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Association of potentially inappropriate medications with prognosis among older patients with non-small cell lung cancer

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BMC GERIATRICS 2024年第1期24卷 550页

作者： Ma, Zhuo Xu, Man Fu, Mengyuan huang, Tao Shi, luwen Zhang, Yuhui Guan, Xiaodong Peking Univ Sch Pharmaceut Sci Dept Pharm Adm & Clin Pharm Beijing 100191 Peoples R China Capital Med Univ Beijing Chao Yang Hosp Dept Pharm Beijing 100020 Peoples R China Capital Med Univ Beijing Obstet & Gynecol Hosp Dept Pharm Beijing 100010 Peoples R China Peking Univ Int Res Ctr Med Adm 38 Xueyuan Rd Beijing 100191 Peoples R China Capital Med Univ Beijing Chao Yang Hosp Beijing Inst Resp Med Dept Resp & Crit Care Med 8 Gongtinan Rd Beijing 100020 Peoples R China

Background Potentially inappropriate medications (PIMs) are common among older adults with cancer, but their association with overall survival (OS) among non-small cell lung cancer (NSCLC) patients remains unclear. This study aimed to investigate the association between the use of PIMs and OS in patients with NSCLC. Methods In this cohort study, we included patients >= 65 years with newly diagnosed NSCLC from January 2014 to December 2020. Potentially inappropriate medication (PIM) is defined by the Beers criteria of 2019 at baseline and within six months following the initiation of systemic therapy. Multivariable Cox regression model was built to assess the association between PIMs and overall survival (OS). Results We finally included 338 patients with a median follow-up for OS of 1777 days. The prevalence of patients receiving at least one PIM was 39.9% (135/338) and 61.2% (71/116) at baseline and after systemic therapy, respectively. The most important factor associated with PIM use was the number of prescribed medications (P < 0.001). Baseline PIM use and PIM after systemic therapy were significantly associated with inferior OS (476 days vs. 844 days, P = 0.044;and 633 days vs. 1600 days, P = 0.007;respectively). In multivariable analysis, both baseline PIM use and PIM after systemic therapy were independent predictors of poor prognosis (adjusted HR, 1.33;95% CI, 1.01-1.75;P = 0.041;and adjusted HR, 1.86;95% CI, 1.11-3.14;P = 0.020;respectively). Conclusions PIMs are prevalent among older patients with NSCLC and are independent predictors of NSCLC prognosis. There is an urgent need for clinicians to conduct medication reconciliation and appropriate deprescribing for this population, especially for patients with multiple PIMs.

关键词： Non-small cell lung cancer Potentially inappropriate medications Older Overall survival

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Waste Remolding - Lime Remolding 24

Waste Remolding - Lime Remolding

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17th International Symposium on Visual Information Communication and Interaction

作者： Jiang, Yuyang Tang, Jia Wu, Xiaotian huang, Zhuoxin Yu, luwen Hu, Zhenhang Hong Kong Univ Sci & Technol Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China China Acad Art Hangzhou Peoples R China Lingnan Univ Hong Kong Peoples R China

ISBN: (纸本)9798400709678

This artwork delves into the themes of urban memory and the revitalization of discarded materials through an innovative installation in the abandoned buildings. The core of this piece is the controlled flow of limewater, regulated by sensors to interact with air, forming new spaces within the ruins. This process highlights the transformation and rebirth from demolition to creation, symbolizing the rebirth of abandoned structures. By emphasizing the importance of recycling and reusing waste, the artwork invites viewers to reflect on the significance of urban decay and renewal. It seeks to evoke a sense of historical continuity and environmental consciousness.

