Hydrogen sulfide (H2S) is a very toxic, acidic, and odorous gas. In this study, a calcined zeolite was used to investigate the adsorption performance of H2S. Among particle size, calcination temperature and time calcination temperature and time had significant effects on the adsorption capacity of H2S on the zeolite. The optimal calcination conditions for the zeolite were 332 °C, 1.8 h, and 10-20 mm size, and the maximum adsorption capacity of H2S was approximately 6219 mg kg-1. Calcination could broaden the channels, remove the adsorbed gases and impurities on the surface of zeolites, and increase the average pore size and point of zero net charge. As the zeolite adsorbed to saturation, it could be regenerated at the temperatures between 200 and 350 °C for 0.5 h. Compared with the natural zeolite, the adsorption capacities of dimethyl disulfide, dimethyl sulfide, toluene, CH3SH, CS2, CO2, and H2S were significantly higher on the calcined zeolite, while the adsorption capacity of CH4 was lower on the calcined zeolite. A gas treatment system by a temperature swing adsorption-regeneration process on honeycomb rotors with calcined zeolites was proposed. These findings are helpful for developing techniques for removing gas pollutants such as volatile sulfur compounds and volatile organic compounds to purify biogas and to limited toxic concentrations in the working environment.
BACKGROUND AND AIM: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. METHODS: In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. RESULTS: In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. CONCLUSIONS: Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.
For the flexible riser systems modeled with partial differential equations (PDEs), this article explores the boundary control problem in depth for the first time using a dynamic event-triggered mechanism (DETM). Given the intrinsic time-space coupling characteristic inherent in PDE computations, implementing a state-dependent DETM for PDE-based flexible risers presents a significant challenge. To overcome this difficulty, a novel dynamic event-triggered control method is introduced for flexible riser systems, focusing on optimizing available control inputs. In order to save computational costs from the controller to the actuator, a dynamic event-triggered adaptive boundary controller is designed to effectively reduce boundary position vibrations. Additionally, considering external disturbances, an adaptive bounded compensation term is incorporated to counteract the influence of external disturbances on the system. Addressing boundary position constraints, a new integral barrier Lyapunov function (iBLF) tailored specifically for flexible riser systems is introduced, thereby alleviating conservatism in the controller design of flexible risers modeled by PDEs. At last, the validity of the proposed method is demonstrated through a simulation example.
Neurotoxicity can cause a range of symptoms and disorders in humans, including neurodegenerative diseases, neurodevelopmental disorders, nerve conduction abnormalities, neuroinflammation, autoimmune disorders, and cognitive deficits. The cyclic guanosine-adenosine synthase (cGAS)-stimulator of interferon genes (STING) pathway and NF-κB pathway are two important signaling pathways involved in the innate immune response. The cGAS-STING pathway is activated by the recognition of intracellular DNA, which triggers the production of type I interferons and pro-inflammatory cytokines, such as tumor necrosis factor, IL-1ß, and IL-6. These cytokines play a role in oxidative stress and mitochondrial dysfunction in neurons. The NF-κB pathway is activated by various stimuli, such as bacterial lipopolysaccharide, viral particle components, and neurotoxins. NF-κB activation may lead to the production of pro-inflammatory cytokines, which promote neuroinflammation and cause neuronal damage. A potential interaction exists between the cGAS-STING and NF-κB pathways, and NF-κB activation blocks STING degradation by inhibiting microtubule-mediated STING transport. This review examines the progress of research on the roles of these pathways in neurotoxicity and their interrelationships. Understanding the mechanisms of these pathways will provide valuable therapeutic insights for preventing and controlling neurotoxicity.
