significantly positive correlations were presented between CISS scores and temperature grades of symptoms triggered. Seven patients were with partial recovery while 6 patients were with complete recovery from negative symptoms. 22 patients reported no change in terms of the symptoms. CONCLUSIONS: Cold intolerance is a common complication after the RHDF for thumb tissue reconstruction, especially in the elderly population. Cold intolerance following RHDF warrants more attention for surgeons to describe the patient expect after the procedure.

In-depth learning of automatic segmentation of shoulder joint magnetic resonance images based on convolutional neural networks.

OBJECTIVE: Magnetic resonance imaging (MRI) is gradually replacing computed tomography (CT) in the examination of bones and joints. The accurate and automatic segmentation of the bone structure in the MRI of the shoulder joint is essential for the measurement and diagnosis of bone injuries and diseases. The existing bone segmentation algorithms cannot achieve automatic segmentation without any prior knowledge, and their versatility and accuracy are relatively low. For this reason, an automatic segmentation algorithm based on the combination of image blocks and convolutional neural networks is proposed. METHODS: First, we establish 4 segmentation models, including 3 U-Net-based bone segmentation models (humeral segmentation model, joint bone segmentation model, humeral head and articular bone segmentation model as a whole) and a block-based Alex Net segmentation model; Then we use 4 segmentation models to obtain the candidate bone area, and accurately detect the location area of the humerus and joint bone by voting. Finally, the Alex Net segmentation model is further used in the detected bone area to segment the bone edge with the accuracy of the pixel level. RESULTS: The experimental data is obtained from 8 groups of patients in the orthopedics department of our hospital. Each group of scan sequence includes about 100 images, which have been segmented and labeled. Five groups of patients were used for training and five-fold cross-validation, and three groups of patients were used to test the actual segmentation effect. The average accuracy of Dice Coefficient, Positive Predicted Value (PPV) and Sensitivity reached 0.91 ±â€¯0.02, respectively. 0.95 ±â€¯0.03 and 0.95 ±â€¯0.02. CONCLUSIONS: The method in this paper is for a small sample of patient data sets, and only through deep learning on 2D medical images, very accurate shoulder joint segmentation results can be obtained, provide clinical diagnostic guidance to orthopedics. At the same time, the proposed algorithm framework has a certain versatility and is suitable for the precise segmentation of specific organs and tissues in MRI based on a small sample data.