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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Author summary: The size and shape of protein structures vary considerably. Accurate protein structure comparison usually relies on structure alignment algorithms. However, superimposing two protein structures is relatively time-consuming, which makes it inappropriate for large-scale protein structure retrieval. Alignment-free algorithms are proposed for efficient protein structure comparison over the last few decades. These algorithms first transform the coordinates of atoms in two proteins to fixed-length vectors. Then, the comparison can be done by measuring the distance or similarity between two vectors, which is much faster than alignment. In this study, we propose a novel protein structure representation method for efficient structure comparison. Compared with other state-of-the-art alignment-free methods, our method achieves better performance on both ranking and multi-class classification tasks due to the powerful representation ability of deep graph neural networks. We dig into the model and observe that the superiority of our method is mainly brought by the learned discriminative residue-level and global descriptors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

[Display omitted] • MoS 2 NSs with different S defects were prepared by adjusting the synthesis temperature. • The activity of MoS 2 NSs is enhanced with the decreasing of S/Mo atomic ratio. • The inhibition experiments verify the catalytic active sites are Mo atoms with S defects. • The catalytic mechanism of peroxidase-like activity of MoS 2 NSs was proposed. MoS 2 nanosheets (NSs) have been found to possess the peroxidase-like activity and widely used in colorimetric sensors. However, the catalytic active sites of MoS 2 NSs are still unclear. Herein, MoS 2 NSs with sulfur defects were prepared using the molten salt method at different heating temperature (800 °C–1100 °C) and following by ultrasonic exfoliation. With the increasing of heating temperature, the crystallinity of MoS 2 NSs is improved, accompanying with the decrement of S/Mo atomic ratio. The increment of sulfur defects in MoS 2 NSs remarkably enhances the peroxidase-like activity of MoS 2 NSs. The catalytic inhibition by phosphate-containing molecules further verifies the catalytic active sites of MoS 2 NSs are Mo atoms with sulfur defects. Finally, the peroxidase-like activity of sulfur defect-rich MoS 2 NSs was applied for the colorimetric detection of H 2 O 2 and the inhibitor. This work helps to understand the peroxidase-like catalytic mechanism of MoS 2 NSs. [ABSTRACT FROM AUTHOR]

Propyl paraben (propyl 4-hydroxybenzoate, PPB), one of the typically used paraben species in various pharmaceutical and personal care products, has been found in different aquatic environment, which could affect the water quality and human health. In this paper, the degradation of PPB by aqueous ferrate (Fe(VI)) was investigated in different water matrix and reaction kinetics as a function of pH was determined. Intermediate products of the degradation process were isolated and characterized by the high performance liquid chromatography/mass spectrometry/mass spectrometry techniques. Acute and chronic toxicities during water treatment of PPB using Fe(VI) were calculated using the ECOSAR program at three trophic levels. The obtained apparent second-order rate constant (kapp) for PPB reaction with Fe(VI) ranged from 99.6 ± 0.4 M−1 s−1 to 15.0 ± 0.1 M−1 s−1 with the half-life (t1/2) ranging from 154 s to 1026 s at pH 6.5-10.0 for an Fe(VI) concentration of 600 μM. The proposed pathway for the oxidation of PPB by Fe(VI) involves one electron transfer of phenoxyl radical and breaking of the ether bond. In general, the oxidation of PPB by ferrate resulted in a significant decrease in toxicity at three trophic levels. [ABSTRACT FROM AUTHOR]

2, 2′-azinobis-3-ethylbenzthiazoline-6-sulfonate (ABTS), 3, 3′, 5, 5′-tetramethylbenzidine (TMB), o-phenylenediamine (OPD) are typical chromogenic substrates to evaluate the activity of peroxidase-like nanozymes. However, these substrates are toxic, unstable, and the coloration reactions are slow. Herein, the classical starch-sodium iodide reagent was utilized as the chromogenic substrate to evaluate peroxidase-like activity of nanozymes. Starch-sodium iodine is less toxic, more stable and faster color reaction than ABTS, TMB and OPD. Under the catalysis of peroxidase-like nanozymes such as Mo 5 N 6 nanosheets, graphene oxide nanosheets, Fe 3 O 4 nanoparticles, CeO 2 nanoparticles, iodide ion (I–) is oxidized by H 2 O 2 to produce blue color complex (starch-I 2). Taking Mo 5 N 6 nanosheets as the peroxidase-like nanozyme, the color reaction completed within 5 min at room temperature (25 ºC), and the chromogenic substrate remained stable within 30 days. The maximum reaction rate (V max) using starch-sodium iodine as the chromogenic substrate was more than 100-fold higher than that of TMB. Based on this, a sensitive colorimetric sensor and facile portable agarose gel test kits were proposed and applied for the detection of H 2 O 2 residue in liquid milk samples with fast response. • Classical chromogenic reagent starch-I 2 was utilized as the substrate of peroxidase-like nanozymes. • Starch-sodium iodine is less toxic, more stable and faster color reaction than ABTS, TMB and OPD. • A sensitive colorimetric sensor and portable agarose gel test kits were proposed for H 2 O 2 in liquid milk. [ABSTRACT FROM AUTHOR]

