MLRR-ATV: A Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization for scRNA-seq Data Clustering.

Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limitations of sequencing technology, the data contains a lot of noise. At the same time, it also has the characteristics of highdimensional and sparse. Clustering is a common method of analyzing scRNA-seq data. This paper proposes a novel singlecell clustering method called Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization (MLRR-ATV). The Adaptive Total-Variation (ATV) regularization is introduced into Low-Rank Representation (LRR) model to reduce the influence of noise through gradient learning. Then, the linear and nonlinear manifold structures in the data are learned through Euclidean distance and cosine similarity, and more valuable information is retained. Because the model is non-convex, we use the Alternating Direction Method of Multipliers (ADMM) to optimize the model. We tested the performance of the MLRRATV model on eight real scRNA-seq datasets and selected nine state-of-the-art methods as comparison methods. The experimental results show that the performance of the MLRRATV model is better than the other nine methods.

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

M3HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction.

Numerous scientific studies have found a link between diverse microorganisms in the human body and complex human diseases. Because traditional experimental approaches are time-consuming and expensive, using computational methods to identify microbes correlated with diseases is critical. In this paper, a new microbe-disease association prediction model is proposed that combines a multi-view multi-modal network and a multi-scale feature fusion mechanism, called M3HOGAT. Firstly, a microbe-disease association network and multiple similarity views are constructed based on multi-source information. Then, consider that neighbor information from disparate orders might be more adept at learning node representations. Consequently, the higher-order graph attention network (HOGAT) is devised to aggregate neighbor information from disparate orders to extract microbe and disease features from different networks and views. Given that the embedding features of microbe and disease from different views possess varying importance, a multi-scale feature fusion mechanism is employed to learn their interaction information, thereby generating the final feature of microbes and diseases. Finally, an inner product decoder is used to reconstruct the microbe-disease association matrix. Compared with five state-of-the-art methods on the HMDAD and Disbiome datasets, the results of 5-fold cross-validations show that M3HOGAT achieves the best performance. Furthermore, case studies on asthma and obesity confirm the effectiveness of M3HOGAT in identifying potential disease-related microbes.

Predictive value of intracranial high-density areas in neurological function.

BACKGROUND: Intracranial high-density areas (HDAs) have attracted considerable attention for predicting clinical outcomes; however, whether HDAs predict worse neurological function and mental health remains controversial and unclear, which requires further investigation. AIM: To investigate the predictive value of intracranial HDAs for neurological function and mental health after endovascular treatment. METHODS: In this prospective study, 96 patients with acute ischemic stroke (AIS) who accepted endovascular mechanical thrombectomy (EMT) were included. The enrolled patients underwent cranial computed tomography (CT) examination within 24 hours after EMT. Clinical data in terms of National Institutes of Health Stroke Scale (NIHSS), the 3-month modified Rankin Scale (mRS), self-rating depression scale (SDS), and self-rating anxiety scale (SAS) scores were collected and compared between patients with HDAs and non-HDAs and between patients with good and poor clinical prognosis. RESULTS: Compared to patients without HDAs, patients with HDAs presented severe neurological deficits (admission NIHSS score: 18 ± 3 vs 19 ± 4), were more likely to have post-stroke disabilities (mRS < 3: 35% vs 62%), and suffered more severe depression (SDS score: 58 ± 16 vs 64 ± 13) and anxiety disorder (SAS score: 52 ± 8 vs 59 ± 10). Compared to patients with a good prognosis, patients with a poor prognosis presented severe neurological deficits (admission NIHSS score: 17 ± 4 vs 20 ± 3), were more likely to have HDAs on CT images (64% vs 33%), and suffered more severe depression (SDS score: 55 ± 19 vs 65 ± 11) and anxiety (SAS score: 50 ± 8 vs 58 ± 12). Multivariate analysis revealed that HDAs were independent negative prognostic factors. CONCLUSION: In conclusion, HDAs on CT images predicted poor prognosis and severe depressive and anxiety symptoms in patients with AIS who underwent EMT.

