[Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021].

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

Urinary metabolomic profiles uncover metabolic pathways in children with asthma.

OBJECTIVE: The prevalence of asthma has gradually increased worldwide in recent years, which has made asthma a global public health problem. However, due to its complexity and heterogeneity, there are a few academic debates on the pathogenic mechanism of asthma. The study of the pathogenesis of asthma through metabolomics has become a new research direction. We aim to uncover the metabolic pathway of children with asthma. METHODS: Liquid chromatography (LC)-mass spectrometry (MS)-based metabolomic analysis was conducted to compare urine metabolic profiles between asthmatic children (n = 30) and healthy controls (n = 10). RESULTS: Orthogonal projections to latent structures-discrimination analysis (OPLS-DA) showed that there were significant differences in metabolism between the asthma group and the control group with three different metabolites screened out, including traumatic acid, dodecanedioic acid, and glucobrassicin, and the levels of traumatic acid and dodecanedioic acid in the urine samples of asthmatic children were lower than those of healthy controls therein. Pathway enrichment analysis of differentially abundant metabolites suggested that α-linolenic acid metabolism was an asthma-related pathway. CONCLUSIONS: This study suggests that there are significant metabolic differences in the urine of asthmatic children and healthy controls, and α-linolenic acid metabolic pathways may be involved in the pathogenesis of asthma.

Multilevel chitosan-gelatin particles loaded with P4HA1 siRNA suppress glioma development.

It has been reported that prolyl 4-hydroxylase subunit alpha 1 (P4HA1) promoted tumor growth and metastasis of glioma; thus, targeting P4HA1 may be a promising therapeutic strategy against glioma. In consideration of the instability of siRNA in vivo, the chitosan-gelatin microspheres loaded with P4HA1 siRNA (P4HA1 siRNA@CGM) were employed. Firstly, the gel electrophoresis and hemolytic test were performed to assess the stability and blood compatibility of P4HA1 siRNA@CGM. Then, methyl thiazolyl tetrazolium (MTT), cell colony formation, Transwell assay, wound healing assay, gliosphere formation, tube formation, and Western blot were performed to assess the effects of P4HA1 siRNA@CGM on the biological functions of glioma. Finally, 125I-labeled P4HA1 siRNA@CGM was injected into the xenograft mice, radionuclide imaging was recorded, Ki67 and terminal deoxynucleoitidyl transferase-mediated nick end labeling (TUNEL) staining was performed to assess the effects of P4HA1 siRNA@CGM on tumor growth and apoptosis of glioma in vivo. The results showed that P4HA1 siRNA and P4HA1 siRNA@CGM not only markedly inhibited the proliferation, metastasis, gliosphere formation, and the protein levels of interstitial markers (N-cadherin and vimentin) and the transcription factors of epithelial-mesenchymal transition (EMT) (Snail, Slug, and Twist1) in glioma cells, but also inhibited the tube formation in human brain microvascular endothelial cells (HBMECs), and P4HA1 siRNA@CGM exhibited the better inhibitory effects than P4HA1 siRNA. Above results suggested the feasibility of P4HA1 siRNA@CGM in the clinical treatment of glioma.

DPM2 serve as novel oncogene and prognostic marker transactivated by ESR1 in breast cancer.

Breast cancer (BRCA) is the most common malignancies worldwide with increasing rate. Dolichol phosphate mannose synthase (DPMS) is a critical mannosyltransferase involved in the posttranslational modification of proteins. At present, there is limited knowledge regarding the function of DPMS in breast cancer. In this study, silica analysis in multiple datasets found that dolichyl-phosphate mannosyltransferase subunit 2 (DPM2) is an unfavorable prognostic marker, suggesting its oncogenic role. Cell counting kit-8 and apoptosis assays show that DPM2-silenced cancer cells exhibit decreased growth potential and enhanced cell death rate. Further, transwell and wound healing assays show reduced invasion and migration capabilities in DPM2 knockdown groups, xenograft nude mice model demonstrated smaller tumor volume in DPM2 silenced BC cells. Then, the underlying downstream mechanism of DPM2 in BC was predicted and analyzed, highlighting classical tumorigenic pathways like JAK/STAT signaling pathway and oxidative phosphorylation activated in the cancer group. Finally, ChIP-seq analysis, expression correlation analysis, inhibitor treatment, and dual luciferase assays show that DPM2 is transcriptionally activated by estrogen receptor1 (ESR1). The results show that high expression of DPM2 mRNA is significantly correlated with shorter overall survival (OS) and disease-free survival (DFS) in breast cancer patients, and in vitro knockdown of DPM2 can significantly inhibit the malignant phenotypes of cells, including proliferation, invasion, migration, and apoptosis. These results suggest that DPM2 may play an important role in breast cancer. Altogether, we first uncovered the tumorigenic and prognostic role of DPM2 in breast cancer, cellular assays, and bioinformatics analysis highlighted DPM2 as oncogene via inhibited cancer-related signaling pathways in breast cancer. Besides, DPM2 is transcriptionally activated by ESR1, the signaling axis of ESR1/DPM2 provides a new strategy for BC-targeted therapy.

