Synthesis Principle and Practice with Radioactive Iodines and Astatine: Advances Made So Far.

Radioactive iodines and astatine, possessing distinct exploitable nuclear properties, play indispensable roles in the realms of nuclear imaging and therapy. Their analogous chemical characteristics shape the design, preparation, and substrate range for tracers labeled with these radiohalogens through interconnected radiosynthetic chemistry. This perspective systematically explores the labeling methods by types of halogenating reagents─nucleophilic and electrophilic─underpinning the rational design of such compounds. It delves into the rapidly evolving synthetic strategies and reactions in radioiodination and radioastatination over the past decade, comparing their intrinsic relationships and highlighting variations. This comparative analysis illuminates potential radiosynthetic methods for exploration. Moreover, stability concerns related to compounds labeled with radioactive iodines and astatine are addressed, offering valuable insights for radiochemists and physicians alike.

A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning.

Twenty-five percent of cervical cancers are classified as endocervical adenocarcinomas (EACs), which comprise a highly heterogeneous group of tumors. A histopathologic risk stratification system known as the Silva pattern system was developed based on morphology. However, accurately classifying such patterns can be challenging. The study objective was to develop a deep learning pipeline (Silva3-AI) that automatically analyzes whole slide image-based histopathologic images and identifies Silva patterns with high accuracy. Initially, a total of 202 patients with EACs and histopathologic slides were obtained from Qilu Hospital of Shandong University for developing and internally testing the Silva3-AI model. Subsequently, an additional 161 patients and slides were collected from seven other medical centers for independent testing. The Silva3-AI model was developed using a vision transformer and recurrent neural network architecture, utilizing multi-magnification patches, and its performance was evaluated based on a class-specific area under the receiver-operating characteristic curve. Silva3-AI achieved a class-specific area under the receiver-operating characteristic curve of 0.947 for Silva A, 0.908 for Silva B, and 0.947 for Silva C on the independent test set. Notably, the performance of Silva3-AI was consistent with that of professional pathologists with 10 years' diagnostic experience. Furthermore, the visualization of prediction heatmaps facilitated the identification of tumor microenvironment heterogeneity, which is known to contribute to variations in Silva patterns.

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature based on H&E-stained pathological specimens to accurately predict the clinical benefit of PD-1 inhibitors in ESCC patients. METHODS: ESCC patients receiving PD-1 inhibitors from Shandong Cancer Hospital were included. WSI images of H&E-stained histological specimens of included patients were collected, and randomly divided into training (70%) and validation (30%) sets. The labels of images were defined by the progression-free survival (PFS) with the interval of 4 months. The pretrained ViT model was used for patch-level model training, and all patches were projected into probabilities after linear classifier. Then the most predictive patches were passed to RNN for final patient-level prediction to construct ESCC-pathomics signature (ESCC-PS). Accuracy rate and survival analysis were performed to evaluate the performance of ViT-RNN survival model in validation cohort. RESULTS: 163 ESCC patients receiving PD-1 inhibitors were included for model training. There were 486,188 patches of 1024*1024 pixels from 324 WSI images of H&E-stained histological specimens after image pre-processing. There were 120 patients with 227 images in training cohort and 43 patients with 97 images in validation cohort, with balanced baseline characteristics between two groups. The ESCC-PS achieved an accuracy of 84.5% in the validation cohort, and could distinguish patients into three risk groups with the median PFS of 2.6, 4.5 and 12.9 months (P < 0.001). The multivariate cox analysis revealed ESCC-PS could act as an independent predictor of survival from PD-1 inhibitors (P < 0.001). A combined signature incorporating ESCC-PS and expression of PD-L1 shows significantly improved accuracy in outcome prediction of PD-1 inhibitors compared to ESCC-PS and PD-L1 anlone, with the area under curve value of 0.904, 0.924, 0.610 for 6-month PFS and C-index of 0.814, 0.806, 0.601, respectively. CONCLUSIONS: The outcome supervised pathomics signature based on deep learning has the potential to enable superior prognostic stratification of ESCC patients receiving PD-1 inhibitors, which convert the images pixels to an effective and labour-saving tool to optimize clinical management of ESCC patients.

Glycolysis Induced by METTL14 Is Essential for Macrophage Phagocytosis and Phenotype in Cervical Cancer.

