Mo5N6 nanosheets for fluorescent quenching and target recognition: Highly selectively sensing of sodium hexametaphosphate.

Typical fluorescent biosensors use fluorescently labeled ssDNA for target recognition and nanomaterials for signal transduction. Herein, we propose a reverse sensing strategy that Mo5N6 nanosheets are used for target recognition while fluorescein (FAM)-labeled ssDNA only serves for signal generation. We discover that Mo5N6 nanosheets show high fluorescence quenching ability (>95%) and selective recognition for sodium hexametaphosphate (SHMP). After FAM-labeled ssDNA is adsorbed on Mo5N6 nanosheets, the fluorescence is quenched due to the photoinduced electron transfer (PET) effect between FAM and Mo5N6 nanosheets. SHMP can specifically displace the adsorbed FAM-labeled ssDNA from Mo5N6 nanosheets, resulting in more than 80% fluorescence recovery on addition of 5 µmol L-1 SHMP. This biosensor can sensitively detect SHMP down to 150 nmol L-1 and selectively recognize SHMP over glucose, lactose, common amino acids, Zn2+, Mg2+, Ca2+ and other phosphates (such as Na2HPO4, sodium pyrophosphate, sodium tripolyphosphate). This biosensor also shows great potential for the detection of SHMP in bacon sample. This work not only provides a facile sensitive and selective biosensor for SHMP but also exploits the application of transition metal nitrides in the field of sensing and biosensing.

Lobar or sublobar resection for early-stage second primary lung cancer ≤ 3 cm in size: a SEER population-based study.

PURPOSE: Surgical strategy for second primary lung cancer (SPLC) may be more conservative due to influence of first primary lung cancer (FPLC). The optimal surgical method for SPLC warrants discussion. We aimed to explore a more suitable surgical approach for early-stage (T1-T2N0, ≤ 3 cm) SPLC and provide insights for clinical practice. METHODS: A retrospective study was conducted using data from the Surveillance, Epidemiology and End Results database between 2004 and 2018, and data of patients with early-stage SPLC who underwent secondary surgery were collected. Propensity score matching (PSM) reduced potential bias between lobar and sublobar resection groups. The effect of lobar and sublobar resection on overall survival (OS) was assessed in all patients and subgroups. RESULTS: A total of 714 patients who met the study entry criteria were enrolled, including 476 patients in the sublobar resection group (66.67%) and 238 patients in the lobar resection group (33.33%). There was no difference in OS between the lobar and sublobar resection groups before and after PSM (P = 0.289) and (P = 0.608), respectively. Subgroup analyses showed that lobar resection achieved a significantly better OS than sublobar resection only in patients with an SPLC tumor size of 2-3 cm (P < 0.05). CONCLUSION: The OS of sublobar resection was not significantly different from that of lobar resection for early-stage SPLC. For SPLC with a 2-3 cm tumor size, lobar resection is more advantageous than sublobar resection.

Identification of 5 hub genes for diagnosis of coronary artery disease.

Background: Coronary artery disease (CAD) is a main cause leading to increasing mortality of cardiovascular disease (CVD) worldwide. We aimed to discover marker genes and develop a diagnostic model for CAD. Methods: CAD-related target genes were searched from DisGeNET. Count expression data and clinical information were screened from the GSE202626 dataset. edgeR package identified differentially expressed genes (DEGs). Using online STRING tool and Cytoscape, protein-protein reactions (PPI) were predicted. WebGestaltR package was employed to functional enrichment analysis. We used Metascape to conduct module-based network analysis. VarElect algorithm provided genes-phenotype correlation analysis. Immune infiltration was assessed by ESTIMATE package and ssGSEA analysis. mRNAsi was determined by one class logistic regression (OCLR). A diagnostic model was constructed by SVM algorithm. Results: 162 target genes were screened by intersection 1,714 DEGs and 1,708 CAD related target genes. 137 target genes of the 162 target genes were obtained using PPI analysis, in which those targets were enriched in inflammatory cytokine pathways, such as chemokine signaling pathway, and IL-17 signaling pathway. From the above 137 target genes, four functional modules (MCODE1-4) were extracted. From the 162 potential targets, CAD phenotype were directly and indirectly associated with 161 genes and 22 genes, respectively. Finally, 5 hub genes (CCL2, PTGS2, NLRP3, VEGFA, LTA) were screened by intersections with the top 20, directly and indirectly, and genes in MCODE1. PTGS2, NLRP3 and VEGFA were positively, while LTA was negatively correlated with immune cells scores. PTGS2, NLRP3 and VEGFA were negatively, while LTA was positively correlated with mRNAsi. A diagnostic model was successfully established, evidenced by 92.59% sensitivity and AUC was 0.9230 in the GSE202625 dataset and 94.11% sensitivity and AUC was 0.9706 in GSE120774 dataset. Conclusion: In this work, we identified 5 hub genes, which may be associated with CAD development.