Research on the Human Motion Recognition Method Based on Wearable.

The accurate analysis of human dynamic behavior is very important for overcoming the limitations of movement diversity and behavioral adaptability. In this paper, a wearable device-based human dynamic behavior recognition method is proposed. The method collects acceleration and angular velocity data through a six-axis sensor to identify information containing specific behavior characteristics in a time series. A human movement data acquisition platform, the DMP attitude solution algorithm, and the threshold algorithm are used for processing. In this experiment, ten volunteers wore wearable sensors on their bilateral forearms, upper arms, thighs, calves, and waist, and movement data for standing, walking, and jumping were collected in school corridors and laboratory environments to verify the effectiveness of this wearable human movement recognition method. The results show that the recognition accuracy for standing, walking, and jumping reaches 98.33%, 96.67%, and 94.60%, respectively, and the average recognition rate is 96.53%. Compared with similar methods, this method not only improves the recognition accuracy but also simplifies the recognition algorithm and effectively saves computing resources. This research is expected to provide a new perspective for the recognition of human dynamic behavior and promote the wider application of wearable technology in the field of daily living assistance and health management.

Infection and intracellular transport of white spot syndrome virus require the ESCRT machinery in shrimp.

The cellular endosomal sorting complex required for transport (ESCRT) system comprises five distinct components and is involved in many different physiological processes. Recent studies have shown that different viruses rely upon the host ESCRT system for viral infection. However, whether this system is involved in white spot syndrome virus (WSSV) infection remains unclear. Here, we identified 24 homologs of ESCRT subunits in kuruma shrimp, Marsupenaeus japonicus, and found that some key components were strongly upregulated in shrimp after WSSV infection. Knockdown of key components of the ESCRT system using RNA interference inhibited virus replication, suggesting that the ESCRT system is beneficial for WSSV infection. We further focused on TSG101, a crucial member of the ESCRT-I family that plays a central role in recognizing cargo and activating the ESCRT-II and ESCRT-III complexes. TSG101 colocalized with WSSV in hemocytes. The addition of N16 (a TSG101 inhibitor) markedly decreased WSSV replication. TSG101 and ALIX of the ESCRT system interact with WSSV envelope proteins. The host proteins TSG101, RAB5, and RAB7, the viral protein VP28, and DNA were detected in endosomes isolated from hemocytes of WSSV-infected shrimp. Knockdown of Rab5 and Rab7 expression reduced viral replication. Taken together, these results suggest that the ESCRT system is hijacked by WSSV for transport through the early to late endosome pathway. Our work identified a novel requirement for the intracellular trafficking and infection of WSSV, and provided novel therapeutic targets for the prevention and control of WSSV in shrimp aquaculture. IMPORTANCE: Viruses utilize the ESCRT machinery in a variety of strategies for their replication and infection. This study revealed that the interaction of ESCRT complexes with WSSV envelope proteins plays a crucial role in WSSV infection in shrimp. The ESCRT system is conserved in the shrimp Marsupenaeus japonicus, and 24 homologs of the ESCRT system were identified in the shrimp. WSSV exploits the ESCRT system for transport and propagation via the interaction of envelope proteins with host TSG101 and ALIX in an endosome pathway-dependent manner. Understanding the underlying mechanisms of WSSV infection is important for disease control and breeding in shrimp aquaculture.

Identification of COP9 signalosome (CSN) subunits and antiviral function analysis of CSN5 in shrimp.

