Search

Your search keyword '"Zeng, Li"' showing total 529 results
Start Over Author "Zeng, Li" Publication Year Range This year
529 results on '"Zeng, Li"'

1. MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs

Author

Cheng, Zhixiang, Xiang, Hongxin, Ma, Pengsen, Zeng, Li, Jin, Xin, Yang, Xixi, Lin, Jianxin, Deng, Yang, Song, Bosheng, Feng, Xinxin, Deng, Changhui, and Zeng, Xiangxiang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them. Our research indicates that as molecular similarity increases, graph-based methods struggle to capture these nuances, whereas image-based approaches effectively retain the distinctions. Thus, we developed MaskMol, a knowledge-guided molecular image self-supervised learning framework. MaskMol accurately learns the representation of molecular images by considering multiple levels of molecular knowledge, such as atoms, bonds, and substructures. By utilizing pixel masking tasks, MaskMol extracts fine-grained information from molecular images, overcoming the limitations of existing deep learning models in identifying subtle structural changes. Experimental results demonstrate MaskMol's high accuracy and transferability in activity cliff estimation and compound potency prediction across 20 different macromolecular targets, outperforming 25 state-of-the-art deep learning and machine learning approaches. Visualization analyses reveal MaskMol's high biological interpretability in identifying activity cliff-relevant molecular substructures. Notably, through MaskMol, we identified candidate EP4 inhibitors that could be used to treat tumors. This study not only raises awareness about activity cliffs but also introduces a novel method for molecular image representation learning and virtual screening, advancing drug discovery and providing new insights into structure-activity relationships (SAR)., Comment: 33 pages, 5 figures

Published
2024

2. Causal estimands and identification of time-varying effects in non-stationary time series from N-of-1 mobile device data

Author

Cai, Xiaoxuan, Zeng, Li, Fowler, Charlotte, Dixon, Lisa, Ongur, Dost, Baker, Justin T., Onnela, Jukka-Pekka, and Valeri, Linda

Subjects
Statistics - Methodology
Abstract

Mobile technology (mobile phones and wearable devices) generates continuous data streams encompassing outcomes, exposures and covariates, presented as intensive longitudinal or multivariate time series data. The high frequency of measurements enables granular and dynamic evaluation of treatment effect, revealing their persistence and accumulation over time. Existing methods predominantly focus on the contemporaneous effect, temporal-average, or population-average effects, assuming stationarity or invariance of treatment effects over time, which are inadequate both conceptually and statistically to capture dynamic treatment effects in personalized mobile health data. We here propose new causal estimands for multivariate time series in N-of-1 studies. These estimands summarize how time-varying exposures impact outcomes in both short- and long-term. We propose identifiability assumptions and a g-formula estimator that accounts for exposure-outcome and outcome-covariate feedback. The g-formula employs a state space model framework innovatively to accommodate time-varying behavior of treatment effects in non-stationary time series. We apply the proposed method to a multi-year smartphone observational study of bipolar patients and estimate the dynamic effect of phone-based communication on mood of patients with bipolar disorder in an N-of-1 setting. Our approach reveals substantial heterogeneity in treatment effects over time and across individuals. A simulation-based strategy is also proposed for the development of a short-term, dynamic, and personalized treatment recommendation based on patient's past information, in combination with a novel positivity diagnostics plot, validating proper causal inference in time series data.

