Combined ion beam irradiation platform and 3D fluorescence microscope for cellular cancer research.

To improve particle radiotherapy, we need a better understanding of the biology of radiation effects, particularly in heavy ion radiation therapy, where global responses are observed despite energy deposition in only a subset of cells. Here, we integrated a high-speed swept confocally-aligned planar excitation (SCAPE) microscope into a focused ion beam irradiation platform to allow real-time 3D structural and functional imaging of living biological samples during and after irradiation. We demonstrate dynamic imaging of the acute effects of irradiation on 3D cultures of U87 human glioblastoma cells, revealing characteristic changes in cellular movement and intracellular calcium signaling following ionizing irradiation.

Enantioselective effects of chiral prothioconazole and its metabolites: Oxidative stress in HepG2 cells and lysozyme activity.

Chiral pesticides may exhibit enantioselectivity in terms of bioconcentration, environmental fate, and reproductive toxicity. Here, chiral prothioconazole and its metabolites were selected to thoroughly investigate their enantioselective toxicity and mechanisms at the molecular and cellular levels. Multispectral techniques revealed that the interaction between chiral PTC/PTCD and lysozyme resulted in the formation of a complex, leading to a change in the conformation of lysozyme. Meanwhile, the effect of different conformations of PTC/PTCD on the conformation of lysozyme differed, and its metabolites were able to exert a greater effect on lysozyme compared to prothioconazole. Moreover, the S-configuration of PTCD interacted most strongly with lysozyme. This conclusion was further verified by DFT calculations and molecular docking as well. Furthermore, the oxidative stress indicators within HepG2 cells were also affected by chiral prothioconazole and its metabolites. Specifically, S-PTCD induced more substantial perturbation of the normal oxidative stress processes in HepG2 cells, and the magnitude of the perturbation varied significantly among different configurations (P > 0.05). Overall, chiral prothioconazole and its metabolites exhibit enantioselective effects on lysozyme conformation and oxidative stress processes in HepG2 cells. This work provides a scientific basis for a more comprehensive risk assessment of the environmental behaviors and effects caused by chiral pesticides, as well as for the screening of highly efficient and less biotoxic enantiomeric monomers.

TFRegNCI: Interpretable Noncovalent Interaction Correction Multimodal Based on Transformer Encoder Fusion.

The interpretability is an important issue for end-to-end learning models. Motivated by computer vision algorithms, an interpretable noncovalent interaction (NCI) correction multimodal (TFRegNCI) is proposed for NCI prediction. TFRegNCI is based on RegNet feature extraction and a transformer encoder fusion strategy. RegNet is a network design paradigm that mainly focuses on local features. Meanwhile, the Vision Transformer is also leveraged for feature extraction, because it can capture global features better than RegNet while lowering the computational cost. Using a transformer encoder as the fusion strategy rather than multilayer perceptron can enhance model performance, due to its emphasis on important features with less parameters. Therefore, the proposed TFRegNCI achieved high accurate prediction (mean absolute error of ∼0.1 kcal/mol) comparing with the coupled cluster single double (triple) (CCSD(T)) benchmark. To further improve the model efficiency, TFRegNCI applies two-dimensional (2D) inputs transformed from three-dimensional (3D) electron density cubes, which saves time (30%), while the model accuracy remains. To improve model interpretability, a visualization module, Gradient-weighted Regression Activation Mapping (Grad-RAM) has been embedded. Grad-RAM is promoted from the classification algorithm, Gradient-weighted Class Activation Mapping, to perform feature visualization for the regression task. With Grad-RAM, the visual location map for features in deep learning models can be displayed. The feature map visualizations suggest that the 2D model has the similar performance as the 3D model, because of equally effective feature extractions from electron density. Moreover, the valid feature region on the location map by the 3D model is consistent with the NCIPLOT NCI isosurface. It is confirmed that the model does extract significant features related to the NCI interaction. The interpretable analyses are carried out through molecular orbital contribution on effective features. Thereby, the proposed model is likely to be a promising tool to reveal some essential information on NCIs, with regard to the level of electronic theory.

