Integrated convolution and self-attention for improving peptide toxicity prediction.

MOTIVATION: Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development. RESULTS: We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs. AVAILABILITY AND IMPLEMENTATION: The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP.

RDscan: Extracting RNA-disease relationship from the literature based on pre-training model.

With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at https://cellknowledge.com.cn/RDscan/.

ERAD-related E2 and E3 enzymes modulate the drought response by regulating the stability of PIP2 aquaporins.

Endoplasmic reticulum-associated degradation (ERAD) is known to regulate plant responses to diverse stresses, yet its underlying molecular mechanisms and links to various stress signaling pathways are poorly understood. Here, we show that the ERAD component ubiquitin-conjugating enzyme UBC32 positively regulates drought tolerance in Arabidopsis thaliana by targeting the aquaporins PIP2;1 and PIP2;2 for degradation. Furthermore, we demonstrate that the RING-type ligase Rma1 acts together with UBC32 and that the E2 activity of UBC32 is essential for the ubiquitination of Rma1. This complex ubiquitinates a phosphorylated form of PIP2;1 at Lys276 to promote its degradation, thereby enhancing plant drought tolerance. Extending these molecular insights into crops, we show that overexpression of Arabidopsis UBC32 also improves drought tolerance in rice (Oryza sativa). Thus, beyond uncovering the molecular basis of an ERAD-regulated stress response, our study suggests multiple potential strategies for engineering crops with improved drought tolerance.

Multifunctional nanoplatform with near-infrared triggered nitric-oxide release for enhanced tumor ferroptosis.

Ferroptosis has emerged as a promising strategy for cancer treatment. Nevertheless, the efficiency of ferroptosis-mediated therapy remains a challenge due to high glutathione (GSH) levels and insufficient endogenous hydrogen peroxide in the tumor microenvironment. Herein, we presented a nitric-oxide (NO) boost-GSH depletion strategy for enhanced ferroptosis therapy through a multifunctional nanoplatform with near-infrared (NIR) triggered NO release. The nanoplatform, IS@ATF, was designed that self-assembled by loading the NO donor L-arginine (L-Arg), ferroptosis inducer sorafenib (SRF), and indocyanine green (ICG) onto tannic acid (TA)-Fe3+‒metal-phenolic networks (MPNs) modified with hydroxyethyl starch. Inside the tumor, SRF could inhibit GSH biosynthesis, impair the activation of glutathione peroxidase 4, and disrupt the ferroptosis defensive system. In conjunction with TA-Fe3+‒MPNs, which has cascaded Fenton catalytic activity, it could navigate the lethal ferroptosis to cancer cells. Upon NIR laser irradiation, the ICG-generated ROS oxidated L-Arg to a substantial quantity of NO, which further depleted the intracellular GSH and caused LPO accumulation, enhancing cell ferroptosis. Moreover, ICG also serves as a photothermal agent that can produce hyperthermia when exposed to irradiation, further potentiating ferroptosis therapy. In addition, the nanoplatform showed significantly improved tumor therapeutic efficacy and anti-metastasis efficiency. This work thus demonstrated that utilizing NO boost-GSH depletion to enhance ferroptosis induction is a feasible and promising strategy for cancer treatment.

CERBERUS is critical for stabilization of VAPYRIN during rhizobial infection in Lotus japonicus.

CERBERUS (also known as LIN) and VAPYRIN (VPY) are essential for infection of legumes by rhizobia and arbuscular mycorrhizal fungi (AMF). Medicago truncatula LIN (MtLIN) was reported to interact with MtVPY, but the significance of this interaction is unclear and the function of VPY in Lotus japonicus has not been studied. We demonstrate that CERBERUS has auto-ubiquitination activity in vitro and is localized within distinct motile puncta in L. japonicus root hairs and in Nicotiana benthamiana leaves. CERBERUS colocalized with the trans-Golgi network/early endosome markers. In L. japonicus, two VPY orthologs (LjVPY1 and LjVPY2) were identified. CERBERUS interacted with and colocalized with both LjVPY1 and LjVPY2. Co-expression of CERBERUS with LjVPY1 or LjVPY2 in N. benthamiana led to increased protein levels of LjVPY1 and LjVPY2, which accumulated as mobile punctate bodies in the cytoplasm. Conversely, LjVPY2 protein levels decreased in cerberus roots after rhizobial inoculation. Mutant analysis indicates that LjVPY1 and LjVPY2 are required for rhizobial infection and colonization by AMF. Our data suggest that CERBERUS stabilizes LjVPY1 and LjVPY2 within the trans-Golgi network/early endosome, where they might function to regulate endocytic trafficking and/or the formation or recycling of signaling complexes during rhizobial and AMF symbiosis.