The constitutive photomorphogenesis 9 (COP9) signalosome (CSN) typically composing of eight subunits (CSN1-8) mediates the process of deneddylation and deubiquitination. The fifth subunit of COP9 signalosome, CSN5, has special characteristics compared with the other seven subunits, and plays vital roles in the deneddylation activity and diverse cellular processes. However, the role of CSN5 in antiviral immunity is not clear. In this study, we identified 8 subunits (CSN1-8) of COP9 signalosome in shrimp Marsupenaeus japonicus. CSN1-6 were existed in all tested tissues, but CSN7-CSN8 were not detected in hepatopancreas. After WSSV challenged, the expression level of Csn1 to Csn4, and Csn6 to Csn8 were highly decreased, but the expression level of Csn5 was conspicuously increased in shrimp challenged by white spot syndrome virus (WSSV). The CSN5 was recombinantly expressed in Escherichia coli and its polyclonal antibody was prepared. The expression level of CSN5 was conspicuously increased at RNA and protein levels in the shrimp challenged by WSSV. After knockdown of Csn5 by RNA interference, the WSSV replication was obviously increased in shrimp. When injected the recombinant protein of CSN5 with the membrane penetrating peptide into shrimp, WSSV replication was inhibited and the survival rate of shrimp was significantly improved compared with control. We further analyzed the expression of antimicrobial peptides (AMPs) in Csn5-RNAi shrimp, and the results showed that the expression of several AMPs was declined significantly. These results indicate that CSN5 inhibits replication of WSSV via regulating expression of AMPs in shrimp, and the recombinant CSN5 might be used in shrimp aquaculture for the white spot syndrome disease control.

FSCME: A Feature Selection Method Combining Copula Correlation and Maximal Information Coefficient by Entropy Weights.

Feature selection is a critical component of data mining and has garnered significant attention in recent years. However, feature selection methods based on information entropy often introduce complex mutual information forms to measure features, leading to increased redundancy and potential errors. To address this issue, we propose FSCME, a feature selection method combining Copula correlation (Ccor) and the maximum information coefficient (MIC) by entropy weights. The FSCME takes into consideration the relevance between features and labels, as well as the redundancy among candidate features and selected features. Therefore, the FSCME utilizes Ccor to measure the redundancy between features, while also estimating the relevance between features and labels. Meanwhile, the FSCME employs MIC to enhance the credibility of the correlation between features and labels. Moreover, this study employs the Entropy Weight Method (EWM) to evaluate and assign weights to the Ccor and MIC. The experimental results demonstrate that FSCME yields a more effective feature subset for subsequent clustering processes, significantly improving the classification performance compared to the other six feature selection methods. The source codes of the FSCME are available online at https://github.com/CDMBlab/FSCME.

Anthropogenic Disturbances Influenced the Island Effect on Both Taxonomic and Phylogenetic Diversity on Subtropical Islands, Pingtan, China.

The investigation of taxonomic diversity within island plant communities stands as a central focus in the field of island biogeography. Phylogenetic diversity is crucial for unraveling the evolutionary history, ecological functions, and species combinations within island plant communities. Island effects (area and isolation effect) may shape species distribution patterns, habitat heterogeneity affects habitat diversity, and anthropogenic disturbances can lead to species extinction and habitat destruction, thus impacting both species diversity and phylogenetic diversity. To investigate how taxonomic and phylogenetic diversity in island natural plant communities respond to island effects, habitat heterogeneity, and anthropogenic disturbances, we took the main island of Haitan (a land-bridge island) and nine surrounding islands (oceanic islands) of varying sizes as the subjects of our study on the Pingtan islands. We aim to elucidate the influence of island effects, habitat heterogeneity, and anthropogenic disturbances on taxonomic and phylogenetic diversity. The results showed that, (1) Both the taxonomic and phylogenetic diversity of plants on the Pingtan islands followed the island area effect, indicating that as the island area increases, both taxonomic and phylogenetic diversity also increase. (2) Island effects and habitat heterogeneity were found to enhance taxonomic and phylogenetic diversity, whereas anthropogenic disturbances were associated with a decrease in both taxonomic and phylogenetic diversity. Furthermore, the synergistic influence of island effects, habitat heterogeneity, and anthropogenic disturbances collectively exerted a negative impact on both taxonomic and phylogenetic diversity. (3) The contribution of explanatory variables of anthropogenic disturbances for taxonomic and phylogenetic diversity was higher than that of island effects and habitat heterogeneity. Additionally, the contribution of the explanatory variables under the combined influence of island effects, habitat heterogeneity, and anthropogenic disturbances is higher than that of the individual variables for island effects and habitat heterogeneity. These findings suggest that anthropogenic disturbances emerged as the dominant factors influencing both taxonomic and phylogenetic diversity. These findings demonstrate the intricate interplay between island effects, habitat heterogeneity, and anthropogenic disturbances, highlighting their combined influence on both taxonomic and phylogenetic diversity on island.

