Alkylation of phosphorothioated thrombin binding aptamers improves the selectivity of inhibition of tumor cell proliferation upon anticoagulation

Background: Recently, aptamers have been extensively researched for therapy and diagnostic applications. Thrombin-binding aptamer is a 15 nt deoxyribonucleic acid screened by SELEX, it can specifically bind to thrombin and inhibit blood coagulation. Since it is also endowed with excellent antitumor activity, the intrinsic anticoagulation advantage converted to a main potential side effect for its further application in antiproliferative therapy. Methods: Site-specific alkylation was conducted through nucleophilic reaction of phosphorothioated TBAs using bromide reagents. Circular dichroism (CD) spectroscopy and surface plasmon resonance (SPR) measurements were used to evaluate anticoagulation activity, and a CCK-8 assay was used to determine cell proliferation activity. Results: The CD spectra of the modified TBAs were weakened, and their affinity for thrombin was dramatically reduced, as reflected by the K-D values. On the other hand, their inhibition of A549 cells was retained. Conclusions: Incorporation of different alkyls apparently disrupted the binding of TBA to thrombin while maintaining the antitumor activity. General significance: A new modification strategy was established for the use of TBA as a more selective antitumor agent.National Natural Science Foundation of China [21332010, 21572013]; Ministry of Science and Technology of the People's Republic of China [2012CB720604]SCI(E)ARTICLE71864-1869186

FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures. However, they are extremely hungry for delicate annotations to train, and the acquisition is laborious and unaffordable. Therefore, we present FreeMask in this work, which resorts to synthetic images from generative models to ease the burden of both data collection and annotation procedures. Concretely, we first synthesize abundant training images conditioned on the semantic masks provided by realistic datasets. This yields extra well-aligned image-mask training pairs for semantic segmentation models. We surprisingly observe that, solely trained with synthetic images, we already achieve comparable performance with real ones (e.g., 48.3 vs. 48.5 mIoU on ADE20K, and 49.3 vs. 50.5 on COCO-Stuff). Then, we investigate the role of synthetic images by joint training with real images, or pre-training for real images. Meantime, we design a robust filtering principle to suppress incorrectly synthesized regions. In addition, we propose to inequally treat different semantic masks to prioritize those harder ones and sample more corresponding synthetic images for them. As a result, either jointly trained or pre-trained with our filtered and re-sampled synthesized images, segmentation models can be greatly enhanced, e.g., from 48.7 to 52.0 on ADE20K. Code is available at https://github.com/LiheYoung/FreeMask.Comment: Accepted by NeurIPS 202

Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation

Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and additional training procedures. Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance. We argue that various data augmentations should be adjusted to better adapt to the semi-supervised scenarios instead of directly applying these techniques from supervised learning. Specifically, we adopt a simplified intensity-based augmentation that selects a random number of data transformations with uniformly sampling distortion strengths from a continuous space. Based on the estimated confidence of the model on different unlabeled samples, we also randomly inject labelled information to augment the unlabeled samples in an adaptive manner. Without bells and whistles, our simple AugSeg can readily achieve new state-of-the-art performance on SSS benchmarks under different partition protocols.Comment: 10 pages, 8 table

Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning

Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples. This practice ensures high-quality pseudo labels, but incurs a relatively low utilization of the whole unlabeled set. In this work, our key insight is that these uncertain samples can be turned into certain ones, as long as the confusion classes for the top-1 class are detected and removed. Invoked by this, we propose a novel method dubbed ShrinkMatch to learn uncertain samples. For each uncertain sample, it adaptively seeks a shrunk class space, which merely contains the original top-1 class, as well as remaining less likely classes. Since the confusion ones are removed in this space, the re-calculated top-1 confidence can satisfy the pre-defined threshold. We then impose a consistency regularization between a pair of strongly and weakly augmented samples in the shrunk space to strive for discriminative representations. Furthermore, considering the varied reliability among uncertain samples and the gradually improved model during training, we correspondingly design two reweighting principles for our uncertain loss. Our method exhibits impressive performance on widely adopted benchmarks. Code is available at https://github.com/LiheYoung/ShrinkMatch.Comment: Accepted by ICCV 202

Diverse Cotraining Makes Strong Semi-Supervised Segmentor

Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it. In this work, we revisit the core assumption that supports co-training: multiple compatible and conditionally independent views. By theoretically deriving the generalization upper bound, we prove the prediction similarity between two models negatively impacts the model's generalization ability. However, most current co-training models are tightly coupled together and violate this assumption. Such coupling leads to the homogenization of networks and confirmation bias which consequently limits the performance. To this end, we explore different dimensions of co-training and systematically increase the diversity from the aspects of input domains, different augmentations and model architectures to counteract homogenization. Our Diverse Co-training outperforms the state-of-the-art (SOTA) methods by a large margin across different evaluation protocols on the Pascal and Cityscapes. For example. we achieve the best mIoU of 76.2%, 77.7% and 80.2% on Pascal with only 92, 183 and 366 labeled images, surpassing the previous best results by more than 5%.Comment: ICCV2023, Camera Ready Version, Code: \url{https://github.com/williamium3000/diverse-cotraining

