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.

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.

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.

Improving tabular data extraction in scanned laboratory reports using deep learning models.

OBJECTIVE: Medical laboratory testing is essential in healthcare, providing crucial data for diagnosis and treatment. Nevertheless, patients' lab testing results are often transferred via fax across healthcare organizations and are not immediately available for timely clinical decision making. Thus, it is important to develop new technologies to accurately extract lab testing information from scanned laboratory reports. This study aims to develop an advanced deep learning-based Optical Character Recognition (OCR) method to identify tables containing lab testing results in scanned laboratory reports. METHODS: Extracting tabular data from scanned lab reports involves two stages: table detection (i.e., identifying the area of a table object) and table recognition (i.e., identifying and extracting tabular structures and contents). DETR R18 algorithm as well as YOLOv8s were involved for table detection, and we compared the performance of PaddleOCR and the encoder-dual-decoder (EDD) model for table recognition. 650 tables from 632 randomly selected laboratory test reports were annotated and used to train and evaluate those models. For table detection evaluation, we used metrics such as Average Precision (AP), Average Recall (AR), AP50, and AP75. For table recognition evaluation, we employed Tree-Edit Distance (TEDS). RESULTS: For table detection, fine-tuned DETR R18 demonstrated superior performance (AP50: 0.774; AP75: 0.644; AP: 0.601; AR: 0.766). In terms of table recognition, fine-tuned EDD outperformed other models with a TEDS score of 0.815. The proposed OCR pipeline (fine-tuned DETR R18 and fine-tuned EDD), demonstrated impressive results, achieving a TEDS score of 0.699 and a TEDS structure score of 0.764. CONCLUSIONS: Our study presents a dedicated OCR pipeline for scanned clinical documents, utilizing state-of-the-art deep learning models for region-of-interest detection and table recognition. The high TEDS scores demonstrate the effectiveness of our approach, which has significant implications for clinical data analysis and decision-making.

The impact of postoperative adjuvant therapy on EGFR-mutated stage IA lung adenocarcinoma with micropapillary pathological subtypes.

BACKGROUND: Micropapillary (MPP) adenocarcinoma is considered one of the most aggressive pathological types of lung adenocarcinoma (LADC). This retrospective study aimed to evaluate the prognostic significance and benefit of postoperative adjuvant therapy (PAT) in stage IA LADC patients with different proportions of MPP components. MATERIALS AND METHODS: We retrospectively examined clinical stage IA LADC patients who underwent surgical resection between August 2012 and December 2019. In terms of the proportion of MPP components (TPM), the tumors were reclassified into three categories: MPP patterns absent (TPMN); low proportions of MPP components (TPML); and high proportions of MPP components (TPMH). The dates of recurrence and metastasis were identified based on physical examinations and were confirmed by histopathological examination. RESULTS: Overall, 505 (TPMN, n = 375; TPML, n = 92; TPMH, n = 38) patients harboring EGFR mutations were enrolled in the study. Male sex (P = 0.044), high pathological stage (P < 0.001), and MPP pathological subtype (P < 0.001) were more frequent in the TPM-positive (TPMP) group than in the TPM-negative (TPMN) group. Five-year disease-free survival (DFS) rates were significantly lower in the TPMP group than in the TPMN group (84.5% vs. 93.4%, P = 0.006). In addition, patients with high proportions (greater than 10%) of MPP components had worse overall survival (OS) (91.0% vs. 98.9%, P = 0.025) than those with low proportions (5%≤ TPM ≤ 10%). However, postoperative EGFR tyrosine kinase inhibitors (TKIs) or adjuvant chemotherapy (ACT) cannot improve DFS and OS between EGFR-mutated patients with different proportions of MPP components. CONCLUSION: MPP was related to earlier recurrence and shortened survival time, even in stage IA. Further research needs a larger sample size to clarify that EGFR-mutated stage IA patients with MPP components obtain survival benefits from adjuvant therapy.

Exploring the Mechanism of H2S Synthesis in Male Bactrian Camel Poll Glands Based on Data Independent Acquisition Proteomics and Non-Targeted Metabolomics.

