Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning

Self-supervised learning is an efficient pre-training method for medical image analysis. However, current research is mostly confined to specific-modality data pre-training, consuming considerable time and resources without achieving universality across different modalities. A straightforward solution is combining all modality data for joint self-supervised pre-training, which poses practical challenges. Firstly, our experiments reveal conflicts in representation learning as the number of modalities increases. Secondly, multi-modal data collected in advance cannot cover all real-world scenarios. In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data. Unlike joint self-supervised learning, MedCoSS assigns different modality data to different training stages, forming a multi-stage pre-training process. To balance modal conflicts and prevent catastrophic forgetting, we propose a rehearsal-based continual learning method. We introduce the k-means sampling strategy to retain data from previous modalities and rehearse it when learning new modalities. Instead of executing the pretext task on buffer data, a feature distillation strategy and an intra-modal mixup strategy are applied to these data for knowledge retention. We conduct continuous self-supervised pre-training on a large-scale multi-modal unlabeled dataset, including clinical reports, X-rays, CT scans, MRI scans, and pathological images. Experimental results demonstrate MedCoSS's exceptional generalization ability across nine downstream datasets and its significant scalability in integrating new modality data. Code and pre-trained weight are available at https://github.com/yeerwen/MedCoSS

Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction

Multi-modal learning focuses on training models by equally combining multiple input data modalities during the prediction process. However, this equal combination can be detrimental to the prediction accuracy because different modalities are usually accompanied by varying levels of uncertainty. Using such uncertainty to combine modalities has been studied by a couple of approaches, but with limited success because these approaches are either designed to deal with specific classification or segmentation problems and cannot be easily translated into other tasks, or suffer from numerical instabilities. In this paper, we propose a new Uncertainty-aware Multi-modal Learner that estimates uncertainty by measuring feature density via Cross-modal Random Network Prediction (CRNP). CRNP is designed to require little adaptation to translate between different prediction tasks, while having a stable training process. From a technical point of view, CRNP is the first approach to explore random network prediction to estimate uncertainty and to combine multi-modal data. Experiments on two 3D multi-modal medical image segmentation tasks and three 2D multi-modal computer vision classification tasks show the effectiveness, adaptability and robustness of CRNP. Also, we provide an extensive discussion on different fusion functions and visualization to validate the proposed model

Polymer-stabilized blue phase liquid crystal with a negative Kerr constant

A polymer-stabilized blue-phase liquid crystal (BPLC) with a negative Kerr constant is reported. In a voltage-on state, the double-twist BPLC molecules within the lattice cylinders are reoriented perpendicular to the applied electric field because of their negative dielectric anisotropy. As a result, the induced birefringence has a negative value, which leads to a negative Kerr constant. The negative sign of Kerr constant is experimentally validated by using a quarter-wave plate and a vertical field switching cell. Such a BPLC shows a negligible (similar to 1%) hysteresis and fast response time (similar to 1ms) at the room temperature, although its Kerr constant is relatively small because the employed host has a small Delta epsilon

Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists

Lung cancer is a leading cause of death worldwide and early screening is critical for improving survival outcomes. In clinical practice, the contextual structure of nodules and the accumulated experience of radiologists are the two core elements related to the accuracy of identification of benign and malignant nodules. Contextual information provides comprehensive information about nodules such as location, shape, and peripheral vessels, and experienced radiologists can search for clues from previous cases as a reference to enrich the basis of decision-making. In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules. The context parsing module first segments the context structure of nodules and then aggregates contextual information for a more comprehensive understanding of the nodule. The prototype recalling module utilizes prototype-based learning to condense previously learned cases as prototypes for comparative analysis, which is updated online in a momentum way during training. Building on the two modules, our method leverages both the intrinsic characteristics of the nodules and the external knowledge accumulated from other nodules to achieve a sound diagnosis. To meet the needs of both low-dose and noncontrast screening, we collect a large-scale dataset of 12,852 and 4,029 nodules from low-dose and noncontrast CTs respectively, each with pathology- or follow-up-confirmed labels. Experiments on several datasets demonstrate that our method achieves advanced screening performance on both low-dose and noncontrast scenarios.Comment: MICCAI 202

OIP5-AS1/CD147/TRPM7 axis promotes gastric cancer metastasis by regulating apoptosis related PI3K-Akt signaling