From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93. [ABSTRACT FROM AUTHOR]

Typical fluorescent biosensors use fluorescently labeled ssDNA for target recognition and nanomaterials for signal transduction. Herein, we propose a reverse sensing strategy that Mo 5 N 6 nanosheets are used for target recognition while fluorescein (FAM)-labeled ssDNA only serves for signal generation. We discover that Mo 5 N 6 nanosheets show high fluorescence quenching ability (>95%) and selective recognition for sodium hexametaphosphate (SHMP). After FAM-labeled ssDNA is adsorbed on Mo 5 N 6 nanosheets, the fluorescence is quenched due to the photoinduced electron transfer (PET) effect between FAM and Mo 5 N 6 nanosheets. SHMP can specifically displace the adsorbed FAM-labeled ssDNA from Mo 5 N 6 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 Na 2 HPO 4 , 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. [Display omitted] • A turn-on sensitive and selective fluorescent biosensor for sodium pyrophosphate was proposed. • Mo 5 N 6 nanosheets showed excellent fluorescence quenching ability as well as SHMP recognition function. • FAM-ssDNA probe served for signal generation and did not require precise design of base sequences. • This work facilitated the application of transition metal nitrides in chemical sensing and biosensing. [ABSTRACT FROM AUTHOR]

The capacity for nanopatterning and functionality is a promising facet of supramolecular self‐assembly. However, the formation of molecular frameworks entirely dependent on van der Waals (vdW) interactions is infrequently explored. Herein, 2D vdW supramolecular structures are synthesized through the self‐assembled cocrystallization of C60 and decanethiol (DT) molecules on Au(111) surface. Notably, the system eliminates the need of functional groups for specific bonding between adjacent units. The conformation C60/DT is delicately manipulated by adjusting molecular coverage and annealing temperature. The absence of directional bonding between C60 and DT molecules facilitates the formation of a variety of stable phases at room temperature (RT), such as 1) porous C60 networks with thiol‐filled pores and 2) self‐synthesized (C60)n nanochains with thiol spacers interspersed between the chains are achieved and visualized by scanning tunneling microscopic imaging under RT. This innovative integration of the vdW interaction unveils new avenues for developing supramolecular patterns characterized by their comparatively weak but exceptionally adaptable bonding. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to validate stage groupings in the 8th edition of the tumor node metastasis (TNM) classification for lung adenocarcinoma and explore the non‐anatomic factors that influence the prognosis of lung adenocarcinoma patients in China. Methods: We retrospectively analyzed the data of 291 lung adenocarcinoma patients at our department between 2008 and 2013. Logrank tests and Cox regression models were used to analyze survival among adjacent stage groupings. Kaplan–Meier curves were used to estimate overall survival (OS). Results: There were significant differences in OS in adjacent stage groupings in early stages in the 8th edition. There were also significant differences between patients treated with radical surgery and limited resection (P = 0.027). Lepidic predominant adenocarcinoma (LPA) had better survival rates than acinar predominant (APA), papillary predominant, and solid predominant with mucin production adenocarcinoma (SPA) (P = 0.008). Survival rates of micropapillary predominant adenocarcinoma were lower than the others (P = 0.003). EGFR mutations were closely associated with lepidic predominant (65%, P = 0.56) but less commonly associated with solid predominant with mucin production adenocarcinoma (24%, P = 0.02). There was no significant difference in survival between EGFR gene mutation‐positive and negative groups (P = 0.402). Conclusion: The 8th edition TNM may be more accurate and applicable than the 7th edition for Chinese lung adenocarcinoma patients who have undergone surgical treatment. Stage IV patients may gain survival improvement from radical surgery. [ABSTRACT FROM AUTHOR]