N 6-methyladenosine (m6A) is the most abundant mRNA modification in mammals and it plays a vital role in various biological processes. However, the roles of m6A on cervical cancer tumorigenesis, especially macrophages infiltrated in the tumor microenvironment of cervical cancer, are still unclear. We analyzed the abnormal m6A methylation in cervical cancer, using CaSki and THP-1 cell lines, that might influence macrophage polarization and/or function in the tumor microenvironment. In addition, C57BL/6J and BALB/c nude mice were used for validation in vivo. In this study, m6A methylated RNA immunoprecipitation sequencing analysis revealed the m6A profiles in cervical cancer. Then, we discovered that the high expression of METTL14 (methyltransferase 14, N6-adenosine-methyltransferase subunit) in cervical cancer tissues can promote the proportion of programmed cell death protein 1 (PD-1)-positive tumor-associated macrophages, which have an obstacle to devour tumor cells. Functionally, changes of METTL14 in cervical cancer inhibit the recognition and phagocytosis of macrophages to tumor cells. Mechanistically, the abnormality of METTL14 could target the glycolysis of tumors in vivo and vitro. Moreover, lactate acid produced by tumor glycolysis has an important role in the PD-1 expression of tumor-associated macrophages as a proinflammatory and immunosuppressive mediator. In this study, we revealed the effect of glycolysis regulated by METTL14 on the expression of PD-1 and phagocytosis of macrophages, which showed that METTL14 was a potential therapeutic target for treating advanced human cancers.

Comparison of different predictive biomarker testing assays for PD-1/PD-L1 checkpoint inhibitors response: a systematic review and network meta-analysis.

Background: Accurate prediction of efficacy of programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) checkpoint inhibitors is of critical importance. To address this issue, a network meta-analysis (NMA) comparing existing common measurements for curative effect of PD-1/PD-L1 monotherapy was conducted. Methods: We searched PubMed, Embase, the Cochrane Library database, and relevant clinical trials to find out studies published before Feb 22, 2023 that use PD-L1 immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), microsatellite instability (MSI), multiplex IHC/immunofluorescence (mIHC/IF), other immunohistochemistry and hematoxylin-eosin staining (other IHC&HE) and combined assays to determine objective response rates to anti-PD-1/PD-L1 monotherapy. Study-level data were extracted from the published studies. The primary goal of this study was to evaluate the predictive efficacy and rank these assays mainly by NMA, and the second objective was to compare them in subgroup analyses. Heterogeneity, quality assessment, and result validation were also conducted by meta-analysis. Findings: 144 diagnostic index tests in 49 studies covering 5322 patients were eligible for inclusion. mIHC/IF exhibited highest sensitivity (0.76, 95% CI: 0.57-0.89), the second diagnostic odds ratio (DOR) (5.09, 95% CI: 1.35-13.90), and the second superiority index (2.86). MSI had highest specificity (0.90, 95% CI: 0.85-0.94), and DOR (6.79, 95% CI: 3.48-11.91), especially in gastrointestinal tumors. Subgroup analyses by tumor types found that mIHC/IF, and other IHC&HE demonstrated high predictive efficacy for non-small cell lung cancer (NSCLC), while PD-L1 IHC and MSI were highly efficacious in predicting the effectiveness in gastrointestinal tumors. When PD-L1 IHC was combined with TMB, the sensitivity (0.89, 95% CI: 0.82-0.94) was noticeably improved revealed by meta-analysis in all studies. Interpretation: Considering statistical results of NMA and clinical applicability, mIHC/IF appeared to have superior performance in predicting response to anti PD-1/PD-L1 therapy. Combined assays could further improve the predictive efficacy. Prospective clinical trials involving a wider range of tumor types are needed to establish a definitive gold standard in future.

Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images.

BACKGROUND: Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM. METHODS: A deep learning (DL) model was trained using the Vision transformer (ViT) and recurrent neural network (RNN) frameworks to predict LNM. This prediction was based on the analysis of 554 histopathological whole-slide images (WSIs) obtained from Qilu Hospital of Shandong University. To validate the model's performance, an external test was conducted using 336 WSIs from four other hospitals. Additionally, the efficiency of the DL model was evaluated using 190 cervical biopsies WSIs in a prospective set. RESULTS: In the internal test set, our DL model achieved an area under the curve (AUC) of 0.919, with sensitivity and specificity values of 0.923 and 0.905, respectively, and an accuracy (ACC) of 0.909. The performance of the DL model remained strong in the external test set. In the prospective cohort, the AUC was 0.91, and the ACC was 0.895. Additionally, the DL model exhibited higher accuracy compared to imaging examination in the evaluation of LNM. By utilizing the transformer visualization method, we generated a heatmap that illustrates the local pathological features in primary lesions relevant to LNM. CONCLUSION: DL-based image analysis has demonstrated efficiency in predicting LNM in early operable cervical cancer through the utilization of biopsies WSI. This approach has the potential to enhance therapeutic decision-making for patients diagnosed with cervical cancer.