Clinical Significance of Sarcopenia in Elderly Patients Undergoing Endoscopic Submucosal Dissection: A Systematic Review and Meta-analysis.

BACKGROUND AND AIMS: As global life expectancy rises and gastrointestinal tumor incidence increases, more elderly patients are undergoing endoscopic submucosal dissection (ESD) for tumor treatment. The current situation highlights the importance of sarcopenia assessment before ESD. This systematic review and meta-analysis aim to assess sarcopenia's role in predicting post-ESD adverse outcomes in the elderly. METHODS: We conducted a systematic review and meta-analysis to investigate the impact of sarcopenia on the prognosis of elderly patients undergoing ESD treatment. A comprehensive search was conducted across three databases (PubMed, Embase, Web of Science). We were using NEWCASTLE-OTTAWA ASSESSMENT SCALE for risk of bias assessment. The data were synthesized using Review Manager 5.3. RESULTS: A total of 9 reports were identified, analyzing 7 indicators, with a combined sample size of 6044. Through a series of analyses, we have derived several highly credible research findings: the overall OR and 95% CI for gastric and colorectal post-ESD perforation between sarcopenia and nonsarcopenia groups were 1.34 [0.92, 1.97], for CTCAE grade > 2 was 2.65 [1.45, 4.82], for upper gastrointestinal post-ESD pneumonia were 1.97 [1.30, 2.99], and for gastric post-ESD mortality within 5 years were 2.96 [1.33, 6.58]. CONCLUSIONS: Sarcopenia is a risk factor for increased incidence of complications (CTCAE > 2) after undergoing gastric and colorectal ESD, increased pneumonia rates, and higher mortality rates within five years following gastric ESD treatment in elderly patients. However, sarcopenia does not lead to an increased perforation rate in elderly patients undergoing gastric and colorectal ESD treatments. Registration and protocol: The protocol for this study was registered on the Open Science Framework in 2024 https://doi.org/10.17605/OSF.IO/7B2CZ . We also conducted pre-registration on PROSPERO (CRD42024532547).

Metabolic determinants of leukemia onset variability in genetically homogeneous AKR mice.

Leukemia is a complex disease shaped by the intricate interplay of genetic and environmental factors. Given our preliminary data showing different leukemia incidence in genetically homogenous AKR mice harboring the spontaneous leukemia-inducing mutation Rmcfs, we sought to unravel the role of metabolites and gut microbiota in the leukemia penetrance. Our metabolomic analysis revealed distinct serum metabolite profiles between mice that developed leukemia and those that did not. We discovered that linoleic acid (LA), an essential ω-6 polyunsaturated fatty acid, was significantly decreased in the leukemia group, with the lower levels observed starting from 25 weeks before the onset. A predictive model based on LA levels demonstrated high accuracy in predicting leukemia development (area under curve 0.82). In vitro experiment confirmed LA's cytotoxic effects against leukemia cells, and in vivo study showed that a diet enriched with LA prolonged survival in AKR mice. Furthermore, gut microbiome analysis identified specific Lachnospiraceae species, that affect host lipid metabolism, are exclusively present in the leukemia group, suggesting their potential influence on LA metabolism and leukemia development. These findings shed light on the complex relationship between metabolites, gut microbiota, and leukemia development, providing valuable insights into the role of non-genetic factors in leukemia penetrance and potential strategies for leukemia prevention.

Lactobacillus delbrueckii subsp. bulgaricus Alleviates Acute Injury in Hypoxic Mice.

