Molecular characteristics of patients with colorectal signet-ring cell carcinoma with different ABO blood groups.

Background: Colorectal signet-ring cell carcinoma (SRCC) is a rare subtype of malignant adenocarcinoma, accounting for approximately 1 % of colorectal cancer (CRC) cases. Its biomarkers and molecular characteristics remain controversial, and there are no specific therapeutic targets or strategies for its clinical treatment. Methods: A retrospective study was conducted between January 2010 and December 2021. 1058 colorectal cancer cases from the Sun Yat-sen University Cancer Center and 489 cases from the Tumor Genome Atlas Project were included in the analysis, of which 64 were SRCC. Data extraction included patient demographics, blood types and risk factors, including clinical variables and genomics (either a 19-gene panel NGS or 1021-gene panel NGS). Univariate analyses were performed to identify factors significantly associated with overall survival. Results: The blood groups of 27 (42.2 %), 18 (28.1 %), 12 (18.8 %), and seven (10.9 %) patients were classified as O, A, B, and AB, respectively. We found that O was a unique blood group characterized by a low frequency of KRAS mutations, a high frequency of heterozygosity at each HLA class I locus, and a high tumor mutational burden (TMB). Patients in blood group A with high-frequency KRAS mutations and those in blood group B with anemia and metabolic abnormalities required targeted treatment. Furthermore, genetic alterations in SRCC differed from those in adenocarcinoma and mucinous adenocarcinoma. Conclusions: Our study revealed genomic changes in SRCC patients across different blood groups, which could advance the understanding and precise treatment of colorectal SRCC.

Highly sensitive response to the toxicity of environmental chemicals in transparent casper zebrafish.

The response sensitivity to toxic substances is the most concerned performance of animal model in chemical risk assessment. Casper (mitfaw2/w2;mpv17a9/a9), a transparent zebrafish mutant, is a useful in vivo model for toxicological assessment. However, the ability of casper to respond to the toxicity of exogenous chemicals is unknown. In this study, zebrafish embryos were exposed to five environmental chemicals, chlorpyrifos, lindane, α-endosulfan, bisphenol A, tetrabromobisphenol A (TBBPA), and an antiepileptic drug valproic acid. The half-lethal concentration (LC50) values of these chemicals in casper embryos were 62-87 % of that in the wild-type. After TBBPA exposure, the occurrence of developmental defects in the posterior blood island of casper embryos was increased by 67-77 % in relative to the wild-type, and the half-maximal effective concentration (EC50) in casper was 73 % of that in the wild-type. Moreover, the casper genetic background significantly increased the hyperlocomotion caused by chlorpyrifos and lindane exposure compared with the wild-type. These results demonstrated that casper had greater susceptibility to toxicity than wild-type zebrafish in acute toxicity, developmental toxicity and neurobehavioral toxicity assessments. Our data will inform future toxicological studies in casper and accelerate the development of efficient approaches and strategies for toxicity assessment via the use of casper.

Improving quantitative prediction of protein subcellular locations in fluorescence images through deep generative models.

Machine learning has been employed in recognizing protein localization at the subcellular level, which highly facilitates the protein function studies, especially for those multi-label proteins that localize in more than one organelle. However, existing works mostly study the qualitative classification of protein subcellular locations, ignoring fraction of one multi-label protein in different locations. Actually, about 50 % proteins are multi-label proteins, and the ignorance of quantitative information highly restricts the understanding of their spatial distribution and functional mechanism. One reason of the lack of quantitative study is the insufficiency of quantitative annotations. To address the data shortage problem, here we proposed a generative model, PLocGAN, which could generate cell images with conditional quantitative annotation of the fluorescence distribution. The model was a conditional generative adversarial network, in which the condition learning utilized partial label learning to overcome the lack of training labels and allowed training with only qualitative labels. Meanwhile, it used contrastive learning to enhance diversity of the generated images. We assessed the PLocGAN on four pixel-fused synthetic datasets and one real dataset, and demonstrated that the model could generate images with good fidelity and diversity, outperforming existing state-of-the-art generative methods. To verify the utility of PLocGAN in the quantitative prediction of protein subcellular locations, we replaced the training images with generated quantitative images and built prediction models, and found that they had a boosting effect on the quantitative estimation. This work demonstrates the effectiveness of deep generative models in bioimage analysis, and provides a new solution for quantitative subcellular proteomics.