Anthropogenically and meteorologically modulated summertime ozone trends and their health implications since China's clean air actions.

Elevated ozone (O3) has emerged as the major air quality concern since China's clean air actions, offsetting the health benefits gained from improved air quality. Given the shifted ozone chemical regimes and recently boosted extreme weather in China, it's essential to rethink the O3 trends since 2013 for evaluations of air pollution mitigation policy. Here, we examine the anthropogenically and meteorologically modulated summertime O3 trends across China at different stages of the clean air actions using multi-source observations combined with multi-model calculations. Ozone increases steadily in China between 2013-2022, with a fast increase rate of 4.4 µg m-3 yr-1 in Phase I and a much smaller 0.6 µg m-3 yr-1 in Phase II of Action Plan. Results highlight that the deteriorative O3 pollution in Phase I and early Phase II is dominated by the nonlinear O3-emission response. Persistent decline in O3 precursors has shifted its chemical regime in urban areas and began to show a positive influence on ozone mitigation in recent years. Meteorological influence on O3 variations is minor until 2019 (∼10%), but it greatly accelerates or relieves the O3 pollution after then, showing comparable contribution to emissions. Epidemiological model predicts totally 0.8-3.0 thousand yr-1 more deaths across China with altered anthropogenic emissions since clean air actions, and additional health burdens by -1.5-0.3 thousand yr-1 from perturbated meteorology. This study calls for stringent emission control and climate adaptation strategies to attain the ozone pollution mitigation in China.

A phase 1a/1b first-in-human study (COMPASSION-01) evaluating cadonilimab in patients with advanced solid tumors.

Simultaneous inhibition of programmed cell death protein-1 (PD-1) and cytotoxic T lymphocyte-associated protein-4 (CTLA-4) with bispecific antibodies may improve efficacy over single-agent treatment while limiting toxicity. Cadonilimab is a humanized, bispecific antibody targeting PD-1 and CTLA-4. This is a phase 1 study of cadonilimab including dose escalation (n = 39) and dose expansion (n = 80). One dose-limiting toxicity event is observed, with the maximum tolerated dose not reached. 6 mg/kg cadonilimab once every 2 weeks is established as the recommended dose for future studies. The most common treatment-related adverse event is infusion-related reaction (18.5%), mostly grade 1/2 in severity. The incidences of any grade and grade ≥3 immune-related adverse events are 44.5% and 6.7%, respectively. The confirmed overall response rate is 13.4%, and the median duration of response is 12.9 months. Cadonilimab is well tolerated and showed promising efficacy in patients with advanced solid tumors. This study is registered with ClinicalTrials.gov: NCT03261011.

Neuronal activity and remyelination: new insights into the molecular mechanisms and therapeutic advancements.

This article reviews the role of neuronal activity in myelin regeneration and the related neural signaling pathways. The article points out that neuronal activity can stimulate the formation and regeneration of myelin, significantly improve its conduction speed and neural signal processing ability, maintain axonal integrity, and support axonal nutrition. However, myelin damage is common in various clinical diseases such as multiple sclerosis, stroke, dementia, and schizophrenia. Although myelin regeneration exists in these diseases, it is often incomplete and cannot promote functional recovery. Therefore, seeking other ways to improve myelin regeneration in clinical trials in recent years is of great significance. Research has shown that controlling neuronal excitability may become a new intervention method for the clinical treatment of demyelinating diseases. The article discusses the latest research progress of neuronal activity on myelin regeneration, including direct or indirect stimulation methods, and the related neural signaling pathways, including glutamatergic, GABAergic, cholinergic, histaminergic, purinergic and voltage-gated ion channel signaling pathways, revealing that seeking treatment strategies to promote myelin regeneration through precise regulation of neuronal activity has broad prospects.