Surfactants Accelerate Isotope Exchange-Based 18F-Fluorination in Water.

Radiochemical yields (RCYs) of isotope exchange-based 18F-fluorination of non-carbon-centered substrates in water are rationally enhanced by adding surfactants, which increases both the rate constant k and local reactant concentrations. Among 12 surfactants, the cationic surfactant cetrimonium bromide (CTAB) and two nonionic surfactants (Tween 20 and Tween 80) were selected for their superior catalytic effects, namely, electrostatic effects or solubilization effects. For a model substrate, bis(4-methoxyphenyl)phosphinic fluoride, the 18F-fluorination rate constant (k) increased up to 7-fold, while its saturation concentration rose up to 15-fold due to micelle formation, encapsulating 70-94% of the substrate. With 30.0 mmol/L CTAB, the required 18F-labeling temperature of a typical organofluorosilicon prosthesis ([18F]SiFA) decreased from 95 °C to room temperature, achieving an RCY of 22%. For an E[c(RGDyK)]2-derived peptide tracer with an organofluorophosphine prosthesis, the RCY in water at 90 °C achieved 25%, correspondingly increasing the molar activity (Am). After high-performance liquid chromatography (HPLC) or solid-phase purification, the residual selected surfactant concentrations in the tracer injections were well below the FDA DII (Inactive Ingredient Database) limits or the LD50 in mice.

Outcome-Supervised Deep Learning on Pathologic Whole Slide Images for Survival Prediction of Immunotherapy in Patients with Non-Small Cell Lung Cancer.

Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in patients with non-small cell lung cancer (NSCLC), approximately 60% of the patients still suffer from recurrence and metastasis after PD-(L)1 inhibitor treatment. To accurately predict the response to PD-(L)1 inhibitors, we presented a deep learning model using a Vision Transformer (ViT) network based on hematoxylin and eosin (H&E)-stained specimens of patients with NSCLC. Two independent cohorts of patients with NSCLC receiving PD-(L)1 inhibitors from Shandong Cancer Hospital and Institute and Shandong Provincial Hospital were enrolled for model training and external validation, respectively. Whole slide images (WSIs) of H&E-stained histologic specimens were obtained from these patients and patched into 1024 × 1024 pixels. The patch-level model was trained based on ViT to identify the predictive patches, and patch-level probability distribution was performed. Then, we trained a patient-level survival model based on the ViT-Recursive Neural Network framework and externally validated it in the Shandong Provincial Hospital cohort. A total of 291 WSIs of H&E-stained histologic specimens from 198 patients with NSCLC in Shandong Cancer Hospital and 62 WSIs from 30 patients with NSCLC in Shandong Provincial Hospital were included in the model training and validation. The model achieved an accuracy of 88.6% in the internal validation cohort and 81% in the external validation cohort. The survival model also remained a statistically independent predictor of survival from PD-(L)1 inhibitors. In conclusion, the outcome-supervised ViT-Recursive Neural Network survival model based on pathologic WSIs could be used to predict immunotherapy efficacy in patients with NSCLC.

Development and validation of a deep learning survival model for cervical adenocarcinoma patients.

BACKGROUND: The aim was to develop a personalized survival prediction deep learning model for cervical adenocarcinoma patients and process personalized survival prediction. METHODS: A total of 2501 cervical adenocarcinoma patients from the surveillance, epidemiology and end results database and 220 patients from Qilu hospital were enrolled in this study. We created our deep learning (DL) model to manipulate the data and evaluated its performance against four other competitive models. We tried to demonstrate a new grouping system oriented by survival outcomes and process personalized survival prediction by using our DL model. RESULTS: The DL model reached 0.878 c-index and 0.09 Brier score in the test set, which was better than the other four models. In the external test set, our model achieved a 0.80 c-index and 0.13 Brier score. Thus, we developed prognosis-oriented risk grouping for patients according to risk scores computed by our DL model. Notable differences among groupings were observed. In addition, a personalized survival prediction system based on our risk-scoring grouping was developed. CONCLUSIONS: We developed a deep neural network model for cervical adenocarcinoma patients. The performance of this model proved to be superior to other models. The results of external validation supported the possibility that the model can be used in clinical work. Finally, our survival grouping and personalized prediction system provided more accurate prognostic information for patients than traditional FIGO stages.

