bluuebunny/crossref_metadata_2025_split huggingface dataset:
Targeted protein degradation via intramolecular bivalent glues
Authors: Hsia Oliver , Hinterndorfer Matthias , Cowan Angus D. , Iso Kentaro , Ishida Tasuku , Sundaramoorthy Ramasubramanian , Nakasone Mark A. , Imrichova Hana , Schätz Caroline , Rukavina Andrea , Husnjak Koraljka , Wegner Martin , Correa-Sáez Alejandro , Craigon Conner , Casement Ryan , Maniaci Chiara , Testa Andrea , Kaulich Manuel , Dikic Ivan , Winter Georg E. , Ciulli Alessio 
Published: February 2024

AbstractTargeted protein degradation is a pharmacological modality that is based on the induced proximity of an E3 ubiquitin ligase and a target protein to promote target ubiquitination and proteasomal degradation. This has been achieved either via proteolysis-targeting chimeras (PROTACs)—bifunctional compounds composed of two separate moieties that individually bind the target and E3 ligase, or via molecular glues that monovalently bind either the ligase or the target1–4. Here, using orthogonal genetic screening, biophysical characterization and structural reconstitution, we investigate the mechanism of action of bifunctional degraders of BRD2 and BRD4, termed intramolecular bivalent glues (IBGs), and find that instead of connecting target and ligase in trans as PROTACs do, they simultaneously engage and connect two adjacent domains of the target protein in cis. This conformational change ‘glues’ BRD4 to the E3 ligases DCAF11 or DCAF16, leveraging intrinsic target–ligase affinities that do not translate to BRD4 degradation in the absence of compound. Structural insights into the ternary BRD4–IBG1–DCAF16 complex guided the rational design of improved degraders of low picomolar potency. We thus introduce a new modality in targeted protein degradation, which works by bridging protein domains in cis to enhance surface complementarity with E3 ligases for productive ubiquitination and degradation.
Research on the ratio of similar materials in water-absorbent mudstone based on fuzzy mathematics
Authors: Zeng Chunlin , Zhou Yuejin , Xiaoding Xu 
Published: February 2024

AbstractTo determine the suitability and credibility of similar water-absorbent mudstone materials in model experiments, the prototype mudstone parameter similarity index was determined based on the similarity theory. Similar materials use cement and Plaster as binders and quartz sand as aggregate. The sensitivity of similar indicators of similar materials to control factors was analyzed through range statistics. Multiple regression analysis was used to establish the quantitative relationship between each control factor and similar indicators. Finally, the optimal matching scheme was refined through the combination of fuzzy mathematics and analytic hierarchy process. The results show that the physical and mechanical property indicators of similar materials with different proportions have a wide distribution range, and under certain similar conditions, they can meet the requirements of rock model tests with different properties. The aggregate-binder ratio is a direct indicator of material density, elastic modulus, and compressive strength. The main controlling factors, material density, elastic modulus, and compressive strength all increase with the decrease in aggregate-binder ratio. The cement-plaster ratio is the main control factor of material water absorption, and the water absorption gradually decreases with the increase of the cement-plaster ratio. The formula obtained through linear analysis can better represent the changing trend and distribution characteristics of various parameters of similar materials with the aggregate-binder ratio and cement-plaster ratio, and initially optimize the proportioning scheme of similar materials. Use fuzzy mathematics to evaluate the membership degree of each parameter index of similar materials, and the optimal ratio scheme was further determined to improve the credibility of later model experiments.
Integrating artificial intelligence in osteosarcoma prognosis: the prognostic significance of SERPINE2 and CPT1B biomarkers
Authors: Qu Haishun , Jiang Jie , Zhan Xinli , Liang Yunxiao , Guo Quan , Liu Peifeng , Lu Ling , Yang Yanwei , Xu Weicheng , Zhang Yitian , Lan Shaohang , Chen Zeshan , Lu Yuanhong , Ou Yufu , Qin Yijue 
Published: February 2024