Published
2024

3. Synthesis of Electron Microbunching Rotation for Generating Isolated Attosecond Soft X-ray Free-electron Laser Pulses

Author

Sun, Hao, Wang, Xiaofan, Zeng, Li, and Zhang, Weiqing

Subjects
Physics - Accelerator Physics
Abstract

Attosecond x-ray pulses play a crucial role in the study of ultrafast phenomena occurring within inner and valence electrons. Especially isolated attosecond pulses with high photon energy and high peak power are of great significance in single-shot imaging in the soft x-ray region, life sciences, and attosecond pump-probe experiments. In modern accelerators, laser manipulation of electrons can be used to tailor the ultrafast properties of free-electron laser (FEL) pulses. In this paper, we propose a novel laser manipulation technique that makes use of two laser beams with mutual delays and tilted wavefronts to synthesize microbunching rotation on the scale of infrared laser wavelengths within the electron bunch for generating isolated attosecond soft x-ray pulses. This microbunching rotation ultimately leads to an enhanced current contrast ratio between the main peak and the surrounding satellite peaks within the bunch. By properly accounting for the longitudinal space charge fields within the FEL undulator, a tapered undulator can further suppress the side peaks in the radiation pulse and enable the selection of an isolated, hundred-attosecond, GW-level soft x-ray pulse.

Published
2024

4. Expert-Token Resonance: Redefining MoE Routing through Affinity-Driven Active Selection

Author

Li, Jing, Sun, Zhijie, Lin, Dachao, He, Xuan, Lin, Yi, Zheng, Binfan, Zeng, Li, Zhao, Rongqian, and Chen, Xin

Subjects
Computer Science - Computation and Language
Abstract

Mixture-of-Experts (MoE) architectures have emerged as a paradigm-shifting approach for large language models (LLMs), offering unprecedented computational efficiency. However, these architectures grapple with challenges of token distribution imbalance and expert homogenization, impeding optimal semantic generalization. We introduce a novel framework that redefines MoE routing through affinity-driven active selection. The innovations for the framework encompass: (1) A rigorous formulation of expert-token affinity metrics. (2) An adaptive bidirectional selection mechanism leveraging resonance between experts and tokens. (3) Theoretical derivation and experimental evidence of reduced expert capacity bounds under dynamic token distribution evolution. It is also integrated with orthogonal feature extraction module and an optimized loss function for expert localization. Our theoretical analysis demonstrates that this approach mitigates expert homogenization while enabling substantial capacity boundary reduction. Experimental validation corroborates these findings: it achieves a 40% reduction in token processed by each expert without compromising model convergence or efficacy. When coupled with communication optimizations, the training efficiency improvements of 5.4% to 46.6% can be observed. After supervised fine-tuning, it exhibits performance gains of 9.7% to 14.1% across GDAD, C-Eval, and TeleQnA benchmarks.

Published
2024

5. EFSA: Towards Event-Level Financial Sentiment Analysis

Author

Chen, Tianyu, Zhang, Yiming, Yu, Guoxin, Zhang, Dapeng, Zeng, Li, He, Qing, and Ao, Xiang

Subjects
Computer Science - Computation and Language
Abstract

In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictions, it has specialized challenges due to the lengthy and discontinuity of events in a financial text. To this end, we reconceptualize the event extraction as a classification task by designing a categorization comprising coarse-grained and fine-grained event categories. Under this setting, we formulate the \textbf{E}vent-Level \textbf{F}inancial \textbf{S}entiment \textbf{A}nalysis~(\textbf{EFSA} for short) task that outputs quintuples consisting of (company, industry, coarse-grained event, fine-grained event, sentiment) from financial text. A large-scale Chinese dataset containing $12,160$ news articles and $13,725$ quintuples is publicized as a brand new testbed for our task. A four-hop Chain-of-Thought LLM-based approach is devised for this task. Systematically investigations are conducted on our dataset, and the empirical results demonstrate the benchmarking scores of existing methods and our proposed method can reach the current state-of-the-art. Our dataset and framework implementation are available at https://anonymous.4open.science/r/EFSA-645E

Published
2024

6. Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning

Author

Pu, Tianle, Fan, Changjun, Shen, Mutian, Lu, Yizhou, Zeng, Li, Nussinov, Zohar, Chen, Chao, and Liu, Zhong

