LanCLs add glutathione to dehydroamino acids generated at phosphorylated sites in the proteome.

Enzyme-mediated damage repair or mitigation, while common for nucleic acids, is rare for proteins. Examples of protein damage are elimination of phosphorylated Ser/Thr to dehydroalanine/dehydrobutyrine (Dha/Dhb) in pathogenesis and aging. Bacterial LanC enzymes use Dha/Dhb to form carbon-sulfur linkages in antimicrobial peptides, but the functions of eukaryotic LanC-like (LanCL) counterparts are unknown. We show that LanCLs catalyze the addition of glutathione to Dha/Dhb in proteins, driving irreversible C-glutathionylation. Chemo-enzymatic methods were developed to site-selectively incorporate Dha/Dhb at phospho-regulated sites in kinases. In human MAPK-MEK1, such "elimination damage" generated aberrantly activated kinases, which were deactivated by LanCL-mediated C-glutathionylation. Surveys of endogenous proteins bearing damage from elimination (the eliminylome) also suggest it is a source of electrophilic reactivity. LanCLs thus remove these reactive electrophiles and their potentially dysregulatory effects from the proteome. As knockout of LanCL in mice can result in premature death, repair of this kind of protein damage appears important physiologically.

A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches.

Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for complex diseases. Traditional biological experiments struggle to keep pace with the growing number of known proteins. With the development of high-throughput sequencing technology, a wide variety of biological data provides the possibility to accurately predict protein functions by computational methods. Consequently, many computational methods have been proposed. Due to the diversity of application scenarios, it is necessary to conduct a comprehensive evaluation of these computational methods to determine the suitability of each algorithm for specific cases. In this study, we present a comprehensive benchmark, BeProf, to process data and evaluate representative computational methods. We first collect the latest datasets and analyze the data characteristics. Then, we investigate and summarize 17 state-of-the-art computational methods. Finally, we propose a novel comprehensive evaluation metric, design eight application scenarios and evaluate the performance of existing methods on these scenarios. Based on the evaluation, we provide practical recommendations for different scenarios, enabling users to select the most suitable method for their specific needs. All of these servers can be obtained from https://csuligroup.com/BEPROF and https://github.com/CSUBioGroup/BEPROF.

GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation.

The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding lncRNA functions. Most of existing lncRNA subcellular localization prediction methods use k-mer frequency features to encode lncRNA sequences. However, k-mer frequency features lose sequence order information and fail to capture sequence patterns and motifs of different lengths. In this paper, we proposed GraphLncLoc, a graph convolutional network-based deep learning model, for predicting lncRNA subcellular localization. Unlike previous studies encoding lncRNA sequences by using k-mer frequency features, GraphLncLoc transforms lncRNA sequences into de Bruijn graphs, which transforms the sequence classification problem into a graph classification problem. To extract the high-level features from the de Bruijn graph, GraphLncLoc employs graph convolutional networks to learn latent representations. Then, the high-level feature vectors derived from de Bruijn graph are fed into a fully connected layer to perform the prediction task. Extensive experiments show that GraphLncLoc achieves better performance than traditional machine learning models and existing predictors. In addition, our analyses show that transforming sequences into graphs has more distinguishable features and is more robust than k-mer frequency features. The case study shows that GraphLncLoc can uncover important motifs for nucleus subcellular localization. GraphLncLoc web server is available at http://csuligroup.com:8000/GraphLncLoc/.

CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features.