Single-cell sequencing reveals novel proliferative cell type: a key player in renal cell carcinoma prognosis and therapeutic response.

Renal cell carcinoma (RCC) is characterized by a variety of subtypes, each defined by unique genetic and morphological features. This study utilizes single-cell RNA sequencing to explore the molecular heterogeneity of RCC. A highly proliferative cell subset, termed as "Prol," was discovered within RCC tumors, and its increased presence was linked to poorer patient outcomes. An artificial intelligence network, encompassing traditional regression, machine learning, and deep learning algorithms, was employed to develop a Prol signature capable of predicting prognosis. The signature demonstrated superior performance in predicting RCC prognosis compared to other signatures and exhibited pan-cancer prognostic capabilities. RCC patients with high Prol signature scores exhibited resistance to targeted therapies and immunotherapies. Furthermore, the key gene CEP55 from the Prol signature was validated by both proteinomics and quantitative real time polymerase chain reaction. Our findings may provide new insights into the molecular and cellular mechanisms of RCC and facilitate the development of novel biomarkers and therapeutic targets.

Effect of Stroke Etiology on Endovascular Treatment for Acute Basilar-Artery Occlusion: A Post Hoc Analysis of the ATTENTION Randomized Trial.

BACKGROUND: Stroke etiology could influence the outcomes in patients with basilar-artery occlusion (BAO). This study aimed to evaluate the differences in efficacy and safety of best medical treatment (BMT) plus endovascular treatment (EVT) versus BMT alone in acute BAO across different stroke etiologies. METHODS: The study was a post hoc analysis of the ATTENTION trial (Trial of Endovascular Treatment of Acute Basilar-Artery Occlusion), which was a multicenter, randomized trial at 36 centers in China from February 2021 to September 2022. Patients with acute BAO were classified into 3 groups according to stroke etiology (large-artery atherosclerosis [LAA], cardioembolism, and undetermined cause/other determined cause [UC/ODC]). The primary outcome was a favorable outcome (modified Rankin Scale score of 0-3) at 90 days. Safety outcomes included symptomatic intracranial hemorrhage and 90-day mortality. RESULTS: A total of 340 patients with BAO were included, 150 (44.1%) had LAA, 72 (21.2%) had cardioembolism, and 118 (34.7%) had UC/ODC. For patients treated with BMT plus EVT and BMT alone, respectively, the rate of favorable outcome at 90 days was 49.1% and 23.8% in the LAA group (odds ratio, 3.08 [95% CI, 1.38-6.89]); 52.2% and 30.8% in the cardioembolism group (odds ratio, 2.45 [95% CI, 0.89-6.77]); and 37.5% and 17.4% in the UC/ODC group (odds ratio, 2.85 [95% CI, 1.16-7.01]), with P=0.89 for the stroke etiology×treatment interaction. The rate of symptomatic intracranial hemorrhage in EVT-treated patients with LAA, cardioembolism, and UC/ODC was 8.3%, 2.2%, and 3.2%, respectively, and none of the BMT-treated patients. Lower 90-day mortality was observed in patients with EVT compared with BMT alone across 3 etiology groups. CONCLUSIONS: Among patients with acute BAO, EVT compared with BMT alone might be associated with favorable outcomes and lower 90-day mortality, regardless of cardioembolism, LAA, or UC/ODC etiologies. The influence of stroke etiology on the benefit of EVT should be explored by further trials. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04751708.

Metabolic reprograming mediated by tumor cell-intrinsic type I IFN signaling is required for CD47-SIRPα blockade efficacy.