During estrus, the poll glands of male Bactrian Camels (Camelus Bactrianus) become slightly raised, exuding a large amount of pale yellow watery secretion with a characteristic odor that may contain hydrogen sulfide (H2S). However, whether H2S can be synthesized in the poll glands of male Bactrian Camels and its role in inducing camel estrus remains unclear. This study aimed to identify differentially expressed proteins (DEPs) and signaling pathways in the poll gland tissues of male Bactrian Camels using data independent acquisition (DIA) proteomics. Additionally, gas chromatography-mass spectrometry (GC-MS) was performed to identify differentially expressed metabolites (DEMs) in the neck hair containing secretions during estrus in male Bactrian Camels, to explore the specific expression patterns and mechanisms in the poll glands of camels during estrus. The results showed that cystathionine-γ-lyase (CTH) and cystathionine-ß-synthase (CBS), which are closely related to H2S synthesis in camel poll glands during estrus, were mainly enriched in glycine, serine, and threonine metabolism, amino acid biosynthesis, and metabolic pathways. In addition, both enzymes were widely distributed and highly expressed in the acinar cells of poll gland tissues in camels during estrus. Meanwhile, the neck hair secretion contains high levels of amino acids, especially glycine, serine, threonine, and cystathionine, which are precursors for H2S biosynthesis. These results demonstrate that the poll glands of male Bactrian Camels can synthesize and secrete H2S during estrus. This study provides a basis for exploring the function and mechanism of H2S in the estrus of Bactrian Camels.

"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.

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.

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.

CALMODULIN-LIKE-38 and PEP1 RECEPTOR 2 integrate nitrate and brassinosteroid signals to regulate root growth.

Plants exhibit remarkable developmental plasticity, enabling them to adapt to adverse environmental conditions such as low nitrogen (N) in the soil. Brassinosteroids (BRs) promote root foraging for nutrients under mild N deficiency, but the crosstalk between the BR- and N-signaling pathways in the regulation of root growth remains largely unknown. Here, we show that CALMODULIN-LIKE-38 (CML38), a calmodulin-like protein, specifically interacts with the PEP1 RECEPTOR 2 (PEPR2), and negatively regulates root elongation in Arabidopsis (Arabidopsis thaliana) in response to low nitrate (LN). CML38 and PEPR2 are transcriptionally induced by treatments of exogenous nitrate and BR. Compared with Col-0, the single mutants cml38 and pepr2 and the double mutant cml38 pepr2 displayed enhanced primary root growth and produced more lateral roots under LN. This is consistent with their higher nitrate absorption abilities, and their stronger expression of nitrate assimilation genes. Furthermore, CML38 and PEPR2 regulate common downstream genes related to BR signaling, and they have positive roles in BR signaling. Low N facilitated BR signal transmission in Col-0 and CML38- or PEPR2-overexpressing plants, but not in the cml38 and pepr2 mutants. Taken together, our results illustrate a mechanism by which CML38 interacts with PEPR2 to integrate LN and BR signals for coordinating root development to prevent quick depletion of N resources in Arabidopsis.

Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation.