AbstractThe principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate goal to enhance prognostic prediction. Expression profiles from 88 OS cases and 396 normal samples were procured from accessible public databases. Prognostic models were established using univariate COX regression analysis and an array of AI methodologies including the XGB method, RF method, GLM method, SVM method, and LASSO regression analysis. Multivariate COX regression analysis was also employed. Immune cell variations in OS were examined using the CIBERSORT software, and a differential analysis was conducted. Routine blood data from 20,679 normal samples and 437 OS cases were analyzed to validate lymphocyte disparity. Histological assessments of the study's postulates were performed through immunohistochemistry and hematoxylin and eosin (HE) staining. AI facilitated the identification of differentially expressed genes, which were utilized to construct a prognostic model. This model discerned that the survival rate in the high-risk category was significantly inferior compared to the low-risk cohort (p < 0.05). SERPINE2 was found to be positively associated with memory B cells, while CPT1B correlated positively with CD8 T cells. Immunohistochemical assessments indicated that SERPINE2 was more prominently expressed in OS tissues relative to adjacent non-tumorous tissues. Conversely, CPT1B expression was elevated in the adjacent non-tumorous tissues compared to OS tissues. Lymphocyte counts from routine blood evaluations exhibited marked differences between normal and OS groups (p < 0.001). The study highlights SERPINE2 and CPT1B as crucial biomarkers for OS prognosis and suggests that dysregulation of lymphocytes plays a significant role in OS pathogenesis. Both SERPINE2 and CPT1B have potential utility as prognostic biomarkers for OS.
Hepa-ToxMOA: a pathway-screening method for evaluating cellular stress and hepatic metabolic-dependent toxicity of natural products
Authors: Park Se-Myo , Choi Mi-Sun , Kim Soojin , Jegal Hyun , Han Hyoung-Yun , Chun Hyang Sook , Kim Sang Kyum , Oh Jung-Hwa 
Published: February 2024

AbstractIn the field of drug discovery, natural products have emerged as therapeutic agents for diseases such as cancer. However, their potential toxicity poses significant obstacles in the developing effective drug candidates. To overcome this limitation, we propose a pathway-screening method based on imaging analysis to evaluate cellular stress caused by natural products. We have established a cellular stress sensing system, named Hepa-ToxMOA, which utilizes HepG2 cells expressing green fluorescent protein (GFP) fluorescence under the control of transcription factor response elements (TREs) for transcription factors (AP1, P53, Nrf2, and NF-κB). Additionally, to augment the drug metabolic activity of the HepG2 cell line, we evaluated the cytotoxicity of 40 natural products with and without S9 fraction-based metabolic activity. Our finding revealed different activities of Hepa-ToxMOA depending on metabolic or non-metabolic activity, highlighting the involvement of specific cellular stress pathways. Our results suggest that developing a Hepa-ToxMOA system based on activity of drug metabolizing enzyme provides crucial insights into the molecular mechanisms initiating cellular stress during liver toxicity screening for natural products. The pathway-screening method addresses challenges related to the potential toxicity of natural products, advancing their translation into viable therapeutic agents.
Insulin and IGF-1 have both overlapping and distinct effects on CD4+ T cell mitochondria, metabolism, and function
Authors: Kiernan Kaitlin , Alwarawrah Yazan , Nichols Amanda G. , Danzaki Keiko , MacIver Nancie J. 
Published: February 2024

AbstractInsulin and insulin-like growth factor 1 (IGF-1) are metabolic hormones with known effects on CD4+ T cells through insulin receptor (IR) and IGF-1 receptor (IGF-1R) signaling. Here, we describe specific and distinct roles for these hormones and receptors. We have found that IGF-1R, but not IR, expression is increased following CD4+ T cell activation or following differentiation toward Th17 cells. Although both insulin and IGF-1 increase the metabolism of CD4+ T cells, insulin has a more potent effect. However, IGF-1 has a unique role and acts specifically on Th17 cells to increase IL-17 production and Th17 cell metabolism. Furthermore, IGF-1 decreases mitochondrial membrane potential and mitochondrial reactive oxygen species (mROS) in Th17 cells, providing a cytoprotective effect. Interestingly, both IR and IGF-1R are required for this effect of IGF-1 on mitochondria, which suggests that the hybrid IR/IGF-1R may be required for mediating the effect of IGF-1 on mitochondrial membrane potential and mROS production.
External validation of the rCAST for patients after in-hospital cardiac arrest: a multicenter retrospective observational study
Authors: Ishii Junki , Nishikimi Mitsuaki , Kikutani Kazuya , Kyo Michihito , Ohki Shingo , Ota Kohei , Fujino Mitsuhiro , Sakuraya Masaaki , Ohshimo Shinichiro , Shime Nobuaki 
Published: February 2024