Apatinib Inhibits Bladder Cancer through Suppression of the VEGFR2- PI3K-AKT Signaling Pathway as Revealed by Network Pharmacology and in vitro Experimental Verification.

AIMS: This study aimed to evaluate the underlying pharmacological mechanisms of Apatinib anti-bladder cancer via network pharmacology and experimental verification. METHODS: Network pharmacology was used to screen the possible signaling pathways of Apatinib in bladder cancer, and the most likely pathway was selected for in vitro validation. CCK-8 and colony formation assay were used to detect the effect of Apatinib on the proliferation of bladder cancer cells. Hoechst staining and flow cytometry detected apoptosis of bladder cancer cells induced by Apatinib. Western blot was performed to distinguish the effect of Apatinib on the expression levels of key targets. RESULTS: Apatinib can affect many signaling pathways and the correlation of the PI3K-AKT signaling pathway was the greatest. In vitro experiments showed that Apatinib could inhibit bladder cancer cell proliferation, induce apoptosis, and up-regulate the expression of apoptosisrelated proteins Cleaved-PARP and down-regulate the expression of Bcl-2. Furthermore, Apatinib could decrease the protein expression of VEGFR2, P-VEGFR2, P-PI3K and P-AKT. CONCLUSIONS: Apatinib could promote apoptosis of bladder cancer cells by inhibiting the VEGFR2- PI3K-AKT signaling pathway.

Isotope Exchange-Based 18F-Labeling Methods.

18F-Labeling methods for the preparation of 18F-labeled molecular probes can be classified into electrophilic fluorination, nucleophilic fluorination, metal-F coordination, and 18F/19F isotope exchange. Isotope exchange-based 18F-labeling methods demonstrate mild conditions featuring water resistance and facile high-performance liquid chromatography-free purification in direct 18F-labeling of substrates. This paper systematically reviews isotope exchange-based 18F-labeling methods sorted by the adjacent atom bonding with F, i.e., carbon and noncarbon atoms (Si, B, P, S, Ga, Fe, etc.). The respective isotope exchange mechanism, radiolabeling condition, radiochemical yield, molar activity, and stability of the 18F-product are mainly discussed for each isotope exchange-based 18F-labeling method as well as the cutting-edge application of the corresponding 18F-labeled molecular probes.

Network-based Pharmacology and In vitro Validation Reveal that Galangin Induces Apoptosis in Bladder Cancer Cells by Promoting the P53 Signaling Pathway.

BACKGROUND: Galangin is one of the flavonoids in Alpinia officinarum. It has various anti-tumor activities, but its anti-bladder cancer effect is unclear. OBJECTIVE: To investigate the mechanism of action of galangin against bladder cancer using a network pharmacology approach. METHODS: The TCM Systematic Pharmacology Database and Analysis Platform (TCMSP), SwissTargetPrediction database, and the Targetnet database were used to predict the targets of action of galangin. Bladder cancer-related targets were obtained through the GeneCards database. The intersection of the two was taken as the target of galangin's action against bladder cancer. The intersecting targets were screened for core targets using the STRING database and Cytoscape 3.9.0 software to build a protein-protein interaction (PPI) network of targets. The core targets were subjected to gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the online annotation and visual integration analysis tool DAVIDBioinformaticsResources (2021Update). A drug-disease-target-pathway network was constructed using Cytoscape 3.9.0 software. The antibladder cancer effect of galangin was observed by cell proliferation, and plate cloning assay; apoptosis of bladder cancer cells induced by galangin was detected by Hoechst33342 staining and flow cytometry; protein immunoblotting (Western-blot) was used to detect the effect of galangin on apoptosis-related proteins Bax, Bcl-2, Cleaved-PARP, p53 signaling pathway p53 and cytc. RESULTS: A total of 115 genes were obtained from galangin against bladder cancer, and 16 core targets were screened. The kEGG pathway enrichment analysis included Pathways in cancer, PI3K-AKT signaling pathway, p53 signaling pathway, etc. In vitro experiments showed that galangin could inhibit bladder cancer cell proliferation, induce apoptosis, upregulate the expression of apoptosis-related proteins Bax and Cleaved-PARP and downregulate the expression of Bcl-2; meanwhile, galangin could promote the upregulation of the expression of p53 and cytc proteins by activating the p53 signaling pathway. CONCLUSION: Galangin induced apoptosis in bladder cancer cells by activating the p53 signaling pathway.

Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.

Background: Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a novel prognostic model to predict the prognosis for operable cervical cancer patients with better accuracy than clinical staging system. Methods: A total of 13,952 operable cervical cancer patients were retrospectively enrolled in this study. The whole dataset was randomly split into a training set (n = 9,068, 65%), validation set (n = 2,442, 17.5%), and testing set (n = 2,442, 17.5%). Cox proportional hazard (CPH) model and random survival forest (RSF) model were used as baseline models for the prediction of overall survival (OS). Then, a deep survival learning model (DSLM) was developed for OS prediction. Finally, a novel prognostic model was explored based on this DSLM. Results: The C-indexes for the CPH and RSF model were 0.731 and 0.753, respectively. DSLM, which had four layers that had 50 neurons in each layer, achieved a C-index of 0.782 in the validation set and a C-index of 0.758 in the testing set. The novel prognostic model based on DSLM showed better performances than the conventional clinical staging system (area under receiver operating curves were 0.826 and 0.689, respectively). Personalized survival curves for individual patient using this novel model also showed notably different survival slopes. Conclusions: Our study developed a novel, practical, personalized prognostic model for operable cervical cancer patients. This novel prognostic model may have the potential to provide a more prognostic information to oncologists.

Preoperative CT-based deep learning model for predicting overall survival in patients with high-grade serous ovarian cancer.

Purpose: High-grade serous ovarian cancer (HGSOC) is aggressive and has a high mortality rate. A Vit-based deep learning model was developed to predicting overall survival in HGSOC patients based on preoperative CT images. Methods: 734 patients with HGSOC were retrospectively studied at Qilu Hospital of Shandong University with preoperative CT images and clinical information. The whole dataset was randomly split into training cohort (n = 550) and validation cohort (n = 184). A Vit-based deep learning model was built to output an independent prognostic risk score, afterward, a nomogram was then established for predicting overall survival. Results: Our Vit-based deep learning model showed promising results in predicting survival in the training cohort (AUC = 0.822) and the validation cohort (AUC = 0.823). The multivariate Cox regression analysis indicated that the image score was an independent prognostic factor in the training (HR = 9.03, 95% CI: 4.38, 18.65) and validation cohorts (HR = 9.59, 95% CI: 4.20, 21.92). Kaplan-Meier survival analysis indicates that the image score obtained from model yields promising prognostic significance to refine the risk stratification of patients with HGSOC, and the integrative nomogram achieved a C-index of 0.74 in the training cohort and 0.72 in the validation cohort. Conclusions: Our model provides a non-invasive, simple, and feasible method to predicting overall survival in patients with HGSOC based on preoperative CT images, which could help predicting the survival prognostication and may facilitate clinical decision making in the era of individualized and precision medicine.

Dearomative di- and trifunctionalization of aryl sulfoxides via [5,5]-rearrangement.

Aromatic [5,5]-rearrangement can in principle be an ideal protocol to access dearomative compounds. However, the lack of competent [5,5]-rearrangement impedes the advance of the protocol. In this Article, we showcase the power of [5,5]-rearrangement recently developed in our laboratory for constructing an intriguing dearomative sulfonium specie which features versatile and unique reactivities to perform nucleophilic 1,2- and 1,4-addition and cyclization, thus achieving dearomative di- and trifunctionalization of easily accessible aryl sulfoxides. Impressively, the dearomatization products can be readily converted to sulfur-removed cyclohexenones, naphthalenones, bicyclic cyclohexadienones, and multi-substituted benzenes. Mechanistic studies shed light on the key intermediates and the remarkable chemo-, regio- and stereoselectivities of the reactions.

A novel deep learning prognostic system improves survival predictions for stage III non-small cell lung cancer.