OBJECTIVES: This study aimed to establish a non-invasive radiomics model based on computed tomography (CT), with favorable sensitivity and specificity to predict EGFR mutation status in GGO-featured lung adenocarcinoma subsequently guiding the administration of targeted therapy. METHODS: Clinical-pathological information and preoperative CT images of 636 lung adenocarcinoma patients (464, 100, and 72 in the training, internal, and external validation sets, respectively) that underwent GGO lesions resection were included. A total of 1476 radiomics features were extracted with gradient boosting decision tree (GBDT). RESULTS: The established radiomics model containing 102 selected features showed an encouraging discrimination performance of EGFR mutation status (mutant or wild type), and the predictive ability was superior to that of the clinical model (AUC: 0.838 vs. 0.674, 0.822 vs. 0.730, and 0.803 vs. 0.746 for the training, internal validation, and external validation sets, respectively). The combined radiomics plus clinical model showed no additional benefit over the radiomics model in predicting EGFR status (AUC: 0.846 vs. 0.838, 0.816 vs. 0.822, and 0.811 vs. 0.803, respectively, in three cohorts). Uniquely, this model was validated in a cohort of lung adenocarcinoma patients who have undertaken adjuvant EGFR-TKI treatment and harbored unresected GGOs during the medication, leading to a significantly improved potency of EGFR-TKIs (response rate: 25.9% vs. 53.8%, p = 0.006; before and after prediction, respectively). CONCLUSION: This presented radiomics model can be served as a non-invasive and time-saving approach for predicting the EGFR mutation status in lung adenocarcinoma presenting as GGO. KEY POINTS: • We developed a GGO-specific radiomics model containing 102 radiomics features for EGFR mutation status differentiation. • An AUC of 0.822 and 0.803 in the internal and external validation cohorts, respectively, were achieved. • The radiomics model was utilized in clinical translation in an adjuvant EGFR-TKI treatment cohort with unresected GGOs. A significant improvement in the potency of EGFR-TKIs was achieved (response rate: 25.9% vs. 53.8%, p = 0.006; before and after prediction).

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora.

BACKGROUND: Clinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information from the original clinical trials is very challenging due to the complex and unstructured texts of such documents. Named entity recognition (NER) is a fundamental and necessary step to process and standardize the unstructured text in clinical trials using Natural Language Processing (NLP) techniques. METHODS: In this study we fine-tuned pre-trained language models to support the NER task on clinical trial eligibility criteria. We systematically investigated four pre-trained contextual embedding models for the biomedical domain (i.e., BioBERT, BlueBERT, PubMedBERT, and SciBERT) and two models for the open domains (BERT and SpanBERT), for NER tasks using three existing clinical trial eligibility criteria corpora. In addition, we also investigated the feasibility of data augmentation approaches and evaluated their performance. RESULTS: Our evaluation results using tenfold cross-validation show that domain-specific transformer models achieved better performance than the general transformer models, with the best performance obtained by the PubMedBERT model (F1-scores of 0.715, 0.836, and 0.622 for the three corpora respectively). The data augmentation results show that it is feasible to leverage additional corpora to improve NER performance. CONCLUSIONS: Findings from this study not only demonstrate the importance of contextual embeddings trained from domain-specific corpora, but also shed lights on the benefits of leveraging multiple data sources for the challenging NER task in clinical trial eligibility criteria text.

CTH/H2S Regulates LPS-Induced Inflammation through IL-8 Signaling in MAC-T Cells.

Hydrogen sulfide (H2S), as an endogenous gaseous signaling molecule, plays an important role in the inflammatory process. Our previous study found that Cystathionine-γ-lyase (CTH) and H2S are correlated with the occurrence and development of Clinical Mastitis (CM) in Holstein cows. However, the functions and regulatory mechanisms of CTH/H2S are still unknown. In this study, the inflammatory mammary cell model based on the MAC-T cell line was established by Lipopolysaccharide (LPS)-induced manner to further explore the function and regulatory mechanism of CTH/H2S in cows with CM. In the inflammatory MAC-T cell, the CTH expression and H2S production were both repressed in an LPS-dose dependent manner, which demonstrated that CTH/H2S is related to the progression of inflammation. The inhibition of CTH/H2S using a selective CTH inhibitor, ß-cyano-l-Alanine (BCA), promoted LPS-induced inflammation response and the expression of inflammatory cytokines. However, this was reversed by the H2S donor NaHS, demonstrating that H2S can protect cells from inflammatory damage. Intriguingly, interleukin-8 (IL-8) showed an inverse expression pattern correlated with the H2S-mediated cell protection effect during the inflammation process, and the inhibition test using a selective IL-8 receptor antagonist, SB225002, showed that IL-8 signaling plays a critical role in mediating endogenous H2S synthesis, and CTH/H2S exerts its anti-inflammation via IL-8-mediated signaling. This study provided support for the prevention and treatment of CM and the development of a novel anti-inflammatory strategy.

WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis.

The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

Immune-related adverse events of a PD-L1 inhibitor plus chemotherapy versus a PD-L1 inhibitor alone in first-line treatment for advanced non-small cell lung cancer: A meta-analysis of randomized control trials.