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Many complex problems encountered in both production and daily life can be conceptualized as combinatorial optimization problems (COPs) over graphs. Recent years, reinforcement learning (RL) based models have emerged as a promising direction, which treat the COPs solving as a heuristic learning problem. However, current finite-horizon-MDP based RL models have inherent limitations. They are not allowed to explore adquately for improving solutions at test time, which may be necessary given the complexity of NP-hard optimization tasks. Some recent attempts solve this issue by focusing on reward design and state feature engineering, which are tedious and ad-hoc. In this work, we instead propose a much simpler but more effective technique, named gauge transformation (GT). The technique is originated from physics, but is very effective in enabling RL agents to explore to continuously improve the solutions during test. Morever, GT is very simple, which can be implemented with less than 10 lines of Python codes, and can be applied to a vast majority of RL models. Experimentally, we show that traditional RL models with GT technique produce the state-of-the-art performances on the MaxCut problem. Furthermore, since GT is independent of any RL models, it can be seamlessly integrated into various RL frameworks, paving the way of these models for more effective explorations in the solving of general COPs.

Published
2024

7. Satellite Federated Edge Learning: Architecture Design and Convergence Analysis

Author

Shi, Yuanming, Zeng, Li, Zhu, Jingyang, Zhou, Yong, Jiang, Chunxiao, and Letaief, Khaled B.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Information Theory, Computer Science - Machine Learning
Abstract

The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate., Comment: 16 pages, 15 figures

Published
2024

8. WindGP: Efficient Graph Partitioning on Heterogenous Machines

Author

Zeng, Li, Huang, Haohan, Zheng, Binfan, Yang, Kang, Shao, Shengcheng, Zhou, Jinhua, Xie, Jun, Zhao, Rongqian, and Chen, Xin

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Graph Partitioning is widely used in many real-world applications such as fraud detection and social network analysis, in order to enable the distributed graph computing on large graphs. However, existing works fail to balance the computation cost and communication cost on machines with different power (including computing capability, network bandwidth and memory size), as they only consider replication factor and neglect the difference of machines in realistic data centers. In this paper, we propose a general graph partitioning algorithm WindGP, which can support fast and high-quality edge partitioning on heterogeneous machines. WindGP designs novel preprocessing techniques to simplify the metric and balance the computation cost according to the characteristics of graphs and machines. Also, best-first search is proposed instead of BFS and DFS, in order to generate clusters with high cohesion. Furthermore, WindGP adaptively tunes the partition results by sophisticated local search methods. Extensive experiments show that WindGP outperforms all state-of-the-art partition methods by 1.35 - 27 times on both dense and sparse distributed graph algorithms, and has good scalability with graph size and machine number., Comment: 19 pages, 15 figures, 18 tables

Published
2024

9. LocMoE: A Low-Overhead MoE for Large Language Model Training

Author

Li, Jing, Sun, Zhijie, He, Xuan, Zeng, Li, Lin, Yi, Li, Entong, Zheng, Binfan, Zhao, Rongqian, and Chen, Xin

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning method for large language models (LLM), which is favored due to its ability to sparsify and expand models efficiently. However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity. Load imbalance may result from existing routing policies that consistently tend to select certain experts. The frequent inter-node communication in the All-to-All procedure also significantly prolongs the training time. To alleviate the above performance problems, we propose a novel routing strategy that combines load balance and locality by converting partial inter-node communication to that of intra-node. Notably, we elucidate that there is a minimum threshold for expert capacity, calculated through the maximal angular deviation between the gating weights of the experts and the assigned tokens. We port these modifications on the PanGu-Sigma model based on the MindSpore framework with multi-level routing and conduct experiments on Ascend clusters. The experiment results demonstrate that the proposed LocMoE reduces training time per epoch by 12.68% to 22.24% compared to classical routers, such as hash router and switch router, without impacting the model accuracy., Comment: 1. Update the font size of all figures. 2. Update the name of the proposed layer Grouped Average Pooling (GrAP). 3. Change the order of the Section Contribution Statement

