Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via Artificial Intelligence-Based Pathology.

INTRODUCTION: Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent. METHODS: Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers. RESULTS: It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925. CONCLUSION: Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.

ß-Lapachone induces ferroptosis of colorectal cancer cells via NCOA4-mediated ferritinophagy by activating JNK pathway.

ß-Lapachone is a natural product that can promote ROS generation and ultimately triggers tumor cells death by inducing DNA damage. Recent studies have indicated that the targeting of ferroptosis or iron metabolism is a feasible strategy for treating cancer. In this study, bulk RNA-seq analysis suggested that ß-Lapachone might induce ferroptosis in CRC cells. We further tested this hypothesis using a xenograft model of human colorectal cancer as an animal model and in SW620 and DLD-1 of CRC cell lines. Western blot was used to determine the key proteins of ferroptosis (SLC7A11, GPX4), autophagy (LC3B, P62, ATG7), ferritinophagy (NCOA4, FTH1, TFRC), and JNK pathway (p-JNK, JNK, p-c-Jun, c-Jun). The levels of MDA, GSH/GSSG, lipid ROS, and intracellular ferrous iron were determined after ß-Lapachone treatment, and inhibitors of various pathways, including NAC, Ferrostatin-1, DFO, 3-MA, and SP600125 were utilized to explore the molecular mechanism underlying ß-Lapachone-mediated ferroptosis. As the result, we identified that ß-Lapachone inhibited cell proliferation and induced apoptosis, autophagy, and ROS generation. In addition, ß-Lapachone induced ferroptosis as demonstrated by intra-cellular iron overload, increased levels of lipid ROS and MDA. Mechanistically, JNK signaling pathway was involved in ß-Lapachone-induced xCT/GPX4-mediated ferroptosis and NCOA4-mediated ferritinophagy in CRC cells. In vivo experiments in nude mice demonstrated that ß-Lapachone significantly inhibited CRC growth and induced ferroptosis and NCOA4-mediated ferritinophagy. These findings not only identify a novel role for ß-Lapachone in ferroptosis but also indicate that ß-Lapachone may be a valuable candidate for the research and development of anti-cancer therapeutic agents.

Paper test strip for fluorescence detection of iron ion based on nitrogen, zinc and copper codoped carbon dots.

In this study, a test strip for fluorometric analysis of iron ion (Fe3+) was constructed based on nitrogen, zinc and copper codoped carbon dots (NZC-CDs) as fluorescence probes. NZC-CDs were synthesized by hydrothermal method. The morphology, size, components, crystal state and optical properties of NZC-CDs were characterized by transmission electron microscope (TEM), Fourier-transform infrared (FT-IR), x-ray photoelectron spectroscopy (XPS), x-ray diffraction (XRD), UV-vis absorption and fluorescence spectroscopy techniques, respectively. NZC-CDs exhibited bright blue fluorescence under UV lamp with a quantum yield at 17.76%. The fluorescence of NZC-CDs was quenched by Fe3+possibly due to the static quenching. The possible fluorescence quenching mechanism was also discussed. The quenching fluorescence was linear with the concentration of Fe3+in the range of 2.5-400µM with a low detection limit of 0.5µM. For the convenient detection, the test strips based on filter paper were employed for Fe3+assay. Moreover, the present approach was successfully applied in the determination of Fe3+in real samples including black fungus, duck blood and pork liver. The sensing method had the potential application in more food analysis.

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.