The relationship between cadmium exposure and hepatitis B susceptibility and the establishment of its prediction model.

Cadmium, a common metal, is an environmental contaminant that is hepatotoxic and immunotoxic. Cadmium exposure may affect hepatitis B immunity. The purpose of this study was to assess the association between cadmium exposure and hepatitis B serology in the US population and to develop a model to predict susceptibility of hepatitis B. The study included 50,588 individuals in the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2016. Univariate and multivariate logistic regression and dose-response curves were used to evaluate the relationship between cadmium exposure and hepatitis B serology. Through multivariate logistic regression results, a predictive model was established, and relevant indicators were used to verify the clinical value of the model and evaluate prognostic value of serum cadmium concentration in patients with hepatitis B. We selected 5989 (≥ 6 years old) participants. Univariate logistic regression analysis showed that gender (aOR = 0.7192, 95% CI = 0.6492-0.7968), age (aOR = 1.030, 95% CI = 1.026-1.033), race (aOR = 0.8974, 95% CI = 0.8591-0.9374), poverty ratio (aOR = 1.042, 95% CI = 0.9872-1.101), body mass index (BMI) (aOR = 1.052, 95% CI = 1.044-1.061), hypertension (aOR = 2.017, 95% CI = 1.763-2.306), diabetes (aOR = 2.673, 95% CI = 2.119-3.370), vigorous recreational activities (aOR = 0.6369, 95% CI = 0.5725-0.7085), moderate recreational activity (aOR = 0.7681, 95% CI = 0.6935-0.8574) and cadmium (aOR = 1.295, 95% CI = 1.168-1.436) were closely related to hepatitis B virus (HBV) susceptibility. After adjusting for these confounding factors, multivariate logistic regression analysis showed that the odds ratio of HBV susceptibility was positively correlated with the level of cadmium in serum. The effectiveness of the model was then evaluated by establishing a nomogram, and by calibration curves, ROC curves, and clinical decision curves. Our study shows that cadmium exposure is positively associated with HBV susceptibility risk in the US population, and the constructed model has clinical significance.

A comparable efficacy and safety between intracardiac echocardiography and transesophageal echocardiography for percutaneous left atrial appendage occlusion.

Background: Accumulated clinical studies utilized intracardiac echocardiography (ICE) to guide percutaneous left atrial appendage occlusion (LAAO). However, its procedural success and safety compared to traditional transesophageal echocardiography (TEE) remained elusive. Therefore, we performed a meta-analysis to compare efficacy and safety of ICE and TEE for LAAO. Methods: We screened studies from four online databases (including the Cochrane Library, Embase, PubMed, and Web of Science) from their inception to 1 December 2022. We used a random or fixed-effect model to synthesize the clinical outcomes and conducted a subgroup analysis to identify the potential confounding factors. Results: A total of twenty eligible studies with 3,610 atrial fibrillation (AF) patients (1,564 patients for ICE and 2,046 patients for TEE) were enrolled. Compared with TEE group, there was no significant difference in procedural success rate [risk ratio (RR) = 1.01; P = 0.171], total procedural time [weighted mean difference (WMD) = -5.58; P = 0.292], contrast volume (WMD = -2.61; P = 0.595), fluoroscopic time (WMD = -0.34; P = 0.705; I2 = 82.80%), procedural complications (RR = 0.82; P = 0.261), and long-term adverse events (RR = 0.86; P = 0.329) in the ICE group. Subgroup analysis revealed that ICE group might be associated with the reduction of contrast use and fluoroscopic time in the hypertension proportion <90 subgroup, with lower total procedure time, contrast volume, and the fluoroscopic time in device type subgroup with multi-seal mechanism, and with the lower contrast use in paroxysmal AF (PAF) proportion ≤50 subgroup. Whereas, ICE group might increase the total procedure time in PAF proportion >50 subgroup and contrast use in multi-center subgroup, respectively. Conclusion: Our study suggests that ICE may have comparable efficacy and safety compared to TEE for LAAO.

DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations.

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://github.com/lennylv/DGCddG.

Tyrosine kinase inhibitors-associated interstitial lung disease used in non-small cell lung cancer: a pharmacovigilance analysis based on the FDA adverse event reporting system database.