Circular RNAs (circRNAs) are reverse-spliced and covalently closed RNAs. Their interactions with RNA-binding proteins (RBPs) have multiple effects on the progress of many diseases. Some computational methods are proposed to identify RBP binding sites on circRNAs but suffer from insufficient accuracy, robustness and explanation. In this study, we first take the characteristics of both RNA and RBP into consideration. We propose a method for discriminating circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features, called CRMSS. For circRNAs, we use sequence ${k}\hbox{-}{mer}$ embedding and the forming probabilities of local secondary structures as features. For RBPs, we combine sequence and structure frequencies of RNA-binding domain regions to generate features. We capture binding patterns with multi-scale residual blocks. With BiLSTM and attention mechanism, we obtain the contextual information of high-level representation for circRNA-RBP binding. To validate the effectiveness of CRMSS, we compare its predictive performance with other methods on 37 RBPs. Taking the properties of both circRNAs and RBPs into account, CRMSS achieves superior performance over state-of-the-art methods. In the case study, our model provides reliable predictions and correctly identifies experimentally verified circRNA-RBP pairs. The code of CRMSS is freely available at https://github.com/BioinformaticsCSU/CRMSS.

Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network.

Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.

A review of enzyme design in catalytic stability by artificial intelligence.

The design of enzyme catalytic stability is of great significance in medicine and industry. However, traditional methods are time-consuming and costly. Hence, a growing number of complementary computational tools have been developed, e.g. ESMFold, AlphaFold2, Rosetta, RosettaFold, FireProt, ProteinMPNN. They are proposed for algorithm-driven and data-driven enzyme design through artificial intelligence (AI) algorithms including natural language processing, machine learning, deep learning, variational autoencoder/generative adversarial network, message passing neural network (MPNN). In addition, the challenges of design of enzyme catalytic stability include insufficient structured data, large sequence search space, inaccurate quantitative prediction, low efficiency in experimental validation and a cumbersome design process. The first principle of the enzyme catalytic stability design is to treat amino acids as the basic element. By designing the sequence of an enzyme, the flexibility and stability of the structure are adjusted, thus controlling the catalytic stability of the enzyme in a specific industrial environment or in an organism. Common indicators of design goals include the change in denaturation energy (ΔΔG), melting temperature (ΔTm), optimal temperature (Topt), optimal pH (pHopt), etc. In this review, we summarized and evaluated the enzyme design in catalytic stability by AI in terms of mechanism, strategy, data, labeling, coding, prediction, testing, unit, integration and prospect.

Automatic ICD-10-CM coding via Lambda-Scaled attention based deep learning model.

The International Classification of Diseases (ICD) serves as a global healthcare administration standard, with one of its editions being ICD-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites, co-morbidities, and causes. These additions facilitate conveying the complexities of various diseases. Currently, ICD-10 coding is widely adopted worldwide. However, public hospitals in Pakistan have yet to implement it and automate the coding process. In this research, we implemented ICD-10-CM coding for a private database and named it Clinical Pool of Liver Transplant (CPLT). Additionally, we proposed a novel deep learning model called Deep Recurrent-Convolution Neural Network with a lambda-scaled Attention module (DRCNN-ATT) using the CPLT database to achieve automatic ICD-10-CM coding. DRCNN-ATT combines a bi-directional long short-term memory network (bi-LSTM), a multi-scale convolutional neural network (MS-CNN), and a lambda-scaled attention module. Experimental results demonstrate that deep recurrent convolutional neural network (DRCNN) faces attention score vanishing problem with a standard attention module for automatic ICD coding. However, adding a lambda-scaled attention module resolves this issue. We evaluated DRCNN-ATT model using two distinct datasets: a private CPLT dataset and a public MIMIC III top 50 dataset. The results indicate that the DRCNN-ATT model outperformed various baselines by generating 0.862 micro F1 and 0.25 macro F1 scores on CPLT dataset and 0.705 micro F1 and 0.655 macro F1 scores on MIMIC III top 50 dataset. Furthermore, we also deployed our model for automatic ICD-10-CM coding using ngrok and the Flask APIs, which receives input, processes it, and then returns the results.

A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods.

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics.

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding.

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions. RESULTS: In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines. AVAILABILITY AND IMPLEMENTATION: The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.

