Synergetic surface enhancement of quantum dots-based electrochemiluminescence with photonic crystal light scattering and metal surface plasmon resonance for sensitive bioanalysis.

Noble metal nanostructures and photonic crystals (PhCs) have been widely investigated as substrates for constructing surface enhanced electrochemiluminescence (SE-ECL) biosensors. However, their applications are hindered by the limited enhancement intensity of surface plasmon resonance (SPR) and an incomplete mechanism for the photonic enhancement effect. Hence, developing a novel SE-ECL strategy with better signal enhanced capability and enriching our understanding of the intrinsic mechanisms for efficient bioanalysis is extremely urgent. Here, a synergistic SE-ECL strategy was developed for the sensitive determination of prostate specific antigen (PSA) protein. The randomly arranged polystyrene (r-PS) spheres and PS PhC arrays were applied to enhance the ECL emission of cadmium sulfide quantum dots (CdS QDs) and the results suggested that the PhC arrays displayed superior intensity (0.22) than the r-PS interface (0.10). Au nanoparticles (NPs) were introduced onto the two kinds of surfaces and further boosted the ECL intensity. According to the ECL measurements, Au NPs modified at the r-PS surface exhibited only a slight increase (0.13), while the PhC arrays showed approximately 5-fold enhancement (0.92), benefiting from the synergistic enhancement. The finite-difference time-domain (FDTD) simulation indicated that the ECL enhancement was ascribed to the coupled electromagnetic (EM) field at the surfaces of PS PhCs and Au NPs. The SE-ECL could achieve a detection range from 1 pg/mL to 1 µg/mL with a detection limit of 0.41 pg/mL (S/N = 3). This study provides the first combination of PhC arrays and metal surface plasmon nanostructure for the synergetic enhancement of SE-ECL systems. It opens a new avenue for the rational design of advanced ECL biosensors and shows great perspective for clinical diagnosis.

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer.

Background: Most of the current research on prognostic model construction for non-small cell lung cancer (NSCLC) only involves in bulk RNA-seq data without integration of single-cell RNA-seq (scRNA-seq) data. Besides, most of the prognostic models are constructed by predictive genes, ignoring other predictive variables such as clinical features. Methods: We obtained scRNA-seq data from GEO database and bulk RNA-seq data from TCGA database. We construct a prognostic model through the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. Furthermore, we performed ESTIMATE, CIBERSORT, immune checkpoint-related analyses and compared drug sensitivity using pRRophetic method judged by IC50 between different risk groups. Results: 14 tumor-related genes were extracted for model construction. The AUC for 1-, 3-, and 5 years overall survival prediction in TCGA and three validation cohorts were almost higher than 0.65, some of which were even higher than 0.7, even 0.8. Besides, calibration curves suggested no departure between model prediction and perfect fit. Additionally, immune-related and drug sensitivity results revealed potential targets and strategies for treatment, which can provide clinical guidance. Conclusion: We integrated traditional bulk RNA-seq and scRNA-seq data, along with predictive clinical features to develop a prognostic model for patients with NSCLC. According to the constructed model, patients in different groups can follow precise and individual therapeutic schedules based on immune characteristics as well as drug sensitivity.

Development and validation of radiomics nomograms for preoperative prediction of characteristics in non-small cell lung cancer and circulating tumor cells.

To develop and validate 3 radiomics nomograms for preoperative prediction of pathological and progression diagnosis in non-small cell lung cancer (NSCLC) as well as circulating tumor cells (CTCs). A total of 224 and 134 patients diagnosed with NSCLC were respectively gathered in 2018 and 2019 in this study. There were totally 1197 radiomics features that were extracted and quantified from the images produced by computed tomography. Then we selected the radiomics features with predictive value by least absolute shrinkage and selection operator and combined them into radiomics signature. Logistic regression models were built using radiomics signature as the only predictor, which were then converted to nomograms for individualized predictions. Finally, the performance of the nomograms was assessed on both cohorts. Additionally, immunohistochemical correlation analysis was also performed. As for discrimination, the area under the curve of pathological diagnosis nomogram and progression diagnosis nomogram in NSCLC were both higher than 90% in the training cohort and higher than 80% in the validation cohort. The performance of the CTC-diagnosis nomogram was somehow unexpected where the area under the curve were range from 60% to 70% in both cohorts. As for calibration, nonsignificant statistics (P > .05) yielded by Hosmer-Lemeshow tests suggested no departure between model prediction and perfect fit. Additionally, decision curve analyses demonstrated the clinically usefulness of the nomograms. We developed radiomics-based nomograms for pathological, progression and CTC diagnosis prediction in NSCLC respectively. Nomograms for pathological and progression diagnosis were demonstrated well-performed to facilitate the individualized preoperative prediction, while the nomogram for CTC-diagnosis prediction needed improvement.