关键词： Art Installation Materials Research Urban Exhibition Lime

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Adaptively Weighted k-Tuple Metric Network for Kinship Verification

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第10期53卷 6173-6186页

作者： huang, Sheng Lin, Jingkai huangfu, luwen Xing, Yun Hu, Junlin Zeng, Daniel Dajun Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China San Diego State Univ Fowler Coll Business San Diego CA 92182 USA San Diego State Univ Ctr Human Dynam Mobile Age San Diego CA 92182 USA Beihang Univ Sch Software Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat Beijing 100045 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

Facial image-based kinship verification is a rapidly growing field in computer vision and biometrics. The key to determining whether a pair of facial images has a kin relation is to train a model that can enlarge the margin between the faces that have no kin relation while reducing the distance between faces that have a kin relation. Most existing approaches primarily exploit duplet (i.e., two input samples without cross pair) or triplet (i.e., single negative pair for each positive pair with low-order cross pair) information, omitting discriminative features from multiple negative pairs. These approaches suffer from weak generalizability, resulting in unsatisfactory performance. Inspired by human visual systems that incorporate both low-order and high-order cross-pair information from local and global perspectives, we propose to leverage high-order cross-pair features and develop a novel end-to-end deep learning model called the adaptively weighted k-tuple metric network (AWk-TMN). Our main contributions are three-fold. First, a novel cross-pair metric learning loss based on k-tuplet loss is introduced. It naturally captures both the low-order and high-order discriminative features from multiple negative pairs. Second, an adaptively weighted scheme is formulated to better highlight hard negative examples among multiple negative pairs, leading to enhanced performance. Third, the model utilizes multiple levels of convolutional features and jointly optimizes feature and metric learning to further exploit the low-order and high-order representational power. Extensive experimental results on three popular kinship verification datasets demonstrate the effectiveness of our proposed AWk-TMN approach compared with several state-of-the-art approaches. The source codes and models are released.1

关键词： Measurement Feature extraction Task analysis Faces Deep learning Convolutional neural networks Genetics Deep learning kinship verification metric learning relation network (RN) triplet loss

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Multi-label out-of-distribution detection via exploiting sparsity and co-occurrence of labels

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IMAGE AND VISION COMPUTING 2022年 126卷

作者： Wang, Lei huang, Sheng huangfu, luwen Liu, Bo Zhang, Xiaohong Chongqing Univ Sch Big Data & Software Engn Chonqqing 400044 Peoples R China Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China San Diego State Univ Fowler Coll Business San Diego CA 92182 USA San Diego State Univ Ctr Human Dynam Mobile Age San Diego CA 92182 USA Walmart Global Tech Sunnyvale CA 94086 USA

Although Out-Of-Distribution (OOD) detection has been extensively studied, the OOD detection under multi -label settings, which is closer to the real world, is still in its infancy. The pioneer work ignores some unique prop-erties of multi-label images, such as the sparsity and co-occurrence of labels. Here, we empirically observe that these properties readily distinguish OOD and in-distribution data. Motivated by this observation, we propose a novel multi-label OOD detection approach named Sparse Label Co-occurrence Scoring (SLCS) to exploit the spar-sity and co-occurrence information of labels. SLCS follows conventions and deems the logits outputted by the penultimate layer of the trained multi-label image classification model as the prediction confidences of a sample to categories in the training label set. A logit sparse filtering process is employed to filter out the low-confidence logits for avoiding the interference of low-confidence predictions while preserving the high-confidence logits to obtain the label sparsity. Then, the label co-occurrence pairs are counted for each sample based on its predicted categories and the label co-occurrence matrix constructed on the training set. Finally, the preserved logits are weighted by the label co-occurrence information and accumulated to produce the OOD detection score for each sample. Extensive experimental results on three well-known multi-label image datasets demonstrate the discriminating power of SLCS, which achieves greatly improved performances compared with the only multi -label OOD detection approach - JointEnergry and the state-of-the-art single-label OOD detection approaches. The performance improvements of SLCS over JointEnergy in FPR95 are 12.85%, 12.41%, and 9.50% on MS-COCO, VOC 2012, and NUS-WIDE datasets respectively. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Multi -label learning Out -of -distribution detection Image classification Sparse learning Label co -occurrence

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An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8652-8661页

作者： Tang, Wenhao huang, Sheng Zhao, Qiming Li, Ren huangfu, luwen Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing 400074 Peoples R China San Diego State Univ Fowler Coll Business San Diego CA 92182 USA

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the stateof-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on https://***/DearCaat/ioplin, and the CQU-BPDD dataset is able to be accessed on https://***/CQU-BPDD/.