Type I interferons have been well recognized for their roles in various types of immune cells during tumor immunotherapy. However, their direct effects on tumor cells are less understood. Oxidative phosphorylation is typically latent in tumor cells. Whether oxidative phosphorylation can be targeted for immunotherapy remains unclear. Here, we find that tumor cell responsiveness to type I, but not type II interferons, is essential for CD47-SIRPα blockade immunotherapy in female mice. Mechanistically, type I interferons directly reprogram tumor cell metabolism by activating oxidative phosphorylation for ATP production in an ISG15-dependent manner. ATP extracellular release is also promoted by type I interferons due to enhanced secretory autophagy. Functionally, tumor cells with genetic deficiency in oxidative phosphorylation or autophagy are resistant to CD47-SIRPα blockade. ATP released upon CD47-SIRPα blockade is required for antitumor T cell response induction via P2X7 receptor-mediated dendritic cell activation. Based on this mechanism, combinations with inhibitors of ATP-degrading ectoenzymes, CD39 and CD73, are designed and show synergistic antitumor effects with CD47-SIRPα blockade. Together, these data reveal an important role of type I interferons on tumor cell metabolic reprograming for tumor immunotherapy and provide rational strategies harnessing this mechanism for enhanced efficacy of CD47-SIRPα blockade.

An Integrated Multi-omics prediction model for stroke recurrence based on Lnet transformer layer and dynamic weighting mechanism.

BACKGROUND AND OBJECTIVE: Stroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data. METHODS: We systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics. RESULTS: We compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model. CONCLUSION: MPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

Model-based federated learning for accurate MR image reconstruction from undersampled k-space data.

Deep learning-based methods have achieved encouraging performances in the field of Magnetic Resonance (MR) image reconstruction. Nevertheless, building powerful and robust deep learning models requires collecting large and diverse datasets from multiple centers. This raises concerns about ethics and data privacy. Recently, federated learning has emerged as a promising solution, enabling the utilization of multi-center data without the need for data transfer between institutions. Despite its potential, existing federated learning methods face challenges due to the high heterogeneity of data from different centers. Aggregation methods based on simple averaging, which are commonly used to combine the client's information, have shown limited reconstruction and generalization capabilities. In this paper, we propose a Model-based Federated learning framework (ModFed) to address these challenges. ModFed has three major contributions: (1) Different from existing data-driven federated learning methods, ModFed designs attention-assisted model-based neural networks that can alleviate the need for large amounts of data on each client; (2) To address the data heterogeneity issue, ModFed proposes an adaptive dynamic aggregation scheme, which can improve the generalization capability and robustness of the trained neural network models; (3) ModFed incorporates a spatial Laplacian attention mechanism and a personalized client-side loss regularization to capture the detailed information for accurate image reconstruction. The effectiveness of the proposed ModFed is evaluated on three in-vivo datasets. Experimental results show that when compared to six existing state-of-the-art federated learning approaches, ModFed achieves better MR image reconstruction performance with increased generalization capability. Codes will be made available at https://github.com/ternencewu123/ModFed.

Phytoavailability, translocation, and accompanying isotopic fractionation of cadmium in soil and rice plants in paddy fields.

Rice consumption is a major pathway for human cadmium (Cd) exposure. Understanding Cd behavior in the soil-rice system, especially under field conditions, is pivotal for controlling Cd accumulation. This study analyzed Cd concentrations and isotope compositions (δ114/110Cd) in rice plants and surface soil sampled at different times, along with urinary Cd of residents from typical Cd-contaminated paddy fields in Youxian, Hunan, China. Soil water-soluble Cd concentrations varied across sampling times, with δ114/110Cdwater lighter under drained than flooded conditions, suggesting supplementation of water-soluble Cd by isotopically lighter Cd pools, increasing Cd phytoavailability. Both water-soluble Cd and atmospheric deposition contributed to rice Cd accumulation. Water-soluble Cd's contribution increased from 28-52% under flooded to 58-87% under drained conditions due to increased soil Cd phytoavailability. Atmospheric deposition's contribution (12-72%) increased with potential atmospheric deposition flux among sampling areas. The enrichment of heavy Cd isotopes occurred from root-stem-grain to prevent rice Cd accumulation. The different extent of enrichment of heavy isotopes in urine indicated different Cd exposure sources. These findings provide valuable insights into the speciation and phytoavailability changes of Cd in the soil-rice system and highlight the potential application of Cd isotopic fingerprinting in understanding the environmental fate of Cd.