Acute mountain sickness (AMS) is a common ailment in high-altitude areas caused by the body's inadequate adaptation to low-pressure, low-oxygen environments, leading to organ edema, oxidative stress, and impaired intestinal barrier function. The gastrointestinal tract, being the first to be affected by ischemia and hypoxia, is highly susceptible to injury. This study investigates the role of Lactobacillus delbrueckii subsp. bulgaricus in alleviating acute hypoxic-induced intestinal and tissue damage from the perspective of daily consumed lactic acid bacteria. An acute hypoxia mouse model was established to evaluate tissue injury, oxidative stress, inflammatory responses, and intestinal barrier function in various groups of mice. The results indicate that strain 4L3 significantly mitigated brain and lung edema caused by hypoxia, improved colonic tissue damage, and effectively increased the content of tight junction proteins in the ileum, reducing ileal permeability and alleviating mechanical barrier damage in the intestines due to acute hypoxia. Additionally, 4L3 helped to rebalance the intestinal microbiota. In summary, this study found that Lactobacillus delbrueckii subsp. bulgaricus strain 4L3 could alleviate acute intestinal damage caused by hypoxia, thereby reducing hypoxic stress. This suggests that probiotic lactic acid bacteria that exert beneficial effects in the intestines may alleviate acute injury under hypoxic conditions in mice, offering new insights for the prevention and treatment of AMS.

Nominating novel proteins for anxiety via integrating human brain proteomes and genome-wide association study.

BACKGROUND: The underlying pathogenesis of anxiety remain elusive, making the pinpointing of potential therapeutic and diagnostic biomarkers for anxiety paramount to its efficient treatment. METHODS: We undertook a proteome-wide association study (PWAS), fusing human brain proteomes from both discovery (ROS/MAP; N = 376) and validation cohorts (Banner; N = 152) with anxiety genome-wide association study (GWAS) summary statistics. Complementing this, we executed transcriptome-wide association studies (TWAS) leveraging human brain transcriptomic data from the Common Mind Consortium (CMC) to discern the confluence of genetic influences spanning both proteomic and transcriptomic levels. We further scrutinized significant genes through a suite of methodologies. RESULTS: We discerned 14 genes instrumental in the genesis of anxiety through their specific cis-regulated brain protein abundance. Out of these, 6 were corroborated in the confirmatory PWAS, with 4 also showing associations with anxiety via their cis-regulated brain mRNA levels. A heightened confidence level was attributed to 5 genes (RAB27B, CCDC92, BTN2A1, TMEM106B, and DOC2A), taking into account corroborative evidence from both the confirmatory PWAS and TWAS, coupled with insights from mendelian randomization analysis and colocalization evaluations. A majority of the identified genes manifest in brain regions intricately linked to anxiety and predominantly partake in lysosomal metabolic processes. LIMITATIONS: The limited scope of the brain proteome reference datasets, stemming from a relatively modest sample size, potentially curtails our grasp on the entire gamut of genetic effects. CONCLUSION: The genes pinpointed in our research present a promising groundwork for crafting therapeutic interventions and diagnostic tools for anxiety.

[A multicenter study of neonatal stroke in Shenzhen, China]. / 深圳市新生儿脑卒中多中心现况调查.

OBJECTIVES: To investigate the incidence rate, clinical characteristics, and prognosis of neonatal stroke in Shenzhen, China. METHODS: Led by Shenzhen Children's Hospital, the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022. The incidence, clinical characteristics, treatment, and prognosis of neonatal stroke in Shenzhen were analyzed. RESULTS: The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137, 1/6 060, and 1/7 704, respectively. Ischemic stroke accounted for 75% (27/36); boys accounted for 64% (23/36). Among the 36 neonates, 31 (86%) had disease onset within 3 days after birth, and 19 (53%) had convulsion as the initial presentation. Cerebral MRI showed that 22 neonates (61%) had left cerebral infarction and 13 (36%) had basal ganglia infarction. Magnetic resonance angiography was performed for 12 neonates, among whom 9 (75%) had involvement of the middle cerebral artery. Electroencephalography was performed for 29 neonates, with sharp waves in 21 neonates (72%) and seizures in 10 neonates (34%). Symptomatic/supportive treatment varied across different hospitals. Neonatal Behavioral Neurological Assessment was performed for 12 neonates (33%, 12/36), with a mean score of (32±4) points. The prognosis of 27 neonates was followed up to around 12 months of age, with 44% (12/27) of the neonates having a good prognosis. CONCLUSIONS: Ischemic stroke is the main type of neonatal stroke, often with convulsions as the initial presentation, involvement of the middle cerebral artery, sharp waves on electroencephalography, and a relatively low neurodevelopment score. Symptomatic/supportive treatment is the main treatment method, and some neonates tend to have a poor prognosis.