[Display omitted] • Cu-g-C 3 N 5 nanozyme with efficient laccase-like activity was proposed. • Triazole groups improve the stability of Cu NPs and facilitate the activation of O 2. • Cu-g-C 3 N 5 nanozyme displays robust catalytic activity. • Cu-g-C 3 N 5 nanozyme was applied for colorimetric detection of BPA. Herein, a new strategy for laccase-like nanozyme is proposed by utilizing copper nanoparticles (Cu NPs) as the active center, carbon nitride as the skeleton and triazole groups as the cofactor. Cu NPs incorporated nitrogen-rich carbon nitride (Cu-g-C 3 N 5) nanocomposites are found to possess excellent laccase-like activity, which can catalyze the oxidation of bisphenol A (BPA) to produce a color change in the presence of 4-aminoantipyrine. The catalytic efficiency of Cu-g-C 3 N 5 nanocomposites is 4-fold higher than their analogue (Cu-g-C 3 N 4 nanocomposites) without triazole groups. Cu-g-C 3 N 5 nanocomposites also display robust catalytic activity with high temperature stability, resistance to acid and alkalinity, and long term stability. Density functional theory calculations indicate that triazole groups are beneficial to improve the stability of Cu NPs via additional Cu-N bonds and facilitate the activation of the adsorbed O 2 molecules. There is a linear relationship between the absorbance and BPA concentration over the range of 0.25–25 mg L−1 with the limit of detection of 0.09 mg L−1. Finally, Cu-g-C 3 N 5 nanocomposites are successfully applied for colorimetric detection of BPA released from polycarbonate microplastics. To the best of our knowledge, this is the first example to utilize laccase-like nanozyme for colorimetric detection of BPA. [ABSTRACT FROM AUTHOR]

Histopathological whole slide images (WSIs) are the gold standard for cancer diagnosis. In prognosis, WSIs can also help predict the overall survival (OS) time of cancer (such as glioblastoma (GBM) patients). However, current region of interest (ROI)-based supervised learning methods for GBM patients are unsatisfactory in accuracy and efficiency. To mitigate the issues, we propose a novel deep synergetic spiking neural (DSSN) P system to perform ROI-free prediction of OS time in GBM patients. Specifically, a dual-task attention network is designed by different synergistic firing mechanisms on new types of neurons to predict OS time and classify genotype simultaneously. The network can not only capture useful cell features but also enrich and share features between the two tasks to improve performance. Four triggering rules are also proposed to cluster pathological images into different phenotypes related to survival to implement ROI-free prediction. Since P systems are highly parallel, multiple preprocessing and ensemble learning of dual-task attention networks with different initial configurations are conducted to further improve OS time prediction accuracy and reduce time complexity. Experimental results show that the DSSN P system outperforms ten other state-of-the-art models, demonstrating the ability of the DSSN P system to predict OS time in GBM patients. • A novel deep synergetic spiking neural (DSSN) P system is proposed. • DSSN P system with dual-task attention network predicts OS time and genotype. • Four classes of synergistic neurons self-assemble into different function blocks. • Four triggering rules are provided to implement ROI-free prediction. • High parallelism of DSSN P system improves accuracy and saves time of computation. [ABSTRACT FROM AUTHOR]

Background: Acquired resistance of chemotherapy, especially cisplatin, is a major challenge in lung cancer treatment. We conducted this study to examine whether a cyclin D kinase 4/6 (CDK4/6) inhibitor, PD 0332991, could reverse cisplatin resistance in human lung cancer cells. In addition, we explored the underlying mechanisms. Method: We used CCK‐8 assay to got the IC50 of PD‐0332991 and cisplatin in A549 and A549/CDDP respectively. CCK‐8 assay, CalcuSyn 2.0 software, cell cycle distribution and apoptosis used to identify PD‐0332991 could reverse the acquired resistance of cisplatin. At last, western‐blot used to show the mechanism of PD‐0332991 enhances the effects of cisplatin. Results: We found that PD‐0332991 potentiated cisplatin‐induced growth inhibition in both cisplatin‐sensitive (A549) and cisplatin‐resistant (A549/CDDP) cells via downregulation of the proliferation, induction of apoptosis (A549 increased to 7.06%; A549/CDDP increased to 7.03%), and G0/G1 cell cycle arrest (A549 increased to 9.15%; A549/CDDP increased to 49.92%). Western blot analysis revealed that PD‐0332991 enhance the effect of cisplatin through inhibit Rb‐E2Fs pathway. Conclusions: These findings suggest that PD‐0332991 could reverse the acquired resistance of cisplatin in lung cancer cells and provide a novel treatment strategy for lung cancer patients with cisplatin resistance. [ABSTRACT FROM AUTHOR]