BackgroundTo explore the mechanism of OIP5-AS1/CD147/TRPM7 axis to gastric cancer (GC) metastasis.MethodsBioinformatic analysis was performed to pick up the candidate genes associated with regulation GC metastasis. Using GC cell lines, AGS and MKN-45 as research objects, identify the effect of candidate genes on GC metastasis, judge cell proliferation status by MTT assay and cell clone number, and detect cell migration by Transwell and Wound-healing assay. The molecular mechanism of CD147/OIP5/TRPM7 axis regulating GC metastasis was further explored by RNA sequencing. The key signaling pathways were subsequently verified by flow cytometry and WB.ResultsBioinformatic analysis suggested OIP5-AS1/CD147/TRPM7 axis may be involving in GC metastasis. The RNA interference experiment proved that after gene interference, the proliferation ability of GC cells decreased significantly (P<0.05), which was manifested in the reduction of the number of cell clones. In addition, the migration ability of GC cells was also affected, which was based on the results of Wound Healing (P<0.05). CD147, OIP5-AS1 and TRPM7 all have harmful effects on GC cells. The relationship between OIP5-AS1 and CD147/TRPM7 was detected by RNA immunoprecipitation. Moreover, the RNA sequencing data indicated that CD147/OIP5-AS1/TRPM7 may coordinately regulate the PI3K-AKT pathway related to GC cell apoptosis, thereby affecting the proliferation and migration of GC cells. After RNA interference, the level of apoptosis increased both in AGS and MKN-45 cells. Meanwhile, the expression of pro-apoptotic proteins Caspase9 and BAX were up-regulated (P<0.05). In addition, the expression of PI3K and AKT proteins was reduced (P<0.05). The mouse tumorigenesis experiment corroborated the results of the in vitro study.ConclusionOIP5-AS1/CD147/TRPM7 axis reduces GC cell proliferation by regulating apoptosis associated with PI3K-AKT signaling, further affecting cancer metastasis

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering betteror best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (ΔG≠ off). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated.We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/ mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect thismethodology to be widely applicable to drug discovery and development

Mechanism of intercalation and deintercalation of lithium ions in graphene nanosheets

Graphene nanosheets (GNSs) were synthesized by reducing exfoliated graphite oxides. Their structure, surface morphology and lithium storage mechanism were characterized and investigated systematically using X-ray diffraction, atomic force microscopy, scanning electron microscopy, charge-discharge tests, cyclic voltammetry and electrochemical impedance spectroscopy. It was found that the GNSs, which were obtained via chemical synthesis, were primarily less than 10 graphene layers. The GNS electrodes, which were fabricated from the reduced GNSs, exhibited an enhanced reversible lithium storage capacity and good cyclic stability when serving as anodes in lithium-ion batteries. Also, the first-cycle irreversible capacities of the system were relatively high, because of the formation of a solid electrolyte interphase film on the surface of the GNS electrode and the spontaneous stacking of GNSs during the first lithiation. The electrochemical impedance spectroscopy results suggest that the solid electrolyte interphase film on the GNS electrode during first lithiation were primarily formed at potentials between 0.95 and 0.7 V. Also, the symmetry factor of the charge transfer was measured to be 0.446.Fundamental Research Funds for the Central Universities[2010LKHX03, 2010QNB04, 2010QNB05]; China University of Mining Technology[ON090237

The Effects of Ambient Temperature on Lumbar Disc Herniation: A Retrospective Study

PurposeThis article was designed to provide critical evidence into the relationship between ambient temperature and intensity of back pain in people with lumbar disc herniation (LDH).MethodsData concerning patient's age, gender, diagnostic logout, admission time, discharge time, residence area, and work area (residence area and work area were used to ensure research area) from 2017 to 2019 were obtained from the Neck-Shoulder and Lumbocrural Pain Hospital in Jinan, China. A total of 1,450 hospitalization records were collected in total. The distributed lag non-linear model (DLNM) was used to evaluate the relationship between lag–response and exposure to ambient temperature. Stratification was based on age and gender. Days 1, 5, 20, and 28 prior to admission were denoted as lags 0, 5, 20, and 28, respectively.ResultsAn average daily temperature of 15–23°C reduced the risk of hospitalization the most in men. Conversely, temperatures <10°C drastically increased hospitalization in men, particularly in lags 0–5 and lags 20–28. Men aged between 40 and 50 years old showed less effect in pain sensation during ambient temperature.ConclusionHigh or low ambient temperature can increase the hospitalization risk of LDH, and sometimes, the temperature effect is delayed