BACKGROUND: The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine kinase inhibitor therapy. EGFR detection requires tissue samples, which are difficult to obtain in some patients, costing them the opportunity for further treatment. To realize EGFR mutation prediction without molecular detection, we aimed to build a high-accuracy deep learning model with only haematoxylin and eosin (H&E)-stained slides. METHODS: We collected 326 H&E-stained non-small cell lung cancer slides from Beijing Chest Hospital, China, and used 226 slides (88 with EGFR mutations) for model training. The remaining 100 images (50 with EGFR mutations) were used for testing. We trained a convolutional neural network based on ResNet-50 to classify EGFR mutation status on the slide level. RESULTS: The sensitivity and specificity of the model were 76% and 74%, respectively, with an area under the curve of 0.82. When applying the double-threshold approach, 33% of the patients could be predicted by the deep learning model as EGFR positive or negative with a sensitivity and specificity of 100.0% and 87.5%. The remaining 67% of the patients got an uncertain result and will be recommenced to perform further examination. By incorporating adenocarcinoma subtype information, we achieved 100% sensitivity in predicting EGFR mutations in 37.3% of adenocarcinoma patients. CONCLUSIONS: Our study demonstrates the potential of a deep learning-based EGFR mutation prediction model for rapid and cost-effective pre-screening. It could serve as a high-accuracy complement to current molecular detection methods and provide treatment opportunities for non-small cell lung cancer patients from whom limited samples are available.

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manual evaluation, invasive non-mucinous lung adenocarcinoma (ADC) is difficult to diagnose. We aim to offer a deep learning solution that automatically classifies invasive non-mucinous lung ADC histological subtypes. DESIGN: For this investigation, 523 whole-slide images (WSIs) were obtained. We divided 376 of the WSIs at random for model training. According to WHO diagnostic criteria, six histological components of invasive non-mucinous lung ADC, comprising lepidic, papillary, acinar, solid, micropapillary and cribriform arrangements, were annotated at the pixel level and employed as the predicting target. We constructed the deep learning model using DeepLab v3, and used 27 WSIs for model validation and the remaining 120 WSIs for testing. The predictions were analysed by senior pathologists. RESULTS: The model could accurately predict the predominant subtype and the majority of minor subtypes and has achieved good performance. Except for acinar, the area under the curve of the model was larger than 0.8 for all the subtypes. Meanwhile, the model was able to generate pathological reports. The NDCG scores were greater than 75%. Through the analysis of feature maps and incidents of model misdiagnosis, we discovered that the deep learning model was consistent with the thought process of pathologists and revealed better performance in recognising minor lesions. CONCLUSIONS: The findings of the deep learning model for predicting the major and minor subtypes of invasive non-mucinous lung ADC are favourable. Its appearance and sensitivity to tiny lesions can be of great assistance to pathologists.

Cooperative Boron and Vanadium Doping of Nickel Phosphides for Hydrogen Evolution in Alkaline and Anion Exchange Membrane Water/Seawater Electrolyzers.

Developing low-cost and high-performance transition metal-based electrocatalysts is crucial for realizing sustainable hydrogen evolution reaction (HER) in alkaline media. Here, a cooperative boron and vanadium co-doped nickel phosphide electrode (B, V-Ni2 P) is developed to regulate the intrinsic electronic configuration of Ni2 P and promote HER processes. Experimental and theoretical results reveal that V dopants in B, V-Ni2 P greatly facilitate the dissociation of water, and the synergistic effect of B and V dopants promotes the subsequent desorption of the adsorbed hydrogen intermediates. Benefiting from the cooperativity of both dopants, the B, V-Ni2 P electrocatalyst requires a low overpotential of 148 mV to attain a current density of -100 mA cm-2  with excellent durability. The B, V-Ni2 P is applied as the cathode in both alkaline water electrolyzers (AWEs) and anion exchange membrane water electrolyzers (AEMWEs). Remarkably, the AEMWE delivers a stable performance to achieve 500 and 1000 mA cm-2  current densities at a cell voltage of 1.78 and 1.92 V, respectively. Furthermore, the developed AWEs and AEMWEs also demonstrate excellent performance for overall seawater electrolysis.

RSL1D1 knockdown induces ferroptosis and mediates ferrous iron accumulation in senescent cells by inhibiting FTH1 mRNA stability.