BACKGROUND: Interstitial lung disease (ILD) was a relatively common cause of drug-induced mortality. However, the safety profile of the whole TKIs induced ILD was largely unknown. RESEARCH DESIGN AND METHODS: The reported cases of ILD associated with TKIs were downloaded from the FDA adverse event reporting system (FAERS) database between 1 January 2004 and 30 April 2022 to detect ILD signals by disproportionality analysis. Furthermore, the fatality rate and time to onset (TTO) of various TKIs were also calculated. RESULTS: The median age of total 2999 reported cases was 67. The largest reported cases came from osimertinib (n = 736, 24.5%). However, gefitinib had the highest ROR of 12.47 (11.4, 13.64) and IC of 3.53 (3.23, 3.86), means the strongest association with ILD. Trametinib, vemurafenib, larotectinib, selpercatinib, and cabozantinib did not show ILD signal. The median age of dead cases was 72 (Q1:62, Q3:83), and 53.02% (n = 579) were female and 41.11% (n = 449) were male. MET group showed the highest fatality rate of 55.17% with the shortest median TTO of 21 days (Q1: 8.5, Q3: 35.5). CONCLUSIONS: TKIs were significantly associated with ILD. More attention should be paid to female, older, MET group with shorter TTO, as their prognosis might be worse.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks.

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides. Here, we propose a deep graph convolutional network (GCN)-based method called GraphPPepIS to predict the interaction sites of protein-peptide complexes using protein and peptide structural information. We also propose a companion method, SeqPPepIS, for assisting with the lack of structural information and the flexibility of peptides. SepPPepIS replaces the peptide structural features in GraphPPepIS by learning features from peptide sequences. We performed a comprehensive evaluation of the benchmark data sets, and the results show that our two methods outperform state-of-the-art methods on the accurate interaction sites of both protein and peptide sides. We show that our methods can help improve protein-peptide docking. For docking data sets, our methods maintain robust performance in identifying binding sites, thereby enhancing the prediction of peptide binding poses. Finally, we visualized the analysis of protein and peptide graph embedding to demonstrate the learning ability of graph convolution in predicting interaction sites, which was mainly obtained through the shared parameters of a protein graph and peptide graph.

The income-related distribution of cognitive function and its mobility among the Chinese elderly over a 14-year period.

BACKGROUND: With population ageing, cognitive function among the elderly is a growing public health concern in China. This study aimed to investigate the trend of income-related inequality in cognitive function, and to track health-related income mobility among the Chinese elderly. METHODS: Data were drawn from the Chinese Longitudinal Healthy Longevity Survey conducted in 2005, 2008, 2011, 2014, and 2018. Cognitive function was evaluated by the Mini-Mental State Examination. Cross-sectional and longitudinal concentration indices were used to measure the magnitudes of inequalities at different length of time. The mobility index was used to capture the discrepancy between short-term and long-term assessments. The contributions of determinants to mobility were estimated by decomposition analysis. RESULTS: The results showed the cognitive function score among the Chinese elderly as 21.13 at the baseline. Men, activities, daily living ability, education, marriage status, income, receipt of community service, vision and hearing condition were positively associated with cognitive function, whereas age, negative well-being, and drinking were negatively associated with cognitive function. The cross-sectional concentration index was positive and significant only at the baseline. In the long run, however, the concentration indices were all positive and became larger over time. After five waves, the mobility index reached -4.84. The largest negative contributor to the mobility index was daily living ability, followed by relaxing activity, domestic activity, and hearing condition. The two largest positive contributors were negative well-being and income. CONCLUSIONS: As a whole, cognitive function did not perform well among the Chinese elderly. In the long term, the weighted cross-sectional concentration indices underestimated the inequality in cognitive function, and good cognitive performance was concentrated more among the rich. When formulating intervention measures, the Chinese government should give priority to vulnerable groups, especially the elderly who are poor or downwardly mobile in income.

Mec1 regulates PAS recruitment of Atg13 via direct binding with Atg13 during glucose starvation-induced autophagy.