MOTIVATION: There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration. RESULTS: In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance. AVAILABILITY AND IMPLEMENTATION: The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.

LncRNA ZFAS1 Alleviated NLRP3 Inflammasome-Mediated Pyroptosis through Regulating miR-96-5p/Smad7 Signaling in Allergic Rhinitis.

INTRODUCTION: The NLR family pyrin domain containing 3 (NLRP3)-mediated pyroptosis was positively correlated with the allergic rhinitis progression and was reported to be regulated by SMAD family member 7 (Smad7). Bioinformatics analysis revealed that Smad7 might be targeted by miR-96-5p, and miR-96-5p might be targeted by long noncoding RNA zinc finger antisense 1 (ZFAS1). However, the effects and regulatory mechanisms of the ZFAS1/miR-96-5p/Smad7 functional axis in allergic rhinitis have not been investigated. METHODS: Human nasal mucosa epithelial cell line RPMI 2650 and C57BL/6 mice were obtained for in vitro and in vivo studies. Dual-luciferase reporter assay and RNA immunoprecipitation were implemented for detecting molecular interactions. Cell counting kit-8 and flow cytometry were used for measuring cell viability and pyroptosis. ELISA was obtained for monitoring cytokine secretion. RT-qPCR and Western blot were examined for determining RNA and protein expression. RESULTS: In vitro studies revealed that ZFAS1 was downregulated in interleukin (IL)-13-treated RPMI 2650 cells, while overexpression of ZFAS1 enhanced cell viability and inhibited NLRP3-mediated pyroptosis and inflammatory response. ZFAS1 directly inhibited miR-96-5p to suppress NLRP3-mediated pyroptosis in IL-13-treated RPMI 2650 cells. MiR-96-5p bound to the 3'-untranslated region of Smad7 and knockdown of Smad7 significantly reversed the effects of miR-96-5p depletion. Moreover, in vivo experiments further confirmed the findings of in vitro studies and showed ZFAS1 overexpression or miR-96-5p inhibition alleviated allergic rhinitis in vivo. CONCLUSION: ZFAS1 downregulated the expression of miR-96-5p to upregulate Smad7 level, which subsequently inhibited NLRP3-mediated pyroptosis and inflammatory response to ameliorate allergic rhinitis.

TFAP2C inhibits cell autophagy to alleviate myocardial ischemia/reperfusion injury by regulating miR-23a-5p/SFRP5/Wnt5a axis.

Myocardial ischemia/reperfusion (MI/R) injury contributes to severe injury for cardiomyocytes. In this study, we aimed to explore the underlying mechanism of TFAP2C on cell autophagy in MI/R injury. MTT assay measured cell viability. The cells injury was evaluated by commercial kits. IF detected the level of LC3B. Dual luciferase reporter gene assay, ChIP or RIP assay were performed to verify the interactions between crucial molecules. We found that TFAP2C and SFRP5 expression were decreased while miR-23a-5p and Wnt5a increased in AC16 cells in response to H/R condition. H/R induction led to cell injury and induced autophagy, which were reversed by TFAP2C overexpression or 3-MA treatment (an autophagy inhibitor). Mechanistically, TFAP2C suppressed miR-23a expression through binding to miR-23a promoter, and SFRP5 was a target gene of miR-23a-5p. Moreover, miR-23a-5p overexpression or rapamycin reversed the protective impacts of TFAP2C overexpression on cells injury and autophagy upon H/R condition. In conclusion, TFAP2C inhibited autophagy to improve H/R-induced cells injury by mediating miR-23a-5p/SFRP5/Wnt5a axis.

Nitrogen-Based Gas Molecule Adsorption on a ReSe2 Monolayer via Single-Atom Doping: A First-Principles Study.