Published
2024

25. GPR50 regulates neuronal development as a mitophagy receptor

Author

Liu, Ji-Chuan, Zhao, Xiu-Yun, Wu, Ming-Lei, Shi, Yi-fan, Huang, Ze-Ping, Fang, Li-Pao, Zhu, Chao, Peng, Xuan, Shi, Zi-Ling, Lan, Li-Jun, Ji, Wen-Li, Luo, Li, Feng, Lei, Zhang, Zeng-Li, Xu, De-en, Li, Shao, Qin, Zheng-Hong, Sun, Yan-Yun, Schachner, Melitta, and Ma, Quan-Hong

Published
2024
Full Text
View/download PDF

26. GPR50 regulates neuronal development as a mitophagy receptor

Author

Ji-Chuan Liu, Xiu-Yun Zhao, Ming-Lei Wu, Yi-fan Shi, Ze-Ping Huang, Li-Pao Fang, Chao Zhu, Xuan Peng, Zi-Ling Shi, Li-Jun Lan, Wen-Li Ji, Li Luo, Lei Feng, Zeng-Li Zhang, De-en Xu, Shao Li, Zheng-Hong Qin, Yan-Yun Sun, Melitta Schachner, and Quan-Hong Ma

Subjects
Cytology, QH573-671
Abstract

Abstract Neurons rely heavily on high mitochondrial metabolism to provide sufficient energy for proper development. However, it remains unclear how neurons maintain high oxidative phosphorylation (OXPHOS) during development. Mitophagy plays a pivotal role in maintaining mitochondrial quality and quantity. We herein describe that G protein-coupled receptor 50 (GPR50) is a novel mitophagy receptor, which harbors the LC3-interacting region (LIR) and is required in mitophagy under stress conditions. Although it does not localize in mitochondria under normal culturing conditions, GPR50 is recruited to the depolarized mitochondrial membrane upon mitophagy stress, which marks the mitochondrial portion and recruits the assembling autophagosomes, eventually facilitating the mitochondrial fragments to be engulfed by the autophagosomes. Mutations Δ502-505 and T532A attenuate GPR50-mediated mitophagy by disrupting the binding of GPR50 to LC3 and the mitochondrial recruitment of GPR50. Deficiency of GPR50 causes the accumulation of damaged mitochondria and disrupts OXPHOS, resulting in insufficient ATP production and excessive ROS generation, eventually impairing neuronal development. GPR50-deficient mice exhibit impaired social recognition, which is rescued by prenatal treatment with mitoQ, a mitochondrially antioxidant. The present study identifies GPR50 as a novel mitophagy receptor that is required to maintain mitochondrial OXPHOS in developing neurons.

Published
2024
Full Text
View/download PDF

27. Analysis of pathological remission degree and influencing factors of radical surgery after neoadjuvant immunotherapy combined with chemotherapy in patients with locally advanced esophageal squamous cell carcinoma