Anti-apoptotic and anti-fibrotic efficacy of exercise training in hypertensive hearts: A systematic review.

Background: This review aims to summarize the antiapoptotic, pro-survival, and antifibrotic effects of exercise training in hypertensive hearts. Methods: Keyword searches were conducted in PubMed, Web of Science, and Scopus in May 2021. Research published in English on the effects of exercise training on the apoptosis, survival, and fibrosis pathways in hypertension was included. The CAMARADES checklist was used to determine the quality of the studies. Two reviewers independently implemented predesigned protocols for the search and selection of studies, the assessment of study quality, and the evaluation of the strength of evidence. Results: Eleven studies were included after selection. The duration of the exercise training ranged from 5 to 27 weeks. Nine studies showed that exercise training improved cardiac survival rates by increasing IGF-1, IGF-1 receptor, p-PI3K, Bcl-2, HSP 72, and p-Akt. Furthermore, 10 studies showed that exercise training reduced apoptotic pathways by downregulating Bid, t-Bid, Bad, Bak, Bax, TNF, and FADD. Finally, two studies reported the modification and subsequent improvement of physiological characteristics of fibrosis and decreased MAPK p38 and PTEN levels by exercise training in the left ventricle of the heart. Conclusions: The findings of the review showed that exercise training could improve cardiac survival rates and attenuate cardiac apoptotic and fibrotic pathways in hypertension, suggesting that exercise training could act as a therapeutic approach to prevent hypertension-induced cardiac apoptosis and fibrosis. Systematic Review Registration: https://www.crd.york.ac.uk, identifier: CRD42021254118.

High-accuracy protein model quality assessment using attention graph neural networks.

Great improvement has been brought to protein tertiary structure prediction through deep learning. It is important but very challenging to accurately rank and score decoy structures predicted by different models. CASP14 results show that existing quality assessment (QA) approaches lag behind the development of protein structure prediction methods, where almost all existing QA models degrade in accuracy when the target is a decoy of high quality. How to give an accurate assessment to high-accuracy decoys is particularly useful with the available of accurate structure prediction methods. Here we propose a fast and effective single-model QA method, QATEN, which can evaluate decoys only by their topological characteristics and atomic types. Our model uses graph neural networks and attention mechanisms to evaluate global and amino acid level scores, and uses specific loss functions to constrain the network to focus more on high-precision decoys and protein domains. On the CASP14 evaluation decoys, QATEN performs better than other QA models under all correlation coefficients when targeting average LDDT. QATEN shows promising performance when considering only high-accuracy decoys. Compared to the embedded evaluation modules of predicted ${C}_{\alpha^{-}} RMSD$ (pRMSD) in RosettaFold and predicted LDDT (pLDDT) in AlphaFold2, QATEN is complementary and capable of achieving better evaluation on some decoy structures generated by AlphaFold2 and RosettaFold. These results suggest that the new QATEN approach can be used as a reliable independent assessment algorithm for high-accuracy protein structure decoys.

[Preliminary Recommendations on the Timing of Lung Surgery after 
Novel Coronavirus Infection in Patients with Pulmonary Nodules and Lung Cancer].