ABI4 mediates antagonistic effects of abscisic acid and gibberellins at transcript and protein levels.

Abscisic acid (ABA) and gibberellins (GAs) are plant hormones which antagonistically mediate numerous physiological processes, and their optimal balance is essential for normal plant development. However, the molecular mechanism underlying ABA and GA antagonism still needs to be determined. Here, we report that ABA-INSENSITIVE 4 (ABI4) is a central factor in GA/ABA homeostasis and antagonism in post-germination stages. ABI4 overexpression in Arabidopsis (OE-ABI4) leads to developmental defects including a decrease in plant height and poor seed production. The transcription of a key ABA biosynthetic gene, NCED6, and of a key GA catabolic gene, GA2ox7, is significantly enhanced by ABI4 overexpression. ABI4 activates NCED6 and GA2ox7 transcription by directly binding to the promoters, and genetic analysis revealed that mutation in these two genes partially rescues the dwarf phenotype of ABI4 overexpressing plants. Consistently, ABI4 overexpressing seedlings have a lower GA/ABA ratio than the wild type. We further show that ABA induces GA2ox7 transcription while GA represses NCED6 expression in an ABI4-dependent manner; and that ABA stabilizes the ABI4 protein whereas GA promotes its degradation. Taken together, these results suggest that ABA and GA antagonize each other by oppositely acting on ABI4 transcript and protein levels.

ABSCISIC ACID-INSENSITIVE 4 negatively regulates flowering through directly promoting Arabidopsis FLOWERING LOCUS C transcription.

During the life cycle of a plant, one of the major biological processes is the transition from the vegetative to the reproductive stage. In Arabidopsis, flowering time is precisely controlled by extensive environmental and internal cues. Gibberellins (GAs) promote flowering, while abscisic acid (ABA) is considered as a flowering suppressor. However, the detailed mechanism through which ABA inhibits the floral transition is poorly understood. Here, we report that ABSCISIC ACID-INSENSITIVE 4 (ABI4), a key component in the ABA signalling pathway, negatively regulates floral transition by directly promoting FLOWERING LOCUS C (FLC) transcription. The abi4 mutant showed the early flowering phenotype whereas ABI4-overexpressing (OE-ABI4) plants had delayed floral transition. Consistently, quantitative reverse transcription-PCR (qRT-PCR) assay revealed that the FLC transcription level was down-regulated in abi4, but up-regulated in OE-ABI4. The change in FT level was consistent with the pattern of FLC expression. Chromatin immunoprecipitation-qPCR (ChIP-qPCR), electrophoretic mobility shift assay (EMSA), and tobacco transient expression analysis showed that ABI4 promotes FLC expression by directly binding to its promoter. Genetic analysis demonstrated that OE-ABI4::flc-3 could not alter the flc-3 phenotype. OE-FLC::abi4 showed a markedly delayed flowering phenotype, which mimicked OE-FLC::WT, and suggested that ABI4 acts upstream of FLC in the same genetic pathway. Taken together, these findings suggest that ABA inhibits the floral transition by activating FLC transcription through ABI4.

Survivin protein expression is involved in the progression of non-small cell lung cancer in Asians: a meta-analysis.

