Metal-ligand cross-link strategy engineered iron-doped dopamine-based superstructure as peroxidase-like nanozymes for detection of glucose.

Nanozymes are nanomaterials with mimetic enzyme properties and the related research has attracted much attention. It is of great value to develop methods to construct nanozymes and to study their application in bioanalysis. Herein, the metal-ligand cross-linking strategy was developed to fabricate superstructure nanozymes. This strategy takes advantage of being easy to operate, adjustable, cheap, and universal. The fabricated superstructure nanozymes possess efficient peroxidase-like catalytic activity. The enzyme reaction kinetic tests demonstrated that for TMB and H2O2, the Km is 0.229 and 1.308 mM, respectively. Furthermore, these superstructure nanozymes are applied to highly efficient and sensitive detection of glucose. The linear range for detecting glucose is 20-2000 µM, and the limit of detection is 17.5 µM. Furthermore, mechanistic research illustrated that this integrated system oxidizes glucose to produce hydrogen peroxide and further catalyzes the production of ·OH and O2·-, which results in a chromogenic reaction of oxidized TMB for the detection of glucose. This work could not only contribute to the development of efficient nanozymes but also inspire research in the highly sensitive detection of other biomarkers.

Structure-based characterization and compound identification of the wild-type THF class-II riboswitch.

Riboswitches are conserved regulatory RNA elements participating in various metabolic pathways. Recently, a novel RNA motif known as the folE RNA motif was discovered upstream of folE genes. It specifically senses tetrahydrofolate (THF) and is therefore termed THF-II riboswitch. To unravel the ligand recognition mechanism of this newly discovered riboswitch and decipher the underlying principles governing its tertiary folding, we determined both the free-form and bound-form THF-II riboswitch in the wild-type sequences. Combining structural information and isothermal titration calorimetry (ITC) binding assays on structure-based mutants, we successfully elucidated the significant long-range interactions governing the function of THF-II riboswitch and identified additional compounds, including alternative natural metabolites and potential lead compounds for drug discovery, that interact with THF-II riboswitch. Our structural research on the ligand recognition mechanism of the THF-II riboswitch not only paves the way for identification of compounds targeting riboswitches, but also facilitates the exploration of THF analogs in diverse biological contexts or for therapeutic applications.

Mechanism interpretation of Guhan Yangshengjing for protection against Alzheimer's disease by network pharmacology and molecular docking.

ETHNOPHARMACOLOGICAL RELEVANCE: Guhan Yangshengjing (GHYSJ) is an effective prescription for delaying progression of Alzheimer's disease (AD) based on the ancient Chinese medical classics excavated from Mawangdui Han Tomb. Comprising a combination of eleven traditional Chinese herbs, the precise protective mechanism through which GHYSJ acts on AD progression remains unclear and has significant implications for the development of new drugs to treat AD. AIM OF THE STUDY: To investigate the mechanism of GHYSJ in the treatment of AD through network pharmacology and validate the results through in vitro experiments. MATERIALS AND METHODS: Chemical composition-target-pathway network and protein-protein interaction network were constructed by network pharmacology to predict the potential targets of GHYSJ for the treatment of AD. The interaction relationship between active ingredients and targets was verified by molecular docking and molecular force. Furthermore, the chemical constituents of GHYSJ were analyzed by LC-MS and HPLC, the effects of GHYSJ on animal tissues were analyzed by H&E staining. An Aß-induced SH-SY5Y cellular model was established to validate the core pathways and targets predicted by network pharmacology and molecular docking. RESULTS: The results of the network pharmacology analysis revealed a total of 155 bioactive compounds capable of crossing the blood-brain barrier and interacting with 677 targets, among which 293 targets specifically associated with AD, which mainly participated in and regulated the amyloid aggregation pathway and PI3K/Akt signaling pathway, thereby treating AD. In addition, molecular docking analysis revealed a robust binding affinity between the principal bioactive constituents of GHYSJ and crucial targets implicated in AD. Our findings were further substantiated by in vitro experiments, which demonstrated that Liquiritigenin and Ginsenosides Rh4, crucial constituents of GHYSJ, as well as GHYSJ pharmaceutic serum, exhibited a significant down-regulation of BACE1 expression in Aß-induced damaged SH-SY5Y cells. This study provides valuable data and theoretical underpinning for the potential therapeutic application of GHYSJ in the treatment of AD and secondary development of GHYSJ prescription. CONCLUSION: Through network pharmacology, molecular docking, LC-MS, and cellular experiments, GHYSJ was initially confirmed to delay the progression of AD by regulating the expression of BACE1 in Amyloid aggregation pathway. Our observations provided valuable data and theoretical underpinning for the potential therapeutic application of GHYSJ in the treatment of AD.