In recent years, the corona virus disease 2019 (COVID-19) pandemic has had a huge impact on the global medical, political and economic fields. Since the beginning of the COVID-19 epidemic, our understanding of the impact of COVID-19 has grown exponentially. Recently, the COVID-19 epidemic has changed rapidly in China, and there has been controversy over how to carry out surgical operations for patients with lung neoplastic lesions. Some studies have shown that lung cancer patients undergoing surgery are more likely to experience respiratory failure and perioperative death after contracting COVID-19 than the general population, however, delays in cancer treatment are also associated with increased mortality among these patients. In particular, the novel coronavirus Omikron variant has a higher transmissibility and may escape the immunity obtained through the previous novel coronavirus infection and vaccination. In order to minimize the risk of novel coronavirus infection in surgical patients, it is necessary to develop new treatment guidelines, expert consensus and preventive measures. However, the current rapid change of the epidemic situation has led to insufficient time and evidence to develop guidelines and consensus. Therefore, thoracic surgeons need to evaluate specific patient populations at higher risk of severe complications before surgery and weigh the benefit of surgical treatment against the risk of novel coronavirus infection. We try to give some recommendations on lung surgery during the current domestic epidemic situation based on the guidelines and consensus of oncology and thoracic surgery organizations in different regions on lung surgery.
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Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network.

Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinity of a protein-ligand complex with the known bound structure because of the potential preference of scoring system. In recent years, deep learning (DL) techniques have been applied to SFs without sophisticated feature engineering. Nevertheless, existing methods cannot model the differential contribution of atoms in various regions of proteins, and the relationship between atom properties and intermolecular distance is also not fully explored. We propose a novel empirical graph neural network for accurate protein-ligand binding affinity prediction (EGNA). Graphs of protein, ligand and their interactions are constructed based on different regions of each bound complex. Proteins and ligands are effectively represented by graph convolutional layers, enabling the EGNA to capture interaction patterns precisely by simulating empirical SFs. The contributions of different factors on binding affinity can thus be transparently investigated. EGNA is compared with the state-of-the-art machine learning-based SFs on two widely used benchmark data sets. The results demonstrate the superiority of EGNA and its good generalization capability.

BindWeb: A web server for ligand binding residue and pocket prediction from protein structures.

Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains challenging. In this study, we introduce an easy-to-use web server BindWeb for ligand-specific and ligand-general binding residue and pocket prediction from protein structures. BindWeb integrates a graph neural network GraphBind with a hybrid convolutional neural network and bidirectional long short-term memory network DELIA to identify binding residues. Furthermore, BindWeb clusters the predicted binding residues to binding pockets with mean shift clustering. The experimental results and case study demonstrate that BindWeb benefits from the complementarity of two base methods. BindWeb is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/BindWeb/.

Neoadjuvant camrelizumab and chemotherapy in patients with resectable stage IIIA squamous non-small-cell lung cancer: Clinical experience of three cases.

Neoadjuvant immunochemotherapy has attracted much attention as a treatment for locally advanced non-small-cell lung cancer. However, there is scarce evidence of the safety and efficacy of camrelizumab as neoadjuvant in lung cancer. Here, we present three patients who were diagnosed with IIIA squamous non-small-cell lung cancer from September to December in 2020 and received two cycles of neoadjuvant camrelizumab plus nab-paclitaxel and nedaplatin, followed by surgical resection. All three patients had a reduction in the tumor size on CT image and not delayed planned surgery. We did not observe grade 3 or 4 adverse events. Two of the three patients achieved a major pathological response (MPR), including one complete tumor regression of the primary lung tumor. Multiplex fluorescent immunohistochemistry revealed that CD8+ T cells, FoxP3+ regulatory T cells, and PD-L1 expression on immune cells in the surgical specimen were much higher than in the pretreatment biopsy sample in patients with MPR. This was not observed in the patient without MPR. Camrelizumab plus chemotherapy could potentially be a neoadjuvant regimen for resectable IIIA squamous non-small-cell lung cancer, with a high MPR proportion, and did not compromise surgical procedure. Our findings should be validated in a future randomized clinical trial.

Survival Benefits for Pulmonary Adenocarcinoma With Malignant Pleural Effusion After Thoracoscopic Surgical Treatment: A Real-World Study.