OsbHLH067, OsbHLH068, and OsbHLH069 redundantly regulate inflorescence axillary meristem formation in rice.

Rice axillary meristems (AMs) are essential to the formation of tillers and panicle branches in rice, and therefore play a determining role in rice yield. However, the regulation of inflorescence AM development in rice remains elusive. In this study, we identified no spikelet 1-Dominant (nsp1-D), a sparse spikelet mutant, with obvious reduction of panicle branches and spikelets. Inflorescence AM deficiency in nsp1-D could be ascribed to the overexpression of OsbHLH069. OsbHLH069 functions redundantly with OsbHLH067 and OsbHLH068 in panicle AM formation. The Osbhlh067 Osbhlh068 Osbhlh069 triple mutant had smaller panicles and fewer branches and spikelets. OsbHLH067, OsbHLH068, and OsbHLH069 were preferentially expressed in the developing inflorescence AMs and their proteins could physically interact with LAX1. Both nsp1-D and lax1 showed sparse panicles. Transcriptomic data indicated that OsbHLH067/068/069 may be involved in the metabolic pathway during panicle AM formation. Quantitative RT-PCR results demonstrated that the expression of genes involved in meristem development and starch/sucrose metabolism was down-regulated in the triple mutant. Collectively, our study demonstrates that OsbHLH067, OsbHLH068, and OsbHLH069 have redundant functions in regulating the formation of inflorescence AMs during panicle development in rice.

Ectopic Expression of Gastrodia Antifungal Protein in Rice Enhances Resistance to Rice Sheath Blight Disease.

Sheath blight (ShB) disease, caused by Rhizoctonia solani Kühn, is one of the most serious rice diseases. Rice breeding against ShB has been severely hindered because no major resistance genes or germplasms are available in rice. Here, we report that introduction of Gastrodia antifungal protein (GAFP) genes from Gastrodia elata B1 into rice significantly enhances resistance to rice ShB. Four GAFP genes were cloned from G. elata B1, and all displayed a strong ability to inhibit R. solani growth in plate assays. Two versions, with or without a signal peptide, for each of the four GAFP genes were introduced into XD3 and R6547 rice cultivars, and all transgenic lines displayed stronger ShB resistance than the corresponding wild-type control in both greenhouse and field conditions. Importantly, GAFP2 showed the highest ShB resistance; GAFPs with and without its signal peptide showed no significant differences in enhancing ShB resistance. We also evaluated the agronomic traits of these transgenic rice and found that ectopic expression of GAFPs in rice at appropriate levels did not affect agronomic traits other than enhancing ShB resistance. Together, these results indicate that GAFP genes, especially GAFP2, have great potential in rice breeding against ShB disease.

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer.

Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC. Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package "maftool" and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package "pRRophetic", CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy. Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy. Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.

Multi-Omics Integrative Analysis of Lung Adenocarcinoma: An in silico Profiling for Precise Medicine.