Factors influencing recurrence and model development for recurrence of minimally invasive percutaneous transhepatic lithotripsy: a single-center retrospective study.

OBJECTIVE: To identify factors influencing recurrence after percutaneous transhepatic choledochoscopic lithotripsy (PTCSL) and to develop a predictive model. METHODS: We retrospectively analyzed clinical data from 354 patients with intrahepatic and extrahepatic bile duct stones treated with PTCSL at Qinzhou First People's Hospital between February 2018 and January 2020. Patients were followed for three years and categorized into non-recurrence and recurrence groups based on postoperative outcome. Univariate analysis identified possible predictors of stone recurrence. Data were split using the gradient boosting machine (GBM) algorithm, assigning 70% as the training set and 30% as the test set. The predictive performance of the GBM model was assessed using the receiver operating characteristic (ROC) curve and calibration curve, and compared with a logistic regression model. RESULTS: Six factors were identified as significant predictors of recurrence: age, diabetes, total bilirubin, biliary stricture, number of stones, and stone diameter. The GBM model, developed based on these factors, showed high predictive accuracy. The area under the ROC curve (AUC) was 0.763 (95% CI: 0.695-0.830) for the training set and 0.709 (95% CI: 0.596-0.822) for the test set. Optimal cutoff values were 0.286 and 0.264, with sensitivities of 62.30% and 66.70%, and specificities of 77.20% and 68.50%, respectively. Calibration curves indicated good agreement between predicted probabilities and observed recurrence rates in both sets. DeLong's test revealed no significant differences between the GBM and logistic regression models in predictive performance (training set: D = 0.003, P = 0.997 > 0.05; test set: D = 0.075, P = 0.940 > 0.05). CONCLUSION: Biliary stricture, stone diameter, diabetes, stone number, age, and total bilirubin significantly influence stone recurrence after PTCSL. The GBM model, based on these factors, demonstrates robust accuracy and discrimination. Both GBM and logistic regression models effectively predicted stone recurrence post-PTCSL.

[Characteristics of leaf stoichiometry and the driving factors of Ammopiptanthus mongolicus, China]. / æ²™å†¬é’å¶ç‰‡åŒ–å­¦è®¡é‡ç‰¹å¾åŠå ¶é©±åŠ¨å› ç´ .

The stoichiometric characteristics of leaves can reflect environmental adaptation of plants, and thus the study of the relationship between them is helpful for exploring plant adaptation strategies. In this study, taking the national second-level key protection species, Ammopiptanthus mongolicus, as the research object, we set up 26 plots to collect samples, and measured the content of carbon (C), nitrogen (N), phosphorus (P) and water use efficiency (WUE) of leaves. We analyzed the relationship between leaf stoichiometric characteristics and WUE, and quantified the contributions of soil, climate, and water use efficiency to the variations of leaf stoichiometry. The results showed that C, N, and P contents in the leaves were (583.99±27.93), (24.31±2.09), and (1.83±0.06) mg·g-1, respectively. The coefficients of variation were 4.8%, 8.6%, and 3.2%, respectively, all belonging to weak variability, indicating that foliar contents of C, N and P tended to a certain stable value. The average value of N:P was 13.3, indicating that the growth of A. mongolicus was mainly limited by N. WUE was not correlated with leaf C content, but was significantly positively correlated with leaf N and P contents and N:P, and significantly negatively correlated with C:N and C:P, indicating that there was a linear synergistic trend between WUE and leaf nutrient content. The main factors influencing leaf C content and C:P were climatic factors, the leaf N content and N:P were mainly affected by soil factors, and the water use efficiency mainly affected leaf P content and C:N, indicating that the driving factors of different stoichiometric characteristics were different. The results could help eva-luate the habitat adaptation of desert plants, which would provide a theoretical basis for the conservation and management of A. mongolicus.

