The impact of PET/CT and brain MRI for metastasis detection among patients with clinical T1-category lung cancer: Findings from a large-scale cohort study.

PURPOSE: [18F]-FDG PET/CT and brain MRI are common approaches to detect metastasis in patients of lung cancer. Current guidelines for the use of PET/CT and MRI in clinical T1-category lung cancer lack risk-based stratification and require optimization. This study stratified patients based on metastatic risk in terms of the lesions' size and morphological characteristics. METHODS: The detection rate of metastasis was measured in different sizes and morphological characteristics (solid and sub-solid) of tumors. To confirm the cut-off value for discriminating metastasis and overall survival (OS) prediction, the receiver operating characteristic (ROC) analysis was performed based on PET/CT metabolic parameters (SUVmax/SUVmean/SULpeak/MTV/TLG), followed by Kaplan-Meier analysis for survival in post-operation patients with and without PET/CT plus MRI. RESULTS: 2,298 patients were included. No metastasis was observed in patients with solid nodules < 8.0 mm and sub-solid nodules < 10.0 mm. The cut-off of PET/CT metabolic parameters on discriminating metastasis were 1.09 (SUVmax), 0.26 (SUVmean), 0.31 (SULpeak), 0.55 (MTV), and 0.81 (TLG), respectively. Patients undergoing PET/CT plus MRI exhibited longer OS compared to those who did not receive it in solid nodules ≥ 8.0 mm & sub-solid nodules ≥ 10.0 mm (HR, 0.44; p < 0.001); in solid nodules ≥ 8.0 mm (HR, 0.12; p<0.001) and in sub-solid nodules ≥ 10.0 mm (HR; 0.61; p=0.075), respectively. Compared to patients with metabolic parameters lower than cut-off values, patients with higher metabolic parameters displayed shorter OS: SUVmax (HR, 12.94; p < 0.001), SUVmean (HR, 11.33; p <0.001), SULpeak (HR, 9.65; p < 0.001), MTV (HR, 9.16; p = 0.031), and TLG (HR, 12.06; p < 0.001). CONCLUSION: The necessity of PET/CT and MRI should be cautiously evaluated in patients with solid nodules < 8.0 mm and sub-solid nodules < 10.0 mm, however, these examinations remained essential and beneficial for patients with solid nodules ≥ 8.0 mm and sub-solid nodules ≥ 10.0 mm.

The activity and immune dynamics of PD-1 inhibition on high-risk pulmonary ground glass opacity lesions: insights from a single-arm, phase II trial.

Immune checkpoint inhibitors targeting the programmed cell death-1 (PD-1) protein significantly improve survival in patients with advanced non-small-cell lung cancer (NSCLC), but its impact on early-stage ground-glass opacity (GGO) lesions remains unclear. This is a single-arm, phase II trial (NCT04026841) using Simon's optimal two-stage design, of which 4 doses of sintilimab (200 mg per 3 weeks) were administrated in 36 enrolled multiple primary lung cancer (MPLC) patients with persistent high-risk (Lung-RADS category 4 or had progressed within 6 months) GGOs. The primary endpoint was objective response rate (ORR). T/B/NK-cell subpopulations, TCR-seq, cytokines, exosomal RNA, and multiplexed immunohistochemistry (mIHC) were monitored and compared between responders and non-responders. Finally, two intent-to-treat (ITT) lesions (pure-GGO or GGO-predominant) showed responses (ORR: 5.6%, 2/36), and no patients had progressive disease (PD). No grade 3-5 TRAEs occurred. The total response rate considering two ITT lesions and three non-intent-to-treat (NITT) lesions (pure-solid or solid-predominant) was 13.9% (5/36). The proportion of CD8+ T cells, the ratio of CD8+/CD4+, and the TCR clonality value were significantly higher in the peripheral blood of responders before treatment and decreased over time. Correspondingly, the mIHC analysis showed more CD8+ T cells infiltrated in responders. Besides, responders' cytokine concentrations of EGF and CTLA-4 increased during treatment. The exosomal expression of fatty acid metabolism and oxidative phosphorylation gene signatures were down-regulated among responders. Collectively, PD-1 inhibitor showed certain activity on high-risk pulmonary GGO lesions without safety concerns. Such effects were associated with specific T-cell re-distribution, EGF/CTLA-4 cytokine compensation, and regulation of metabolism pathways.