Soft-lithography is widely used to fabricate microstructured surfaces on plastics and elastomers for designable physical properties such as wetting and adhesions. However, it remains a big challenge to construct high-aspect-ratio microstructures on the surface of hydrogels due to the difficulty in demolding from the gel with low strength and stiffness. Demonstrated here is the engineering of tough hydrogels by soft-lithography to form well-defined micropillars. The mechanical properties of poly(acrylamide-co-methacrylic acid) hydrogels with dense hydrogen-bond associations severely depend on temperature, with Young's modulus increasing from 8.1 MPa at 15 °C to 821.8 MPa at -30 °C, enabling easy demolding at low temperatures. Arrays of micropillars are maintained on the surface of the gel, and can be used at room temperature when the gel restores soft and stretchable. The hydrogel also exhibits good shape-memory property, favoring tailoring the morphology with a switchable tilt angle of micropillars. Consequently, the hydrogel shows tunable wetting and adhesion properties, as manifested by varying contact angles and adhesion strengths. These surface properties can also be tuned by geometry and arrangement of micropillars. This facile strategy by harnessing tunable viscoelasticity of supramolecular hydrogels should be applicable to other soft materials, and broaden their applications in biomedical and engineering fields.
The forest experience is good for people's physical and mental health. However, few studies on the effects of pure forest based on the duration and way of experience on people's physical and mental recovery. In this study, we took 180 first-year college students as research objects and conducted experiments in Pinus sylvestris and Betula platyphylla and the control group of grass plot. The changes of physiological and psychological indexes of the subjects were compared by two perception methods (onsite perception, video perception) and three perception duration (10 min, 20 min, 30 min). The results indicated that: (1) Differences between the two pure forests were mainly reflected in short-term recovery of diastolic blood pressure (DBP) and long-term recovery of total mood disorder (TMD). (2) Video perception was more conducive to short-term recovery of systolic blood pressure (SBP) and diastolic blood pressure (DBP). (3) Viewing the Pinus sylvestris for 20 min in different ways was the best way to relieve stress. It is suggested that, Pinus sylvestris can be used as the rehabilitation perception material, and reasonable path length or perception time can be selected for landscape construction in future. These results can provide scientific reference for landscape design based on forest health and environmental perception.
Blood Pressure , Forests , Humans , Male , Female , Young Adult , Blood Pressure/physiology , Betula , Perception , Pinus sylvestris , Adult , Time Factors
Lung adenocarcinoma (LUAD) is the most prevalent histological type of lung cancer. Previous studies have reported that specific long non-coding RNAs (lncRNAs) are involved in cancer development and progression. The phenotype and mechanism of ENST00000440028, named MSL3P1, a lncRNA which we referring to a cancer-testis gene with potential roles in tumorigenesis and progression, have not been reported. We found that MSL3P1 is overexpressed in LUAD tumor tissues, which is significantly associated with clinical characteristics, metastasis, and poor clinical prognosis. MSL3P1 promotes the metastasis of LUAD in vitro and in vivo. The enhancer reprogramming in LUAD tumor tissue is the major driver of the aberrantly expression of MSL3P1. Mechanistically, due to the competitive binding to CUL3 mRNA with ZFC3H1 protein (a protein involved in targeting polyadenylated RNA to exosomes and promoting the degradation of target mRNA), MSL3P1 can prevent the ZFC3H1-mediated RNA degradation of CUL3 mRNA and transport it to the cytoplasm. This activates the downstream epithelial-to-mesenchymal transition signaling pathway, and promote tumor invasion and metastasis. Implications: This study indicates that lncRNA MSL3P1 regulates CUL3 mRNA stability and promotes the metastasis and holds potential as a prognostic biomarker and therapeutic target in LUAD.
Gonadotropin-inhibitory hormone (GnIH) plays a critical role of reproduction in vertebrate since its discovery. Recently, a regulatory role of GnIH in appetite and the energy metabolism has emerged, despite its precise physiological mechanisms remain unknown. Thus, the present study evaluated the effects of a single or long-term GnIH treatments (administered via intraperitoneal injection) on the food intake, weight and glucolipid metabolism of chickens, while investigated the possible neuroendocrinology factors and its mechanism that involved in GnIH-induced obesity and glucolipid metabolism disorder. Our results showed that the intraperitoneal administration of GnIH to chickens resulted in marked body mass increased, hyperlipidemia, hyperglycemia and glucose intolerance. Subsequently, the results of metabolomics and pharmacological inhibition of 5-HT2C receptor studies revealed that blocked 5-HT2C receptor reinforced the effects of GnIH on food intake, body weight and the levels of blood glucose and lipid, resulted in GnIH-induced hyperglycaemia, hyperlipidemia and hepatic lipid deposition even worse, suggesting that peripheral 5-HT via 5-HT2C receptor may act as a negative feedback regulator to interplay with GnIH and jointly homeostatic control of energy balance in chickens. Our present study provide evidence of the cross-talk between GnIH and 5-HT in food intake and energy metabolism at the in vivo pharmacological level and to propose a molecular basis for these interactions, suggesting that functional interaction between GnIH and 5-HT may open new avenues to understand the mechanism of neuroendocrine network involved in appetite and energy metabolism as well as provide a new therapeutic strategy to prevent obesity, diabetes and metabolic disorders.