AbstractNo established predictive or risk classification tool exists for the neurological outcomes of post-cardiac arrest syndrome (PCAS) in patients with in-hospital cardiac arrest (IHCA). This study aimed to investigate whether the revised post-cardiac arrest syndrome for therapeutic hypothermia score (rCAST), which was developed to estimate the prognosis of PCAS patients with out-of-hospital cardiac arrest (OHCA), was applicable to patients with IHCA. A retrospective, multicenter observational study of 140 consecutive adult IHCA patients admitted to three intensive care units. The area under the receiver operating characteristic curves (AUCs) of the rCAST for poor neurological outcome and mortality at 30 days were 0.88 (0.82–0.93) and 0.83 (0.76–0.89), respectively. The sensitivity and specificity of the risk classification according to rCAST for poor neurological outcomes were 0.90 (0.83–0.96) and 0.67 (0.55–0.79) for the low, 0.63 (0.54–0.74) and 0.67 (0.55–0.79) for the moderate, and 0.27 (0.17–0.37) and 1.00 (1.00–1.00) for the high-severity grades. All 22 patients classified with a high-severity grade showed poor neurological outcomes. The rCAST showed excellent predictive accuracy for neurological prognosis in patients with PCAS after IHCA. The rCAST may be useful as a risk classification tool for PCAS after IHCA.
Endothelium-related biomarkers enhanced prediction of kidney support therapy in critically ill patients with non-oliguric acute kidney injury
Authors: Salmito Francisco Thiago Santos , Mota Sandra Mara Brasileira , Holanda Francisco Márcio Tavares , Libório Santos Leticia , Silveira de Andrade Luana , Meneses Gdayllon Cavalcante , Lopes Nicole Coelho , de Araújo Leticia Machado , Martins Alice Maria Costa , Libório Alexandre Braga 
Published: February 2024

AbstractAcute kidney injury (AKI) is a common condition in hospitalized patients who often requires kidney support therapy (KST). However, predicting the need for KST in critically ill patients remains challenging. This study aimed to analyze endothelium-related biomarkers as predictors of KST need in critically ill patients with stage 2 AKI. A prospective observational study was conducted on 127 adult ICU patients with stage 2 AKI by serum creatinine only. Endothelium-related biomarkers, including vascular cell adhesion protein-1 (VCAM-1), angiopoietin (AGPT) 1 and 2, and syndecan-1, were measured. Clinical parameters and outcomes were recorded. Logistic regression models, receiver operating characteristic (ROC) curves, continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used for analysis. Among the patients, 22 (17.2%) required KST within 72 h. AGPT2 and syndecan-1 levels were significantly greater in patients who progressed to the KST. Multivariate analysis revealed that AGPT2 and syndecan-1 were independently associated with the need for KST. The area under the ROC curve (AUC-ROC) for AGPT2 and syndecan-1 performed better than did the constructed clinical model in predicting KST. The combination of AGPT2 and syndecan-1 improved the discrimination capacity of predicting KST beyond that of the clinical model alone. Additionally, this combination improved the classification accuracy of the NRI and IDI. AGPT2 and syndecan-1 demonstrated predictive value for the need for KST in critically ill patients with stage 2 AKI. The combination of AGPT2 and syndecan-1 alone enhanced the predictive capacity of predicting KST beyond clinical variables alone. These findings may contribute to the early identification of patients who will benefit from KST and aid in the management of AKI in critically ill patients.
Network analysis-guided drug repurposing strategies targeting LPAR receptor in the interplay of COVID, Alzheimer’s, and diabetes
Authors: Malar Dicson Sheeja , Verma Kanika , Prasanth Mani Iyer , Tencomnao Tewin , Brimson James Michael 
Published: February 2024