The Excellent Mechanical Performance of Polymer-Coated Ceramsite Particles for Efficient Fracturing: An Explanation from a Surface, Tribological Perspective.

Hydraulic fracturing using micro-particles is an effective technology in the petroleum industry since the particles facilitate crack propagation of the shale layer, creating pathways for oil and gas. A new kind of polymer-coated ceramsite particles (PCP) was generated. The friction and wear properties of the particles under different loads and speeds were also studied. The tribological relationship between the newly fabricated polymer-coated ceramsite particles and the fracturing fluid was studied through tribological experiments under the condition of fracturing fluid lubrication. The results show that, in contrast, the wear of the new-generation particles is relatively stable, indicating that it has good adjustable friction properties. In addition, under the lubrication condition of fracturing fluid, the new-generation particles have better hydrophobicity, high-pressure resistance, and low reflux rate, which have an important value as a practical engineering application for improving shale gas production efficiency and production.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.

During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi'an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.

Transmission dynamics informed neural network with application to COVID-19 infections.

Since the end of 2019 the COVID-19 repeatedly surges with most countries/territories experiencing multiple waves, and mechanism-based epidemic models played important roles in understanding the transmission mechanism of multiple epidemic waves. However, capturing temporal changes of the transmissibility of COVID-19 during the multiple waves keeps ill-posed problem for traditional mechanism-based epidemic compartment models, because that the transmission rate is usually assumed to be specific piecewise functions and more parameters are added to the model once multiple epidemic waves involved, which poses a huge challenge to parameter estimation. Meanwhile, data-driven deep neural networks fail to discover the driving factors of repeated outbreaks and lack interpretability. In this study, aiming at developing a data-driven method to project time-dependent parameters but also merging the advantage of mechanism-based models, we propose a transmission dynamics informed neural network (TDINN) by encoding the SEIRD compartment model into deep neural networks. We show that the proposed TDINN algorithm performs very well when fitting the COVID-19 epidemic data with multiple waves, where the epidemics in the United States, Italy, South Africa, and Kenya, and several outbreaks the Omicron variant in China are taken as examples. In addition, the numerical simulation shows that the trained TDINN can also perform as a predictive model to capture the future development of COVID-19 epidemic. We find that the transmission rate inferred by the TDINN frequently fluctuates, and a feedback loop between the epidemic shifting and the changes of transmissibility drives the occurrence of multiple waves. We observe a long response delay to the implementation of control interventions in the four countries, while the decline of the transmission rate in the outbreaks in China usually happens once the implementation of control interventions. The further simulation show that 17 days' delay of the response to the implementation of control interventions lead to a roughly four-fold increase in daily reported cases in one epidemic wave in Italy, which suggest that a rapid response to policies that strengthen control interventions can be effective in flattening the epidemic curve or avoiding subsequent epidemic waves. We observe that the transmission rate in the outbreaks in China is already decreasing before enhancing control interventions, providing the evidence that the increasing of the epidemics can drive self-conscious behavioural changes to protect against infections.

NAD+ biosynthesis metabolism predicts prognosis and indicates immune microenvironment for breast cancer.

The growing evidence implies that tumor cells need to increase NAD+ levels by upregulating NAD+ biosynthesis to satisfy their growth demand. NAD+ biosynthesis metabolism is implicated in tumor progression. Breast cancer (BC) is the most common malignant malignancy in the world. Nevertheless, the prognostic significance of NAD+ biosynthesis and its relationship with the tumor immune microenvironment in breast cancer still need further investigation. In this study, we obtained the mRNA expression data and clinical information of BC samples from public databases and calculated the level of NAD+ biosynthesis activity by single-sample gene set enrichment analysis (ssGSEA). We then explored the relationship between the NAD+ biosynthesis score, infiltrating immune cells, prognosis significance, immunogenicity and immune checkpoint molecules. The results demonstrated that patients with high NAD+ biosynthetic score displayed poor prognosis, high immune infiltration, high immunogenicity, elevated PD-L1 expression, and might more benefit from immunotherapy. Taken together, our studies not only deepened the understanding of NAD+ biosynthesis metabolism of breast cancer but also provided new insights into personalized treatment strategies and immunological therapies to improve the outcomes of breast cancer patients.

A cross-sectional study: a breathomics based pulmonary tuberculosis detection method.

BACKGROUND: Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection. METHOD: Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients. RESULTS: The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961. CONCLUSIONS: The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.

Ultra-Small High-Entropy Alloy Nanoparticles: Efficient Nanozyme for Enhancing Tumor Photothermal Therapy.