BACKGROUND: Flow diverter devices (FDs) are increasingly used for treating unruptured intracranial aneurysms (UIAs), but limited studies compared different FDs. OBJECTIVE: To conduct a propensity score matched analysis comparing the Pipeline embolization device (PED) and Tubridge embolization device (TED) for UIAs. METHODS: Patients with UIAs treated with either PED or TED between July 2016 and July 2022 were included. Propensity score matching was performed to adjust for age, sex, comorbidities, smoking, drinking, aneurysm size, morphology, neck, location, parent artery diameter, adjunctive coiling, and angiographic follow-up duration. Perioperative complications and clinical and angiographic outcomes were compared after matching. RESULTS: 735 patients treated by PED and 290 patients treated by TED were enrolled. Compared with the PED group, patients in the TED group had a greater number of women and patients with ischemia, a smaller proportion of vertebrobasilar and non-saccular aneurysms, a smaller size and neck, and fewer adjunctive coils and overlapping stents, but a larger parent artery diameter and lumen disparities. After adjusting for these differences, 275 pairs were matched. No differences were found in perioperative complications (4.4% vs 2.5%, P=0.350), in-stent stenosis (16.0% vs 15.6%, P>0.999), or favorable prognosis (98.9% vs 98.5%, P>0.999). However, PED showed a trend towards better complete occlusion over a median 8-month angiographic follow-up (81.8% vs 75.3%, P=0.077). CONCLUSION: Compared with PED, TED provides a comparable rate of perioperative and short-term outcomes. Nevertheless, a better occlusion status in the PED group needs to be further verified over a longer follow-up period.
BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC. MATERIALS AND METHODS: Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined. RESULTS: The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer-Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56). CONCLUSION: This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients' survival, utilizing intratumoral and peritumoral radiomics features.
During the process of malignant tumor treatment, photodynamic therapy (PDT) exerts poor efficacy due to the hypoxic environment of the tumor cells, and long-time chemotherapy reduces the sensitivity of tumor cells to chemotherapy drugs due to the presence of drug-resistant proteins on the cell membranes for drug outward transportation. Therefore, we reported a nano platform based on mesoporous silica coated with polydopamine (MSN@PDA) loading PDT enhancer MnO2, photosensitizer indocyanine green (ICG) and chemotherapeutic drug doxorubicin (DOX) (designated as DMPIM) to achieve a sequential release of different drugs to enhance treatment of malignant tumors. MSN was first synthesized by a template method, then DOX was loaded into the mesoporous channels of MSN, and locked by the PDA coating. Next, ICG was modified by π-π stacking on PDA, and finally, MnO2layer was accumulated on the surface of DOX@MSN@PDA- ICG@MnO2, achieving orthogonal loading and sequential release of different drugs. DMPIM first generated oxygen (O2) through the reaction between MnO2and H2O2after entering tumor cells, alleviating the hypoxic environment of tumors and enhancing the PDT effect of sequentially released ICG. Afterwards, ICG reacted with O2in tumor tissue to produce reactive oxygen species, promoting lysosomal escape of drugs and inactivation of p-glycoprotein (p-gp) on tumor cell membranes. DOX loaded in the MSN channels exhibited a delay of approximately 8 h after ICG release to exert the enhanced chemotherapy effect. The drug delivery system achieved effective sequential release and multimodal combination therapy, which achieved ideal therapeutic effects on malignant tumors. This work offers a route to a sequential drug release for advancing the treatment of malignant tumors.