BACKGROUND: The addition of chemotherapy to a programmed death 1/programmed death ligand 1 (PD-L1) inhibitor is a more effective option as a first-line treatment for advanced non-small cell lung cancer (NSCLC). It might also inhibit an overactive immune response and thereby reduce immune-related adverse events (irAEs). This meta-analysis assessed the rate of irAEs with a PD-(L)1 inhibitor plus chemotherapy (I+C) versus a PD-(L)1 inhibitor alone (I) and evaluated the indirect relative risk (RR) of I+C versus I. METHODS: The protocol of this study was registered with PROSPERO (CRD42020139923). The pooled rates of irAEs at different grades were calculated by a single-arm meta-analysis weighted by sample size, and RRs were determined by direct meta-analysis and indirect treatment comparison. RESULTS: Overall, I+C had a lower rate of grade 3 or higher irAEs than I (7.1% vs 10.6%; indirect RR, 0.516; 95% confidence interval [CI], 0.291-0.916), although irAEs of any grade were similar. The rate of pneumonitis with I+C was lower than the rate with I for any grade (5.9% vs 7.1%; indirect RR, 0.217; 95% CI, 0.080-0.588) and for grade 3 or higher. In the endocrine system, I+C was associated with a lower overall ratein comparison with I (16.1% vs 20.1%; indirect RR, 0.260; 95% CI, 0.120-0.564), whereas irAEs of the digestive system were similar with I+C and I. In other systems, I+C decreased the rate of skin reactions, including rash, in comparison with I (10.4% vs 12.9%; indirect RR, 0.474; 95% CI, 0.299-0.751). The rate of grade 3 or higher skin reactions (excluding rash) also decreased with I+C versus I (1.1% vs 2.0%) with an indirect RR of 0.158 (95% CI, 0.032-0.765), whereas other included irAEs were similar. CONCLUSIONS: In comparison with a PD-(L)1 inhibitor alone, a combination with chemotherapy for the first-line treatment of NSCLC decreased the rates of most irAEs, such as pneumonitis and endocrine and skin reactions, and the overall rate. LAY SUMMARY: In the first-line treatment of advanced non-small cell lung cancer (NSCLC), the addition of chemotherapy to a programmed death 1/programmed death ligand 1 (PD-(L)1) inhibitor is a more effective option. Adding chemotherapy might reduce immune-related adverse events (irAEs). Thus, this article assesses the rate of irAEs with a PD-(L)1 inhibitor plus chemotherapy (I+C) in comparison with a PD-(L)1 inhibitor alone (I) and evaluates the indirect relative risk (RR) with I+C versus I. The key finding is that in comparison with a PD-(L)1 inhibitor alone, a combination with chemotherapy for the first-line treatment of NSCLC decreases the rates of most irAEs, such as pneumonitis and endocrine and skin reactions, and the overall rate.

Reference Electrode Standardization Interpolation Technique (RESIT): A Novel Interpolation Method for Scalp EEG.

"Bad channels" are common phenomena during scalp electroencephalography (EEG) recording that arise due to various technique-related reasons, and reconstructing signals from bad channels is an inevitable choice in EEG processing. However, current interpolation methods are all based on purely mathematical interpolation theory, ignoring the neurophysiological basis of the EEG signals, and their performance needs to be further improved, especially when there are many scattered or adjacent bad channels. Therefore, a new interpolation method, named the reference electrode standardization interpolation technique (RESIT), was developed for interpolating scalp EEG channels. Resting-state and event-related EEG datasets were used to investigate the performance of the RESIT. The main results showed that (1) assuming 10% bad channels, RESIT can reconstruct the bad channels well; (2) as the percentage of bad channels increased (from 2% to 85%), the absolute and relative errors between the true and RESIT-reconstructed signals generally increased, and the correlations between the true and RESIT signals decreased; (3) for a range of bad channel percentages (2% ~ 85%), the RESIT had lower absolute error (approximately 2.39% ~ 33.5% reduction), lower relative errors (approximately 1.3% ~ 35.7% reduction) and higher correlations (approximately 2% ~ 690% increase) than traditional interpolation methods, including neighbor interpolation (NI) and spherical spline interpolation (SSI). In addition, the RESIT was integrated into the EEG preprocessing pipeline on the WeBrain cloud platform ( https://webrain.uestc.edu.cn/ ). These results suggest that the RESIT is a promising interpolation method for both separate and simultaneous EEG preprocessing that benefits further EEG analysis, including event-related potential (ERP) analysis, EEG network analysis, and strict group-level statistics.