Two-dimensional materials have shown immense promise for gas-sensing applications due to their remarkable surface-to-volume ratios and tunable chemical properties. However, despite their potential, the utilization of ReSe2 as a gas-sensing material for nitrogen-containing molecules, including NO2, NO, and NH3, has remained unexplored. The choice of doping atoms in ReSe2 plays a pivotal role in enhancing the gas adsorption and gas-sensing capabilities. Herein, the adsorption properties of nitrogen-containing gas molecules on metal and non-metal single-atom (Au, Pt, Ni, P, and S)-doped ReSe2 monolayers have been evaluated systematically via ab initio calculations based on density functional theory. The findings strongly suggest that intrinsic ReSe2 has better selectivity toward NO2 than toward NO and NH3. Moreover, our results provide compelling evidence that all of the dopants, with the exception of S, significantly enhance both the adsorption strength and charge transfer between ReSe2 and the investigated molecules. Notably, P-decorated ReSe2 showed the highest adsorption energy for NO2 and NO (-1.93 and -1.52 eV, respectively) with charge transfer above 0.5e, while Ni-decorated ReSe2 exhibited the highest adsorption energy for NH3 (-0.76 eV). In addition, on the basis of transition theory, we found that only Au-ReSe2 and Ni-ReSe2 can serve as reusable chemiresisitve gas sensors for reliable detection of NO and NH3, respectively. Hence, our findings indicate that gas-sensing applications can be significantly improved by utilizing a single-atom-doped ReSe2 monolayer.

Dexmedetomidine Prevents Chronic Incisional Pain After Brain Tumor Resection: A Secondary Analysis of the Randomized Control Trial.

BACKGROUND: Dexmedetomidine was reported to reduce postoperative acute pain after neurosurgery. However, the efficacy of dexmedetomidine for preventing chronic incisional pain is uncertain. METHODS: This article is a secondary analysis of a randomized, double-blind, placebo-controlled trial. Eligible patients were randomly allocated to either the dexmedetomidine group or the placebo group. Patients assigned to the dexmedetomidine group were given a 0.6 µg kg -1 dexmedetomidine bolus followed by a 0.4 µg kg -1 h -1 maintenance dose until dural closure; placebo patients were given comparable amounts of normal saline. The primary end point was the incidence of incisional pain at 3 months after craniotomy evaluated by numerical rating scale scores and defined as any score >0. The secondary end points were postoperative acute pain scores, sleep quality, and Short-Form McGill Pain Questionnaire (SF-MPQ-2) at 3 months after craniotomy. RESULTS: From January 2021 to December 2021, a total of 252 patients were included in the final analysis: the dexmedetomidine group (n = 128) and the placebo group (n = 124). The incidence of chronic incisional pain was 23.4% (30 of 128) in the dexmedetomidine group versus 42.7% (53 of 124) in the placebo group (risk ratio, 0.55; 95% confidence interval, 0.38-0.80; P = .001). The overall severity of chronic incisional pain was mild in both groups. Patients in the dexmedetomidine group had lower acute pain severity on movement than those in the placebo group for the first 3 days after surgery (all adjusted P < .01). Sleep quality did not differ between groups. However, the SF-MPQ-2 total sensory ( P = .01) and neuropathic pain descriptor ( P = .023) scores in the dexmedetomidine group were lower than those in the placebo group. CONCLUSIONS: Prophylactic intraoperative dexmedetomidine infusion reduces the incidence of chronic incisional pain as well as acute pain score after elective brain tumor resections.

Thermostable vacuum foam dried Newcastle disease vaccine: Process optimization and pilot-scale study.