BACKGROUND: Surviving expression might serve as a prognostic biomarker predicting the clinical outcome of non-small cell lung cancer (NSCLC). The study was conducted to explore the potential correlation of survivin protein expression with NSCLC and its clinicopathologic characteristics. METHODS: PubMed, Medline, Cochrane Library, CNKI and Wanfang database were searched through January 2016 with a set of inclusion and exclusion criteria. Data was extracted from these articles and all statistical analysis was conducted by using Stata 12.0. RESULTS: A total of 28 literatures (14 studies in Chinese and 14 studies in English) were enrolled in this meta-analysis, including 3206 NSCLC patients and 816 normal controls. The result of meta-analysis demonstrated a significant difference of survivin positive expression between NSCLC patients and normal controls (RR = 7.16, 95 % CI = 4.63-11.07, P < 0.001). To investigate the relationship of survivin expression and clinicopathologic characteristics, we performed a meta-analysis in NSCLC patients. Our results indicates survivin expression was associated with histological differentiation, tumor-node-metastasis (TNM) stage and lymph node metastasis (LNM) (RR = 0.80, 95 % CI = 0.73-0.87, P < 0.001; RR = 0.75, 95 % CI = 0.67-0.84, P < 0.001; RR = 1.14, 95 % CI = 1.01-1.29, P = 0.035, respectively), but not pathological type and tumor size. (RR = 1.00, 95 % CI = 0.93-1.07, P = 0.983; RR = 0.95, 95 % CI = 0.86-1.05, P = 0.336, respectively). CONCLUSION: Higher expression of survivin in NSCLC patients was found when compared to normal controls. Survivin expression was associated with the clinicopathologic characteristics of NSCLC and may serves as an important biomarker for NSCLC progression.

Acceptability of palliative sleeve lobectomy with microscopic margin disease in patients with non-small cell lung cancer: A retrospective study.

OBJECTIVE: For patients with non-small cell lung cancer, microscopic residual disease (R1) is sometimes inevitable after sleeve lobectomy. However, the necessity for extensive pneumonectomy after sleeve lobectomy with R1 status remains unclear, especially when the patient cannot tolerate surgery. METHODS: We retrospectively collected the clinical data of 366 patients who underwent sleeve lobectomy for microscopic residual disease (SLobR1) or pneumonectomy between 2015 and 2019 at Shanghai Chest Hospital, China. We used propensity score matching to balance the baseline characteristics between the SLobR1 and pneumonectomy groups and then analyzed the survival outcomes (overall survival and progression-free survival. RESULTS: Propensity score matching balanced the baseline characteristics, resulting in 93 patients per group. Overall survival and progression-free survival did not differ between the SLobR1 and pneumonectomy groups. However, the subgroup analysis indicated that residual disease negatively affected early stage I disease in the SLobR1 group compared with the pneumonectomy group. In addition, the causes of death did not differ between the groups. Moreover, radiotherapy improved overall survival (P = .021) and considerably decreased the incidence of distant recurrence, similar to other studies. However, it increased the risk of extrathoracic lymph node metastasis. CONCLUSION: Palliative SLobR1 is acceptable, especially for patients who cannot tolerate extensive pneumonectomy. Furthermore, radiotherapy is necessary to reduce the recurrence risk.

MAT1A activation of glycolysis to promote NSCLC progression depends on stabilizing CCND1.

Non-small cell lung cancer (NSCLC) remains a cause for concern as the leading cause of cancer-related death worldwide. Amidst ongoing debates on the role and mechanisms of methionine adenosyltransferase 1A (MAT1A) in cancer, our study sheds light on its significance in NSCLC. Leveraging TCGA database and immunohistochemical staining, we systematically analyzed MAT1A expression in NSCLC, uncovering its marked upregulation. To unravel the functional and mechanistic underpinnings, we implemented stable knockdown of MAT1A in NSCLC cell lines. Our findings converged to demonstrate that suppression of MAT1A expression effectively impeded the proliferation and migratory capabilities of NSCLC cells, while concurrently enhancing apoptosis. Mechanistically, we discovered that MAT1A depletion accelerated the degradation of CCND1, a key cell cycle regulator, through S-phase kinase-associated protein 2 (SKP2)-mediated ubiquitination. Notably, CCND1 emerged as a crucial MAT1A partner, jointly orchestrating glycolytic metabolism in NSCLC cells. This intricate interplay suggests that MAT1A promotes NSCLC progression by safeguarding CCND1 protein stability and activating glycolytic pathways, thereby sustaining tumorigenesis. In summary, our study not only identifies MAT1A as a prognostic marker for poor survival in NSCLC patients but also elucidates its mechanistic contributions to cancer progression. These findings pave the way for the development of targeted therapies aimed at disrupting the deleterious MAT1A-CCND1-glycolysis axis in NSCLC.