High-entropy alloys nanoparticles (HEANPs) are receiving extensive attention due to their broad compositional tunability and unlimited potential in bioapplication. However, developing new methods to prepare ultra-small high-entropy alloy nanoparticles (US-HEANPs) faces severe challenges owing to their intrinsic thermodynamic instability. Furthermore, there are few reports on studying the effect of HEANPs in tumor therapy. Herein, the fabricated PtPdRuRhIr US-HEANPs act as bifunctional nanoplatforms for the highly efficient treatment of tumors. The US-HEANPs are engineered by the universal metal-ligand cross-linking strategy. This simple and scalable strategy is based on the aldol condensation of organometallics to form the target US-HEANPs. The synthesized US-HEANPs exhibit excellent peroxidase-like (POD-like) activity and can catalyze the endogenous hydrogen peroxide to produce highly toxic hydroxyl radicals. Furthermore, the US-HEANPs possess a high photothermal conversion effect for converting 808 nm near-infrared light into heat energy. In vivo and in vitro experiments demonstrated that under the synergistic effect of POD-like activity and photothermal action, the US-HEANPs can effectively ablate cancer cells and treat tumors. It is believed that this work not only provides a new perspective for the fabrication of HEANPs, but also opens the high-entropy nanozymes research direction and their biomedical application.

Dual-Functional Capping Agent-Mediated Transformation of Silver Nanotriangles to Silver Nanoclusters for Dual-Mode Biosensing.

The localized surface plasmon resonance (LSPR) property, depending on the structure (morphology and assembly) of nanoparticles, is very sensitive to the environmental fluctuation. Retaining the colorimetric effect derived from the LSPR property while introducing new optical properties (such as fluorescence) that provide supplementary information is an effective means to improve the controllability in structures and reproducibility in optical properties. DNA as a green and low-cost etching agent has been demonstrated to effectively control the morphology and optical properties (the blue shift of the LSPR peak) of the plasmonic nanoparticles. Herein, taking silver nanotriangles (AgNTs) as a proof of concept, we report a novel strategy to induce precisely tunable LSPR and fluorescence-composited dual-mode signals by using mono-DNA first as an etching agent for etching the morphology of AgNTs and later as a template for synthesizing fluorescent silver nanoclusters (AgNCs). In addition, common templates for synthesizing AgNCs, such as l-glutathione and bovine serum albumin, were demonstrated to have the capability to serve as etching agents. More importantly, these biomolecules as dual-functional capping agents (etching agents and templates) follow the size-dependent rule: as the size of the thiolated biomolecule increases, the blue shift of the LSPR peak increases; at the same time, the fluorescence intensity increases. The enzyme that can change the molecular weight (size) of the biomolecular substrates (DNA, peptides, and proteins) through an enzymatic cleavage reaction was explored to regulate the LSPR and fluorescent properties of the resulting nanoparticles (by etching of AgNTs and synthesis of AgNCs), achieving excellent performance in detection of cancer-related proteases. This study can be expanded to other biopolymers to impact both fundamental nanoscience and applications and provide powerful new tools for bioanalytical biosensors and nanomedicine.

Recent Advances of Seed-Mediated Growth of Metal Nanoparticles: from Growth to Applications.

Unprecedented advances in metal nanoparticle synthesis have paved the way for broad applications in sensing, imaging, catalysis, diagnosis, and therapy by tuning the optical properties, enhancing catalytic performance, and improving chemical and biological properties of metal nanoparticles. The central guiding concept for regulating the size and morphology of metal nanoparticles is identified as the precise manipulation of nucleation and subsequent growth, often known as seed-mediated growth methods. However, since the growth process is sensitive not only to the metal seeds but also to capping agents, metal precursors, growth solution, growth/incubation time, reductants, and other influencing factors, the precise control of metal nanoparticle morphology is multifactorial. Further, multiple reaction parameters are entangled with each other, so it is necessary to clarify the mechanism by which each factor precisely regulates the morphology of metal nanoparticles. In this review, to exploit the generality and extendibility of metal nanoparticle synthesis, the mechanisms of growth influencing factors in seed-mediated growth methods are systematically summarized. Second, a variety of critical properties and applications enabled by grown metal nanoparticles are focused upon. Finally, the current progress and offer insights on the challenges, opportunities, and future directions for the growth and applications of grown metal nanoparticles are reviewed.

Biological nitrogen removal mechanisms during anaerobic digestion of swine manure: Effects of biogas circulation and activated carbon addition.

