Cardiovascular calcification in chronic kidney disease: Risk factors and effect of α-keto acid tablets

Purpose: To investigate the effect of α-keto acid tablets, and risk factors for cardiovascular calcification in patients with chronic kidney disease (CKD).Methods: A total of 128 CKD patients were enrolled in this study. They were randomly assigned to study and control groups, each with 64 patients. Control patients received symptomatic treatment, while the study group patients received α-keto acid tablets plus. Indices of cardiovascular calcification, blood lipids and mineral metabolism were determined in the 2 groups of patients and compared. Risk factors for cardiovascular calcification were also analyzed.Results: After treatment, the two groups had decreased CACS scores and reduced serum FGF-23levels, with lower values in patients in the study group. Levels of Klotho and fetuin-A were significantly elevated after treatment, with higher values observed in study group patients. The degree of cardiovascular calcification was markedly lower in study group than that in controls. There was no significant difference in blood Ca level between the control and study groups before and after treatment. Logistic multivariate analysis demonstrated that hyperlipidemia, hyperphosphatemia, hypercalcemia, hypertension and diabetes put patients at risk for cardiovascular calcification.Conclusion: Compound α-keto acid tablets delay cardiovascular calcification in patients with CKD, and alleviate symptoms of related risk factors for cardiovascular calcification

Gastroprotective effect of the root extract of Alpinia officinarum Hance (Zingiberoside) against acute indomethacin-induced gastric injuries in rats: Involvement of H+/K+-ATPase and prostaglandin E receptors

Purpose: To investigate the protective effects of Alpinia officinarum root ethanol extract (AOE) and galangin against acute indomethacin-induced injury on rat gastric mucosaMethods: Sprague-Dawley rats were daily treated with bismuth potassium citrate (0.08 g/kg), AOE at doses of 0.09, 0.18 and 0.36 g/kg; and galangin (0.2 g/kg) for 15 days. Then, gastric injury on rats was induced by intragastric administration of indomethacin (30 mg/kg). Blood flow and thickness of gastric mucosa were determined using neutral red clearance test and Alcian blue staining. The activity of H+/K+-ATPase was assayed using a biochemical kit. Prostaglandin E receptor expressions were assayed by western blotting.Results: High doses of ethanol extract of Alpinia officinarum root significantly inhibited H+/K+-ATPase activity by 8.12 % (p < 0.01), increased gastric mucosal blood flow (p < 0.001), enhanced mucus thickness (p < 0.05), and elevated the activities of prostaglandin E receptors 1 and 4 (p < 0.05).Galangin significantly inhibited H+/K+-ATPase activity by 4.82 % (p < 0.05) and increased gastric mucosal blood flow (p < 0.01).Conclusion: The ethanol extract of Alpinia officinarum root attenuates indomethacin-induced gastric injury by reinforcing gastric mucosal barrier and inhibiting excessive gastric acid secretion. Thus, the extract can be potentially developed for management of gastric injuries. Keywords: Galangin, Gastric mucosal barrier, Gastric acid, Prostaglandin, Indomethaci

Efficient Neural Neighborhood Search for Pickup and Delivery Problems

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. Our implementation for N2S is available online.Comment: Accepted at IJCAI 2022 (short oral

BeamSearchQA: Large Language Models are Strong Zero-Shot QA Solver

Open-domain question answering is a crucial task that often requires accessing external information. Existing methods typically adopt a single-turn retrieve-then-read approach, where relevant documents are first retrieved, and questions are then answered based on the retrieved information. However, there are cases where answering a question requires implicit knowledge that is not directly retrievable from the question itself. In this work, we propose a novel question-answering pipeline called BeamSearchQA. Our approach leverages large language models to iteratively generate new questions about the original question, enabling an iterative reasoning process. By iteratively refining and expanding the scope of the question, our method aims to capture and utilize hidden knowledge that may not be directly obtainable through retrieval. We evaluate our approach on the widely-used open-domain NQ and WebQ datasets. The experimental results demonstrate that BeamSearchQA significantly outperforms other zero-shot baselines, indicating its effectiveness in tackling the challenges of open-domain question answering.Comment: Work in progres

LEAD: Liberal Feature-based Distillation for Dense Retrieval

Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model. Traditional knowledge distillation methods include response-based methods and feature-based methods. Response-based methods are used the most widely but suffer from lower upper limit of model performance, while feature-based methods have constraints on the vocabularies and tokenizers. In this paper, we propose a tokenizer-free method liberal feature-based distillation (LEAD). LEAD aligns the distribution between teacher model and student model, which is effective, extendable, portable and has no requirements on vocabularies, tokenizer, or model architecture. Extensive experiments show the effectiveness of LEAD on several widely-used benchmarks, including MS MARCO Passage, TREC Passage 19, TREC Passage 20, MS MARCO Document, TREC Document 19 and TREC Document 20.Comment: Work in progres

Deciphering the role of HPV-mediated metabolic regulation in shaping the tumor microenvironment and its implications for immunotherapy in HNSCC

Head and neck squamous cell carcinoma (HNSCC), as a complex and variable malignancy, poses a significant threat to human health. Since the intricate association between HPV and HNSCC emerged, its role within the TME has garnered extensive attention. HPV+HNSCC exhibits distinct immunological characteristics within the TME, intricately intertwined with mechanisms of immune evasion. HPV employs multifaceted pathways to intervene in metabolic regulation within the TME, exerting influence over immune cell functionality and neoplastic cell genesis. Furthermore, the heightened immune reactivity exhibited by HPV+HNSCC within the TME augments responses to immune interventions such as immune checkpoint inhibitors. Therefore, amidst the current limitations of therapeutic approaches, immunotherapy stands as a promising strategy to overcome the conventional confines of treating HNSCC. This article comprehensively outlines the impact of HPV on the inception and progression of HNSCC while discussing the amalgamation of metabolic regulation within the TME and immunotherapeutic strategies. By intervening in the reciprocal interactions between HPV and HNSCC within the TME, the potential to modulate the efficacy of immune-based treatments becomes evident. Concurrently, a synthesis of pertinent biomarker development is summarized. Such endeavors hold paramount significance for personalized therapeutic approaches and the more effective management of HNSCC patients