Author

LIAO Ziyi, PENG Yang, ZENG Beilei, MA Yingying, ZENG Li, GAN Kelun, MA Daiyuan

Subjects
esophageal squamous cell carcinoma, neoadjuvant immunotherapy combined with chemotherapy, radical surgery, pathological remission degree, safety, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background and purpose: Radical surgery after neoadjuvant immunotherapy combined with chemotherapy (nICT) in patients with locally advanced esophageal squamous cell carcinoma (LAESCC) has good efficacy and safety, and it can improve the patients’ pathological complete remission (pCR) rate, main pathologic response (MPR) rate and R0 resection rate. The prognosis of patients with pCR/MPR after nICT is significantly better compared with patients without pCR. The prognosis of patients achieving pCR/MPR after neoadjuvant therapy has been demonstrated to be significantly better than that of patients with non-pCR/MPR. Therefore, finding predictive factors of pCR/MPR is beneficial for us to screen out the advantageous populations for combination therapy. The aim of this study was to investigate the value of clinical data of patients with LAESCC before and after nICT in predicting the degree of remission of different pathologies after radical surgery following neoadjuvant treatment and to observe the safety of the treatment. Methods: Data of patients with locally LAESCC who underwent radical surgery after nICT from January 2019 to June 2023 at the Affiliated Hospital of Chuanbei Medical College were collected. The clinical data of all patients as well as some blood, inflammation and nutritional indexes of patients before and after neoadjuvant therapy were collected, and the patients were grouped according to the different degrees of pathological remission after neoadjuvant therapy. Data were analyzed by multi-group comparative analysis of variance (ANOVA) and LSD-t post-hoc test. We explored the factors that had an influence on the different degrees of pathological remission, and collected and recorded the patients’ adverse reactions during neoadjuvant therapy as well as their eventual surgeries. Results: Data of 62 patients with LAESCC treated with nICT who underwent radical surgery were collected. Only one patient showed grade 4 myelosuppression during neoadjuvant therapy, and the rest of the patients had adverse reactions ≤grade 2. The R0 resection rate of the surgery was 98.39%. The present study was compared with the previous studies of LAESCC treated with neoadjuvant chemotherapy followed by radical surgery performed in Affiliated Hospital of Chuanbei Medical College. Compared with the previous studies conducted in our center, no significant difference was observed in terms of operation time, intraoperative bleeding, postoperative hospitalization time and surgical complications. The postoperative pathologic results showed that the pCR rate was 22.58% (14/62), and the MPR rate was 40.32% (25/62). According to the different tumor regression grade (TRG) after surgery, patients were divided into 3 groups of TRG1, TRG2 and TRG3-4, and differences in the platelet distribution width (PDW) before neoadjuvant therapy and the preoperative neutrophil-to-lymphocyte ratio (NLR) after neoadjuvant therapy were statistically significant among the 3 groups (P

Published
2024
Full Text
View/download PDF

28. Comparison of Third-Generation Sequencing and Routine Polymerase Chain Reaction in Genetic Analysis of Thalassemia

Author

Xu, Zhen, Hu, Lanping, Liu, Yinyin, Peng, Can, Zeng, Guo, Zeng, Li, Yang, Mengyue, Linpeng, Siyuan, Bu, Xiufen, Jiang, Xuanyu, Xie, Tiantian, Chen, Libao, Zhou, Shihao, and He, Jun

Subjects
Nucleotide sequencing -- Analysis -- Genetic aspects, Blood -- Medical examination, Hemoglobin -- Analysis, DNA sequencing -- Analysis -- Genetic aspects, Thalassemia -- Genetic aspects, Polymerase chain reaction -- Analysis -- Genetic aspects, Health
Abstract

* Context.--Thalassemia is the most widely distributed monogenic autosomal recessive disorder in the world. Accurate genetic analysis of thalassemia is crucial for thalassemia prevention. Objective.--To compare the clinical utility of a third-generation sequencing-based approach termed comprehensive analysis of thalassemia alleles with routine polymerase chain reaction (PCR) in genetic analysis of thalassemia and explore the molecular spectrum of thalassemia in Hunan Province. Design.--Subjects in Hunan Province were recruited, and hematologic testing was performed. Five hundred four subjects positive on hemoglobin testing were then used as the cohort, and third-generation sequencing and routine PCR were used for genetic analysis. Results.--Of the 504 subjects, 462 (91.67%) had the same results, whereas 42 (8.33%) exhibited discordant results between the 2 methods. Sanger sequencing and PCR testing confirmed the results of third-generation sequencing. In total, third-generation sequencing correctly detected 247 subjects with variants, whereas PCR identified 205, which showed an increase in detection of 20.49%. Moreover, [alpha] triplications were identified in 1.98% (10 of 504) hemoglobin testing-positive subjects in Hunan Province. Seven hemoglobin variants with potential pathogenicity were detected in 9 hemoglobin testing-positive subjects. Conclusions.--Third-generation sequencing is a more comprehensive, reliable, and efficient approach for genetic analysis of thalassemia than PCR, and allowed for a characterization of the thalassemia spectrum in Hunan Province., Thalassemia is an inherited disorder characterized by defects in globin synthesis. It is the most widely distributed monogenic autosomal recessive disorder in the world. (1-3) In China, thalassemia is most [...]