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features.

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction.

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

VEGF165 attenuates the Th17/Treg imbalance that exists when transplanting allogeneic skeletal myoblasts to treat acute myocardial infarction.

OBJECTIVES: To investigate whether Th17/Treg imbalance exists, and whether VEGF(165) attenuates the imbalance in allogeneic skeletal myoblast transplantation (allo-SMT) for acute myocardial infarction (AMI). METHODS: On days 1, 2, 4, and 7 after allo-SMT, the percentages and ratios of Th17 and Treg cells were analyzed by flow cytometry in three groups-the AMI group, the AMI-S group (allo-SMT) and the AMI-V group (with VEGF(165) treatment). Subsequently, related proinflammatory and regulatory cytokines and key transcription factors, ROR-γt mRNA and Foxp3 mRNA expression, were examined by Bio-plex and real-time polymerase chain reaction, respectively. RESULTS: On days 1, 2, 4, and 7, the percentage of Tregs, related cytokine concentrations and transcript factor Foxp3 mRNA in the AMI-S group were lower than those in the AMI group, while those in the AMI-V group were higher than those in the AMI group. However, the percentage of Th17 cells, related cytokine concentrations and ROR-γt mRNA in the AMI-S group were higher than those in the AMI group; those in the AMI-V group were lower than those in the AMI group. Compared with the AMI group, the ratios of Th17/Treg cells significantly increased in the AMI-S group and decreased in the AMI-V group. CONCLUSIONS: Th17/Treg imbalance participated in the formation and development of the inflammatory and immune response after allo-SMT. However, transfected VEGF(165) was able to relieve the severity of the Th17/Treg imbalance.

The Implication of Liquid Biopsy in the Non-small Cell Lung Cancer: Potential and Expectation.

Nowadays, lung cancer has remained the most lethal cancer, despite great advances in diagnosis and treatment. However, a large proportion of patients were diagnosed with locally advanced or metastatic disease and have poor prognosis. Immunotherapy and targeted drugs have greatly improved the survival and prognosis of patients with advanced lung cancer. However, how to identify the optimal patients to accept those therapies and how to monitor therapeutic efficacy are still in dispute. In the past few decades, tissue biopsy, including percutaneous fine needle biopsy and surgical excision, has still been the gold standard for examining the gene mutation such as EGFR, ALK, ROS, and PD-1/PD/L1, which can indicate the follow-up treatment. Nevertheless, the biopsy techniques mentioned above were invasive and unrepeatable, which were not suitable for advanced patients. Liquid biopsy, accounting for heterogeneity compared with tissue biopsy, is an alternative technique for monitoring the mutation, and a large quantity of research has demonstrated its feasibility to detect the circulating tumor cell, cell-free DNA, circulating tumor DNA, and extracellular vesicles from peripheral venous blood. The proposal of the concept of precision medicine brings a novel medical model developed with the rapid progress of genome sequencing technology and the cross-application of bioinformation, which was based on personalized medicine. The emerging method of liquid biopsy might contribute to promoting the development of precision medicine. In this review, we intend to describe the liquid biopsy in non-small cell lung cancer in detail in the aspect of screening, diagnosis, monitoring, treatment, and drug resistance.

Identification of DNA-binding proteins by Kernel Sparse Representation via L2,1-matrix norm.