Prompt Tuning in Biomedical Relation Extraction.

Biomedical relation extraction (RE) is critical in constructing high-quality knowledge graphs and databases as well as supporting many downstream text mining applications. This paper explores prompt tuning on biomedical RE and its few-shot scenarios, aiming to propose a simple yet effective model for this specific task. Prompt tuning reformulates natural language processing (NLP) downstream tasks into masked language problems by embedding specific text prompts into the original input, facilitating the adaption of pre-trained language models (PLMs) to better address these tasks. This study presents a customized prompt tuning model designed explicitly for biomedical RE, including its applicability in few-shot learning contexts. The model's performance was rigorously assessed using the chemical-protein relation (CHEMPROT) dataset from BioCreative VI and the drug-drug interaction (DDI) dataset from SemEval-2013, showcasing its superior performance over conventional fine-tuned PLMs across both datasets, encompassing few-shot scenarios. This observation underscores the effectiveness of prompt tuning in enhancing the capabilities of conventional PLMs, though the extent of enhancement may vary by specific model. Additionally, the model demonstrated a harmonious balance between simplicity and efficiency, matching state-of-the-art performance without needing external knowledge or extra computational resources. The pivotal contribution of our study is the development of a suitably designed prompt tuning model, highlighting prompt tuning's effectiveness in biomedical RE. It offers a robust, efficient approach to the field's challenges and represents a significant advancement in extracting complex relations from biomedical texts. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-024-00162-9.

Reverse charge transfer and decomposition in Ca-Te compounds under high pressure.

Pressure alters the nature of chemical bonds and triggers novel reactions. Here, we employed first-principles calculations combined with the CALYPSO structural search technique to reveal the charge transfer reversal between Ca and Te under high pressure in the calcium-tellurium compound (CaxTe1-x, x = 1/4, 1/3, 1/2, 2/3). We predict several new phases with conventional and unconventional compounds and found an unfamiliar phenomenon: the Ca-Te compounds will reverse charge transfer between Ca and Te atoms and decompose into elemental solids under pressure. The Bader charge analyses indicate that the Ca2+ ion gains electrons and becomes an anion under high pressure. This leads to a weakened electrostatic interaction between Ca and Te and ultimately results in decomposition. The calculated band occupation number suggests that the occupation of Ca 3d orbitals under high pressure corresponds to this atypical phenomenon. Our results demonstrated the reverse charge transfer between Ca and Te and, in addition, clarified the mechanism of CaxTe1-x decomposition into solid Ca and Te elements under high pressure, providing important insights into the evolution of the properties of alkaline-earth chalcogenide compounds under high pressure.

Ensemble pretrained language models to extract biomedical knowledge from literature.

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). RESULTS: Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. DISCUSSION AND CONCLUSION: Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.

AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events.

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs' capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs' potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.

Upfront surgery for stage IIIA/B non-small cell lung cancer: retrospective cohort study.