Iron metabolism plays an important role in maintaining cellular multiple biological functions. Dysfunction of iron homeostasis-maintaining systems was observed in many diseases, including cancer. Ribosomal L1 domain-containing 1 (RSL1D1) is an RNA-binding protein involved in multiple cellular processes, including cellular senescence, proliferation and apoptosis. However, the regulatory mechanism of RSL1D1 underlying cellular senescence and its biological process in colorectal cancer (CRC) is not clearly understood. Here, we report that RSL1D1 expression is downregulated by ubiquitin-mediated proteolysis in senescence-like CRC cells. RSL1D1, as an anti-senescence factor, is frequently upregulated in CRC, and elevated RSL1D1 prevents CRC cells from senescence-like phenotype, and correlated with poor prognosis of CRC patients. Knockdown of RSL1D1 inhibited cell proliferation, and induced cell cycle arrest and apoptosis. Notably, RSL1D1 plays important roles in regulating iron metabolism of cancer cells. In RSL1D1-knockdown cells, FTH1 expression was significantly decreased, while transferrin receptor 1 expression was increased, leading to intracellular ferrous iron accumulation, which subsequently promoted ferroptosis, indicated by the increased malondialdehyde and decreased GPX4 levels. Mechanically, RSL1D1 directly bounds with 3' untranslated region of FTH1 and subsequently promoted the mRNA stability. Moreover, RSL1D1-mediated downregulation of FTH1 was also observed in H2O2-induced senescence-like cancer cells. Taken together, these findings support RSL1D1 plays an important role in regulating intracellular iron homeostasis in CRC, and suggest that RSL1D1 could be a potential therapeutic target for cancer treatment.

Rapid Screening of Phenolic Compounds with Anti-Enteritis Activity from Camellia oleifera Oil Using a Smurf Drosophila Model and Molecular Docking Methods.

Screening and identifying the active compounds in foods are important for the development and utilization of functional foods. In this study, the anti-enteritis activity of ethanol extract from Camellia oleifera oil (PECS) was quickly evaluated using a Smurf Drosophila model and the metabolomics approach, combined with molecular docking techniques, were performed to rapidly screen and identify compounds with potential anti-enteritis activity in PECS. PECS showed good anti-enteritis activity and inhibited the activity of 5-lipoxygenase (LOX), cyclooxygenase 2 (COX-2) and inducible nitric oxide synthase (iNOS). In particular, wighteone and p-octopamine were newly identified in C. oleifera oil and were proven to have good anti-enteritis activity. The inhibitory activity of kaempferitrin (IC50 = 0.365 mmol L-1) was higher than that of wighteone (IC50 = 0.424 mmol L-1) and p-octopamine (IC50 = 0.402 mmol L-1). Of note, the IC50 value of salazosulfapyridine was 0.810 mmol L-1. Inhibition of LOX activity is likely one of the anti-enteritis mechanisms of PECS. These new findings lay the foundation for further investigations into the underlying mechanisms of anti-enteritis activity in C. oleifera oil.

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning.

The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists' confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists.

Protocol of Kupffer phase whole liver scan for metastases: A single-center prospective study.