Objectives: Malignant cells in the pleural fluid or pleural metastasis are classified as stage IV non-small cell lung cancer. Radical surgery is generally considered not suitable for such patients. The aim of our study was to discuss the effectiveness of video-assisted thoracoscopic surgery (VATS) in such patients. Methods: A retrospective analysis of the clinical records of 195 patients was performed. These patients were all diagnosed with locally advanced pulmonary adenocarcinomas with malignant pleural effusion (MPE, M1a) but no distant organ metastasis. The 195 patients included 96 patients who underwent VATS plus chemotherapy and 99 patients who received thoracic drainage plus chemotherapy. The baseline characteristics of the patients included age, gender, smoking history, Eastern Cooperative Oncology Group (ECOG) score, and number of chemotherapy cycles (2-4 cycles or >4 cycles); we also analyzed clinical characteristics including the specific surgical options of the VATS group. Results: In multivariate analysis, when compared to the thoracic drainage group, the VATS group remained significantly associated with the overall survival [HR=0.480 (95%CI 0.301-0.765)]; when compared to the lobectomy, the sub-lobectomy and the palliative surgery, remained significantly associated with the overall survival [HR=0.637 (95%CI 0.409-0.993) and HR=0.548 (95%CI 0.435-0.832), respectively]. The median survival time (MST) of patients who underwent VATS (n = 96, 49.2%) was 25 months (95% CI 22.373-27.627) whereas the patients who received thoracic drainage (n = 99, 50.8%) was 11 months (95% CI 9.978-12.022). For patients who underwent VATS, the MST of patients who received a lobectomy (n = 50, 52.1%) was 27 months (95% CI 22.432-31.568), the MST of patients who received a sub-lobectomy plus pleurodesis (n = 26, 27.1%) was 27 months (95% CI 19.157-34.843), and the MST of patients who received only pleurodesis (n = 20, 20.8%) was 12 months (95% CI 7.617-16.383). Conclusion: For pulmonary adenocarcinomas with MPE, receiving a lobectomy or sub-lobectomy plus pleurodesis with VATS was associated with improved survival compared with patients who only received thoracic drainage and chemotherapy. Our results and previously published data may justify the use of VATS for treating pulmonary adenocarcinomas with MPE.

Molecular Features of a Primary Transmural Tracheal Schwannoma: Clinical Experience and Review of the Literature.

Primary tracheal schwannoma is a rare disease with no specific symptoms. At the molecular level, neurofibromatosis type 2 (NF2) gene mutation of Schwann cells is the major tumorigenic element. Herein, we present the case of a 54-year-old man with refractory shortness of breath and dry cough, which was resistant to bronchodilator treatment. Computed tomography revealed a transmural mass in the dorsolateral trachea. The tumor was surgically resected, and the diagnosis of schwannoma was confirmed by pathological examination. Furthermore, for this case, we performed whole-exome sequencing and identified several novel mutated schwannoma genes. The specific roles of these mutations need further confirmation.

Fast protein structure comparison through effective representation learning with contrastive graph neural networks.

Protein structure alignment algorithms are often time-consuming, resulting in challenges for large-scale protein structure similarity-based retrieval. There is an urgent need for more efficient structure comparison approaches as the number of protein structures increases rapidly. In this paper, we propose an effective graph-based protein structure representation learning method, GraSR, for fast and accurate structure comparison. In GraSR, a graph is constructed based on the intra-residue distance derived from the tertiary structure. Then, deep graph neural networks (GNNs) with a short-cut connection learn graph representations of the tertiary structures under a contrastive learning framework. To further improve GraSR, a novel dynamic training data partition strategy and length-scaling cosine distance are introduced. We objectively evaluate our method GraSR on SCOPe v2.07 and a new released independent test set from PDB database with a designed comprehensive performance metric. Compared with other state-of-the-art methods, GraSR achieves about 7%-10% improvement on two benchmark datasets. GraSR is also much faster than alignment-based methods. We dig into the model and observe that the superiority of GraSR is mainly brought by the learned discriminative residue-level and global descriptors. The web-server and source code of GraSR are freely available at www.csbio.sjtu.edu.cn/bioinf/GraSR/ for academic use.

Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks.