关键词： Pavement distress detection Convolutional Neural Networks Expectation-Maximization algorithm image classification object localization

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Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification 24

Category-Prompt Refined Feature Learning for Long-Tailed Mul...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yan, Jiexuan huang, Sheng Mu, Nankun huangfu, luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification (LTMLC). In such contexts, imbalanced data distribution and multi-object recognition pose significant hurdles. To address this issue, we propose a novel and effective approach for LTMLC, termed Category-Prompt Refined Feature Learning (CPRFL), utilizing semantic correlations between different categories and decoupling category-specific visual representations for each category. Specifically, CPRFL initializes category-prompts from the pretrained CLIP's embeddings and decouples category-specific visual representations through interaction with visual features, thereby facilitating the establishment of semantic correlations between the head and tail classes. To mitigate the visual-semantic domain bias, we design a progressive Dual-Path Back-Propagation mechanism to refine the prompts by progressively incorporating context-related visual information into prompts. Simultaneously, the refinement process facilitates the progressive purification of the category-specific visual representations under the guidance of the refined prompts. Furthermore, taking into account the negative-positive sample imbalance, we adopt the Asymmetric Loss as our optimization objective to suppress negative samples across all classes and potentially enhance the head-to-tail recognition performance. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over *** code is available at https://***/jiexuanyan/CPRFL. © 2024 ACM.

关键词： Semantics

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Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition 29th

Kernel Inversed Pyramidal Resizing Network for Efficient Pav...

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29th International Conference on Neural Information Processing

作者： Qin, Rong huangfu, luwen Hood, Devon Ma, James huang, Sheng Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China San Diego State Univ Fowler Coll Business San Diego CA 92182 USA San Diego State Univ Ctr Human Dynam Mobile Age San Diego CA 92182 USA San Diego State Univ Coll Sci San Diego CA 92182 USA Univ Colorado Dept Business Anal Colorado Springs CO 80918 USA Chongqing Univ Key Lab Dependable Serv Comp Cyber Ph Minist Educ Chongqing 400044 Peoples R China

ISBN: (纸本)9789819916443;9789819916450

Pavement Distress Recognition (PDR) is an important step in pavement inspection and can be powered by image-based automation to expedite the process and reduce labor costs. Pavement images are often in high-resolution with a low ratio of distressed to non-distressed areas. Advanced approaches leverage these properties via dividing images into patches and explore discriminative features in the scale space. However, these approaches usually suffer from information loss during image resizing and low efficiency due to complex learning frameworks. In this paper, we propose a novel and efficient method for PDR. A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing, and can be flexibly plugged into the image classification network as a pre-network to exploit resolution and scale information. In KIPRN, pyramidal convolution and kernel inversed convolution are specifically designed to mine discriminative information across different feature granularities and scales. The mined information is passed along to the resized images to yield an informative image pyramid to assist the image classification network for PDR. We applied our method to three well-known Convolutional Neural Networks (CNNs), and conducted an evaluation on a large-scale pavement image dataset named CQU-BPDD. Extensive results demonstrate that KIPRN can generally improve the pavement distress recognition of these CNN models and show that the simple combination of KIPRN and EfficientNet-B3 significantly outperforms the state-of-the-art patch-based method in both performance and efficiency.

关键词： Pavement Distress Recognition Image Classification Resizing Network

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Inhibition of Mitochondrial Succinate Dehydrogenase with Dimethyl Malonate Promotes M2 Macrophage Polarization by Enhancing STAT6 Activation

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INFLAMMATION 2025年 1-15页

作者： He, Chaowen Chen, Pengfei Ning, luwen huang, Xiuping Sun, Huimin Wang, Yuanyuan Zhao, Yanli Zeng, Changchun huang, Dongsheng Gao, Hanchao Cao, Mengtao Shenzhen Longhua Dist Cent Hosp Dept Resp Med Shenzhen 518110 Peoples R China Shenzhen Univ Affiliated Hosp 1 Shenzhen Peoples Hosp 2 Hlth Sci Ctr Shenzhen Peoples R China Shenzhen Longhua Dist Cent Hosp Dept Nephrol Shenzhen Longhua Dist Key Lab Diag & Treatment Chr Shenzhen 518110 Peoples R China