Introduction: As the presence of hepatic metastases is very important to cancer patients' clinical stage which would directly affect the selection and application of anti-cancer treatments. Although conventional ultrasound is commonly performed as a screening tool, most of the examinations have relatively poor sensitivity and specificity for detecting liver metastases. Contrast-enhanced ultrasound (CEUS) with Sonazoid has been reported to have the advantage of the diagnosis and therapeutic support of focal hepatic lesions and its specific Kupffer phase whole liver scan (KPWLS) is believed to be sensitive to detect liver metastases. And the purpose of this study is to determine the number, size, location and diagnosis of metastatic lesions, and to compare the results with conventional ultrasound and contrast-enhanced computed tomography (CECT), thus to clarify the application value, indications of Sonazoid-CEUS in screening liver metastasis. Methods and analysis: Kupffer phase whole liver scan for metastases (KPWLSM) is a self-control, blind map-reading, single-center, prospective superiority trial. Approved by the institutional review committee, the study period is planned to be from 1 January 2022 to 31 December 2025. Our study will include 330 patients with history of malignant tumors that cling to metastasize to liver. All patients will undergo the examinations of conventional ultrasound, Sonazoid-CEUS, and contrast-enhanced magnetic resonance imaging (CEMRI), and 65 of them should have additional CECT scans. The primary endpoint is the comparative analysis of the numbers of detected liver metastatic lesions among Sonazoid-CEUS, conventional ultrasound and CECT in screening liver metastases. Subjective conditions of patient after injection of Sonazoid will be followed up 3 and 30 days after KPWLSM, and any short-term and long-term adverse events are to be recorded with telephone interviews. Ethics and dissemination: This study has been granted by the Ethics Committee of Shanghai Jiao Tong University Affiliated Sixth People's Hospital (Approval No: 2021-197). When the KPWLSM is completed, we will publish it in an appropriate journal to promote further widespread use. Registration: Trial Registration Number and Date of Registration: Chinese Clinical Trial Registry, ChiCTR2100054385, December 16, 2021.

Effect of catheter ablation combined with left appendage occlusion for non-valvular atrial fibrillation: a meta-analysis.

OBJECTIVE: To estimate the effect of catheter ablation combined with left appendage occlusion in the treatment of non-valvular atrial fibrillation (NVAF) by a method of meta-analysis. METHODS: Pubmed, Embase, and Cochrane Library were searched for the studies about catheter ablation combined with left appendage occlusion in treating NVAF. The data analysis was performed using R 4.0.5 software. The retrieval time was from inception to May 12, 2021. RESULTS: A total of 18 published studies were identified in the meta-analysis, including 1385 participants. During the perioperative period of catheter ablation combined with left appendage occlusion in treating NVAF, the pooled incidences of pericardial effusion, major or minor bleeding events, and residual flow documented were 0.5%(95%CI 0.0002-0.0099), 1.42%(95% CI 0.00-0.04), 7.24%(95% CI 0.0447-0.0975), respectively. During the follow-up, the incidences of all-cause mortality, embolism events, and bleeding events were 0.32%(95%CI 0.0000-0.0071), 1.29%(95%CI 0.0037-0.0222), 2.07%(95% CI 0.0075-0.0339), respectively. In the follow-up period of the transesophageal echocardiography, the most complication was residual flow event with an incident rate of 10.80%(95% CI 0.054-0.1620). The maximum occurrence probability of residual flow events was 17.92% (95% CI 0.1133-0.2452). Moreover, the incident rate of NVAF recurrence was 29.23% (95% CI 0.2222-0.3623). CONCLUSION: The meta-analysis suggests that the "one-stop" procedure is effective for those patients undergoing NVAF. However, Patients with more residual blood flow have a higher incidence of bleeding complications. More studies are warranted to verify the safety and efficacy of catheter ablation combined with left appendage occlusion in terms of the "one-stop" procedure.

Ultra-low pressure PES ultrafiltration membrane with high-flux and enhanced anti-oil-fouling properties prepared via in-situ polycondensation of polyamic acid.