A novel signature to predict the neoadjuvant chemotherapy response of bladder carcinoma: Results from a territory multicenter real-world study.

Background: Although neoadjuvant chemotherapy (NAC) has become the standard treatment option for muscle invasive bladder carcinoma (MIBC), its application is still limited because of the lack of biomarkers for NAC prediction. Methods: We conducted a territory multicenter real-world study to summarize NAC practice in China and its associated clinicopathologic variables with NAC response. Then, we developed and validated a robust gene-based signature for accurate NAC prediction using weighted correlation network analysis (WGCNA), the least absolute shrinkage and selector operation (LASSO) algorithm, a multivariable binary logistic regression model, and immunohistochemistry (IHC). Results: In total, we collected 69 consecutive MIBC patients treated with NAC from four clinical centers. The application of NAC in the real world was relatively safe, with only two grade Ⅳ and seven grade Ⅲ AEs and no treatment-related deaths being reported. Among these patients, 16 patients gave up surgery after NAC, leaving 53 patients for further analysis. We divided them into pathological response and non-response groups and found that there were more patients with a higher grade and stage in the non-response group. Patients with a pathological response could benefit from a significant overall survival (OS) improvement. In addition, univariate and multivariate logistic analyses indicated that tumor grade and clinical T stage were both independent factors for predicting NAC response. Importantly, we developed and validated a five-gene-based risk score for extremely high predictive accuracy for NAC response. Conclusion: NAC was relatively safe and could significantly improve OS for MIBC patients in the real-world practice. Our five-gene-based risk score could guide personalized therapy and promote the application of NAC.

Neoadjuvant immunotherapy, chemotherapy, and combination therapy in muscle-invasive bladder cancer: A multi-center real-world retrospective study.

To parallelly compare the efficacy of neoadjuvant immunotherapy (tislelizumab), neoadjuvant chemotherapy (gemcitabine and cisplatin), and neoadjuvant combination therapy (tislelizumab + GC) in patients with muscle-invasive bladder cancer (MIBC) and explore the efficacy predictors, we perform a multi-center, real-world cohort study that enrolls 253 patients treated with neoadjuvant treatments (combination therapy: 98, chemotherapy: 107, and immunotherapy: 48) from 15 tertiary hospitals. We demonstrate that neoadjuvant combination therapy achieves the highest complete response rate and pathological downstaging rate compared with neoadjuvant immunotherapy or chemotherapy. We develop and validate an efficacy prediction model consisting of pretreatment clinical characteristics, which can pinpoint candidates to receive neoadjuvant combination therapy. We also preliminarily reveal that patients who achieve pathological complete response after neoadjuvant treatments plus maximal transurethral resection of the bladder tumor may be safe to receive bladder preservation therapy. Overall, this study highlights the benefit of neoadjuvant combination therapy based on tislelizumab for MIBC.

Intercalary frozen autografts for reconstruction of bone defects following meta-/diaphyseal tumor resection at the extremities.