Doxorubicin , Drug Liberation , Indocyanine Green , Indoles , Manganese Compounds , Oxides , Photochemotherapy , Photosensitizing Agents , Polymers , Photochemotherapy/methods , Doxorubicin/chemistry , Doxorubicin/pharmacology , Doxorubicin/administration & dosage , Indocyanine Green/chemistry , Indoles/chemistry , Animals , Manganese Compounds/chemistry , Humans , Polymers/chemistry , Cell Line, Tumor , Oxides/chemistry , Photosensitizing Agents/chemistry , Silicon Dioxide/chemistry , Mice , Antineoplastic Agents/chemistry , Antineoplastic Agents/pharmacology , Antineoplastic Agents/administration & dosage , Neoplasms/drug therapy , Reactive Oxygen Species/metabolism , Drug Delivery Systems , Nanoparticles/chemistry , Drug Carriers/chemistry , Porosity
PURPOSE: Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies. METHODS: Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets' machine learning prediction with important features and rationales, generating interpretable analytical reports. RESULTS: In this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers. CONCLUSION: The novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.
Antibodies, Bispecific , Machine Learning , Antibodies, Bispecific/pharmacology , Humans , Neoplasms/drug therapy , Neoplasms/immunology
The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.
Adenocarcinoma of Lung , Drug Resistance, Neoplasm , Gefitinib , Lung Neoplasms , RNA, Long Noncoding , Tumor Microenvironment , Humans , Tumor Microenvironment/genetics , Tumor Microenvironment/immunology , Gefitinib/therapeutic use , Gefitinib/pharmacology , RNA, Long Noncoding/genetics , Adenocarcinoma of Lung/genetics , Adenocarcinoma of Lung/drug therapy , Adenocarcinoma of Lung/pathology , Adenocarcinoma of Lung/metabolism , Prognosis , Lung Neoplasms/drug therapy , Lung Neoplasms/genetics , Lung Neoplasms/pathology , Drug Resistance, Neoplasm/genetics , Nomograms , Female , Male , Gene Expression Regulation, Neoplastic , Antineoplastic Agents/therapeutic use , Antineoplastic Agents/pharmacology , Middle Aged , Aged
The development and performance of two mass spectrometry (MS) workflows for the intraoperative diagnosis of isocitrate dehydrogenase (IDH) mutations in glioma is implemented by independent teams at Mayo Clinic, Jacksonville, and Huashan Hospital, Shanghai. The infiltrative nature of gliomas makes rapid diagnosis necessary to guide the extent of surgical resection of central nervous system (CNS) tumors. The combination of tissue biopsy and MS analysis used here satisfies this requirement. The key feature of both described methods is the use of tandem MS to measure the oncometabolite 2-hydroxyglutarate (2HG) relative to endogenous glutamate (Glu) to characterize the presence of mutant tumor. The experiments i) provide IDH mutation status for individual patients and ii) demonstrate a strong correlation of 2HG signals with tumor infiltration. The measured ratio of 2HG to Glu correlates with IDH-mutant (IDH-mut) glioma (P < 0.0001) in the tumor core data of both teams. Despite using different ionization methods and different mass spectrometers, comparable performance in determining IDH mutations from core tumor biopsies was achieved with sensitivities, specificities, and accuracies all at 100%. None of the 31 patients at Mayo Clinic or the 74 patients at Huashan Hospital were misclassified when analyzing tumor core biopsies. Robustness of the methodology was evaluated by postoperative re-examination of samples. Both teams noted the presence of high concentrations of 2HG at surgical margins, supporting future use of intraoperative MS to monitor for clean surgical margins. The power of MS diagnostics is shown in resolving contradictory clinical features, e.g., in distinguishing gliosis from IDH-mut glioma.