This study investigated the biological nitrogen removal mechanisms during the anaerobic digestion of swine manure and the effects of biogas circulation and activated carbon (AC) addition. Biogas circulation, AC addition, and their combination increased the methane yield by 25.9%, 22.3%, and 44.1%, respectively, when compared to the control. Nitrogen species analysis and metagenomic results indicated that nitrification-denitrification was the dominant ammonia removal pathway in all digesters with little oxygen, while anammox did not occur. Biogas circulation could promote mass transfer and induce air infiltration to enrich nitrification- and denitrification-related bacteria and functional genes. And AC might act as an electron shuttle to facilitate ammonia removal. The combined strategies showed a synergetic effect on the enrichment of nitrification and denitrification bacteria and functional genes, significantly lowering the total ammonia nitrogen by 23.6%. A single digester with biogas circulation and AC addition could enhance methanogenesis and ammonia removal via nitrification and denitrification.

An NIR fluorescent/photoacoustic dual-mode probe of NADPH for tumor imaging.

A novel probe was synthesized with a turn-on NIR fluorescent (NIRF)/photoacoustic (PA) response to NADPH, which was successfully applied in both monitoring intracellular NADPH and dual-modal imaging of tumor-bearing mice. It exhibits good potential in studying and understanding the tumor energy metabolism and treatment process related to NADPH.

Detecting latent tuberculosis infection with a breath test using mass spectrometer: A pilot cross-sectional study.

Mycobacterium tuberculosis (M.tb) infects a quarter of the world's population and may progress to active tuberculosis (ATB). There is no gold standard for diagnosing latent tuberculosis infection (LTBI). Some immunodiagnostic tests are recommended to detect LTBI but can not distinguish ATB from LTBI. The breath test is useful for diagnosing ATB compared to healthy subjects but was never studied for LTBI. This proof-of-concept study (Chinese Clinical Trials Registry number: ChiCTR2200058346) was the first to explore a novel, rapid, and simple LTBI detection method via breath test on high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). The case group of LTBI subjects (n = 185) and the control group (n = 250), which included ATB subgroup (n = 121) and healthy control (HC) subgroup (n = 129), were enrolled. The LTBI detection model indicated that a breath test via HPPI-TOFMS could distinguish LTBI from the control with a sensitivity of 80.0% (95% CI: 67.6%, 92.4%) and a specificity of 80.8% (95% CI: 71.8%, 89.9%). Nevertheless, further intensive studies with a larger sample size are required for clinical application.

[Review of classical prescriptions in treatment of ulcerative colitis].

Ulcerative colitis(UC) is a continuous inflammatory bowel disease with the main clinical manifestations of abdominal pain, diarrhea, and mucous bloody stools, mainly attacking the colorectal mucosa and submucosa. It is characterized by high recurrence rate, difficult cure, and clustering and regional occurrence. Chinese medicinal prescriptions for the treatment of UC have good therapeutic effect, multi-target regulation, slight toxicity, and no obvious side effects. In particular, the classical prescriptions highlight the characteristics and advantages of traditional Chinese medicine theory and have attracted much attention in recent years. To enable researchers to timely and comprehensively understand the classical prescriptions in the treatment of UC, we reviewed the studies about the pharmacodynamic material basis, quality control, action mechanism, and clinical application of relevant classical prescriptions. We first introduced the latest research progress in the active components such as alkaloids, polysaccharides, saponins, and flavonoids in relevant classical prescriptions. Then, we reviewed the latest research achievements on the quality control of classical prescriptions for the treatment of UC by gas chromatography, liquid chromatography, mass spectrometry, liquid chromatography-mass spectrometry and the like. Further, we summarized the research advances in the mechanisms of relevant prescriptions in the treatment of UC based on network pharmacology, molecular docking, integrated pharmacology platform, and animal experiments. Finally, we generalized the clinical application of the classical prescriptions for clearing heat and removing dampness, mildly regulating cold and heat, soothing liver and regulating spleen, strengthening spleen and invigorating Qi, and tonifying spleen and stomach. By systematic summary of the research progress in relevant classical prescriptions, we hope to promote the application and development of such prescriptions in UC treatment.