Uptake of gadolinium and dexamethasone in rat inner ear and facial nerve using different administrations.

BACKGROUND: The pathway by which drugs are injected subcutaneously behind the ear to act on the inner ear has not been fully elucidated. OBJECTIVES: To compare the uptake of gadopentetate dimeglumine (Gd-DTPA) and dexamethasone (Dex) in the cochlea and facial nerve of rats following different administrations. MATERIALS AND METHODS: Magnetic resonance imaging was applied to observe the distribution of Gd-DTPA in the facial nerve and inner ear. We observed the uptake of Dex after it was injected with different methods. RESULTS: Images of the intravenous (IV) and intramuscular (IM) groups showed that the bilateral cochlea of the rat was visualized almost simultaneously. While in the left post-auricular (PA) injection group, it was asynchronous. The maximum accumulation (Cmax) of the Gd in the left facial nerve of the PA group (35.406 ± 5.32) was substantially higher than that of the IV group (16.765 ± 3.7542) (p < .01). CONCLUSIONS: Compared with systemic administration, PA has the advantages of long Gd and Dex action time and high accumulation concentration to treat facial nerve diseases. SIGNIFICANCE: The distribution of Gd and Dex in the inner ear and facial nerve of rats following PA injection might be unique.

Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection.

Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently. Then, the unlabeled EEG features are fed into the convolutional autoencoder (CAE) for training, which effectively represents EEG features by optimizing the loss between the input and output features. This unsupervised feature learning process can better combine and optimize EEG features from unlabeled data. After that, the pre-trained encoder part of the model is used for further feature learning of labeled data to obtain its low-dimensional feature representation and achieve classification. This model is performed on the neonatal EEG dataset collected at the University of Helsinki Hospital, which has a high discriminative ability to detect seizures, with an accuracy of 92.34%, precision of 93.61%, recall rate of 98.74%, and F1-score of 95.77%, respectively. The results show that unsupervised learning by CAE is beneficial to the characterization of EEG signals, and the proposed Fd-CAE method significantly improves classification performance.

Unveiling the methionine cycle: a key metabolic signature and NR4A2 as a methionine-responsive oncogene in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a deadly malignancy with notable metabolic reprogramming, yet the pivotal metabolic feature driving ESCC progression remains elusive. Here, we show that methionine cycle exhibits robust activation in ESCC and is reversely associated with patient survival. ESCC cells readily harness exogenous methionine to generate S-adenosyl-methionine (SAM), thus promoting cell proliferation. Mechanistically, methionine augments METTL3-mediated RNA m6A methylation through SAM and revises gene expression. Integrative omics analysis highlights the potent influence of methionine/SAM on NR4A2 expression in a tumor-specific manner, mediated by the IGF2BP2-dependent stabilization of methylated NR4A2 mRNA. We demonstrate that NR4A2 facilitates ESCC growth and negatively impacts patient survival. We further identify celecoxib as an effective inhibitor of NR4A2, offering promise as a new anti-ESCC agent. In summary, our findings underscore the active methionine cycle as a critical metabolic characteristic in ESCC, and pinpoint NR4A2 as a novel methionine-responsive oncogene, thereby presenting a compelling target potentially superior to methionine restriction.

Chemoselective Synthesis of 3-Bromomethyloxindoles via Visible-Light-Induced Radical Cascade Bromocyclization of Alkenes.

