The Janus kinase/signal transducers and activators of transcription (JAK-STAT) control natural killer (NK) cells development and cytotoxic functions, however, whether long non-coding RNAs (lncRNAs) are involved in this pathway remains unknown. We found that miR155HG was elevated in activated NK cells and promoted their proliferation and effector functions in both NK92 and induced-pluripotent stem cells (iPSCs)-derived NK (iPSC-NK) cells, without reliance on its derived miR-155 and micropeptide P155. Mechanistically, miR155HG bound to miR-6756 and relieved its repression of JAK3 expression, thereby promoting the JAK-STAT pathway and enhancing NK cell proliferation and function. Further investigations disclosed that upon cytokine stimulation, STAT3 directly interacts with miR155HG promoter and induces miR155HG transcription. Collectively, we identify a miR155HG-mediated positive feedback loop of the JAK-STAT signaling. Our study will also provide a power target regarding miR155HG for improving NK cell generation and effector function in the field of NK cell adoptive transfer therapy against cancer, especially iPSC-derived NK cells.

Objectives:To propose a method based on machine learning to predict the neurological function-al status of patients with traumatic cervical spinal cord injury(TCSCI).Methods:The clinical data of 180 pa-tients with TCSCI admitted to Shanghai Changzheng Hospital were retrospectively analyzed,including cervical spine MRI images and American Spinal Injury Association(ASIA)scores within 24 hours after injury and the ASIA scores at 1-year follow-up after injury.The 180 patients were randomly divided into a training set of 144 patients and a test set of 36 patients in a ratio of 8∶2.Overall,a new clinical-imaging prediction method was proposed using the two-stage integration concept,which used the ASIA scores and MRI images within 24 hours after TCSCI to achieve a full-feature prediction of the patient's sensory and motor function one year after injury.In the first stage,models such as GradentBoosting,GaussianNB,KNeighbors,Decision-Tree,RandomForest,and support vector classifier were used to independently predict the sensory-motor func-tion recovery of 132 skin nodes and muscle nodes.In the second stage,the optimal model for each feature prediction was screened out through horizontal and vertical comparison of performance indicators,so as to fi-nally achieve the best prognostic prediction of neurological function at 56 light touch and 56 acupuncture skin nodes,and 20 key muscle nodes.After the constructed prediction model is trained and verified,the pre-diction of the test set is evaluated using accuracy,precision,recall,average precision and F1 score.Results:In terms of the overall performance of this prediction model in predicting sensory-motor function in TCSCI patients 1 year after injury,all models in the test set achieved accuracy ≥0.886,recall ≥ 0.845,precision ≥0.875,average precision ≥0.853,and F1 score≥0.859,demonstrating that the correct prediction ability of each model and the quality and completeness of the actual prediction results were relatively high.Moreover,the two-stage prediction model can optimize the prediction effect of each model on each feature,and the prediction performance is better.Conclusions:This sensory-motor full-feature prediction method can effective-ly predict the neurological function recovery of TCSCI patients at 1-year follow-up after injury.Its predictive ability is significantly higher than that of a single model,and is expected to provide a useful reference for the personalized diagnosis,treatment and rehabilitation of TCSCI patients.

Objectives:To propose a method based on machine learning to predict the neurological function-al status of patients with traumatic cervical spinal cord injury(TCSCI).Methods:The clinical data of 180 pa-tients with TCSCI admitted to Shanghai Changzheng Hospital were retrospectively analyzed,including cervical spine MRI images and American Spinal Injury Association(ASIA)scores within 24 hours after injury and the ASIA scores at 1-year follow-up after injury.The 180 patients were randomly divided into a training set of 144 patients and a test set of 36 patients in a ratio of 8∶2.Overall,a new clinical-imaging prediction method was proposed using the two-stage integration concept,which used the ASIA scores and MRI images within 24 hours after TCSCI to achieve a full-feature prediction of the patient's sensory and motor function one year after injury.In the first stage,models such as GradentBoosting,GaussianNB,KNeighbors,Decision-Tree,RandomForest,and support vector classifier were used to independently predict the sensory-motor func-tion recovery of 132 skin nodes and muscle nodes.In the second stage,the optimal model for each feature prediction was screened out through horizontal and vertical comparison of performance indicators,so as to fi-nally achieve the best prognostic prediction of neurological function at 56 light touch and 56 acupuncture skin nodes,and 20 key muscle nodes.After the constructed prediction model is trained and verified,the pre-diction of the test set is evaluated using accuracy,precision,recall,average precision and F1 score.Results:In terms of the overall performance of this prediction model in predicting sensory-motor function in TCSCI patients 1 year after injury,all models in the test set achieved accuracy ≥0.886,recall ≥ 0.845,precision ≥0.875,average precision ≥0.853,and F1 score≥0.859,demonstrating that the correct prediction ability of each model and the quality and completeness of the actual prediction results were relatively high.Moreover,the two-stage prediction model can optimize the prediction effect of each model on each feature,and the prediction performance is better.Conclusions:This sensory-motor full-feature prediction method can effective-ly predict the neurological function recovery of TCSCI patients at 1-year follow-up after injury.Its predictive ability is significantly higher than that of a single model,and is expected to provide a useful reference for the personalized diagnosis,treatment and rehabilitation of TCSCI patients.