BACKGROUND: Accurate prognostic prediction plays a crucial role in the clinical setting. However, the TNM staging system fails to provide satisfactory individual survival prediction for stage III non-small cell lung cancer (NSCLC). The performance of the deep learning network for survival prediction in stage III NSCLC has not been explored. OBJECTIVES: This study aimed to develop a deep learning-based prognostic system that could achieve better predictive performance than the existing staging system for stage III NSCLC. METHODS: In this study, a deep survival learning model (DSLM) for stage III NSCLC was developed based on the Surveillance, Epidemiology, and End Results (SEER) database and was independently tested with another external cohort from our institute. DSLM was compared with the Cox proportional hazard (CPH) and random survival forest (RSF) models. A new prognostic system for stage III NSCLC was also proposed based on the established deep learning model. RESULTS: The study included 16,613 patients with stage III NSCLC from the SEER database. DSLM showed the best performance in survival prediction, with a C-index of 0.725 in the validation set, followed by RSF (0.688) and CPH (0.683). DSLM also showed C-indices of 0.719 and 0.665 in the internal and real-world external testing datasets, respectively. In addition, the new prognostic system based on DSLM (AUROC = 0.744) showed better performance than the TNM staging system (AUROC = 0.561). CONCLUSION: In this study, a new, integrated deep learning-based prognostic model was developed and evaluated for stage III NSCLC. This novel approach may be valuable in improving patient stratification and potentially provide meaningful prognostic information that contributes to personalized therapy.

Serum/glucocorticoid-regulated kinase 1-targeted transient receptor potential oxalate subtype 1 regulates bladder smooth muscle cell proliferation due to bladder outlet obstruction in mice via activated T cell nuclear factor transcription factor 2.

Bladder outlet obstruction (BOO) is a type of chronic disease that is mainly caused by benign prostatic hyperplasia. Previous studies discovered the involvements of both serum/glucocorticoid-regulated kinase 1 (SGK1) and activated T cell nuclear factor transcription factor 2 (NFAT2) in the proliferation of smooth muscle cells after BOO. However, the relationship between these two molecules is yet to be explored. Thus, this study explored the specific mechanism of the SGK1-NFAT2 signaling pathway in mouse BOO-mediated bladder smooth muscle cell proliferation in vivo and in vitro. In vivo experiments were performed by suturing 1/2 of the external urethra of female BALB/C mice to cause BOO for 2 weeks. In vitro, mouse bladder smooth muscle cells (MBSMCs) were treated with dexamethasone (Dex) or dexamethasone + SB705498 for 12 h and were transfected with SGK1 siRNA for 48 h. The expression and distribution of SGK1, transient receptor potential oxalate subtype 1 (TRPV1), NFAT2, and proliferating cell nuclear antigen (PCNA) were measured by Western blotting, polymerase chain reaction, and immunohistochemistry. The relationship between SGK1 and TRPV1 was analyzed by coimmunoprecipitation. The proliferation of MBSMCs was examined by 5-ethynyl-2'-deoxyuridine and cell counting kit 8 assays. Bladder weight, smooth muscle thickness, and collagen deposition in mice after 2 weeks of BOO were examined. Bladder weight, smooth muscle thickness, the collagen deposition ratio, and the expression of SGK1, TRPV1, NFAT2, and PCNA were significantly increased in mice after 2 weeks of BOO. Compared with the control, 10 µM Dex promoted the expression of these four molecules and the proliferation of MBSMCs. After inhibiting TRPV1, only the expression of SGK1 was not affected, and the proliferation of MBSMCs was inhibited. After silencing SGK1, the expression of these four molecules and the proliferation of MBSMCs decreased. Coimmunoprecipitation suggested that SGK1 acted directly on TRPV1. In this study, SGK1 targeted TRPV1 to regulate the proliferation of MBSMCs mediated by BOO in mice through NFAT2 and then affected the process of bladder remodeling after BOO. This finding may provide a strategy for BOO drug target screening.

Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer.