A novel visible-light-induced radical cascade bromocyclization of N-arylacrylamides has been accomplished. This reaction overcomes the overbromination at the benzene rings suffered in traditional electrophilic reactions, thus enabling the first highly chemoselective synthesis of valuable 3-bromomethyloxindoles. The combination of pyridine and anhydrous medium is identified as the key factor for the high chemoselectivity in the current photoreaction system, which might work by suppressing the in situ generation of low-concentration Br2 from N-bromosuccinimide. Moreover, the mild reaction conditions ensure the generation of a wide range of the new desired products with excellent functional group tolerance.

Deep Self-Reconstruction Fusion Similarity Hashing for the Diagnosis of Alzheimer's Disease on Multi-Modal Data.

The pathogenesis of Alzheimer's disease (AD) is extremely intricate, which makes AD patients almost incurable. Recent studies have demonstrated that analyzing multi-modal data can offer a comprehensive perspective on the different stages of AD progression, which is beneficial for early diagnosis of AD. In this paper, we propose a deep self-reconstruction fusion similarity hashing (DS-FSH) method to effectively capture the AD-related biomarkers from the multi-modal data and leverage them to diagnose AD. Given that most existing methods ignore the topological structure of the data, a deep self-reconstruction model based on random walk graph regularization is designed to reconstruct the multi-modal data, thereby learning the nonlinear relationship between samples. Additionally, a fused similarity hash based on anchor graph is proposed to generate discriminative binary hash codes for multi-modal reconstructed data. This allows sample fused similarity to be effectively modeled by a fusion similarity matrix based on anchor graph while modal correlation can be approximated by Hamming distance. Especially, extracted features from the multi-modal data are classified using deep sparse autoencoders classifier. Finally, experiments conduct on the AD Neuroimaging Initiative database show that DS-FSH outperforms comparable methods of AD classification. To conclude, DS-FSH identifies multi-modal features closely associated with AD, which are expected to contribute significantly to understanding of the pathogenesis of AD.

Transarterial Chemoembolization Combined with Tyrosine Kinase Inhibitors Plus Immune Checkpoint Inhibitors Versus Tyrosine Kinase Inhibitors Plus Immune Checkpoint Inhibitors in Unresectable Hepatocellular Carcinoma with First- or Lower-Order Portal Vein Tumor Thrombosis.

PURPOSE: To compare the efficacy of transarterial chemoembolization (TACE) combined with tyrosine kinase inhibitors (TKIs) plus immune checkpoint inhibitors (ICIs) (TACE-TKI-ICI) versus TKIs plus ICIs (TKI-ICI) for unresectable hepatocellular carcinoma (HCC) with first- or lower-order portal vein tumor thrombosis (PVTT). MATERIALS AND METHODS: A retrospective study was performed in HCC patients with first- or lower-order PVTT receiving TKIs (Lenvatinib or sorafenib) plus ICIs (camrelizumab, sintilimab, or atezolizumab) with or without TACE from four institutions between January 2019 and January 2022. Propensity score-based method was performed to minimize bias by confounding factors. Tumor response, progression-free survival (PFS), overall survival (OS), and adverse events (AEs) were evaluated and compared between the two groups. RESULTS: After inverse probability of treatment weighting, two balanced pseudopopulations were created: 106 patients in the TACE-TKI-ICI group and 109 patients in the TKI-ICI group. The objective response rate was higher in the TACE-TKI-ICI group (50.9% vs. 28.4%, P < 0.001). The median PFS and OS were significantly longer in the TACE-TKI-ICI group than in the TKI-ICI group (PFS: 9.1 vs. 5.0 months, P = 0.005; OS: 19.1 vs. 12.7 months, P = 0.002). In Cox regression, TACE-TKI-ICI treatment was an independent predictor of favorable OS. Treatment-related grade 3/4 AEs were comparable between the two groups (22.6% vs. 17.9%, P = 0.437). CONCLUSION: TACE-TKI-ICI therapy contributed to better tumor control, PFS and OS than TKI-ICI therapy in unresectable HCC patients with first- or lower-order PVTT.