BACKGROUND: For patients with malignant limb tumors, salvage surgery can be achieved using endoprosthesis or biological reconstructions like allograft or autograft. In carefully selected patients, resected bone can be recycled after sterilization using methods like autoclaving, irradiation, pasteurization or freezing with liquid nitrogen. We evaluated the clinical outcome and complications of malignant limb tumors treated with intercalary resection and frozen autograft reconstruction. METHODS: We reviewed 33 patients whose malignant bone tumors were treated by wide resection and reconstruction with recycling liquid nitrogen-treated autografts between 2006 and 2017. Limb function, bone union at the osteotomy site and complications were evaluated. Functional outcome was assessed using the Musculoskeletal Tumor Society (MSTS) scoring system. RESULTS: The cohort comprised 16 males and 17 females, with a mean age of 35.4 years (14-76 years). The most common tumor was osteosarcoma (7 cases). Tumors were located in the humerus (5), ulna (1), femur (10) and tibia (17). The mean follow-up was 49.9 months (range 12-127 months). Of the 33 patients, 16 remained disease-free, and 3 were alive with disease. The mean size of the defect after tumor resection was 11.6 cm (range 6-25 cm). Bone union was achieved in 32 patients, with a mean union time of 8.8 months (range 4-18 months). Complications included 1 graft nonunion, 2 infections (1 superficial, 1 deep infection), 1 leg length discrepancy, 2 graft fractures and 3 local recurrences. The mean MSTS score was 87.2% (range 70-100%). CONCLUSION: Liquid nitrogen-treated tumor-bearing autograft is an effective option for biological reconstruction after meta-/diaphyseal tumor resection of long bones. This method has excellent clinical outcomes and is especially recommended for patients with no severe osteolytic bone tumors.

The regulation mechanism of different hair types in inner Mongolia cashmere goat based on PI3K-AKT pathway and FGF21.

There is genetic diversity of hair types in the Inner Mongolia cashmere goat population. Previous studies have found that fibroblast growth factor 21 (FGF21) and PI3K-AKT signal pathways may be related to different hair types in Inner Mongolia cashmere goats. Therefore, the purpose of this study was to explore the effects of the PI3K-AKT signal pathway on different hair types, the expression of mRNA and protein expression sites of FGF21 in the hair follicles of cashmere goats with different hair types, so as to lay a foundation for understanding the molecular mechanism of different hair types and the role of skin hair follicle development. In this experiment, the skin tissues of long hair type (LHG) and short hair type (SHG) of Inner Mongolia cashmere goat were collected in three key periods of secondary hair follicle growth, namely, anagen (September), catagen (December), and telogen (March). The relative expression of FGF21 and PI3K-AKT signal pathway candidate gene mRNA in different periods and different hair types was detected by real-time fluorescence quantitative technique (qRT-PCR), and the expression site of FGF21 protein was located by immunohistochemical technique. Through qRT-PCR, it was found that the relative expression of FGF21, FGFR1, AKT3, BRCA1, PKN3, SPP1, and GNG4 was significantly different between LHG and SHG. The expression of FGF21 in the skin of LHG was significantly higher than that of SHG in the three periods. Through immunohistochemical test, it was found that FGF21 protein was mainly expressed in primary hair follicle connective tissue sheath, primary hair follicle outer root sheath, secondary hair follicle outer root sheath, and sebaceous glands. It was also found that the expression of LHG skin tissue in the outer root sheath of primary hair follicles was higher than that of SHG in three periods. In summary, it is suggested that the PI3K-AKT signal pathway may play an important role in the formation of different hair types in Inner Mongolia cashmere goats.

There is genetic diversity of hair types in Inner Mongolia cashmere goat population. The purpose of this study was to explore the effects of the PI3K-AKT signal pathway on different hair types, the expression of mRNA and protein expression sites of FGF21 in the hair follicles of cashmere goats with different hair types, so as to lay a foundation for understanding the molecular mechanism of different hair types. It was found that the relative expression of FGF21, FGFR1, AKT3, BRCA1, PKN3, SPP1, and GNG4 was significantly different between LHG and SHG. It was found that FGF21 protein was mainly expressed in primary hair follicle connective tissue sheath, primary hair follicle outer root sheath, secondary hair follicle outer root sheath, and sebaceous glands. It was also found that the expression of LHG skin tissue in the outer root sheath of primary hair follicles was higher than that of SHG in three periods. So, it is suggested that the PI3K-AKT signal pathway and FGF21 may play an important role in the formation of different hair types in Inner Mongolia cashmere goats.

DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.19 kcal/mol to ∼0.2 kcal/mol for a NCI molecular database (>1000 molecules). The representativeness of the joint features can be visualized by t-distributed stochastic neighbor embedding (t-SNE), where they can distinguish categorized NCI systems quite well. Therefore, the fused model performs better than its component networks. In addition, the 3D CNN takes electron density as inputs that are in the same range, despite the size of molecular systems, so it can promote model applicability and transferability. To clarify the applicability of DeepNCI, an application domain (AD) has been defined with merged features using the K-nearest-neighbor method. The calculations for external test sets are shown that AD can properly monitor the reliability for a prediction. The model transferability is tested with a small database of homolysis bond dissociation energy including only dozens of samples. With NCI database pretrained parameters, the same or better performance than the reported results is achieved by transfer learning. This suggests that the DeepNCI model is transferable and it may transfer to other relative tasks, which possibly can resolve some small sampling problems. The source code of DeepNCI can be freely accessed at https://github.com/wenzelee/DeepNCI.

ITGBL1 promotes EMT, invasion and migration by activating NF-κB signaling pathway in prostate cancer.

Background: Integrin beta-like 1 (ITGBL1) was extensively demonstrated to contribute the metastasis and progression in a variety of cancers. However, its role of ITGBL1 in prostate cancer (PCa) is still not reported. Methods: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blot were performed to detect ITGBL1 expression in PCa tissues and cell lines. Immunohistochemical (IHC) staining of ITGBL1 in 174 PCa tissues was performed. The influence of ITGL1 expression in PCa cells epithelial-mesenchymal transition (EMT), migration and invasion was investigated. Notably, the possible mechanisms underlying the action of ITGBL1 in vivo and vitro assays were explored. Results: We analyzed PCa dataset from The Cancer Genome Atlas (TCGA) and found that ITGBL1 was upregulated in PCa tissues. Overexpression of ITGBL1 is positively associated with the progression and lymph node metastasis in PCa patients. Furthermore, upregulating ITGBL1 enhanced the invasion, migration abilities and EMT in PCa cells. Conversely, downregulating ITGBL1 exhibited an opposite effect. Our findings further demonstrated that ITGBL1 promoted invasion and migration via activating NF-κB signaling in PCa cells. Conclusion: Therefore, our results identify a novel metastasis-related gene in PCa, which will help to develop a novel therapeutic strategy in metastatic PCa.

Efficient Corrections for DFT Noncovalent Interactions Based on Ensemble Learning Models.

Machine learning has exhibited powerful capabilities in many areas. However, machine learning models are mostly database dependent, requiring a new model if the database changes. Therefore, a universal model is highly desired to accommodate the widest variety of databases. Fortunately, this universality may be achieved by ensemble learning, which can integrate multiple learners to meet the demands of diversified databases. Therefore, we propose a general procedure for learning ensemble establishment based on noncovalent interactions (NCIs) databases. Additionally, accurate NCI computation is quite demanding for first-principles methods, for which a competent machine learning model can be an efficient solution to obtain high NCI accuracy with minimal computational resources. In regard to these aspects, multiple schemes of ensemble learning models (Bagging, Boosting, and Stacking frameworks), are explored in this study. The models are based on various low levels of density functional theory (DFT) calculations for the benchmark databases S66, S22, and X40. All NCIs computed by the DFT calculations can be improved to high-level accuracy (root-mean-square error RMSE = 0.22 kcal/mol in contrast to CCSD(T)/CBS benchmark) by established ensemble learning models. Compared with single machine learning models, ensemble models show better accuracy (RMSE of the best model is further lowered by ∼25%), robustness and goodness-of-fit according to evaluation parameters suggested by the OECD. Among ensemble learning models, heterogeneous Stacking ensemble models show the most valuable application potential. The standardized procedure of constructing learning ensembles has been well utilized on several NCI data sets, and this procedure may also be applicable for other chemical databases.

A machine learning correction for DFT non-covalent interactions based on the S22, S66 and X40 benchmark databases.