Learning to Coordinate with Anyone

In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates. Recent studies attempted to generate diverse teammates to enhance the generalizable coordination ability, but were restricted by pre-defined teammates. In this work, our aim is to train agents with strong coordination ability by generating teammates that fully cover the teammate policy space, so that agents can coordinate with any teammates. Since the teammate policy space is too huge to be enumerated, we find only dissimilar teammates that are incompatible with controllable agents, which highly reduces the number of teammates that need to be trained with. However, it is hard to determine the number of such incompatible teammates beforehand. We therefore introduce a continual multi-agent learning process, in which the agent learns to coordinate with different teammates until no more incompatible teammates can be found. The above idea is implemented in the proposed Macop (Multi-agent compatible policy learning) algorithm. We conduct experiments in 8 scenarios from 4 environments that have distinct coordination patterns. Experiments show that Macop generates training teammates with much lower compatibility than previous methods. As a result, in all scenarios Macop achieves the best overall coordination ability while never significantly worse than the baselines, showing strong generalization ability

Segmentation of kidney and kidney tumor by cascaded fusion FCNs with soft-boundary regression

To produce reliable kidney and kidney tumor semantic segmentation, we proposed a two-stage method to automatically segment kidney and tumor. Specifically, in the first stage, to crop input into a small region, we train a small network to locate kidney and tumor with down-sampled image. In second stage, we train three types of networks to segment kidney, tumor, kidney and tumor respectively. Then we combine these networks together with ensemble method to produce reliable kidney and tumor segmentation. Our method can achieve an overall approximate score of 85.1% in DSC in Kits19 Challenge, with 96.9% for kidney and 73.3% for kidney tumor

Annealing novel nucleobase-lipids with oligonucleotides or plasmid DNA based on H-bonding or π-π interaction:Assemblies and transfections

Lipid derivatives of nucleoside analogs have been highlighted for their potential for effective gene delivery. A novel class of nucleobase-lipids are rationally designed and readily synthesized, comprising thymine/cytosine, an ester/amide linker and an oleyl lipid. The diversity of four nucleobase-lipids termed DXBAs (DOTA, DNTA, DOCA and DNCA) is investigated. Besides, DNCA is demonstrated to be an effective neutral transfection material for nucleic acid delivery, which enbles to bind to oligonucleotides via H-bonding and π-π stacking with reduced toxicity in vitro and in vivo. Several kinds of nucleic acid drugs including aptamer, ssRNA, antisense oligonucleotide, and plasmid DNAs can be delivered by DXBAs, especially DNCA. In particular, G4-aptamer AS1411 encapsulated by DNCA exhibits cellular uptake enhancement, lysosome degradation reduction, cell apoptosis promotion, cell cycle phase alteration in vitro and duration prolongation in vivo, resulting in significant anti-proliferative activity. Our results demonstrate that DNCA is a promising transfection agent for G4-aptamers and exhibites bright application prospects in the permeation improvement of single-stranded oligonucleotides or plasmid DNAs

Posterior Circulation Mechanical Thrombectomy through Primitive Trigeminal Artery: A Case Report

Introduction: Primitive trigeminal artery (PTA) is a rare intracranial vascular malformation, and mechanical thrombectomy and revascularization via PTA are rarely reported. Case Presentation: We reported a case of mechanical thrombectomy through PTA in a patient who presented with sudden slurred speech and had a National Institutes of Health Stroke Scale score of 12. Digital subtraction angiography of the cerebral vasculature showed PTA formation in the right internal carotid artery cavernous segment, with acute occlusion of the distal basilar artery at the PTA junction, and bilateral vertebral arteries and proximal basilar artery were underdeveloped. Therefore, we chose mechanical thrombectomy via PTA, but unfortunately, the vessel failed to recanalize. Follow-up at 1-month post-procedure indicated that the patient had passed away. We present the endovascular process and analyze and summarize the reasons for the failure to provide a reference for subsequent mechanical thrombectomy via PTA. Conclusions: PTA increases the risk of ischemic stroke and adds to the complexity of mechanical thrombectomy post-stroke. However, in certain situations, PTA can be used as a thrombectomy channel to increase the first-line possibility of timely endovascular treatment to save ischemic brain tissue

A simple LC-ESI-MS method for the determination of norvancomycin in rat plasma and application to pharmacokinetic study

A simple and sensitive LC-ESI-MS method for determination of norvancomycin in plasma was developed and validated over the concentration range of 20-2,000 ng/mL. After addition of vancomycin as internal standard (IS), protein precipitation with 5 % trichloroacetic acid was employed for the sample preparation. Chromatographic separation was performed on a Zorbax SB-C18 (100 mm×2.1 mm, 3.5 μm) column with 10:90 (v/v) acetonitrile-0.1 % formic acid as mobile phase. The MS data acquisition was accomplished by selective ions monitoring (SIM) mode with positive electrospray ionization (ESI) interface. The limit of quantification (LOQ) was 20 ng/mL. For inter-day and intra-day tests, the precision (RSD) for the entire validation was less than 12 %. The developed method was successfully applied to pharmacokinetic studies of norvancomycin in rats following single intravenous administration dose of 10 mg/Kg.Colegio de Farmacéuticos de la Provincia de Buenos Aire