Published
2024
Full Text
View/download PDF

32. The predictors of short and long term urinary continence recovery after laparoscopic radical prostatectomy: a single cancer center report in China

Author

Lei Liu, Shukui Zhou, Dandan Song, Zeng Li, Shengke Yang, Yi Wu, Guiying Zhang, Duocai Tang, Junfeng Liu, Hong Liao, and Chuan Zhang

Subjects
Laparoscopic radical prostatectomy, Urinary continence, Predictors, Magnetic resonance imaging, Membranous urethral length, Surgery, RD1-811, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Purpose To evaluate the predictors for short and long term urinary continence (UC) recovery after laparoscopic radical prostatectomy (LRP) from clinical and oncological variables. Methods We retrospectively collected data from 142 prostate cancer patients who underwent LRP between September 2014 and June 2021 at a tumor specialist diagnosis and treatment center in China. The rate of post-prostatectomy incontinence (PPI) was evaluated from immediate and at 3, 6 and 12 mo after LRP, and UC was defined as the use of no or one safety pad. Sixteen clinical and oncological variables were analyzed by univariate and multivariate regression analysis to determine whether they were associated with short (3 mo) or long term (12 mo) UC recovery after LRP. Results After eliminating patients who were lost to follow-up, 129 patients were eventually included. The mean ± SD age was 68 ± 6.3 years. The UC rates of immediate, 3, 6 and 12 mo after the operation were 27.9%, 54.3%, 75.2% and 88.4%, respectively. Multivariate analyses revealed that membranous urethral length (MUL) was a protective predictor of UC after catheter extraction(P

Published
2024
Full Text
View/download PDF

33. Comparison of the Predictive Value of Four Nutritional Evaluation Methods for Postoperative Complications of Lumbar Degenerative Diseases

Author

ZENG Li, WANG Huihui, WANG Xiaoyu, GONG Weiyi, HU Chunqiu

Subjects
lumbar degenerative disease, nutritional evaluation method, prognostic nutritional index, controlling nutritional status, naples prognostic score, high-sensitive modified glasgow prognostic score, postoperative complications, Medicine
Abstract

Background The occurrence of postoperative complications in lumbar degenerative disease (LDD) is closely related to malnutrition, and currently there is a lack of nutritional evaluation methods that can effectively, objectively, and comprehensively assess the nutritional status of LDD patients and predict the occurrence of postoperative complications. Objective To compare the predictive value of four nutritional evaluation methods of Prognostic Nutritional Index (PNI), Controlling Nutritional Status (CONUT), Naples Prognostic Score (NPS) and High-sensitivity Modified Glasgow Prognostic Score (HS-mGPS) for postoperative complications in patients with LDD, so as to formulate a precise nutritional intervention plan and effectively prevent complications. Methods A total of 201 patients diagnosed with LDD and underwent open surgery in Spine Surgery Department of Xiangya Hospital of Central South University from December 2021 to July 2022 were selected as the study subjects, four nutritional evaluation methods of PNI, CONUT, NPS and HS-mGPS were applied to assess the preoperative nutritional status of the patients, respectively, and occurrence of postoperative complications from postoperative period to hospital discharge were observed. The patients were divided into the complication group and non-complication group according to the occurrence of postoperative complications to analyze and compare the difference in clinical characteristics between the two groups; the optimal cut-off values of different nutritional evaluation methods were calculated by plotting the receiver operating characteristic (ROC) curve, and the prediction efficiency of different evaluation methods was compared, the area under curve (AUC) was compared by using MedCalc software. Multivariate Logistic regression model was used to analyze the correlation between different nutritional evaluation methods and postoperative complications. Results Postoperative complications occurred in 60 patients with an incidence rate of 29.8%. The comparison of clinical characteristics data showed statistically significant differences in age, gender, intraoperative bleeding, surgical segment, PNI, CONUT, and NPS scores between the complication group and non-complication group (P

