Self-Hemotherapy for the Treatment of one Case of Hyperglycemia

The clinical use of autohemotherapy for the treatment of hyperglycemia in type 2 diabetes mellitus has a remarkable effect

Multi-modal Queried Object Detection in the Wild

We introduce MQ-Det, an efficient architecture and pre-training strategy design to utilize both textual description with open-set generalization and visual exemplars with rich description granularity as category queries, namely, Multi-modal Queried object Detection, for real-world detection with both open-vocabulary categories and various granularity. MQ-Det incorporates vision queries into existing well-established language-queried-only detectors. A plug-and-play gated class-scalable perceiver module upon the frozen detector is proposed to augment category text with class-wise visual information. To address the learning inertia problem brought by the frozen detector, a vision conditioned masked language prediction strategy is proposed. MQ-Det's simple yet effective architecture and training strategy design is compatible with most language-queried object detectors, thus yielding versatile applications. Experimental results demonstrate that multi-modal queries largely boost open-world detection. For instance, MQ-Det significantly improves the state-of-the-art open-set detector GLIP by +7.8% zero-shot AP on the LVIS benchmark and averagely +6.3% AP on 13 few-shot downstream tasks, with merely 3% pre-training time required by GLIP. Code is available at https://github.com/YifanXu74/MQ-Det.Comment: Under revie

Open Vocabulary Object Detection with Proposal Mining and Prediction Equalization

Open-vocabulary object detection (OVD) aims to scale up vocabulary size to detect objects of novel categories beyond the training vocabulary. Recent work resorts to the rich knowledge in pre-trained vision-language models. However, existing methods are ineffective in proposal-level vision-language alignment. Meanwhile, the models usually suffer from confidence bias toward base categories and perform worse on novel ones. To overcome the challenges, we present MEDet, a novel and effective OVD framework with proposal mining and prediction equalization. First, we design an online proposal mining to refine the inherited vision-semantic knowledge from coarse to fine, allowing for proposal-level detection-oriented feature alignment. Second, based on causal inference theory, we introduce a class-wise backdoor adjustment to reinforce the predictions on novel categories to improve the overall OVD performance. Extensive experiments on COCO and LVIS benchmarks verify the superiority of MEDet over the competing approaches in detecting objects of novel categories, e.g., 32.6% AP50 on COCO and 22.4% mask mAP on LVIS

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to fully reflect the performance of MLLM, lacking a comprehensive evaluation. In this paper, we fill in this blank, presenting the first MLLM Evaluation benchmark MME. It measures both perception and cognition abilities on a total of 14 subtasks. In order to avoid data leakage that may arise from direct use of public datasets for evaluation, the annotations of instruction-answer pairs are all manually designed. The concise instruction design allows us to fairly compare MLLMs, instead of struggling in prompt engineering. Besides, with such an instruction, we can also easily carry out quantitative statistics. A total of 10 advanced MLLMs are comprehensively evaluated on our MME, which not only suggests that existing MLLMs still have a large room for improvement, but also reveals the potential directions for the subsequent model optimization.Comment: https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model