Lung adenocarcinoma (LUAD) is one of the most common histological subtypes of lung cancer. The aim of this study was to construct consensus clusters based on multi-omics data and multiple algorithms. In order to identify specific molecular characteristics and facilitate the use of precision medicine on patients we used gene expression, DNA methylation, gene mutations, copy number variation data, and clinical data of LUAD patients for clustering. Consensus clusters were obtained using a consensus ensemble of five multi-omics integrative algorithms. Four molecular subtypes were identified. The CS1 and CS2 subtypes had better prognosis. Based on the immune and drug sensitivity predictions, we inferred that CS1 may be less responsive to immunotherapy and less sensitive to chemotherapeutic drugs. The high immune infiltration of CS2 cells may respond well to immunotherapy. Additionally, the CS2 subtype may also respond to EGFR molecular targeted therapy. The CS3 and CS4 subtypes were associated with poor prognosis. These two subtypes had more mutations, especially TP53 ones, as well as higher sensitivity to chemotherapeutics for lung cancer. However, CS3 was enriched in immune-related pathways and may respond to anti-PD1 immunotherapy. In addition, CS1 and CS4 were less sensitive to ferroptosis inhibitors. We performed a comprehensive analysis of the five types of omics data using five clustering algorithms to reveal the molecular characteristics of LUAD patients. These findings provide new insights into LUAD subtypes and potential clinical treatment strategies to guide personalized management and treatment.

Identification of a DNA Damage Response and Repair-Related Gene-Pair Signature for Prognosis Stratification Analysis in Hepatocellular Carcinoma.

Background: Nowadays, although the cause of hepatocellular carcinoma (HCC) mortality and recurrence remains at a high level, the 5-year survival rate is still very low. The DNA damage response and repair (DDR) pathway may affect HCC patients' survival by influencing tumor development and therapeutic response. It is necessary to identify a prognostic DDR-related gene signature to predict the outcome of patients. Methods: Level 3 mRNA expression and clinical information were extracted from the TCGA website. The GSE14520 datasets, ICGC-LIRI datasets, and a Chinese HCC cohort were served as validation sets. Univariate Cox regression analysis and LASSO-penalized Cox regression analysis were performed to construct the DDR-related gene pair (DRGP) signature. Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) analysis curves were calculated to determine the predictive ability of this prognostic model. Then, a prognostic nomogram was established to help clinical management. We investigated the difference in biological processes between HRisk and LRisk by conducting several enrichment analyses. The TIDE algorithm and R package "pRRophetic" were applied to estimate the immunotherapeutic and chemotherapeutic response. Results: We constructed the prognostic signature based on 23 DDR-related gene pairs. The patients in the training datasets were divided into HRisk and LRisk groups at median cut-off. The HRisk group had significantly poorer OS than the LRisk group, and the signature was an independent prognostic indicator in HCC. Furthermore, a nomogram of the riskscore combined with TNM stage was constructed and detected by the calibration curve and decision curve. The LRisk group was associated with higher expression of HBV oncoproteins and metabolism pathways, while DDR-relevant pathways and cell cycle process were enriched in the HRisk group. Moreover, patients in the LRisk group may be more beneficial from immunotherapy. We also found that TP53 gene was more frequently mutated in the HRisk group. As for chemotherapeutic drugs commonly used in HCC, the HRisk group was highly sensitive to 5-fluorouracil, while the LRisk group presented with a significantly higher response to gefitinib and gemcitabine. Conclusion: Overall, we developed a novel DDR-related gene pair signature and nomogram to assist in predicting survival outcomes and clinical treatment of HCC patients. It also helps understand the underlying mechanisms of different DDR patterns in HCC.

Corrigendum: Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.

[This corrects the article DOI: 10.3389/fneur.2022.755492.].

Identification of 9H-purin-6-amine derivatives as novel aldose reductase inhibitors for the treatment of diabetic complications.

A series of 9H-purin-6-amine derivatives as aldose reductase (ALR) inhibitors were designed and synthesized. Most of these derivatives, having a C6-substituted benzylamine side chain and N9 carboxylic acid on the core structure, were found to be potent and selective ALR inhibitors, with submicromolar IC50 values against ALR2. Particularly, compound 4e was the most active with an IC50 value of 0.038 µM, and it was also proved to be endowed with excellent inhibitory selectivity. The structure-activity relationship and molecular docking studies highlighted the importance of the carboxylic acid head group along with different halogen substituents on the C6 benzylamine side chain of the 9H-purin-6-amine scaffold for the construction of strong and selective ALR inhibitors.

Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.