AbstractThe COVID-19 pandemic caused by the SARS-CoV-2 virus has greatly affected global health. Emerging evidence suggests a complex interplay between Alzheimer’s disease (AD), diabetes (DM), and COVID-19. Given COVID-19’s involvement in the increased risk of other diseases, there is an urgent need to identify novel targets and drugs to combat these interconnected health challenges. Lysophosphatidic acid receptors (LPARs), belonging to the G protein-coupled receptor family, have been implicated in various pathological conditions, including inflammation. In this regard, the study aimed to investigate the involvement of LPARs (specifically LPAR1, 3, 6) in the tri-directional relationship between AD, DM, and COVID-19 through network analysis, as well as explore the therapeutic potential of selected anti-AD, anti-DM drugs as LPAR, SPIKE antagonists. We used the Coremine Medical database to identify genes related to DM, AD, and COVID-19. Furthermore, STRING analysis was used to identify the interacting partners of LPAR1, LPAR3, and LPAR6. Additionally, a literature search revealed 78 drugs on the market or in clinical studies that were used for treating either AD or DM. We carried out docking analysis of these drugs against the LPAR1, LPAR3, and LPAR6. Furthermore, we modeled the LPAR1, LPAR3, and LPAR6 in a complex with the COVID-19 spike protein and performed a docking study of selected drugs with the LPAR-Spike complex. The analysis revealed 177 common genes implicated in AD, DM, and COVID-19. Protein–protein docking analysis demonstrated that LPAR (1,3 & 6) efficiently binds with the viral SPIKE protein, suggesting them as targets for viral infection. Furthermore, docking analysis of the anti-AD and anti-DM drugs against LPARs, SPIKE protein, and the LPARs-SPIKE complex revealed promising candidates, including lupron, neflamapimod, and nilotinib, stating the importance of drug repurposing in the drug discovery process. These drugs exhibited the ability to bind and inhibit the LPAR receptor activity and the SPIKE protein and interfere with LPAR-SPIKE protein interaction. Through a combined network and targeted-based therapeutic intervention approach, this study has identified several drugs that could be repurposed for treating COVID-19 due to their expected interference with LPAR(1, 3, and 6) and spike protein complexes. In addition, it can also be hypothesized that the co-administration of these identified drugs during COVID-19 infection may not only help mitigate the impact of the virus but also potentially contribute to the prevention or management of post-COVID complications related to AD and DM.
Reimagining plastics waste as energy solutions: challenges and opportunities
Authors: Tan Angie F. J. , Yu Sam , Wang Cheng , Yeoh Guan Heng , Teoh Wey Yang , Yip Alex C. K. 
Published: February 2024

AbstractRecent statistics portray a stark reality, particularly highlighting the inadequate recycling measures and the consequent environmental threats, most notably in developing nations. The global ramifications of plastic pollution are elucidated, specifically focusing on the alarming accumulation in regions such as the “Great Pacific Garbage Patch” and evolving waste management practices in Southeast Asian countries. We emphasize the significance of Waste-to-Energy (W2E) and Waste-to-Fuel (W2F) technologies, e.g., pyrolysis and gasification, for converting difficult-to-recycle plastic waste into a dense-energy source. However, we identify a critical gap in current research: the emission of CO2 during these processes. This perspective spotlights emergent CO2 capture and utilization technologies, underscoring their role as a robust turnkey solution in making W2E and W2F methods more sustainable and unleashing the huge potential of using waste plastics as a dense-energy source. The scientific community is urged to develop tailored solutions for reducing CO2 emissions in plastic waste conversion processes. This approach promotes circular resource utilization and realizes the socio-economic and environmental advantages of plastic waste utilization technologies, advocating their implementation in economically disadvantaged regions.
Diagnosis of Mycobacterium tuberculosis using palladium–platinum bimetallic nanoparticles combined with paper-based analytical devices
Authors: Tung Cheng-Yang , Tsai Tsung-Ting , Chiu Ping-Yeh , Viter Roman , Ramanavičius Arũnas , Yu Cheng-Ju , Chen Chien-Fu 
Published: February 2024

This study demonstrates that palladium–platinum bimetallic nanoparticles (Pd@Pt NPs) can be used as the nanozyme for highly sensitive Mycobacterium tuberculosis diagnosis on a multi-layer paper-based analytical device.
PCRLB for batch discrete‐time estimation through state decoupling
Authors: Bao Xiao‐Chuan , Luo Ji‐An 
Published: February 2024