An understanding of DNA-binding proteins is helpful in exploring the role that proteins play in cell biology. Furthermore, the prediction of DNA-binding proteins is essential for the chemical modification and structural composition of DNA, and is of great importance in protein functional analysis and drug design. In recent years, DNA-binding protein prediction has typically used machine learning-based methods. The prediction accuracy of various classifiers has improved considerably, but researchers continue to spend time and effort on improving prediction performance. In this paper, we combine protein sequence evolutionary information with a classification method based on kernel sparse representation for the prediction of DNA-binding proteins, and based on the field of machine learning, a model for the identification of DNA-binding proteins by sequence information was finally proposed. Based on the confirmation of the final experimental results, we achieved good prediction accuracy on both the PDB1075 and PDB186 datasets. Our training result for cross-validation on PDB1075 was 81.37%, and our independent test result on PDB186 was 83.9%, both of which outperformed the other methods to some extent. Therefore, the proposed method in this paper is proven to be effective and feasible for predicting DNA-binding proteins.

Prediction of strand-specific and cell-type-specific G-quadruplexes based on high-resolution CUT&Tag data.

G-quadruplex (G4), a non-classical deoxyribonucleic acid structure, is widely distributed in the genome and involved in various biological processes. In vivo, high-throughput sequencing has indicated that G4s are significantly enriched at functional regions in a cell-type-specific manner. Therefore, the prediction of G4s based on computational methods is necessary instead of the time-consuming and laborious experimental methods. Recently, G4 CUT&Tag has been developed to generate higher-resolution sequencing data than ChIP-seq, which provides more accurate training samples for model construction. In this paper, we present a new dataset construction method based on G4 CUT&Tag sequencing data and an XGBoost prediction model based on the machine learning boost method. The results show that our model performs well within and across cell types. Furthermore, sequence analysis indicates that the formation of G4 structure is greatly affected by the flanking sequences, and the GC content of the G4 flanking sequences is higher than non-G4. Moreover, we also identified G4 motifs in the high-resolution dataset, among which we found several motifs for known transcription factors (TFs), such as SP2 and BPC. These TFs may directly or indirectly affect the formation of the G4 structure.

Integration of Glutamate Dehydrogenase and Nanoporous Gold for Electrochemical Detection of Glutamate.

Glutamate, a non-essential amino acid produced by fermentation, plays a significant role in disease diagnosis and food safety. It is important to enable the real-time monitoring of glutamate concentration for human health and nutrition. Due to the challenges in directly performing electrochemical oxidation-reduction reactions of glutamate, this study leverages the synergistic effect of glutamate dehydrogenase (GLDH) and nanoporous gold (NPG) to achieve the indirect and accurate detection of glutamate within the range of 50 to 700 µM by measuring the generated quantity of NADH during the enzymatic reaction. The proposed biosensor demonstrates remarkable performance characteristics, including a detection sensitivity of 1.95 µA mM-1 and a limit of detection (LOD) of 6.82 µM. The anti-interference tests indicate an average recognition error ranging from -3.85% to +2.60%, spiked sample recovery rates between 95% and 105%, and a relative standard deviation (RSD) of less than 4.97% for three replicate experiments. Therefore, the GLDH-NPG/GCE biosensor presented in this work exhibits excellent accuracy and repeatability, providing a novel alternative for rapid glutamate detection. This research contributes significantly to enhancing the precise monitoring of glutamate concentration, thereby offering more effective guidance and control for human health and nutrition.

Tracheal tunica adventitia invasion after lobectomy in patients with non-small cell lung cancer.