Background: Computed tomography (CT) plays an essential role in classifying stroke, quantifying penumbra size and supporting stroke-relevant radiomics studies. However, it is difficult to acquire standard, accurate and repeatable images during follow-up. Therefore, we invented an intelligent CT to evaluate stroke during the entire follow-up. Methods: We deployed a region proposal network (RPN) and V-Net to endow traditional CT with intelligence. Specifically, facial detection was accomplished by identifying adjacent jaw positions through training and testing an RPN on 76,382 human faces using a preinstalled 2-dimensional camera; two regions of interest (ROIs) were segmented by V-Net on another training set with 295 subjects, and the moving distance of scanning couch was calculated based on a pre-generated calibration table. Multiple cohorts including 1,124 patients were used for performance validation under three clinical scenarios. Results: Cranial Automatic Planbox Imaging Towards AmeLiorating neuroscience (CAPITAL)-CT was invented. RPN model had an error distance of 4.46 ± 0.02 pixels with a success rate of 98.7% in the training set and 100% with 2.23 ± 0.10 pixels in the testing set. V-Net-derived segmentation maintained a clinically tolerable distance error, within 3 mm on average, and all lines presented with a tolerable angle error, within 3° on average in all boundaries. Real-time, accurate, and repeatable automatic scanning was accomplished with and a lower radiation exposure dose (all P < 0.001). Conclusions: CAPITAL-CT generated standard and reproducible images that could simplify the work of radiologists, which would be of great help in the follow-up of stroke patients and in multifield research in neuroscience.

Identification of Immune Function-Related Subtypes in Cutaneous Melanoma.

Tumour immunotherapy combined with molecular typing is a new therapy to help select patients. However, molecular typing algorithms related to tumour immune function have not been thoroughly explored. We herein proposed a single sample immune signature network (SING) method to identify new immune function-related subtypes of cutaneous melanoma of the skin. A sample-specific network and tumour microenvironment were constructed based on the immune annotation of cutaneous melanoma samples. Then, the differences and heterogeneity of immune function among different subtypes were analysed and verified. A total of 327 cases of cutaneous melanoma were divided into normal and immune classes; the immune class had more immune enrichment characteristics. After further subdividing the 327 cases into three immune-related subtypes, the degree of immune enrichment in the "high immune subtype" was greater than that in other subtypes. Similar results were validated in both tumour samples and cell lines. Sample-specific networks and the tumour microenvironment based on immune annotation contribute to the mining of cutaneous melanoma immune function-related subtypes. Mutations in B2M and PTEN are considered potential therapeutic targets that can improve the immune response. Patients with a high immune subtype can generally obtain a better immune prognosis effect, and the prognosis may be improved when combined with TGF-ß inhibitors.

Identification of Immune-Related Subtypes and Characterization of Tumor Microenvironment Infiltration in Bladder Cancer.

Tumors are closely related to the tumor microenvironment (TME). The complex interaction between tumor cells and the TME plays an indisputable role in tumor development. Tumor cells can affect the TME, promote tumor angiogenesis and induce immune tolerance by releasing cell signaling molecules. Immune cell infiltration (ICI) in the TME can affect the prognosis of patients with bladder cancer. However, the pattern of ICI of the TME in bladder cancer has not yet been elucidated. Herein, we identified three distinct ICI subtypes based on the TME immune infiltration pattern of 584 bladder cancer patients using the ESTIMATE and CIBERSORT algorithms. Then, we identified three gene clusters based on the differentially expressed genes (DEGs) between the three ICI subtypes. In addition, the ICI score was determined using single sample gene set enrichment analysis (ssGSEA). The results suggested that patients in the high ICI score subgroup had a favorable prognosis and higher expression of checkpoint-related and immune activity-related genes. The high ICI score subgroup was also linked to increased tumor mutation burden (TMB) and neoantigen burden. A cohort treated with anti-PD-L1 immunotherapy confirmed the therapeutic advantage and clinical benefit of patients with higher ICI scores. In the end, our study also shows that the ICI score represents an effective prognostic predictor for evaluating the response to immunotherapy. In conclusion, our study deepened the understanding of the TME, and it provides new ideas for improving patients' response to immunotherapy and promoting individualized tumor immunotherapy in the future.