CDK5-dependent BAG3 degradation modulates synaptic protein turnover

阿尔茨海默病（AD）是严重威胁人类健康的重大神经系统疾病，AD的发生发展与衰老密切相关，目前临床治疗方法十分有限。因此迫切需要从AD致病早期入手，发现和鉴定导致AD神经功能紊乱的机制和靶点，为AD的早期防治提供基础。张杰教授及其团队从高通量磷酸化蛋白质组学入手，系统研究了CDK5在神经细胞中的磷酸化底物，鉴定出了在蛋白质量控制中发挥重要功能的BAG3蛋白是CDK5的全新底物。课题组从磷酸化蛋白质组学入手，发现和阐明了细胞周期蛋白激酶5（CDK5）通过调控BAG3在维持突触蛋白水平调控中的作用机制，及其在阿尔茨海默病（AD）发生发展中的机理。 该研究是多个团队历时8年合作完成的，香港中文大学的周熙文教授、美国匹兹堡大学的Karl Herrup教授、美国Sanford-Burnham研究所的许华曦教授、美国梅奥医学中心的卜国军教授，厦门大学医学院的文磊教授、张云武教授、赵颖俊教授、薛茂强教授，军事医学科学院的袁增强教授等都参与了该工作。 厦门大学医学院2012级博士生周杰超等为文章的第一作者，张杰教授为通讯作者。Background Synaptic protein dyshomeostasis and functional loss is an early invariant feature of Alzheimer’s disease (AD), yet the unifying etiological pathway remains largely unknown. Knowing that cyclin-dependent kinase 5 (CDK5) plays critical roles in synaptic formation and degeneration, its phosphorylation targets were re-examined in search for candidates with direct global impacts on synaptic protein dynamics, and the associated regulatory network was also analyzed. Methods Quantitative phospho-proteomics and bioinformatics analyses were performed to identify top-ranked candidates. A series of biochemical assays were used to investigate the associated regulatory signaling networks. Histological, electrochemical and behavioral assays were performed in conditional knockout, shRNA-mediated knockdown and AD-related mice models to evaluate its relevance to synaptic homeostasis and functions. Results Among candidates with known implications in synaptic modulations, BCL2-associated athanogene-3 (BAG3) ranked the highest. CDK5-mediated phosphorylation on Ser297/Ser291 (Mouse/Human) destabilized BAG3. Loss of BAG3 unleashed the selective protein degradative function of the HSP70 machinery. In neurons, this resulted in enhanced degradation of a number of glutamatergic synaptic proteins. Conditional neuronal knockout of Bag3 in vivo led to impairment of learning and memory functions. In human AD and related-mouse models, aberrant CDK5-mediated loss of BAG3 yielded similar effects on synaptic homeostasis. Detrimental effects of BAG3 loss on learning and memory functions were confirmed in these mice, and such were reversed by ectopic BAG3 re-expression. Conclusions Our results highlight that neuronal CDK5-BAG3-HSP70 signaling axis plays a critical role in modulating synaptic homeostasis. Dysregulation of the signaling pathway directly contributes to synaptic dysfunction and AD pathogenesis.This work was supported by the National Science Foundation in China (Grant: 31571055, 81522016, 81271421 to J.Z.; 81801337 to L.L; 81774377 and 81373999 to L.W.); Fundamental Research Funds for the Central Universities of China-Xiamen University (Grant: 20720150062, 20720180049 and 20720160075 to J.Z.); Fundamental Research Funds for Fujian Province University Leading Talents (Grant JAT170003 to L.L); Hong Kong Research Grants Council (HKUST12/CRF/13G, GRF660813, GRF16101315, AoE/M-05/12 to K.H.; GRF16103317, GRF16100718 and GRF16100219 to H.-M,C.); Offices of Provost, VPRG and Dean of Science, HKUST (VPRGO12SC02 to K.H.); Chinese University of Hong Kong (CUHK) Improvement on Competitiveness in Hiring New Faculty Funding Scheme (Ref. 133), CUHK Faculty Startup Fund and Alzheimer’s Association Research Fellowship (AARF-17-531566) to H.-M, C. 该研究受到了国家自然科学基金、厦门大学校长基金、福建省卫生教育联合攻关基金等的资助

Individual Patient Data Pooled Analysis of Randomized Trials of Bivalirudin versus Heparin in Acute Myocardial Infarction: Rationale and Methodology

Background: Individual randomized controlled trials (RCTs) of periprocedural anticoagulation with bivalirudin versus heparin during percutaneous coronary intervention (PCI) have reported conflicting results. Study-level meta-analyses lack granularity to adjust for confounders, explore heterogeneity, or identify subgroups that may particularly benefit or be harmed. Objective: To overcome these limitations, we sought to develop an individual patient-data pooled database of RCTs comparing bivalirudin versus heparin. Methods: We conducted a systematic review to identify RCTs in which ≥1,000 patients with acute myocardial infarction (AMI) undergoing PCI were randomized to bivalirudin versus heparin. Results: From 738 identified studies, 8 RCTs met the prespecified criteria. The principal investigators of each study agreed to provide patient-level data. The data were pooled and checked for accuracy against trial publications, with discrepancies addressed by consulting with the trialists. Consensus-based definitions were created to resolve differing antithrombotic, procedural, and outcome definitions. The project required 3.5 years to complete, and the final database includes 27,409 patients (13,346 randomized to bivalirudin and 14,063 randomized to heparin). Conclusion: We have created a large individual patient database of bivalirudin versus heparin RCTs in patients with AMI undergoing PCI. This endeavor may help identify the optimal periprocedural anticoagulation regimen for patient groups with different relative risks of adverse ischemic versus bleeding events, including those with ST-segment and non-ST-segment elevation MI, radial versus femoral access, use of a prolonged bivalirudin infusion or glycoprotein inhibitors, and others. Adherence to standardized techniques and rigorous validation processes should increase confidence in the accuracy and robustness of the results