BACKGROUND: For patients with non-small cell lung cancer, a negative margin status is required for radical pulmonary surgery. Residual disease of the margin has been thoroughly studied in the past few decades. However, the prognostic significance of tracheal tunica adventitia invasion after lobectomy remains unclear. In this study, we aimed to investigate the clinical influence of tracheal tunica adventitia invasion after lobectomy. METHODS: We retrospectively collected the clinical data of 591 patients who consecutively underwent pulmonary lobectomy, including sleeve lobectomy, between 2012 and 2018 at Shanghai Chest Hospital. According to the tracheal tunica adventitia invasion status, we allocated the patients into 2 groups (tracheal tunica adventitia invasion and non-tracheal tunica adventitia). Disease-free and overall survival were evaluated, and we discussed the necessity of radiotherapy in patients with tracheal tunica adventitia. RESULTS: After propensity score matching to balance baseline characteristics, there were 167 individuals in the tracheal tunica adventitia invasion and non-tracheal tunica adventitia groups. In the hazard analysis, we found that tracheal tunica adventitia increased the risk of recurrence (hazard ratio: 0.652; P = .002) and impaired long-term survival (P < .001). Subgroup analysis revealed that tracheal tunica adventitia was an important risk factor, especially when the hilar lymph nodes were positive. In addition, tracheal tunica adventitia invasion promoted extra-thoracic lymph node metastasis. We discovered that radiotherapy did not improve the prognosis of patients in the tracheal tunica adventitia invasion group. CONCLUSIONS: After lobectomy, tracheal tunica adventitia invasion is a risk factor for non-small cell lung cancer and potentially increases extra-thoracic lymph node metastasis. Moreover, tracheal tunica adventitia invasion is not sensitive to postoperative radiotherapy.

Enhanced electricity generation and storage by nitrogen-doped hierarchically porous carbon modification of the capacitive bioanode in microbial fuel cells.

Microbial fuel cells (MFCs) can potentially be utilized for power generation, but their low power density and low energy storage capabilities remain major bottlenecks for their large-scale development. In this research, a simplistic nitrogen-doped hierarchically porous carbon material (HPC-A) was developed through a one-step carbonization and activation process and was successfully hot-pressed on the carbon cloth (CC) substrate. This process fabricates capacitive bioanodes (HPC-A-CC) that can enhance electricity generation and storage in MFCs. The as-prepared HPC-A-CC anode delivered a power density of 2043.6 mW·m-2 and a cumulative total charge (Qm) of 426.4 ± 13.4C·m-2 at each cycle, which was 2.1 and 34.8 times higher than that of the plain CC anode, respectively. This was a result of the hierarchical and interconnected porous structure, improved hydrophilic surface, and increased number of active centers which host the bacteria for enhanced electron transfer. Electrochemical measurements indicated the superior electrochemical activity and capacitive behavior of the HPC-A-CC anode. Furthermore, biofilm analysis revealed that the HPC-A-CC biofilm exhibited higher cell viability and a more uniform spatial distribution. These findings not only demonstrate the potential of HPC-A-CC for power enhancement in MFCs but also provide a feasible solution to the problem of power generation and demand mismatch in MFC applications.

A pilot study comparing the disinfecting effects of commercialized stable ClO2 solution (free of activation) with conventional H2O2 on dental unit waterlines in the dental practice setting.

Disinfection of dental unit waterlines (DUWLs) plays a key role in control and prevention of nosocomial infection in a dental clinic. The most conventional disinfectant is hydrogen peroxide (H2O2), while chlorine dioxide (ClO2) has been considered however was limited by the "activation" procedures. With the availability of commercialized stable ClO2 solution (free of activation), direct application of ClO2 in the dental practice became possible. This study was designed to compare the disinfecting effects of stable 5 ppm of ClO2 solution with conventional 0.24% of H2O2 on DUWLs in dental practice. Studies of colony-forming units (CFUs), confocal laser scanning microscopy (CLSM) and scanning electron microscope (SEM) were employed for evaluation. In CFUs studies, we found that the efficiency of ClO2 was no less than those of H2O2. In the morphological studies, the stronger disinfecting effects of ClO2 was verified by both CLSM and SEM studies for removal and prevention of biofilm. Importantly, ClO2 solution achieved a better disinfecting efficiency not only at the surface of bacterial biofilm, but also, it has penetrating effects, presented disinfecting effects from the surface to the bottom of the biofilm. This pilot study provided evidence regarding the efficiency of stable ClO2 solution on disinfection of DUWLs in the dental practice setting. Application of stable ClO2 solution in dental practice is therefore become possible.