OsMLH1 interacts with OsMLH3 to regulate synapsis and interference-sensitive crossover formation during meiosis in rice.

Meiotic recombination is essential for reciprocal exchange of genetic information between homologous chromosomes and their subsequent proper segregation in sexually reproducing organisms. MLH1 and MLH3 belong to meiosis-specific members of the MutL-homolog family, which are required for normal level of crossovers (COs) in some eukaryotes. However, their functions in plants need to be further elucidated. Here, we report the identification of OsMLH1 and reveal its functions during meiosis in rice. Using CRISPR-Cas9 approach, two independent mutants, Osmlh1-1 and Osmlh1-2, are generated and exhibited significantly reduced male fertility. In Osmlh1-1, the clearance of PAIR2 is delayed and partial ZEP1 proteins are not loaded into the chromosomes, which might be due to the deficient in resolution of interlocks at late zygotene. Thus, OsMLH1 is required for the assembly of synapsis complex. In Osmlh1-1, CO number is dropped by ~53% and the distribution of residual COs is consistent with predicted Poisson distribution, indicating that OsMLH1 is essential for the formation of interference-sensitive COs (class I COs). OsMLH1 interacts with OsMLH3 through their C-terminal domains. Mutation in OsMLH3 also affects the pollen fertility. Thus, our experiments reveal that the conserved heterodimer MutLγ (OsMLH1-OsMLH3) is essential for the formation of class I COs in rice.

Novel 3,4-dihydroquinolin-2(1H)-one derivatives as dual inhibitor targeting AKR1B1/ROS for treatment of diabetic complications: Design, synthesis and biological evaluation.

AKR1B1 (Aldose reductase) has been used as therapeutic intervention target for treatment of diabetic complications over 50 years, and more recently for inflammation and cancer. However, most developed small molecule inhibitors have the defect of low bioactivity. To address this limitation, novel series of 3,4-dihydroquinolin-2(1H)-one derivatives as dual inhibitor targeting AKR1B1/ROS (Reactive Oxygen Species) were designed and synthesized. Most of these derivatives were found to be potent and selective against AKR1B1, and compound 8a was the most active with an IC50 value of 0.035 µM. Moreover, some prepared derivatives showed strong anti-ROS activity, and among them the phenolic 3,5-dihydroxyl compound 8b was proved to be the most potent, even comparable to that of the well-known antioxidant Trolox at a concentration of 100 µM. Thus the results suggested a success in the construction of potent dual inhibitor for the therapeutic intervention target of AKR1B1/ROS.

Fine mapping of qSTV11TQ, a major gene conferring resistance to rice stripe disease.

The indica rice cultivar, Teqing, shows a high level of resistance to rice stripe virus (RSV). It is believed that this resistance is controlled by the gene, qSTV11(TQ). For positional cloning of the resistance gene, a set of chromosome single segment substitution lines (CSSSLs) was constructed, all of which had the genetic background of the susceptible japonica cultivar, Lemont, with different single substituted segments of Teqing on chromosome 11. By identifying the resistance of the CSSSLs-2006 in a field within a heavily diseased area, the resistance gene qSTV11(TQ) was mapped between the markers Indel7 and RM229. Furthermore, in that region, six new markers were developed and 52 subregion CSSSLs (CSSSLs-2007) were constructed. The natural infection experiment was conducted again at different sites, with two replicates used in each site in order to identify the resistance phenotypes of the CSSSLs-2007 and resistant/susceptible controls in 2007. Through the results of 2007, qSTV11(TQ) was localized in a region defined by the markers, CAPs1 and Indel4. In order to further confirm the position of qSTV11(TQ), another set of subregion CSSSLs (CSSSLs-2009) was constructed. Finally, qSTV11(TQ) was localized to a 55.7 kb region containing nine annotated genes according to the genome sequence of japonica Nipponbare. The relationship between qSTV11(TQ) and Stvb-i (Hayano-Saito et al. in Theor Appl Genet 101:59-63, 2000) and the reliability of the markers used on both sides of qSTV11(TQ) for marker-assisted breeding of resistance to rice stripe disease are discussed.