Macrophages exhibit diverse phenotypes depending on environment status, which contribute to physiological and pathological processes of immunological diseases, including sepsis, asthma, multiple sclerosis and colitis. The alternative activation of macrophages is tightly regulated to avoid excessive activation and damage of tissues and organs. Certain works characterized that succinate dehydrogenase (SDH) altered function of macrophages and promoted inflammatory response in M1 macrophages via mitochondrial reactive oxygen species (ROS). However, the effect of succinate dehydrogenase on M2 macrophage polarization remains incompletely understood. We employed dimethyl malonate (DMM) to inhibit succinate dehydrogenase activity and took use of RNA-seq to analyze the changes of inflammatory response of LPS-activated M1 macrophages or IL 4-activated M2 macrophages. Our data revealed that inhibition of SDH with DMM increased expression of M2 macrophages-associated signature genes, including Arg1, Ym1 and Mrc1. Consistent with previous work, we also observed that inhibition of SDH decreased the expression of IL-1 beta and enhanced the levels of IL-10 in M1 macrophages. Additionally, inhibition of SDH with DMM inhibited the production of chemokines, such as Cxcl3, Cxcl12, Ccl20 and Ccl9. DMM also amplified the M2 macrophages-related signature genes in IL-13-activated M2 macrophages. Mechanistic studies revealed that DMM promoted M2 macrophages polarization through mitochondrial ROS dependent STAT6 activation. Blocking ROS with mitoTEMPO or inhibiting STAT6 activation with ruxolitinib abrogated the promotion effect of DMM on M2 macrophages. Finally, dimethyl malonate treatment promoted peritoneal M2 macrophages differentiation and exacerbated OVA-induced allergy asthma in vivo. Collectively, we identified SDH as a braker to suppress M2 macrophage polarization via mitochondrial ROS, suggesting a novel strategy to treatment of M2 macrophages-mediated inflammatory diseases.

关键词： Succinate dehydrogenase M2 macrophage polarization JAK-STAT6 pathway Asthma

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Prevalence of aggressive care among patients with cancer near the end of life: a systematic review and meta-analysis

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ECLINICALMEDICINE 2024年 71卷 102561-102561页

作者： Ma, Zhuo Li, huangqianyu Zhang, Lan huang, Guo Zhang, Yichen Shi, luwen Liu, Wei An, Zhuoling Guan, Xiaodong Peking Univ Sch Pharmaceut Sci Dept Pharm Adm & Clin Pharm Beijing Peoples R China Capital Med Univ Beijing Chao Yang Hosp Dept Pharm Beijing Peoples R China Univ Hong Kong Sch Nursing Hong Kong Peoples R China Peking Univ Int Res Ctr Med Adm Beijing Peoples R China Peking Univ Canc Hosp & Inst Palliat Care Ctr & Day Care Key Lab Carcinogenesis & Translat ResMinist Educ Beijing Peoples R China 38 Xueyuan Rd Beijing Peoples R China