The Impact of High Intensity Focused Ultrasound (HIFU) on Tumor-Specific Immune Responses of Prostate Cancer.

High intensity focused ultrasound (HIFU), also referred to as focused ultrasound surgery (FUS), has garnered recent attention as a non-invasive therapeutic strategy for prostate cancer. It utilizes focused acoustic energy to achieve localized thermal ablation, while also potentially exerting immunomodulatory effects. This review aims to elucidate the mechanisms underlying how HIFU influences tumor-specific immune responses in prostate cancer. These mechanisms include the release of tumor-associated antigens and damage-associated molecular patterns, the activation of innate immune cells, the facilitation of antigen presentation to adaptive immune cells, the enhancement of activation and proliferation of tumor-specific cytotoxic T lymphocytes, and the attenuation of the immunosuppressive tumor microenvironment by reducing the activity of regulatory T cells and myeloid-derived suppressor cells. Both preclinical investigations and emerging clinical data in prostate cancer models highlight HIFU's potential to modulate the immune system, as evidenced by increased infiltration of effector immune cells, elevated levels of pro-inflammatory cytokines, and improved responsiveness to immune checkpoint inhibitors. HIFU induces immunogenic cell death, leading to the release of tumor antigens and danger signals that activate dendritic cells and facilitate cross-presentation to cytotoxic T cells. Additionally, FUS ablation reduces immunosuppressive cells and increases infiltration of CD8+ T cells into the tumor, reshaping the tumor microenvironment. By priming the immune system while overcoming immunosuppression, combining FUS with other immunotherapies like checkpoint inhibitors and cancer vaccines holds promise for synergistic anti-tumor effects. Despite challenges in optimizing parameters and identifying suitable patients, FUS represents a novel frontier by modulating the tumor microenvironment and enhancing anti-tumor immunity through a non-invasive approach.

Special Distribution of Nanoplastics in the Central Nervous System of Zebrafish during Early Development.

There is growing concern about the distribution of nanoplastics (NPs) in the central nervous system (CNS), whereas intrusion is poorly understood. In this study, fluorescent-labeled polystyrene NPs (PS-NPs) were microinjected into different areas of zebrafish embryo to mimic different routes of exposure. PS-NPs were observed in the brain, eyes, and spinal cord through gametal exposure. It indicated that maternally derived PS-NPs were specially distributed in the CNS of zebrafish during early development. Importantly, these NPs were stranded in the CNS but not transferred to other organs during development. Furthermore, using neuron GFP-labeled transgenic zebrafish, colocalization between NPs and the neuron cells revealed that NPs were mostly enriched in the CNS surrounded but not the neurons. Even so, the intrusion of NPs into the CNS induced the significant upregulation of some neurotransmitter receptors, leading to an inhibited effect on the movement of zebrafish larvae. This work provides insights into understanding the intrusion and distribution of NPs in the CNS and the subsequent potential adverse effects.

Performance-Enhanced Piezoelectric Micromachined Ultrasonic Transducers by PDMS Acoustic Lens Design.

This paper delves into enhancing the performance of ScAlN-based Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) through the implementation of Polydimethylsiloxane (PDMS) acoustic lenses. The PMUT, encapsulated in PDMS, underwent thorough characterization through the utilization of an industry-standard hydrophone calibration instrument. The experimental results showed that the ScAlN-based PMUT with the PDMS lenses achieved an impressive sensitivity of -160 dB (re: 1 V/µPa), an improvement of more than 8 dB compared to the PMUT with an equivalent PDMS film. There was a noticeable improvement in the -3 dB main lobe width within the frequency response when comparing the PMUT with PDMS encapsulation, both with and without lenses. The successful fabrication of high-performance PDMS lenses proved instrumental in significantly boosting the sensitivity of the PMUT. Comprehensive performance evaluations underscored that the designed PMUT in this investigation surpassed its counterparts reported in the literature and commercially available transducers. This encouraging outcome emphasizes its substantial potential for commercial applications.