Mec1 is a DNA damage sensor, which performs an essential role in the DNA damage response pathway and glucose starvation-induced autophagy. However, the functions of Mec1 in autophagy remain unclear. In response to glucose starvation, Mec1 forms puncta, which are recruited to mitochondria through the adaptor protein Ggc1. Here, we show that Mec1 puncta also contact the phagophore assembly site (PAS) via direct binding with Atg13. Functional analysis of the Atg13-Mec1 interaction revealed two previously unrecognized protein regions, the Mec1-Binding Region (MBR) on Atg13 and the Atg13-Binding Region (ABR) on Mec1, which mediate their mutual association under glucose starvation conditions. Disruption of the MBR or ABR impairs the recruitment of Mec1 puncta and Atg13 to the PAS, consequently blocking glucose starvation-induced autophagy. Additionally, the MBR and ABR regions are also crucial for DNA damage-induced autophagy. We thus propose that Mec1 regulates glucose starvation-induced autophagy by controlling Atg13 recruitment to the PAS.

Electrochemical immunosensor for ultrasensitive detection of human papillomaviruse type 16 L1 protein based on Ag@AuNPs-GO/SPA.

Human papillomaviruse type 16 (HPV16) is a high-risk serotype. As the main protective antigen protein, L1 protein is also the target protein for diagnosis. A simple label free electrochemical immunosensor (ECIS) was fabricated for ultrasensitive detection of HPV16 L1 protein in this work. Quasi-spherical Ag@Au core-shell nanoparticles on graphene oxide (Ag@AuNPs-GO) was developed as current response amplifier and characterized by UV-Vis Spectroscopy, Transmission Electron Microscopy and energy dispersive X-ray spectroscopy. Staphylococcal protein A was decorated on the modified electrode and utilized to immobilized the Fc portion of the monoclonal antibody specific for HPV16 L1 protein. Cyclic Voltammetry, Differential Pulse Voltammetry and Electrochemical Impedance Spectroscopy were used to verify the electrochemical performance and interfacial kinetic property. The increased concentration of HPV16 L1 protein led to slow electron transport and linearly decreased differential pulse voltammetry peak current with a detection limit of 0.002 ng mL-1 and a wide linear relationship in the range of 0.005-400 ng mL-1at a regression coefficient (R2) of 0.9948. Furthermore, this ECIS demonstrated acceptable accuracy with good reproducibility, stability and selectivity, suggesting a promising immunological strategy for HPV typing and early screening.

ctP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation.

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP2ISP to improve the prediction of protein-protein interaction sites. ctP2ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP2ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP2ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.

DeepNup: Prediction of Nucleosome Positioning from DNA Sequences Using Deep Neural Network.

Nucleosome positioning is involved in diverse cellular biological processes by regulating the accessibility of DNA sequences to DNA-binding proteins and plays a vital role. Previous studies have manifested that the intrinsic preference of nucleosomes for DNA sequences may play a dominant role in nucleosome positioning. As a consequence, it is nontrivial to develop computational methods only based on DNA sequence information to accurately identify nucleosome positioning, and thus intend to verify the contribution of DNA sequences responsible for nucleosome positioning. In this work, we propose a new deep learning-based method, named DeepNup, which enables us to improve the prediction of nucleosome positioning only from DNA sequences. Specifically, we first use a hybrid feature encoding scheme that combines One-hot encoding and Trinucleotide composition encoding to encode raw DNA sequences; afterwards, we employ multiscale convolutional neural network modules that consist of two parallel convolution kernels with different sizes and gated recurrent units to effectively learn the local and global correlation feature representations; lastly, we use a fully connected layer and a sigmoid unit serving as a classifier to integrate these learned high-order feature representations and generate the final prediction outcomes. By comparing the experimental evaluation metrics on two benchmark nucleosome positioning datasets, DeepNup achieves a better performance for nucleosome positioning prediction than that of several state-of-the-art methods. These results demonstrate that DeepNup is a powerful deep learning-based tool that enables one to accurately identify potential nucleosome sequences.

Effects of high-dose opioid analgesia on survival, pain relief, quality of life and adverse drug reactions in cancer and neuropathic pain patients: a retrospective cohort study in real-world clinical practice.