BACKGROUND: Stage III non-small cell lung cancer is a heterogeneous disease. Several international guidelines recommend neoadjuvant treatment before surgery; however, upfront surgery is the preferred approach for technically resectable non-small cell lung cancer in East Asia. The aim of this retrospective study was to evaluate the long-term outcomes of curative-intent upfront surgery in stage IIIA/B non-small cell lung cancer. METHODS: Patients who underwent curative-intent upfront surgery with stage cIIIA/B non-small cell lung cancer were identified. The clinical and pathological variables and survival outcomes were evaluated. RESULTS: Overall, 664 patients were identified, of whom 320 (48.8%) had N2 disease, 66.7% were males, 49.4% had a smoking history, and 61.2% had lung adenocarcinoma. Lobectomy was the most performed surgical procedure (84.9%). A total of 40 patients (6.02%) had positive margins (R1/R2). The grade III adverse event rate was 2.0% (13 of 664). The median follow-up was 30.6 (range 1.9-97.7) months. At follow-up, the mortality rate was 13.3% (88 of 664) and 37.2% of patients (247 of 664) had recurrence. Lung (101 of 247 (40.9%)) and brain (53 of 247 (21.5%)) were the most common sites of recurrence. The median overall survival was 60.0 (95% c.i. 51.5 to 67.6) months, with overall survival probability at 1, 2, 3, and 5 years being 89.6%, 77.8%, 67.2%, and 49.0% respectively. The R0 cohort showed an improved median overall survival compared with the R1/R2 cohort (67.4 versus 26.5 months respectively; P = greater than 0.001). The multivariable analysis revealed that age greater than or equal to 65 years (HR 1.51, 95% c.i. 1.08 to 2.12; reference = age less than 65 years), tumour size (greater than or equal to 5 cm (HR 2.13, 95% c.i. 1.41 to 3.21) and greater than or equal to 3 cm but less than 5 cm (HR 1.15, 95% c.i. 0.78 to 1.71); reference = less than 3 cm), and adjuvant treatment (chemotherapy (HR 0.69, 95% c.i. 0.49 to 0.96) and targeted therapy (HR 0.30, 95% c.i. 0.12 to 0.76); reference = none) significantly predicted overall survival. CONCLUSION: Upfront surgery is an option for the management of stage IIIA/B non-small cell lung cancer.

Extracting Drug-Protein Relation from Literature Using Ensembles of Biomedical Transformers.

Automatic extraction of relations between drugs/chemicals and proteins from ever-growing biomedical literature is required to build up-to-date knowledge bases in biomedicine. To promote the development of automated methods, BioCreative-VII organized a shared task - the DrugProt track, to recognize drug-protein entity relations from PubMed abstracts. We participated in the shared task and leveraged deep learning-based transformer models pre-trained on biomedical data to build ensemble approaches to automatically extract drug-protein relation from biomedical literature. On the main corpora of 10,750 abstracts, our best system obtained an F1-score of 77.60% (ranked 4th among 30 participating teams), and on the large-scale corpus of 2.4M documents, our system achieved micro-averaged F1-score of 77.32% (ranked 2nd among 9 system submissions). This demonstrates the effectiveness of domain-specific transformer models and ensemble approaches for automatic relation extraction from biomedical literature.

Loratidine is associated with improved prognosis and exerts antineoplastic effects via apoptotic and pyroptotic crosstalk in lung cancer.

BACKGROUND: Tumor-associated inflammation suggests that anti-inflammatory medication could be beneficial in cancer therapy. Loratadine, an antihistamine, has demonstrated improved survival in certain cancers. However, the anticancer mechanisms of loratadine in lung cancer remain unclear. OBJECTIVE: This study investigates the anticancer mechanisms of loratadine in lung cancer. METHODS: A retrospective cohort of 4,522 lung cancer patients from 2006 to 2018 was analyzed to identify noncancer drug exposures associated with prognosis. Cellular experiments, animal models, and RNA-seq data analysis were employed to validate the findings and explore the antitumor effects of loratadine. RESULTS: This retrospective study revealed a positive association between loratadine administration and ameliorated survival outcomes in lung cancer patients, exhibiting dose dependency. Rigorous in vitro and in vivo assays demonstrated that apoptosis induction and epithelial-mesenchymal transition (EMT) reduction were stimulated by moderate loratadine concentrations, whereas pyroptosis was triggered by elevated dosages. Intriguingly, loratadine was found to augment PPARγ levels, which acted as a gasdermin D transcription promoter and caspase-8 activation enhancer. Consequently, loratadine might incite a sophisticated interplay between apoptosis and pyroptosis, facilitated by the pivotal role of caspase-8. CONCLUSION: Loratadine use is linked to enhanced survival in lung cancer patients, potentially due to its role in modulating the interplay between apoptosis and pyroptosis via caspase-8.

Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation.

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions. CONCLUSIONS: Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

The role of the primary sensorimotor system in generalized epilepsy: Evidence from the cerebello-cerebral functional integration.

The interaction between cerebellum and cerebrum participates widely in function from motor processing to high-level cognitive and affective processing. Because of the motor symptom, idiopathic generalized epilepsy (IGE) patients with generalized tonic-clonic seizure have been recognized to associate with motor abnormalities, but the functional interaction in the cerebello-cerebral circuit is still poorly understood. Resting-state functional magnetic resonance imaging data were collected for 101 IGE patients and 106 healthy controls. The voxel-based functional connectivity (FC) between cerebral cortex and the cerebellum was contacted. The functional gradient and independent components analysis were applied to evaluate cerebello-cerebral functional integration on the voxel-based FC. Cerebellar motor components were further linked to cerebellar gradient. Results revealed cerebellar motor functional modules were closely related to cerebral motor components. The altered mapping of cerebral motor components to cerebellum was observed in motor module in patients with IGE. In addition, patients also showed compression in cerebello-cerebral functional gradient between motor and cognition modules. Interestingly, the contribution of the motor components to the gradient was unbalanced between bilateral primary sensorimotor components in patients: the increase was observed in cerebellar cognitive module for the dominant hemisphere primary sensorimotor, but the decrease was found in the cerebellar cognitive module for the nondominant hemisphere primary sensorimotor. The present findings suggest that the cerebral primary motor system affects the hierarchical architecture of cerebellum, and substantially contributes to the functional integration evidence to understand the motor functional abnormality in IGE patients.

Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data.

INTRODUCTION: The rapid development of COVID-19 vaccines has provided crucial tools for pandemic control, but the occurrence of vaccine-related adverse events (AEs) underscores the need for comprehensive monitoring. METHODS: This study analyzed the Vaccine Adverse Event Reporting System (VAERS) data from 2020-2022 using statistical methods such as zero-truncated Poisson regression and logistic regression to assess associations with age, gender groups, and vaccine manufacturers. RESULTS: Logistic regression identified 26 System Organ Classes (SOCs) significantly associated with age and gender. Females displayed especially higher odds in SOC 19 (Pregnancy, puerperium and perinatal conditions), while males had higher odds in SOC 25 (Surgical and medical procedures). Older adults (>65) were more prone to symptoms like Cardiac disorders, whereas those aged 18-65 showed susceptibility to AEs like Skin and subcutaneous tissue disorders. Moderna and Pfizer vaccines induced fewer SOC symptoms compared to Janssen and Novavax. The zero-truncated Poisson regression model estimated an average of 4.243 symptoms per individual. CONCLUSION: These findings offer vital insights into vaccine safety, guiding evidence-based vaccination strategies and monitoring programs for precise and effective outcomes.

"Listen to your heart": A novel interoceptive strategy for real-time fMRI neurofeedback training of anterior insula activity.

Real-time fMRI (rt-fMRI) neurofeedback (NF) training is a novel non-invasive technique for volitional brain modulation. Given the important role of the anterior insula (AI) in human cognitive and affective processes, it has become one of the most investigated regions in rt-fMRI studies. Most rt-fMRI insula studies employed emotional recall/imagery as the regulation strategy, which may be less effective for psychiatric disorders characterized by altered emotional processing. The present study thus aimed to examine the feasibility of a novel interoceptive strategy based on heartbeat detection in rt-fMRI guided AI regulation and its associated behavioral changes using a randomized double-blind, sham feedback-controlled between-subject design. 66 participants were recruited and randomly assigned to receive either NF from the left AI (LAI) or sham feedback from a control region while using the interoceptive strategy. N = 57 participants were included in the final data analyses. Empathic and interoceptive pre-post training changes were collected as behavioral measures of NF training effects. Results showed that participants in the NF group exhibited stronger LAI activity than the control group with LAI activity being positively correlated with interoceptive accuracy following NF training, although there were no significant increases of LAI activity over training sessions. Importantly, ability of LAI regulation could be maintained in a transfer session without feedback. Successful LAI regulation was associated with strengthened functional connectivity of the LAI with cognitive control, memory and learning, and salience/interoceptive networks. The present study demonstrated for the first time the efficacy of a novel regulation strategy based on interoceptive processing in up-regulating LAI activity. Our findings also provide proof of concept for the translational potential of this strategy in rt-fMRI AI regulation of psychiatric disorders characterized by altered emotional processing.