Amphipathic helix (AH)features the segregation of polar and nonpolar residues and plays important roles in many membrane-associated biological processes through interacting with both the lipid and the soluble phases. Although the AH structure has been discovered for a long time, few ab initio machine learning-based prediction models have been reported, due to the limited amount of training data. In this study, we report a new deep learning-based prediction model, which is composed of a residual neural network and the uneven-thresholds decision algorithm. It is constructed on 121 membrane proteins, in total 51640 residue samples, which are curated from an up-to-date membrane protein structure database. Through a rigid 10-fold nested cross-validation experiment, we demonstrate that our model can achieve promising predictions and exceed current state-of-the-art approaches in this field. This presents a new avenue for accurately predicting AHs. Analysis on the contribution of the input residues and some cases further reveals the high interpretability and the generalization of our model.

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.

MOTIVATION: Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state and register. However, most of the existing computational tools only focus on one of them. RESULTS: Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e. CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b and e are more crucial for the oligomeric state prediction. AVAILABILITY AND IMPLEMENTATION: CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. The datasets used in this research can also be downloaded from the website. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[A Case Report and Literature Review of Pulmonary Wegener' Granulomatosis].

Wegener' granulomatosis is an autoimmune diseases, often involving the lung and kidney, has a high mortality rate in nontreatment patients. The low incidence and nonspecific features, often lead to misdiagnosis and delayed treatment. This paper reported the diagnosis and treatment of a 55-year-old female patient with primary Wegener' granuloma of the lung diagnosed by percutaneous lung biopsy of pulmonary nodules, and reviews the relevant literature.
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Salt-template preparation of Mo5N6 nanosheets with peroxidase- and catalase-like activities and application for colorimetric determination of 4-aminophenol.

Mo5N6 nanosheets were synthesized by a nickel-induced growth method and were found to possess peroxidase-like activity in acidic condition and catalase-like activity in weak basic condition. In acidic condition, Mo5N6 nanosheets can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) by H2O2 to form a blue color product (TMBOX). At the co-existence of 4-aminophenol (4-AP), 4-AP can react with H2O2 and TMBOX, resulting in the decrease of TMBOX and the fading of blue color. Therefore, a facile, sensitive colorimetric method for the quantitative detection of 4-AP was developed. The linear range for 4-AP was 1.0 to 80.0 µmol⋅L‒1 (R2 = 0.999), and the detection limit was 0.56 µmol⋅L‒1 based on 3σ/k. Resorcinol, aniline, humic acid, and common ions and anions in surface water did not interfere the determination of 4-AP. This colorimetric method was applied to measure the 4-AP in real water sample from Wulong River in Fujian Province of China. The relative standard deviation for the determination of 4-AP was ranged from 0.03 to 1.88%, and the recoveries from spiked samples were ranged between 99.2 and 107.6%. The determination results were consistent with those obtained by HPLC.

Prediction Model for Lung Cancer in High-Risk Nodules Being Considered for Resection: Development and Validation in a Chinese Population.

BACKGROUND: Determining benign and malignant nodules before surgery is very difficult when managing patients with pulmonary nodules, which further makes it difficult to choose an appropriate treatment. This study aimed to develop a lung cancer risk prediction model for predicting the nature of the nodule in patients' lungs and deciding whether to perform a surgical intervention. METHODS: This retrospective study included patients with pulmonary nodules who underwent lobectomy or sublobectomy at Tianjin Medical University General Hospital between 2017 and 2020. All subjects were further divided into training and validation sets. Multivariable logistic regression models with backward selection based on the Akaike information criterion were used to identify independent predictors and develop prediction models. RESULTS: To build and validate the model, 503 and 260 malignant and benign nodules were used. Covariates predicting lung cancer in the current model included female sex, age, smoking history, nodule type (pure ground-glass and part-solid), nodule diameter, lobulation, margin (smooth, or spiculated), calcification, intranodular vascularity, pleural indentation, and carcinoembryonic antigen. The final model of this study showed excellent discrimination and calibration with a concordance index (C-index) of 0.914 (0.890-0.939). In an independent sample used for validation, the C-index for the current model was 0.876 (0.825-0.927) compared with 0.644 (0.559-0.728) and 0.681 (0.605-0.757) for the Mayo and Brock models. The decision curve analysis showed that the current model had higher discriminatory power for malignancy than the Mayo and the Brock models. CONCLUSIONS: The current model can be used in estimating the probability of lung cancer in nodules requiring surgical intervention. It may reduce unnecessary procedures for benign nodules and prompt diagnosis and treatment of malignant nodules.