Brain Neoplasms , Glioma , Isocitrate Dehydrogenase , Mutation , Glioma/genetics , Glioma/surgery , Glioma/pathology , Isocitrate Dehydrogenase/genetics , Humans , Brain Neoplasms/genetics , Brain Neoplasms/surgery , Brain Neoplasms/pathology , Tandem Mass Spectrometry/methods , Glutarates/metabolism , Mass Spectrometry/methods , Glutamic Acid/metabolism , Glutamic Acid/genetics
The special properties of rare earth elements have effectively broadened their application fields. How to accurately recognize and efficiently separate target rare earth ions with similar radii and chemical properties remains a formidable challenge. Here, we constructed precise two-dimensional (2D) heterogeneous channels using engineered E. coli membranes between graphene oxidse (GO) layers. The difference in binding ability and corresponding conformational change between Lanmodulin (LanM) and rare earth ions in the heterogeneous channel allow for precisely recognizing and sieving of scandium ions (Sc3+). The engineered E. coli membranes not only can protect the integrity of structure and functionality of LanM, the rich lipids and sugars but also help the E. coli membranes closely tile on the GO nanosheets through interaction, preventing swelling and controlling interlayer spacing accurately down to the sub-nanometer. Apparently, the 2D heterogeneous channels showcase excellent selectivity for trivalent ions (SFFe 3+ /Sc 3+≈3), especially for Sc3+ ions in rare earth elements with a high selectivity (SFCe/Sc≈167, SFLa/Sc≈103). The long-term stability and tensile strain tests verify the membrane's outstanding stability. Thus, this simple, efficient, and cost-effective work provides a suggestion for constructing two-dimensional interlayer heterogeneous channels for precise sieving, and the valuable strategy is proposed for the efficient extraction of Sc. This article is protected by copyright. All rights reserved.
In this study, we investigated the molecular mechanisms underlying the impact of extracellular vesicles (EVs) derived from bone marrow stromal cells (BMSCs) on colorectal cancer (CRC) development. The focus was on the role of MAGI2-AS3, delivered by BMSC-EVs, in regulating USP6NL DNA methylation-mediated MYC protein translation modification to promote CDK2 downregulation. Utilizing bioinformatics analysis, we identified significant enrichment of MAGI2-AS3 related to copper-induced cell death in CRC. In vitro experiments demonstrated the downregulation of MAGI2-AS3 in CRC cells, and BMSC-EVs were found to deliver MAGI2-AS3 to inhibit CRC cell proliferation, migration, and invasion. Further exploration revealed that MAGI2-AS3 suppressed MYC protein translation modification by regulating USP6NL DNA methylation, leading to CDK2 downregulation and prevention of colorectal cancer. Overexpression of MYC reversed the functional effects of BMSC-EVs-MAGI2-AS3. In vivo experiments validated the inhibitory impact of BMSC-EVs-MAGI2-AS3 on CRC tumorigenicity by promoting CDK2 downregulation through USP6NL DNA methylation-mediated MYC protein translation modification. Overall, BMSC-EVs-MAGI2-AS3 may serve as a potential intervention to prevent CRC occurrence by modulating key molecular pathways.
Protein ubiquitination regulates a wide range of cellular processes. The degree of protein ubiquitination is determined by the delicate balance between ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase (DUB)-mediated deubiquitination. In comparison to the E3-substrate interactions, the DUB-substrate interactions (DSIs) remain insufficiently investigated. To address this challenge, we introduce a protein sequence-based ab initio method, TransDSI, which transfers proteome-scale evolutionary information to predict unknown DSIs despite inadequate training datasets. An explainable module is integrated to suggest the critical protein regions for DSIs while predicting DSIs. TransDSI outperforms multiple machine learning strategies against both cross-validation and independent test. Two predicted DUBs (USP11 and USP20) for FOXP3 are validated by "wet lab" experiments, along with two predicted substrates (AR and p53) for USP22. TransDSI provides new functional perspective on proteins by identifying regulatory DSIs, and offers clues for potential tumor drug target discovery and precision drug application.