Mapping Vaccine Names in Clinical Trials to Vaccine Ontology using Cascaded Fine-Tuned Domain-Specific Language Models.

Background: Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects. ClinicalTrials.gov is a valuable repository of clinical trial information, but the vaccine data in them lacks standardization, leading to challenges in automatic concept mapping, vaccine-related knowledge development, evidence-based decision-making, and vaccine surveillance. Results: In this study, we developed a cascaded framework that capitalized on multiple domain knowledge sources, including clinical trials, Unified Medical Language System (UMLS), and the Vaccine Ontology (VO), to enhance the performance of domain-specific language models for automated mapping of VO from clinical trials. The Vaccine Ontology (VO) is a community-based ontology that was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Our methodology involved extracting and annotating data from various sources. We then performed pre-training on the PubMedBERT model, leading to the development of CTPubMedBERT. Subsequently, we enhanced CTPubMedBERT by incorporating SAPBERT, which was pretrained using the UMLS, resulting in CTPubMedBERT + SAPBERT. Further refinement was accomplished through fine-tuning using the Vaccine Ontology corpus and vaccine data from clinical trials, yielding the CTPubMedBERT + SAPBERT + VO model. Finally, we utilized a collection of pre-trained models, along with the weighted rule-based ensemble approach, to normalize the vaccine corpus and improve the accuracy of the process. The ranking process in concept normalization involves prioritizing and ordering potential concepts to identify the most suitable match for a given context. We conducted a ranking of the Top 10 concepts, and our experimental results demonstrate that our proposed cascaded framework consistently outperformed existing effective baselines on vaccine mapping, achieving 71.8% on top 1 candidate's accuracy and 90.0% on top 10 candidate's accuracy. Conclusion: This study provides a detailed insight into a cascaded framework of fine-tuned domain-specific language models improving mapping of VO from clinical trials. By effectively leveraging domain-specific information and applying weighted rule-based ensembles of different pre-trained BERT models, our framework can significantly enhance the mapping of VO from clinical trials.

The impact of adjuvant EGFR-TKIs and 14-gene molecular assay on stage I non-small cell lung cancer with sensitive EGFR mutations.

Background: Currently, the role of EGFR-TKIs as adjuvant therapy for stage I, especially IA NSCLC, after surgical resection remains unclear. We aimed to compare the effect of adjuvant EGFR-TKIs with observation in such patients by incorporating an established 14-gene molecular assay for risk stratification. Methods: This retrospective cohort study was conducted at the First Affiliated Hospital of Guangzhou Medical University (Study ID: ChNCRCRD-2022-GZ01). From March 2013 to February 2019, completely resected stage I NSCLC (8th TNM staging) patients with sensitive EGFR mutation were included. Patients with eligible samples for molecular risk stratification were subjected to the 14-gene prognostic assay. Inverse probability of treatment weighting (IPTW) was employed to minimize imbalances in baseline characteristics. Findings: A total of 227 stage I NSCLC patients were enrolled, with 55 in EGFR-TKI group and 172 in the observation group. The median duration of follow-up was 78.4 months. After IPTW, the 5-year DFS (HR = 0.30, 95% CI, 0.14-0.67; P = 0.003) and OS (HR = 0.26, 95% CI, 0.07-0.96; P = 0.044) of the EGFR-TKI group were significantly better than the observation group. For subgroup analyses, adjuvant EGFR-TKIs were associated with favorable 5-year DFS rates in both IA (100.0% vs. 84.5%; P = 0.007), and IB group (98.8% vs. 75.3%; P = 0.008). The 14-gene assay was performed in 180 patients. Among intermediate-high-risk patients, EGFR-TKIs were associated with a significant improvement in 5-year DFS rates compared to observation (96.0% vs. 70.5%; P = 0.012), while no difference was found in low-risk patients (100.0% vs. 94.9%; P = 0.360). Interpretation: Our study suggested that adjuvant EGFR-TKI might improve DFS and OS of stage IA and IB EGFR-mutated NSCLC, and the 14-gene molecular assay could help patients that would benefit the most from treatment. Funding: This work was supported by China National Science Foundation (82022048, 82373121).

Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study.

BACKGROUND: There is an unmet clinical need for accurate non-invasive tests to facilitate the early diagnosis of lung cancer. We propose a combined model of clinical, imaging, and cell-free DNA methylation biomarkers that aims to improve the classification of pulmonary nodules. METHODS: We conducted a prospective specimen collection and retrospective masked evaluation study. We recruited participants with a solitary pulmonary nodule sized 5-30 mm from 24 hospitals across 20 cities in China. Participants who were aged 18 years or older and had been referred with 5-30 mm non-calcified and solitary pulmonary nodules, including solid nodules, part solid nodules, and pure ground-glass nodules, were included. We developed a combined clinical and imaging biomarkers (CIBM) model by machine learning for the classification of malignant and benign pulmonary nodules in a cohort (n=839) and validated it in two cohorts (n=258 in the first cohort and n=283 in the second cohort). We then integrated the CIBM model with our previously established circulating tumour DNA methylation model (PulmoSeek) to create a new combined model, PulmoSeek Plus (n=258), and verified it in an independent cohort (n=283). The clinical utility of the models was evaluated using decision curve analysis. A low cutoff (0·65) for high sensitivity and a high cutoff (0·89) for high specificity were applied simultaneously to stratify pulmonary nodules into low-risk, medium-risk, and high-risk groups. The primary outcome was the diagnostic performance of the CIBM, PulmoSeek, and PulmoSeek Plus models. Participants in this study were drawn from two prospective clinical studies that were registered (NCT03181490 and NCT03651986), the first of which was completed, and the second of which is ongoing because 25% of participants have not yet finished the required 3-year follow-up. FINDINGS: We recruited a total of 1380 participants. 1097 participants were enrolled from July 7, 2017, to Feb 12, 2019; 839 participants were used for the CIBM model training set, and the rest (n=258) for the first CIBM validation set and the PulmoSeek Plus training set. 283 participants were enrolled from Oct 26, 2018, to March 20, 2020, as an independent validation set for the PulmoSeek Plus model and the second validation set for the CIBM model. The CIBM model validation cohorts had area under the curves (AUCs) of 0·85 (95% CI 0·80-0·89) and 0·85 (0·81-0·89). The PulmoSeek Plus model had better discrimination capacity compared with the CIBM and PulmoSeek models with an increase of 0·05 in AUC (PulmoSeek Plus vs CIBM, 95% CI 0·022-0·087, p=0·001; and PulmoSeek Plus vs PulmoSeek, 0·018-0·083, p=0·002). The overall sensitivity of the PulmoSeek Plus model was 0·98 (0·97-0·99) at a fixed specificity of 0·50 for ruling out lung cancer. A high sensitivity of 0·98 (0·96-0·99) was maintained in early-stage lung cancer (stages 0 and I) and 0·99 (0·96-1·00) in 5-10 mm nodules. The decision curve showed that if an invasive intervention, such as surgical resection or biopsy, was deemed necessary at more than the risk threshold score of 0·54, the PulmoSeek Plus model would provide a standardised net benefit of 82·38% (76·06-86·79%), equivalent to correctly identifying approximately 83 of 100 people with lung cancer. Using the PulmoSeek Plus model to classify pulmonary nodules with two cutoffs (0·65 and 0·89) would have reduced 89% (105/118) of unnecessary surgeries and 73% (308/423) of delayed treatments. INTERPRETATION: The PulmoSeek Plus Model combining clinical, imaging, and cell-free DNA methylation biomarkers aids the early diagnosis of pulmonary nodules, with potential application in clinical decision making for the management of pulmonary nodules. FUNDING: The China National Science Foundation, the Key Project of Guangzhou Scientific Research Project, the High-Level University Construction Project of Guangzhou Medical University, the National Key Research & Development Programme, the Guangdong High Level Hospital Construction "Reaching Peak" Plan, the Guangdong Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, The Leading Projects of Guangzhou Municipal Health Sciences Foundation, the Key Research and Development Plan of Shaanxi Province of China, the Scheme of Guangzhou Economic and Technological Development District for Leading Talents in Innovation and Entrepreneurship, the Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship, the Scheme of Guangzhou for Leading Team in Innovation, the Guangzhou Development Zone International Science and Technology Cooperation Project, and the Science and Technology Planning Project of Guangzhou.