BACKGROUND AND PURPOSE: This study aims to develop a risk model to predict esophageal fistula in esophageal cancer (EC) patients by learning from both clinical data and computerized tomography (CT) radiomic features. MATERIALS AND METHODS: In this retrospective study, computerized tomography (CT) images and clinical data of 186 esophageal fistula patients and 372 controls (1:2 matched by the diagnosis time of EC, sex, marriage, and race) were collected. All patients had esophageal cancer and did not receive esophageal surgery. 70% patients were assigned into training set randomly and 30% into validation set. We firstly use a novel attentional convolutional neural network for radiographic descriptor extraction from nine views of planes of contextual CT, segmented tumor and neighboring structures. Then clinical factors including general, diagnostic, pathologic, therapeutic and hematological parameters are fed into neural network for high-level latent representation. The radiographic descriptors and latent clinical factor representations are finally associated by a fully connected layer for patient level risk prediction using SoftMax classifier. RESULTS: 512 deep radiographic features and 32 clinical features were extracted. The integrative deep learning model achieved C-index of 0.901, sensitivity of 0.835, and specificity of 0.918 on validation set with superior performance than non-integrative model using CT imaging alone (C-index = 0.857) or clinical data alone (C-index = 0.780). CONCLUSION: The integration of radiomic descriptors from CT and clinical data significantly improved the esophageal fistula prediction. We suggest that this model has the potential to support individualized stratification and treatment planning for EC patients.

Z-Selective α-Arylation of α,ß-Unsaturated Nitriles via [3,3]-Sigmatropic Rearrangement.

The Morita-Baylis-Hillman (MBH) reaction and [3, 3]-sigmatropic rearrangement are two paradigms in organic synthesis. We have merged the two types of reactions to achieve [3,3]-rearrangement of aryl sulfoxides with α,ß-unsaturated nitriles. The reaction was achieved by sequentially treating both coupling partners with electrophilic activator (Tf2 O) and base, offering an effective approach to prepare synthetically versatile α-aryl α,ß-unsaturated nitriles with Z-selectivity through direct α-C-H arylation of unmodified α,ß-unsaturated nitriles. The control experiments and DFT calculations support a four-stage reaction sequence, including the assembly of Tf2 O activated aryl sulfoxide with α,ß-unsaturated nitrile, MBH-like Lewis base addition, [3,3]-rearrangement, and E1cB-elimination. Among these stages, the Lewis base addition is diastereoselective and E1cB-elimination is cis-selective, which could account for the remarkable Z-selectivity of the reaction.

Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach.

Introduction: The International Federation of Gynecology and Obstetrics (FIGO) staging system is considered the most powerful prognostic factor in patients with cervical cancer. In addition, other surgical-pathological risk factors have been demonstrated to have significance in predicting the prognosis of patients. Therefore, the purpose of this study was to investigate the effects of the FIGO staging system and surgical-pathological risk factors on the prognosis of cervical cancer patients. Methods: A retrospective study was performed on patients diagnosed with cervical cancer at FIGO stage IB1-IIA2. Kaplan-Meier, Cox proportional hazards regression analysis and the support vector machine (SVM) algorithm were used to assess and validate the high-risk factors related to recurrence and death. Results: A total of 647 patients were included. Kaplan-Meier analysis showed that five high-risk factors, including FIGO stage, status of pelvic lymph node, parametrial involvement, tumor size, and depth of cervical cancer, had a significant effect on the prognosis of patients. In multivariate analysis, pelvic lymph node metastasis (hazard ratio [HR] 2.415, 95% confidence interval [CI] 1.471-3.965), parametrial involvement (HR 2.740, 95% CI 1.092-6.872) and >2/3 depth of cervical invasion (HR 2.263, 95% CI 1.045-4.902) were three independent risk factors of disease-free survival. Pelvic lymph node metastasis (HR 3.855, 95% CI 2.125-6.991) and parametrial involvement (HR 3.871, 95% CI 1.375-10.900) were two independent risk factors for overall survival. When all five high-risk factors were assembled and used for classification prediction through SVM, it achieved the highest prediction accuracy of recurrence (accuracy = 69.1%). The highest prediction accuracy for survival was 94.3% when only using the two independent predictors (the pathological status of lymph nodes and parametrium involvement) by SVM classifiers. Among the 13 groups of intermediate-risk factor, the combination of tumor size, histology and grade of differentiation was more accurate in predicting prognosis than the intermediate-risk factors in the Sedlis criteria (recurrence: 86.8% vs. 60.0%; death: 92.0% vs. 71.6%). Conclusions: The combination of FIGO stage and surgical-pathological risk factors can further enhance the prediction accuracy of the prognosis in patients with early-stage cervical cancer. Histology and grade of differentiation can further improve the prediction accuracy of intermediate-risk factors in the Sedlis criteria.