BACKGROUND: Non-covalent interactions (NCIs) play critical roles in supramolecular chemistries; however, they are difficult to measure. Currently, reliable computational methods are being pursued to meet this challenge, but the accuracy of calculations based on low levels of theory is not satisfactory and calculations based on high levels of theory are often too costly. Accordingly, to reduce the cost and increase the accuracy of low-level theoretical calculations to describe NCIs, an efficient approach is proposed to correct NCI calculations based on the benchmark databases S22, S66 and X40 (Hobza in Acc Chem Rev 45: 663-672, 2012; Rezác et al. in J Chem Theory Comput 8:4285, 2012). RESULTS: A novel type of NCI correction is presented for density functional theory (DFT) methods. In this approach, the general regression neural network machine learning method is used to perform the correction for DFT methods on the basis of DFT calculations. Various DFT methods, including M06-2X, B3LYP, B3LYP-D3, PBE, PBE-D3 and ωB97XD, with two small basis sets (i.e., 6-31G* and 6-31+G*) were investigated. Moreover, the conductor-like polarizable continuum model with two types of solvents (i.e., water and pentylamine, which mimics a protein environment with ε = 4.2) were considered in the DFT calculations. With the correction, the root mean square errors of all DFT calculations were improved by at least 70 %. Relative to CCSD(T)/CBS benchmark values (used as experimental NCI values because of its high accuracy), the mean absolute error of the best result was 0.33 kcal/mol, which is comparable to high-level ab initio methods or DFT methods with fairly large basis sets. Notably, this level of accuracy is achieved within a fraction of the time required by other methods. For all of the correction models based on various DFT approaches, the validation parameters according to OECD principles (i.e., the correlation coefficient R (2), the predictive squared correlation coefficient q (2) and [Formula: see text] from cross-validation) were >0.92, which suggests that the correction model has good stability, robustness and predictive power. CONCLUSIONS: The correction can be added following DFT calculations. With the obtained molecular descriptors, the NCIs produced by DFT methods can be improved to achieve high-level accuracy. Moreover, only one parameter is introduced into the correction model, which makes it easily applicable. Overall, this work demonstrates that the correction model may be an alternative to the traditional means of correcting for NCIs.Graphical abstractA machine learning correction model efficiently improved the accuracy of non-covalent interactions(NCIs) calculated by DFT methods. The application of the correction model is easy and flexible, so it may be an alternative correction means for NCIs by first-principle calculations.

Photosynthetic redox imbalance governs leaf sectoring in the Arabidopsis thaliana variegation mutants immutans, spotty, var1, and var2.

We hypothesized that chloroplast energy imbalance sensed through alterations in the redox state of the photosynthetic electron transport chain, measured as excitation pressure, governs the extent of variegation in the immutans mutant of Arabidopsis thaliana. To test this hypothesis, we developed a nondestructive imaging technique and used it to quantify the extent of variegation in vivo as a function of growth temperature and irradiance. The extent of variegation was positively correlated (R(2) = 0.750) with an increase in excitation pressure irrespective of whether high light, low temperature, or continuous illumination was used to induce increased excitation pressure. Similar trends were observed with the variegated mutants spotty, var1, and var2. Measurements of greening of etiolated wild-type and immutans cotyledons indicated that the absence of IMMUTANS increased excitation pressure twofold during the first 6 to 12 h of greening, which led to impaired biogenesis of thylakoid membranes. In contrast with IMMUTANS, the expression of its mitochondrial analog, AOX1a, was transiently upregulated in the wild type but permanently upregulated in immutans, indicating that the effects of excitation pressure during greening were also detectable in mitochondria. We conclude that mutations involving components of the photosynthetic electron transport chain, such as those present in immutans, spotty, var1, and var2, predispose Arabidopsis chloroplasts to photooxidation under high excitation pressure, resulting in the variegated phenotype.