Published
2024
Full Text
View/download PDF

34. Ecological filter walls for efficient pollutant removal from urban surface water

Author

Menglong Liao, Ye Qiu, Yan Tian, Zeng Li, Tongtong liu, Xinlei Feng, Guohong Liu, and Yujie Feng

Subjects
Urban water remediation, Ecological filter walls, Shortcut nitrification-denitrification, Dissolved oxygen gradient, Nitrogen removal, Environmental sciences, GE1-350, Environmental technology. Sanitary engineering, TD1-1066
Abstract

Urban surface water pollution poses significant threats to aquatic ecosystems and human health. Conventional nitrogen removal technologies used in urban surface water exhibit drawbacks such as high consumption of carbon sources, high sludge production, and focus on dissolved oxygen (DO) concentration while neglecting the impact of DO gradients. Here, we show an ecological filter walls (EFW) that removes pollutants from urban surface water. We utilized a polymer-based three-dimensional matrix to enhance water permeability, and emergent plants were integrated into the EFW to facilitate biofilm formation. We observed that varying aeration intensities within the EFW's aerobic zone resulted in distinct DO gradients, with an optimal DO control at 3.19 ± 0.2 mg L−1 achieving superior nitrogen removal efficiencies. Specifically, the removal efficiencies of total organic carbon, total nitrogen, ammonia, and nitrate were 79.4%, 81.3%, 99.6%, and 79.1%, respectively. Microbial community analysis under a 3 mg L−1 DO condition revealed a shift in microbial composition and abundance, with genera such as Dechloromonas, Acinetobacter, unclassified_f__Comamonadaceae, SM1A02 and Pseudomonas playing pivotal roles in carbon and nitrogen elimination. Notably, the EFW facilitated shortcut nitrification-denitrification processes, predominantly contributing to nitrogen removal. Considering low manufacturing cost, flexible application, small artificial trace, and good pollutant removal ability, EFW has promising potential as an innovative approach to urban surface water treatment.

Published
2024
Full Text
View/download PDF

35. Prognostic nomogram for patients with advanced unresectable hepatocellular carcinoma treated with TAE combined with HAIC

Author

Li-xin Du, Guo-li Sheng, An-da Shi, Kang-shuai Li, Zeng-li Liu, Yong-chang Tang, Yi Liu, and Zong-li Zhang

Subjects
unresectable hepatocellular carcinoma, HAIC, TAE, survival analysis, prognostic model, Therapeutics. Pharmacology, RM1-950
Abstract

BackgroundHepatocellular carcinoma (HCC) is the most common primary liver cancer and often arises in the context of chronic liver disease, such as hepatitis B or C infection, and cirrhosis. Advanced unresectable HCC (uHCC) presents significant treatment challenges due to its advanced stage and inoperability. One efficient treatment method for advanced uHCC is the use of hepatic arterial infusion chemotherapy (HAIC) combined with transcatheter arterial embolization (TAE).Patients and MethodsIn this study, we conducted a retrospective collection of clinical data, including basic information, radiological data, and blood test parameters, for patients with advanced uHCC who underwent TAE + HAIC treatment from August 2020 to February 2023. A total of 743 cases involving 262 patients were included. Ultimately, the covariates included in the analysis were the Child-Pugh score, extrahepatic metastasis, tumor number, tumor size, and treatment method.ResultsIn the study, we performed univariable and multivariable analysis on 23 clinical factors that were screened by LASSO regression, indicating that the five variables aforementionedly were identified as independent factors influencing patient prognosis. Then we developed a nomogram of the sensitive model and calculated concordance indices of prognostic survival models.ConclusionBased on the uHCC patient cohort, we have developed a prognostic model for OS in patients who received TAE + HAIC treatment. This model can accurately predict OS and has the potential to assist in personalized clinical decision-making.