A feasibility study of Covid-19 detection using breath analysis by high-pressure photon ionization time-of-flight mass spectrometry.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused a tremendous threat to global health. polymerase chain reaction (PCR) and antigen testing have played a prominent role in the detection of SARS-CoV-2-infected individuals and disease control. An efficient, reliable detection tool is still urgently needed to halt the global COVID-19 pandemic. Recently, the food and drug administration (FDA) emergency approved volatile organic component (VOC) as an alternative test for COVID-19 detection. In this case-control study, we prospectively and consecutively recruited 95 confirmed COVID-19 patients and 106 healthy controls in the designated hospital for treatment of COVID-19 patients in Shenzhen, China. Exhaled breath samples were collected and stored in customized bags and then detected by high-pressure photon ionization time-of-flight mass spectrometry for VOCs. Machine learning algorithms were employed for COVID-19 detection model construction. Participants were randomly assigned in a 5:2:3 ratio to the training, validation, and blinded test sets. The sensitivity (SEN), specificity (SPE), and other general metrics were employed for the VOCs based COVID-19 detection model performance evaluation. The VOCs based COVID-19 detection model achieved good performance, with a SEN of 92.2% (95% CI: 83.8%, 95.6%), a SPE of 86.1% (95% CI: 74.8%, 97.4%) on blinded test set. Five potential VOC ions related to COVID-19 infection were discovered, which are significantly different between COVID-19 infected patients and controls. This study evaluated a simple, fast, non-invasive VOCs-based COVID-19 detection method and demonstrated that it has good sensitivity and specificity in distinguishing COVID-19 infected patients from controls. It has great potential for fast and accurate COVID-19 detection.

Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study.

Background: Breathomics testing has been considered a promising method for detection and screening for lung cancer. This study aimed to identify breath biomarkers of lung cancer through perioperative dynamic breathomics testing. Methods: The discovery study was prospectively conducted between Sept 1, 2020 and Dec 31, 2020 in Peking University People's Hospital in China. High-pressure photon ionisation time-of-flight mass spectrometry was used for breathomics testing before surgery and 4 weeks after surgery. 28 volatile organic compounds (VOCs) were selected as candidates based on a literature review. VOCs that changed significantly postoperatively in patients with lung cancer were selected as potential breath biomarkers. An external validation was conducted to evaluate the performance of these VOCs for lung cancer diagnosis. Multivariable logistic regression was used to establish diagnostic models based on selected VOCs. Findings: In the discovery study of 84 patients with lung cancer, perioperative breathomics demonstrated 16 VOCs as lung cancer breath biomarkers. They were classified as aldehydes, hydrocarbons, ketones, carboxylic acids, and furan. In the external validation study including 157 patients with lung cancer and 368 healthy individuals, patients with lung cancer showed elevated spectrum peak intensity of the 16 VOCs after adjusting for age, sex, smoking, and comorbidities. The diagnostic model including 16 VOCs achieved an area under the curve (AUC) of 0.952, sensitivity of 89.2%, specificity of 89.1%, and accuracy of 89.1% in lung cancer diagnosis. The diagnostic model including the top eight VOCs achieved an AUC of 0.931, sensitivity of 86.0%, specificity of 87.2%, and accuracy of 86.9%. Interpretation: Perioperative dynamic breathomics is an effective approach for identifying lung cancer breath biomarkers. 16 lung cancer-related breath VOCs (aldehydes, hydrocarbons, ketones, carboxylic acids, and furan) were identified and validated. Further studies are warranted to investigate the underlying mechanisms of identified VOCs. Funding: National Natural Science Foundation of China (82173386) and Peking University People's Hospital Scientific Research Development Founds (RDH2021-07).

UbiBrowser 2.0: a comprehensive resource for proteome-wide known and predicted ubiquitin ligase/deubiquitinase-substrate interactions in eukaryotic species.

As an important post-translational modification, ubiquitination mediates ∼80% of protein degradation in eukaryotes. The degree of protein ubiquitination is tightly determined by the delicate balance between specific ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase-mediated deubiquitination. In 2017, we developed UbiBrowser 1.0, which is an integrated database for predicted human proteome-wide E3-substrate interactions. Here, to meet the urgent requirement of proteome-wide E3/deubiquitinase-substrate interactions (ESIs/DSIs) in multiple organisms, we updated UbiBrowser to version 2.0 (http://ubibrowser.ncpsb.org.cn). Using an improved protocol, we collected 4068/967 known ESIs/DSIs by manual curation, and we predicted about 2.2 million highly confident ESIs/DSIs in 39 organisms, with >210-fold increase in total data volume. In addition, we made several new features in the updated version: (i) it allows exploring proteins' upstream E3 ligases and deubiquitinases simultaneously; (ii) it has significantly increased species coverage; (iii) it presents a uniform confidence scoring system to rank predicted ESIs/DSIs. To facilitate the usage of UbiBrowser 2.0, we also redesigned the web interface for exploring these known and predicted ESIs/DSIs, and added functions of 'Browse', 'Download' and 'Application Programming Interface'. We believe that UbiBrowser 2.0, as a discovery tool, will contribute to the study of protein ubiquitination and the development of drug targets for complex diseases.