Pressure induced weakness of electrostatic interaction and solid decomposition in Cs-I compounds.

This work utilized first-principles calculations and the CALYPSO structure search technique to systematically investigate the crystal structure stability of CsxIy compounds under high pressures ranging from 0 to 500 GPa. Several new phases with both conventional and unconventional stoichiometries were predicted. Interestingly, we discovered a counter-intuitive phenomenon where Cs-I compounds decompose into Cs and I elemental solids under pressure. To understand the physical mechanism behind this pressure-induced decomposition, we examine the phenomenon from two distinct perspectives: enthalpy of formation and interatomic interactions. Our results suggest that the main cause is the weakening of electrostatic interactions leading to the decomposition, while the weak covalent interaction plays a minor role. From an energy perspective, the decrease in the formation of enthalpy (ΔH) is primarily due to a reduction in the difference of internal energy (ΔU). These findings provide valuable insights into the decomposition mechanism and high-pressure properties of alkali metal halides. The counterintuitive phenomenon of high-pressure charge transfer and decomposition may inspire new ideas and perspectives in the fields of geology and the study of alkali metal halides under extreme conditions.

Correction to: Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations.

[This corrects the article DOI: 10.1007/s11571-020-09577-7.].

Characterization of the signalling pathways involved in the repression of root nitrate uptake by nitrate in Arabidopsis thaliana.

In Arabidopsis thaliana, root high-affinity nitrate (NO3-) uptake depends mainly on NRT2.1, 2.4, and 2.5, which are repressed by high NO3- supply at the transcript level. For NRT2.1, this regulation is due to the action of (i) feedback down-regulation by N metabolites and (ii) repression by NO3- itself mediated by the transceptor NRT1.1(NPF6.3). However, for NRT2.4 and NRT2.5, the signalling pathway(s) remain unknown as do the molecular elements involved. Here we show that unlike NRT2.1, NRT2.4 and NRT2.5 are not induced in an NO3- reductase mutant but are up-regulated following replacement of NO3- by ammonium (NH4+) as the N source. Moreover, increasing the NO3- concentration in a mixed nutrient solution with constant NH4+ concentration results in a gradual repression of NRT2.4 and NRT2.5, which is suppressed in an nrt1.1 mutant. This indicates that NRT2.4 and NRT2.5 are subjected to repression by NRT1.1-mediated NO3- sensing, and not to feedback repression by reduced N metabolites. We further show that key regulators of NRT2 transporters, such as HHO1, HRS1, PP2C, LBD39, BT1, and BT2, are also regulated by NRT1.1-mediated NO3- sensing, and that several of them are involved in NO3- repression of NRT2.1, NRT2.4, and NRT2.5. Finally, we provide evidence that it is the phosphorylated form of NRT1.1 at the T101 residue, which is most active in triggering the NRT1.1-mediated NO3- regulation of all these genes. Altogether, these data led us to propose a regulatory model for high-affinity NO3- uptake in Arabidopsis, highlighting several NO3- transduction cascades downstream of the phosphorylated form of the NRT1.1 transceptor.