AbstractThis letter aims to obtain a closed‐form performance metric using the smoothing posterior Cramér–Rao lower bound (PCRLB) for a class of direct discrete‐time kinematic models. By decoupling the state‐space model into separate position, velocity and acceleration components using an independent dynamic representation, the velocity component in the a priori distribution is converted into a position component. Based on the above derivations, the closed‐form PCRLB is developed. The effectiveness of the proposed bound is demonstrated by contrasting the proposed smoothing PCRLB with the traditional recursive PCRLB in a bearings‐only target tracking scenario.
T‐Skeleton: Accurate scene text detection via instance‐aware skeleton embedding
Authors: Li Haiyan , Hu Xingfei , Lu Hongtao 
Published: February 2024

AbstractExisting segmentation‐based methods have made considerable progress in arbitrarily shaped text detection due to the advantage of dealing with shape variation. However, there still exist challenges to detecting accurate text instances with dense layouts, inaccurate annotations, and complex backgrounds. Many recent works have focused on improving arbitrary boundary prediction, but it may be difficult to accurately distinguish each instance of dense layouts because their boundary pixels may be mistakenly classified to produce inaccurate results (i.e., adhesive texts) with inaccurate annotation and complex backgrounds. Considering the local and long‐range dependencies, this paper proposes an efficient text detector, namely T‐Skeleton, to obtain more reliable segmentation detections. In the spirit of object skeletonization, we introduce the text instance skeleton highlighting the semantically significant structure (similar to the skeleton of a fish) to explicitly capture the long‐range dependencies of text instances. The key idea of T‐Skeleton is to calibrate the coarse text proposals by embedding text instance skeletons to separate crowd texts accurately and robustly. We further design a channel attention module to enlarge the performance margin between T‐Skeleton and the segmentation baseline. Experimental results on four publicly available datasets show the superiority of T‐Skeleton in handling long and curved texts.
An algorithm for detecting dense small objects in aerial photography based on coordinate position attention module
Authors: Wu Huixin , Zhu Yang , Cao Mengdi 
Published: February 2024

AbstractTo address the challenges of detecting a large number of objects and a high proportion of small objects in aerial drone imagery, an aerial dense small object detection algorithm called coordinate position attention module you only look once (CPAM‐YOLO) is proposed based on the coordinate position attention module (CPAM). In the backbone network of CPAM‐YOLO, a CPAM is proposed and embedded that decomposes channel attention into two 1D feature encoding processes, and selectively combines the features of each position through the weighted sum of all position features. Finally, features are aggregated along two spatial directions, increasing the effective information utilization of input feature positions and channels. The backbone network, feature enhancement network, and detection heads have been optimized to improve detection accuracy while ensuring a lightweight detection network. Using lightweight backbone networks to significantly reduce the number of parameters while using high‐resolution feature enhancement networks to retain more semantic and detailed features. The algorithm's performance was evaluated using the publicly available VisDrone2019 dataset. Compared to the baseline network YOLOv5l, CPAM‐YOLO achieved a 4.5% improvement in mAP0.5 and a 3.2% improvement in mAP0.95. These experimental results demonstrate the strong practicality of the CPAM‐YOLO object detection network for detecting dense small objects in aerial image.
Packets distribution over asymmetric paths using concurrent multipath transfer
Authors: Fetoh Hatem , Amin Khaled M. , Hamad Ahmed M. 
Published: February 2024

AbstractConcurrent multipath transfer (CMT) is a transport layer protocol used for transferring data over multiple paths concurrently. In the case of asymmetric paths, the dissimilarity of delay, bandwidth, and loss rate among paths leads to challenges such as out‐of‐order packets, receiver buffer blocking, and throughput reduction. A framework that uses the end‐to‐end delay of paths to distribute the packets over asymmetric paths in ordering policy is proposed. The proposed framework detects network congestion in the assigned path and reduces the congestion window. Furthermore, the proposed framework predicts receiver buffer blocking and deactivates the highest delay path that causes this problem. The simulation results show that the proposed framework achieves highest throughput, lower entropy and lower average application end‐to‐end delay than the previous algorithms.