Pharmacokinetic study and neuropharmacological effects of atractylenolide â ¢ to improve cognitive impairment via PI3K/AKT/GSK3ß pathway in intracerebroventricular-streptozotocin rats.

ETHNOPHARMACOLOGICAL RELEVANCE: The traditional Chinese herbal remedy Atractylodes macrocephala Koidz is renowned for its purported gastrointestinal regulatory properties and immune-enhancing capabilities. Atractylenolide III (ATL III), a prominent bioactive compound in Atractylodes macrocephala Koidz, has demonstrated significant pharmacological activities. However, its impact on neuroinflammation, oxidative stress, and therapeutic potential concerning Alzheimer's disease (AD) remain inadequately investigated. AIM OF THE STUDY: This study aims to assess the plasma pharmacokinetics of ATL III in Sprague-Dawley (SD) rats and elucidate its neuropharmacological effects on AD via the PI3K/AKT/GSK3ß pathway. Through this research, we endeavor to furnish experimental substantiation for the advancement of novel therapeutics centered on ATL III. MATERIALS AND METHODS: The pharmacokinetic profile of ATL III in SD rat plasma was analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). AD models were induced in SD rats through bilateral intracerebroventricular (ICV) administration of streptozotocin (STZ). ATL III was administered at doses of 0.6 mg/kg, 1.2 mg/kg, and 2.4 mg/kg, while donepezil (1 mg/kg) served as control. Cognitive function assessments were conducted employing behavioral tests including the Morris Water Maze and Novel Object Recognition. Neuronal pathology and histological changes were evaluated through Nissl staining and Hematoxylin-Eosin (HE) staining, respectively. Oxidative stress levels were determined by quantifying malondialdehyde (MDA) content and total superoxide dismutase (T-SOD) activity. Molecular docking analysis was employed to explore the direct binding between ATL III and its relevant targets, followed by validation using Western blot (WB) experiments to assess the expression of p-Tau, PI3K, AKT, GSK3ß, and their phosphorylated forms. RESULTS: Within the concentration range of 5-500 ng/mL, ATL III demonstrated exceptional linearity (R2 = 0.9991), with a quantification limit of 5 ng/mL. In male SD rats, ATL III exhibited a Tmax of 45 min, a t1/2 of 172.1 min, a Cmax of 1211 ng/L, and an AUC(0-t) of 156031 ng/L*min. Treatment with ATL III significantly attenuated Tau hyperphosphorylation in intracerebroventricular-streptozotocin (ICV-STZ) rats. Furthermore, ATL III administration mitigated neuroinflammation and oxidative stress, as evidenced by reduced Nissl body loss, alleviated histological alterations, decreased MDA content, and enhanced T-SOD activity. Molecular docking analyses revealed strong binding affinity between ATL III and the target genes PI3K, AKT, and GSK3ß. Experimental validation corroborated that ATL III stimulated the phosphorylation of PI3K and AKT while reducing the phosphorylation of GSK3ß. CONCLUSIONS: Our results indicate that ATL III can mitigate Tau protein phosphorylation through modulation of the PI3K/AKT/GSK3ß pathway. This attenuation consequently ameliorates neuroinflammation and oxidative stress, leading to enhanced learning and memory abilities in ICV-STZ rats.

Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.

BACKGROUND: Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. OBJECTIVE: We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. METHODS: Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. RESULTS: Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. CONCLUSION: The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

Exploring active ingredients and mechanisms of Coptidis Rhizoma-ginger against colon cancer using network pharmacology and molecular docking.