Background: Pain is a common symptom among cancer patients and directly affects their prognosis. As the leading drug for pain management, opioids are widely prescribed. So it is necessary to get people a correct understanding and application of opioids. In order to examine whether the use of high-dose opioids might affect survival and quality of life, this retrospective cohort study was performed to explore the outcomes of patients receiving high-dose opioids for pain management in a first-class tertiary hospital in China. Methods: We retrospectively searched medical records of inpatients and outpatients with pain who were treated with opioids in The First Affiliated Hospital, Zhejiang University School of Medicine from July to December 2021. Forty-three cases who were treated with high-dose opioids meeting inclusion criteria. Among these patients, 37 had cancer pain and 6 had neuropathic pain. All patients had regular follow-up when readmission until to April 7, 2022. Medical records of patients on high-dose opioids (equivalent to morphine ≥300 mg/d) was collected, including numerical rating scale (NRS), Karnofsky performance score (KPS), survival and adverse drug reactions (ADRs). Pain relief, quality of life, survival, and ADRs of patients after pain treatment were analyzed and evaluated. Results: The NRS score was significantly reduced and pain was relieved after high-dose opioid treatment. The before and after average NRS score of cancer pain was 5.2±1.6 vs. 2.2±1.1 points (P<0.001), neuropathic pain was 5.0±2.2 vs. 1.3±1.2 points (P<0.05), respectively. Although there is no statistical difference, quality of life showed a trend of improvement compared with before treatment. The before and after average KPS scores of cancer pain patients was 55.7±17.3 vs. 62.4±20.0, and neuropathic pain patients was 71.7±9.0 vs. 83.3±4.7. There were no intolerable ADRs. The median survival time was 238 days and 83 days in patients with cancer pain who received high-dose opioids and ultra-high dose opioids (equivalent to morphine ≥600 mg/d). Conclusions: Multimodal high-dose opioid pain treatments are important approaches to effectively relieve moderate to severe pain and improve the quality of life of patients. This study provides a clinical basis for future pain treatment with high-dose opioids.

Evaluation of the vulnerability to public health events in the Guangdong-Hong Kong-Macao Greater Bay Area.

With the continuous improvement in the integration of urban agglomeration, a multi-functional, socialized, and complex dynamic system, effective prevention and control of emergent public health events have become increasingly important. Based on the Public-Health Vulnerability-Assessment-System of Urban Agglomeration (PVUA), the temporal and spatial differentiation characteristics of vulnerability in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) for the period of 2015-2019 are explored, and the vulnerable cities to public health events are identified in this area. The results can be summarized as follows: (1) The overall vulnerability to public health events in GBA decreases in the investigated period. (2) In the temporal dimension, accompanied by social and economic development, the sensitivity to public health events increases in GBA, and the coping capacity change from stable fluctuation to rapid improvement. (3) From the spatial dimension, the sensitivity level in GBA is low in the west, relatively high in the middle, and high in the southeast; the coping capacity is high in the southeast and low in the northwest; the collaborative governance capacity presents a spatial pattern of being low in the south and high in the north. (4) In the period of study, the vulnerability to public health events in Guangzhou and Jiangmen is stable at the lowest level, while that in Zhaoqing, Foshan, and Hong Kong SAR (Special Administrative Region) gradually reduces; the vulnerability in Shenzhen, Zhuhai, and Dongguan is fluctuating, and that in Huizhou, Zhongshan, and Macao SAR is continually maintained at a higher and the highest level.

Interleukin 13 participates in terminal differentiation of esophageal squamous cell carcinoma cells.

Background: In China, esophageal squamous cell carcinoma (ESCC) accounts for more than 90% of all esophageal cancer cases. Interleukin 13 (IL-13) was widely reported to play a key role in tumor progression. Our previous study reported that IL-13 was a favorable predictive marker for the overall survival of esophageal squamous cell carcinoma (ESCC) patients, but how IL-13 contributes to ESCC progression remains unknown. This study aims to explore the role of IL-13 and its underlying downstream molecular mechanisms in ESCC progression. Methods: Tissue microarrays including 262 primary ESCC tumor tissues were collected and analyzed. The expression of IL-13 in ESCC tumor tissue was detected with immunohistochemistry staining (IHC). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to qualify the expressions of KRT13, KRT4 and 15-lipoxygenase-1 (15-LOX-1) in cultured ESCC cell lines with recombinant IL-13 treatment. Results: IL-13 was expressed in the esophageal epithelium cells and ESCC tumor cells. High IL-13 expression in ESCC tumor cells predicted a good prognosis for patients. Recombinant human IL-13 raised KRT13 and 15-LOX-1 mRNA levels, but lowered KRT4 mRNA level 15-LOX-1 in ESCC cells in vitro. Conclusions: In summary, our study suggests that IL-13 might improve the prognosis of ESCC by promoting the terminal differentiation of ESCC cells. This may offer potential new therapeutic target for early treatment of ESCC.