Stable nitrogen-rich scandium nitrides and their bonding features under ambient conditions.

All-nitrogen salts have attracted extensive attention because of their unique chemical, physical properties, and potential applications as high-energy density materials. Using first-principles calculations and a particle swarm optimization structure search method, the pressure versus composition phase diagram of the Sc-N system is established. A new stable phase of C2/m-ScN5 with the intriguing 2D N106- is identified for the first time and we also found ScN3 with the P1[combining macron] structure which has been reported before. Under ambient conditions, both of them have high kinetic and thermodynamic stability with high energy density (2.40 kJ g-1 and 4.23 kJ g-1 relative to ScN and N2 gas). The analyses of chemical bonding pattern indicate that the nitrogen atoms in the N66- chains are connected by covalent bonds with a combined σ and π bond character. We also give a possible high-pressure experimental route to P1[combining macron]-ScN3 and C2/m-ScN5 at modest pressure. We expect that our theoretical research could encourage experimental realization in the future and contribute to the understanding of the bonding features of nitrogen chains.

Arabidopsis NRT1.2 interacts with the PHOSPHOLIPASE Dα1 (PLDα1) to positively regulate seed germination and seedling development in response to ABA treatment.

NRT1.2 has been characterized as a low-affinity nitrate transporter and an abscisic acid (ABA) transporter in Arabidopsis. In this study, we demonstrate that NRT1.2 positively regulated the ABA response during germination and seedling development. The transgenic Arabidopsis NRT1.2-over-expressionors showed increased sensitivity to ABA during these processes. qRT-PCR assays indicated that NRT1.2 over-production in 7-days-old seedlings up-regulated the expression of ABA-responsive genes: ABI1, ABI2, ABI3, ABI4, ABI5, RAB18, RD29A, and RD29B and PHOSPHOLIPASE Dα1 (PLDα1). The expression of these genes was suppressed in the nrt1.2 mutant in comparison with the wild type following ABA treatment. Importantly, bimolecular fluorescence complementation assays indicated that NRT1.2 interacts with PLDα1 at the plasma membrane. Their interaction was further confirmed by using yeast two hybrid (Y2H) experiments with the mating-based split ubiquitin system (MbSUS). Moreover, genetic assays indicated that PLDα1 acts epistatically on NRT1.2 to affect ABA signaling. Taken together, our results provide detailed mechanisms of NRT1.2 in ABA-mediated seed germination and seedling development.

Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak is evolving rapidly worldwide. OBJECTIVE: To evaluate the risk of serious adverse outcomes in patients with COVID-19 by stratifying the comorbidity status. METHODS: We analysed data from 1590 laboratory confirmed hospitalised patients from 575 hospitals in 31 provinces/autonomous regions/provincial municipalities across mainland China between 11 December 2019 and 31 January 2020. We analysed the composite end-points, which consisted of admission to an intensive care unit, invasive ventilation or death. The risk of reaching the composite end-points was compared according to the presence and number of comorbidities. RESULTS: The mean age was 48.9 years and 686 (42.7%) patients were female. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached the composite end-points. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD (HR (95% CI) 2.681 (1.424-5.048)), diabetes (1.59 (1.03-2.45)), hypertension (1.58 (1.07-2.32)) and malignancy (3.50 (1.60-7.64)) were risk factors of reaching the composite end-points. The hazard ratio (95% CI) was 1.79 (1.16-2.77) among patients with at least one comorbidity and 2.59 (1.61-4.17) among patients with two or more comorbidities. CONCLUSION: Among laboratory confirmed cases of COVID-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.