BACKGROUND: Colon cancer is the most prevalent and rapidly increasing malignancy globally. It has been suggested that some of the ingredients in the herb pair of Coptidis Rhizoma and ginger (Zingiber officinale), a traditional Chinese medicine, have potential anti-colon cancer properties. OBJECTIVE: This study aimed to investigate the molecular mechanisms underlying the effects of the Coptidis Rhizoma-ginger herb pair in treating colon cancer, using an integrated approach combining network pharmacology and molecular docking. METHODS: The ingredients of the herb pair Coptidis Rhizoma-ginger, along with their corresponding protein targets, were obtained from the Traditional Chinese Medicine System Pharmacology and Swiss Target Prediction databases. Target genes associated with colon cancer were retrieved from the GeneCards and OMIM databases. Then, the protein targets of the active ingredients in the herb pair were identified, and the disease-related overlapping targets were determined using the Venn online tool. The protein-protein interaction (PPI) network was constructed using STRING database and analyzed using Cytoscape 3.9.1 to identify key targets. Then, a compound-target-disease-pathway network map was constructed. The intersecting target genes were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses for colon cancer treatment. Molecular docking was performed using the Molecular Operating Environment (MOE) software to predict the binding affinity between the key targets and active compounds. RESULTS: Besides 1922 disease-related targets, 630 targets associated with 20 potential active compounds of the herb pair Coptidis Rhizoma-ginger were collected. Of these, 229 intersection targets were obtained. Forty key targets, including STAT3, Akt1, SRC, and HSP90AA1, were further analyzed using the ClueGO plugin in Cytoscape. These targets are involved in biological processes such as miRNA-mediated gene silencing, phosphatidylinositol 3-kinase (PI3K) signaling, and telomerase activity. KEGG enrichment analysis showed that PI3K-Akt and hypoxia-inducible factor 1 (HIF-1) signaling pathways were closely related to colon cancer prevention by the herb pair Coptidis Rhizoma-ginger. Ten genes (Akt1, TP53, STAT3, SRC, HSP90AA1, JAK2, CASP3, PTGS2, BCl2, and ESR1) were identified as key genes for validation through molecular docking simulation. CONCLUSIONS: This study demonstrated that the herb pair Coptidis Rhizoma-ginger exerted preventive effects against colon cancer by targeting multiple genes, utilizing various active compounds, and modulating multiple pathways. These findings might provide the basis for further investigations into the molecular mechanisms underlying the therapeutic effects of Coptidis Rhizoma-ginger in colon cancer treatment, potentially leading to the development of novel drugs for combating this disease.

Influence of the Pd Oxidation State in PdNi Thin Films on Surface Acoustic Wave Hydrogen Sensing Performance.

PdNi alloy thin films demonstrate exceptional hydrogen sensing performance and exhibit significant potential for application in surface acoustic wave (SAW) hydrogen sensors. However, the long-term stability of SAW H2 sensors utilizing PdNi films as catalysts experiences a substantial decrease during operation. In this paper, X-ray photoelectron spectroscopy (XPS) is employed to investigate the failure mechanisms of PdNi thin films under operational conditions. The XPS analysis reveals that the formation of PdO species on PdNi thin films plays a crucial role in the failure of hydrogen sensing. Additionally, density functional theory (DFT) calculations indicate that hydrogen atoms encounter a diffusion energy barrier during the penetration process from the PdNiOx surface to the subsurface region. The identification of PdNi film failure mechanisms through XPS and DFT offers valuable insights into the development of gas sensors with enhanced long-term stability. Guided by these mechanisms, we propose a method to restore the hydrogen sensing response time and magnitude to a certain extent by reducing the partially oxidized surface of the PdNi alloy under a hydrogen atmosphere at 70 °C, thereby restoring Pd to its metallic state with zero valence.

Palladium-Catalyzed Three-Component Cascade Carbonylation Reaction to Construct Benzofuran Derivatives.

A novel three-component cyclization carbonylation reaction of iodoarene-tethered propargyl ethers with amine and CO is reported. This palladium-catalyzed cascade reaction undergoes a sequence of oxidative addition, unsaturated bond migration, carbonyl insertion, and nucleophilic attack to deliver the benzofuran skeleton. Both aromatic amines and aliphatic amines could proceed smoothly in this transformation under one atm of CO.