AffinityNet: semi-supervised few-shot learning for disease type prediction

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the "big p, small N" problem (i.e., a relatively small number of samples with high-dimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data. The code is freely available at https://github.com/BeautyOfWeb/AffinityNet .Comment: 14 pages, 6 figure

Efficacy and safety of percutaneous transforaminal endoscopic surgery (PTES) compared with MIS-TLIF for surgical treatment of lumbar degenerative disease in elderly patients: A retrospective cohort study

ObjectivesTo evaluate the efficacy and safety of PTES for surgical treatment of lumbar degenerative disease (LDD) including lumbar disc herniation, lateral recess stenosis, intervertebral foraminal stenosis and central spinal canal stenosis in elderly patients compared with MIS-TLIF.MethodsFrom November 2016 to December 2018, 84 elderly patients (>70 years old) of single-level LDD with neurologic symptoms underwent the surgical treatment. 45 patients were treated using PTES under local anesthesia in group 1 and 39 patients treated using MIS-TLIF in group 2. Preoperative, postoperative back and leg pain were evaluated using Visual analog scale (VAS) and the results were determined with Oswestry disability index (ODI) at 2-year follow-up. All complications were recorded.ResultsPTES group shows significantly less operation time (55.6 ± 9.7 min vs. 97.2 ± 14.3 min, P < 0.001), less blood loss [11(2–32) ml vs. 70(35–300) ml, P < 0.001], shorter incision length (8.4 ± 1.4 mm vs. 40.6 ± 2.7 mm, P < 0.001), less fluoroscopy frequency [5(5–10) times vs. 7(6–11) times, P < 0.001] and shorter hospital stay[3(2–4) days vs. 7(5–18) days, P < 0.001] than MIS-TLIF group does. Although there was no statistical difference of leg VAS scores between two groups, back VAS scores in PTES group were significantly lower than those in MIS-TLIF group during follow-ups after surgery (P < 0.001). ODI of PTES group was also significantly lower than that of MIS-TLIF group at 2-year follow-up (12.3 ± 3.6% vs. 15.7 ± 4.8%, P < 0.001).ConclusionBoth PTES and MIS-TLIF show favorable clinical outcomes for LDD in elderly patients. Compared with MIS-TLIF, PTES has the advantages including less damage of paraspinal muscle and bone, less blood loss, faster recovery, lower complication rate, which can be performed under local anesthesia

How to predict the culprit segment in percutaneous transforaminal endoscopic surgery under local anesthesia for surgical treatment of lumbar degenerative diseases? Radiologic images or clinical symptoms

ObjectivePercutaneous transforaminal endoscopic surgery (PTES) is a novel, minimally invasive technique used to treat lumbar degenerative diseases (LDDs). PTES under local anesthesia was performed to treat the culprit segment of LDDs predicted by radiologic images or clinical symptoms, and the efficacy, security, and feasibility were evaluated.MethodsEighty-seven cases of LDDs with nerve root symptoms, which were not consistent with lumbar degenerative levels and degrees on MRI and CT, were treated with PTES under local anesthesia in a day surgery ward from January 2015 to December 2019. Forty-two patients, whose culprit segments were predicted by radiologic images, were included in group A. The other 45 patients, whose culprit segments were predicted by clinical symptoms, were included in group B. Leg pain VAS and ODI scores before and after PTES were recorded. The outcome was defined according to the MacNab grade at the 2-year follow-up. Postoperative complications were recorded.ResultsIn group A, 2 patients underwent PTES for one segment, 37 patients underwent PTES for two segments, and 3 patients underwent PTES for three segments. One of the one-segment PTES patients had no relief from symptoms and underwent another PTES for other culprit segments 3 months after surgery. In group B, 44 of 45 patients were treated using PTES for one segment and 1 patient was treated for two segments. Group B showed significantly less operative duration, less blood loss, and less fluoroscopy frequency than group A (p < 0.001). The leg pain VAS score and the ODI score significantly decreased after the operation in both groups (p < 0.001), and the excellent and good rates were 97.6% (41/42) in group A and 100% (45/45) in group B at the 2-year follow-up. The leg pain VAS score of group B was significantly lower than that of group A immediately and 1 week, 1 month, 2 months, and 3 months after surgery (p < 0.001). There was no statistical difference in ODI scores and the excellent and good rates between the two groups. No complications, such as wound infection or permanent nerve injury, were observed.ConclusionIt is much more accurate to predict the culprit segment according to clinical symptoms than radiologic images in PTES under local anesthesia for surgical treatment of LDDs

An overview of hyperbaric oxygen preconditioning against ischemic stroke

Ischemic stroke (IS) has become the second leading cause of morbidity and mortality worldwide, and the prevention of IS should be given high priority. Recent studies have indicated that hyperbaric oxygen preconditioning (HBO-PC) may be a protective nonpharmacological method, but its underlying mechanisms remain poorly defined. This study comprehensively reviewed the pathophysiology of IS and revealed the underlying mechanism of HBO-PC in protection against IS. The preventive effects of HBO-PC against IS may include inducing antioxidant, anti-inflammation, and anti-apoptosis capacity; activating autophagy and immune responses; upregulating heat shock proteins, hypoxia-inducible factor-1, and erythropoietin; and exerting protective effects upon the blood-brain barrier. In addition, HBO-PC may be considered a safe and effective method to prevent IS in combination with stem cell therapy. Although the benefits of HBO-PC on IS have been widely observed in recent research, the implementation of this technique is still controversial due to regimen differences. Transferring the results to clinical application needs to be taken carefully, and screening for the optimal regimen would be a daunting task. In addition, whether we should prescribe an individualized preconditioning regimen to each stroke patient needs further exploration

Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we introduce an innovative pre-training approach, Grounded Point Colorization (GPC), to bridge the gap between data and labels by teaching the model to colorize LiDAR point clouds, equipping it with valuable semantic cues. To tackle challenges arising from color variations and selection bias, we incorporate color as "context" by providing ground-truth colors as hints during colorization. Experimental results on the KITTI and Waymo datasets demonstrate GPC's remarkable effectiveness. Even with limited labeled data, GPC significantly improves fine-tuning performance; notably, on just 20% of the KITTI dataset, GPC outperforms training from scratch with the entire dataset. In sum, we introduce a fresh perspective on pre-training for 3D object detection, aligning the objective with the model's intended role and ultimately advancing the accuracy and efficiency of 3D object detection for autonomous vehicles