Published
2024
Full Text
View/download PDF

39. A novel GSK3β inhibitor 5n attenuates acute kidney injury

Author

Yu-ting Cai, Zeng Li, Yue-yue Wang, Chao Li, and Qiu-ying Ma

Subjects
Acute kidney injury, GSK3β, PP2Ac, NF-κB, Inflammation, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Acute kidney injury (AKI) is a clinical syndrome with high morbidity and mortality caused by various factor. The specific strategies for AKI are still lacking. GSK3β is widely expressed in the kidneys. In acute models of injury, GSK3β promotes the systemic inflammatory response, increases the proinflammatory release of cytokines, induces apoptosis, and alters cell proliferation. We screened a series of 3-(4-pyridyl)-5-(4-sulfamido-phenyl)-1,2,4-oxadiazole derivatives which are recognized as new GSK3β inhibitors, and found that 5n had the least toxicity and the best cell protection. We then tested the anti-inflammatory and reno-protective effect of 5n in cisplatin-treated tubular epithelial cells. 5n had anti-inflammation effect indicated by phosphor–NF–κB detection. Finally, we found that 5n ameliorated renal injury and inflammation in cisplatin-induced AKI mouse model. Silencing GSK3β inhibited cell injury and inflammation induced by cisplatin. We found that GSK3β interacted with PP2Ac to modulate the activity of NF-κB. In conclusion, 5n, the novel GSK3β inhibitor, protects against AKI via PP2Ac-dependent mechanisms which may provide a potential strategy for the treatment of AKI in clinic.

Published
2024
Full Text
View/download PDF

40. Anlotinib in Chinese patients aged ≥70 years with advanced non-squamous non-small cell lung cancer without prior chemotherapy: a multicenter, single-arm pilot trial

Author

Da Zhao, Zhengguo Li, Xinli Hou, Lei Yang, Zeng Li, Li Yan, Hongling Li, Hua Liu, Xiaoping Liu, Feixue Song, Guixiang Li, Yu Zhang, and Xiaoming Hou

Subjects
non-squamous, non-small-cell lung carcinoma, anlotinib, elderly, pilot trial, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

BackgroundBased on pharmacoeconomics, drug availability and actual treatment, optimal treatment regimens for Chinese non-small-cell lung carcinoma (NSCLC) patients over 70 years old are needed.MethodsThis multicenter, single-arm pilot trial enrolled patients with advanced non-squamous NSCLC who refused systemic chemotherapy. Eligible patients received anlotinib (12 mg/day, d1-14, Q3W) until disease progression, intolerant toxicities, or withdrawal from the study. The primary endpoint was progression-free survival (PFS).ResultsForty-nine patients were screened between January 2019 and September 2021, of whom 40 patients were eligible. The median age was 76 years. With a median follow-up period of 16.20 (95% CI: 8.77, 25.10) months, the median PFS was 5.45 months (95% CI: 3.52-9.23) and the median overall survival was 10.32 months (95% CI: 6.44-12.78). Three patients achieved a partial response and 34 had stable disease, with an objective response rate of 7.5% and a disease control rate of 92.5%. Thirty-three (82.5%; 33/40) patients reported treatment-related adverse events (TRAEs) of any grade, and the incidence rate of grade ≥3 TRAEs was 35% (14/40). The most common grade ≥3 TRAEs were hypertension (4/40; 10.0%), hand-foot syndrome (3/40; 7.5%), and proteinuria (2/40; 5.0%).ConclusionAnlotinib treatment was feasible and safe in Chinese elderly patients with advanced non-squamous NSCLC who did not receive any systemic chemotherapy.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results