Vacuum foam drying (VFD) has been shown to improve the thermostability and long-term shelf life of Newcastle Disease Virus (NDV). This study optimized the VFD process to improve the shelf life of NDV at laboratory-scale and then tested the optimized conditions at pilot-scale. The optimal NDV to T5 formulation ratio was determined to be 1:1 or 3:2. Using the 1:1 virus to formulation ratio, the optimal filling volumes were determined to be 13-17% of the vial capacity. The optimized VFD process conditions were determined to be at a shelf temperature of 25℃ with a minimum overall drying time of 44 h. The vaccine samples prepared using these optimized conditions at laboratory-scale exhibited virus titer losses of ≤ 1.0 log10 with residual moisture content (RMC) below 3%. Furthermore, these samples were transported for 97 days around China at ambient temperature without significant titer loss, thus demonstrating the thermostability of the NDV-VFD vaccine. Pilot-scale testing of the NDV-VFD vaccine at optimized conditions showed promising results for up-scaling the process as the RMC was below 3%. However, the virus titer loss was slightly above 1.0 log10 (approximately 1.1 log10). Therefore, the NDV-VFD process requires further optimization at pilot scale to obtain a titer loss of ≤ 1.0 log10. Results from this study provide important guidance for possible industrialization of NDV-VFD vaccine in the future. KEY POINTS: • The process optimization and scale-up test of thermostable NDV vaccine prepared through VFD is reported for the first time in this study. • The live attenuated NDV-VFD vaccine maintained thermostability for 97 days during long distance transportation in summer without cold chain conditions. • The optimized NDV-VFD vaccine preparations evaluated at pilot-scale maintained acceptable levels of infectivity after preservation at 37℃ for 90 days, which demonstrated the feasibility of the vaccine for industrialization.

Key point calibrating and clustering for hard example mining of dense analogs in the fish-eye lens.

Recent years have witnessed widespread applications of the fish-eye lens with a wide field-of-view. However, its inherent distortion poses a big challenge to the intelligent recognition of dense analogs (IRDA) by convolutional neural networks (CNN). The major bottleneck of existing CNN models lies in their limited modeling capacity for distorted objects in fish-eye images, leading to the misclassification of hard examples. To further improve the accuracy of IRDA, we propose a novel key point calibrating and clustering (KPCC) algorithm based on the hemispherical projection model. Our method can effectively correct the hard example misclassification predicted by the CNN, significantly enhancing the performance of the IRDA. The experiments show that, as a light-weight computation calibrating and stable adaptive clustering method, the KPCC increases the precision and recall rate of IRDA on the intelligent retail dataset by 8.55% and 8.07%, respectively; compared with the classic Focalloss, QFocalloss, and OHEM (online hard example mining), it can mine hard examples more sufficiently, especially in the scene of distorted dense analog detection.

Risk/benefit trade-off of habitual physical activity and air pollution on mortality: A large-scale prospective analysis in the UK Biobank.

BACKGROUND: Previous observational studies have indicated associations of physical activity (PA) and air pollution with mortality. A few studies have evaluated air pollution and PA interactions for health. Still, the trade-off between the harmful effects of air pollution exposure and the protective effects of PA remains controversial and unclear. OBJECTIVE: This study aimed to investigate the joint association of air pollution and PA with mortality risks. METHODS: This prospective cohort study included 322,092 participants from 2006 to 2010 and followed up to 2021 in the UK Biobank study. The concentrations of air pollutants (2006-2010), including particulate matter (PM) with diameters <=2.5 mm (PM2.5), <=10 mm (PM10), and between 2.5 and 10 mm (PM2.5-10), and nitrogen oxides (NO2 and NOx) were obtained. Information on PA measured by the International Physical Activity Questionnaire short form (2006-2010) and wrist-worn accelerometer (2013-2015) were collected. All-cause and cause-specific mortalities were recorded. Cox proportional hazard models were used to investigate the associations of air pollution exposure and PA with mortality risks. The additive and multiplicative interactions were also examined. RESULTS: During a mean follow-up of 11.83 years, 16629 deaths were recorded. Compared with participants reporting low PA, higher PA was negatively associated with all-cause [hazard ratio (HR), 0.74; 95% CI, 0.71-0.78], cancer (HR, 0.85; 95% CI, 0.80-0.90), CVD (HR, 0.79; 95% CI, 0.71-0.87), and respiratory disease-specific mortality (HR, 0.51; 95% CI, 0.44-0.60). Exposure to PM2.5 (HR, 1.05; 95% CI, 1.00-1.09) and NOx (HR, 1.06; 95% CI, 1.02-1.10) was connected with increased all-cause mortality risk, and significant PM2.5-associated elevated risks for CVD mortality and NOx-associated elevated risks for respiratory disease mortality were observed. No obvious interaction between PA and PM2.5 or NOx exposure was detected. CONCLUSIONS: Our study provides additional evidence that higher PA and lower air pollutant levels are independently connected with reduced mortality risk. The benefits of PA are not significantly affected by long-term air pollution exposure, indicating PA can be recommended to prevent mortality regardless of air pollution levels. Our findings highlight the importance of public health policies and interventions facilitating PA and reducing air pollution in reducing mortality risks and maximizing health benefits. Future investigation is urgently needed to identify these findings in areas with severe air pollution conditions.