Background Aggressive care near patients ' end -of -life (EOL) entails limited therapeutic values, high costs, and compromised quality of life (QoL). In this study, we aimed to estimate the global prevalence of aggressive care in patients with cancer and explore potential subgroup differences. Methods We searched PubMed, Embase, and the Cochrane Library from database inception to Feb 16, 2024. Eligible studies reported the prevalence of aggressive EOL care using at least one quanti fi able measure. Random -effects models were used to derive the pooled prevalence and subgroup analyses were performed to investigate differences in the prevalence of aggressive care across regions, the country ' s level of economic development, tumor types, ages, and sample sizes. This review is registered with PROSPERO, number CRD42023467839. Findings A total of 129 studies were included in this systematic review, of which 118 (91.5%) were from highincome countries. Studies were mostly conducted in the Americas (60, 46.5%), Europe (34, 26.4%), and Western Paci fi c (31, 24.0%). Measures of aggressive care were inconsistent across studies, with the most commonly used measure being the use of chemotherapy in the last 14 days of life (DOLs) (n = 87, 67.4%) and intensive care unit (ICU) stay in the last 30 DOLs (n = 87, 67.4%). The prevalence of the fi ve claims -based measures of aggressive care, i.e., chemotherapy in the last 14 DOLs, ICU stay in the last 30 DOLs, repeated hospital admission in the last 30 DOLs, repeated emergency room (ER) visit in the last 30 DOLs, and hospice care <3 days before death were 11.6% (95% CI, 9.8% - 13.4%), 14.4% (95% CI, 11.8% - 17.0%), 17.9% (95% CI, 14.4% - 21.4%), 14.8% (95% CI, 12.0% - 17.6%), and 14.4% (95% CI, 11.2% - 17.6%), respectively. Regional differences were statistically signi fi cant in the prevalence of ICU stay and repeated hospital admission in the last 30 DOLs (p < 0.01;p = 0.03). Patients with hematologic malignancies were more likely t

关键词： Aggressive care Cancer End of life Prevalence

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Distribution of Pathogenic Bacteria and Drug Resistance in ICU of a Newly Built Hospital

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INFECTION AND DRUG RESISTANCE 2024年 17卷 4945-4954页

作者： Zeng, Hui Liu, Rong Cheng, Chuanli Yang, Nana Luo, luwen Long, Shengshuang Zhou, Renjia Yan, Kai huang, Huantao Zunyi Med Univ Affiliated Hosp 2 Nursing Dept Zunyi 563000 Guizhou Peoples R China Zunyi Med Univ Sch Nursing Zunyi 563000 Guizhou Peoples R China Zunyi Med Univ Affiliated Hosp 2 Infect Dept Zunyi 563000 Guizhou Peoples R China Zunyi Med Univ Affiliated Hosp 2 ICU Zunyi 563000 Guizhou Peoples R China

Objective: This study investigated the distribution and resistance patterns of pathogens in the intensive care unit of a newly established hospital in Guizhou Province to promote the rational use of antibiotics to reduce multidrug resistance. Methods: A retrospective analysis was conducted on the distribution of pathogens and changes in drug resistance in the ICU of a newly built hospital in Guizhou Province from March 2019 to December 2023. WHONET 5.6 was used to analyze the results. Results: A total of 2444 culture samples were received, predominantly sputum (34.66%) and blood (23.36%) samples, with a steady annual increase in specimen types. A total of 572 pathogenic strains were isolated, predominantly from respiratory specimens (54.02%), including 345 Gram-negative bacteria (60.31%), 135 Gram-positive cocci (23.60%), and 92 fungi (16.08%). The most frequent pathogens included Acinetobacter baumannii (30.77%), Candida albicans (11.71%), and Klebsiella pneumoniae (9.97%). Drug sensitivity tests indicated a fluctuating resistance rate of Acinetobacter baumannii over the past five years. Staphylococcus aureus displayed strong in vitro activity against vancomycin, tigecycline, and linezolid, with no resistant strains identified. The detection rates of carbapenem-resistant Acinetobacter baumannii (CR-AB), carbapenem-resistant Pseudomonas aeruginosa (CR-PA), methicillinresistant Staphylococcus aureus (MRSA), and strains producing extended-spectrum beta-lactamases (ESBL) were 86.78%, 26.79%, 32.45%, 70.27%, and 23.54%, respectively. Conclusion: Compared with other countries in the world, China has increased its data on the prevalence of MDR pathogens and antibiotic *** high resistance rate of Acinetobacter baumannii in the ICU underscores the need for effective infection control measures. Enhanced monitoring of CR-AB, ESBL-producing bacteria, and MRSA is essential, along with improved management of antibacterial drugs and the pursuit of new therapeutic optio

关键词： new-built hospital intensive care unit pathogens multidrug-resistant bacteria antimicrobial resistance

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