Deubiquitinating Enzymes , Proteome , Ubiquitination , Humans , Proteome/metabolism , Deubiquitinating Enzymes/metabolism , Deubiquitinating Enzymes/genetics , Deep Learning , Ubiquitin Thiolesterase/metabolism , Ubiquitin Thiolesterase/genetics , Ubiquitin Thiolesterase/chemistry , Substrate Specificity , Forkhead Transcription Factors/metabolism , Forkhead Transcription Factors/genetics , Tumor Suppressor Protein p53/metabolism , Tumor Suppressor Protein p53/genetics , Machine Learning , Protein Binding , Amino Acid Sequence , Thiolester Hydrolases
China proposed establishing a carbon emission trading market in its 12th Five-Year Plan to reduce carbon dioxide emissions through market mechanisms, promote the development of science and technology and help China become an environment-friendly country. To examine the impact of carbon emission trading on green technology innovation in Chinese energy enterprises, data from 1993 to 2020 were collected from 494 A-share-listed energy enterprises. Enterprises located in the pilot area of carbon emissions trading were assigned to the treatment group, while those in the non-pilot area were assigned to the control group. The propensity-score-matching method was utilized to match the treatment group with the control group, and the resulting samples were used as the actual sample data. The difference-in-differences method was then employed to assess the net impact of carbon emission trading and investigate its effect on green technology innovation in energy enterprises. This empirical study suggested that carbon emission trading has a positive impact on green technology innovation in energy enterprises, particularly state-owned ones. Larger enterprises are more willing to engage in green technological innovation than small enterprises. Furthermore, when faced with a carbon emission trading system, 'mature' companies tend to pay more attention to green technology innovation than younger enterprises do. This study puts forward policy measures for establishing a national-level carbon emission market in China in the future.
The impact of early life exposure to residential greenness on childhood rhinitis and its interaction with particulate matter (PM) of different size fractions remain inconsistent. Herein, we recruited 40,486 preschool children from randomly selected daycare centers in 7 cities in China from 2019 to 2020, and estimated exposure to residential greenness by the normalized difference vegetation index (NDVI) with a 500â¯m buffer. Exposure to ambient PM (PM1, PM2.5, and PM10) was evaluated using a satellite-based prediction model (daily, at a resolution of 1â¯kmâ¯×â¯1â¯km). By mixed-effect logistic regression, NDVI values during pregnancy, in the first (0-1â¯year old) and the second (1-2â¯years old) year of life were negatively associated with lifetime rhinitis (LR) and current rhinitis (CR) (Pâ¯<â¯0.001). PM in the same time windows was associated with increased risks of LR and CR in children, with smaller size fraction of PM showing greater associations. The negative associations between prenatal and postnatal NDVI and LR and CR in preschool children remained robust after adjusting for concomitant exposure to PM, whereas the associations of postnatal NDVI and rhinitis showed significant interactions with PM. At lower levels of PM, postnatal NDVI remained negatively associated with rhinitis and was partly mediated by PM (10.0-40.9â¯%), while at higher levels of PM, the negative associations disappeared or even turned positive. The cut-off levels of PM were identified for each size fraction of PM. In conclusion, prenatal exposure to greenness had robust impacts in lowering the risk of childhood rhinitis, while postnatal exposure to greenness depended on the co-exposure levels to PM. This study revealed the complex interplay of greenness and PM on rhinitis in children. The exposure time window in prenatal or postnatal period and postnatal concomitant PM levels played important roles in influencing the associations between greenness, PM and rhinitis.
Mirikizumab is a humanized anti-interleukin-23p19 monoclonal antibody being developed for ulcerative colitis (UC) and Crohn's disease. We characterized the relationship of mirikizumab systemic exposure with efficacy and safety end points in patients with UC using phase II (NCT02589665) and III (NCT03518086, NCT03524092) trial data. Exposure-response models were developed for clinical remission, clinical response, endoscopic remission, and change in modified Mayo score following induction (50-1,000 mg i.v. every 4 weeks) and maintenance (200 mg s.c. every 4 or 12 weeks) treatment. These models evaluated observed and pharmacokinetic model-predicted mirikizumab exposures as the exposure measure. Key safety event rates were compared across mirikizumab exposure quartiles in the phase III trial. Mirikizumab efficacy in patients with UC showed an apparent positive association with systemic exposure following both induction and maintenance. However, further analysis found this relationship to be overstated by the presence of confounding factors that were not among the tested patient covariates. While prior biologic experience and baseline disease severity showed statistically significant influences on estimated placebo effect, no patient factors affected the mirikizumab effect parameters in any of the phase III exposure-response models. There was no apparent mirikizumab concentration relationship with any adverse event of special interest. When the phase II and III data and confounding are considered together, efficacy was unlikely to be strongly affected by variation in exposures across individual patients at the phase III dose. Together with the previously demonstrated mirikizumab exposure insensitivity to patient factors, these findings indicate that mirikizumab dose adjustment to patient characteristics is not required.