A Nomogram Prediction Model for Persistent Pulmonary Hypertension of the Newborn in Neonates Hospitalized for the First Time After Birth.

OBJECTIVE: Persistent pulmonary hypertension of the newborn (PPHN) is one of the critical neonatal diseases associated with high morbidity and mortality. This study attempted to conduct a nomogram prediction model for performing early identification of PPHN and providing effective information for clinical practice. METHODS: A total of 456 newborns who first admitted to the hospital after birth were included in the analysis, including 138 newborns with PPHN and 318 newborns without PPHN (as controls). The optimal predictive variables selection was performed based on LASSO (least absolute shrinkage and selection operator) regression and multivariate logistic regression. Using the selected variables, a nomogram prediction model was developed. To validate the model, the model was assessed using the receiver operating characteristic curve, calibration plot, and clinical impact curve. RESULTS: Six predictors, namely, gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pao2, were identified by LASSO and multivariate logistic regression analysis from the original 30 variables studied. The constructed model, using these predictors, exhibited favorable predictive ability for PPHN, with an area under the receiver operating characteristic of 0.897 (sensitivity = 0.876, specificity = 0.785) in the training set and 0.871 (sensitivity = 0.902, specificity = 0.695) in the validation set, and was well calibrated, as indicated by the PHosmer-Lemeshow test values of 0.233 and 0.876 for the training and validation sets, respectively. CONCLUSIONS: The model included gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pao2 had good prediction performance for predicting PPHN among newborns first admitted to the hospital after birth.

In Situ Growth of COF/PVA-Carrageenan Hydrogel Using the Impregnation Method for the Purpose of Highly Sensitive Ammonia Detection.

Flexible ammonia (NH3) gas sensors have gained increasing attention for their potential in medical diagnostics and health monitoring, as they serve as a biomarker for kidney disease. Utilizing the pre-designable and porous properties of covalent organic frameworks (COFs) is an innovative way to address the demand for high-performance NH3 sensing. However, COF particles frequently encounter aggregation, low conductivity, and mechanical rigidity, reducing the effectiveness of portable NH3 detection. To overcome these challenges, we propose a practical approach using polyvinyl alcohol-carrageenan (κPVA) as a template for in the situ growth of two-dimensional COF film and particles to produce a flexible hydrogel gas sensor (COF/κPVA). The synergistic effect of COF and κPVA enhances the gas sensing, water retention, and mechanical properties. The COF/κPVA hydrogel shows a 54.4% response to 1 ppm NH3 with a root mean square error of less than 5% and full recovery compared to the low response and no recovery of bare κPVA. Owing to the dual effects of the COF film and the particles anchoring the water molecules, the COF/κPVA hydrogel remained stable after 70 h in atmospheric conditions, in contrast, the bare κPVA hydrogel was completely dehydrated. Our work might pave the way for highly sensitive hydrogel gas sensors, which have intriguing applications in flexible electronic devices for gas sensing.