Polyamic acid (PAA) is a flexible polymer and has abundant valuable hydrophilic groups. Herein, we developed an ultra-low pressure ultrafiltration (UF) membrane by integrating PAA into the polyethersulfone (PES) matrix via the "in-situ polycondensation" method. PAA was well compatible with PES and distributed uniformly in the membrane. The introduction of PAA improved membrane hydrophilicity. Meanwhile, the membrane pore structures were also refined. The membrane exhibited an excellent permeability under ultra-low pressure due to its improvement of hydrophilicity and pore structures. Under 0.3 bar, compare with the water flux of PES membrane, PES/PAA membrane improved nearly 2 times (571.05 L/(m2·h)), with a high BSA rejection (≥90%). Even under a lower pressure, 0.1 bar, >300 L/(m2·h) still can be achieved. Interestingly, the membrane we developed could maintain a high performance after drying, and then is very suitable for dry preservation. PES/PAA membrane showed a high oil removal (≥92%) and could remove oil from water effectively. Besides, the membrane exhibited excellent anti-oil-fouling properties. The flux recovery rate of PES/PAA (70.0%) far exceeds that of PES (37.9%) after three filtration and cleaning cycles. The membrane we developed is very valuable in oily wastewater treatment.

Artificial Intelligence Assisted Topographic Mapping System for Endoscopic Submucosal Dissection Specimens.

Background: Endoscopic submucosal dissection (ESD), a minimally invasive surgery used to treat early gastrointestinal malignancies, has been widely embraced around the world. The gross reconstruction of ESD specimens can facilitate a more precise pathological diagnosis and allow endoscopists to explore lesions thoroughly. The traditional method of mapping is time-consuming and inaccurate. We aim to design a topographic mapping system via artificial intelligence to perform the job automatically. Methods: The topographic mapping system was built using computer vision techniques. We enrolled 23 ESD cases at the Peking Union Medical College Hospital from September to November 2019. The reconstruction maps were created for each case using both the traditional approach and the system. Results: Using the system, the time saved per case ranges from 34 to 3,336 s. Two approaches revealed no significant variations in the shape, size, or tumor area. Conclusion: We developed an AI-assisted system that would help pathologists complete the ESD topographic mapping process rapidly and accurately.

Development of a Colloidal Gold Immunochromatographic Strip for Rapid Detection of Cyfra 21-1 in Lymph Node Metastasis of Thyroid Cancer.

Thyroid cancer is the most common endocrine tumor, and the rate of early lymph node metastasis may be as high as 60%. Currently, detection of lymph node metastasis of thyroid cancer during surgery is limited and time-consuming. Elevated levels of Cyfra 21-1, the proteolytic portion of cytokeratin, are associated with the metastasis and progression of thyroid cancer and are an effective biomarker for the prognosis and diagnosis of thyroid cancer. In this study, an immunochromatographic strip test based on colloidal gold nanoparticles was developed to semi-quantitatively detect the levels of Cyfra 21-1 in lymph nodes within 15 min. The standard (calibration) curve equation was Y = 0.003708 × X + 0.1101, and the detection limit was 0.55-1.14 ng mL-1. The strip did not detect other protein markers of epithelial cells at a concentration of 500 ng mL-1, including cytokeratin 8, cytokeratin 18, epithelial membrane antigen, and epidermal surface antigen. The ability of the strip to differentiate positive from negative metastasis in 40 lymph node specimens was 100% concordant with that of immunohistochemical staining for Cyfra 21-1. In an assessment of 20 lymph node specimens that had been determined by postoperative histopathology to be positive for lymph node metastasis and 20 specimens that were negative, the sensitivity and specificity of the strip were 100% and 95%, respectively. The sensitivity of the strip remained stable when stored at room temperature for 6 months. Together, these results indicated that although further testing using a larger sample size will be required, this immunochromatographic strip test may be useful for rapid intraoperative detection of thyroid cancer metastasis to lymph nodes.

Comprehensive Analysis of mTORC1 Signaling Pathway-Related Genes in the Prognosis of HNSCC and the Response to Chemotherapy and Immunotherapy.

Objective: The mammalian target of the rapamycin complex 1 (mTORC1) signaling pathway has emerged as a crucial player in the oncogenesis and development of head and neck squamous cell carcinoma (HNSCC), however, to date, no relevant gene signature has been identified. Therefore, we aimed to construct a novel gene signature based on the mTORC1 pathway for predicting the outcomes of patients with HNSCC and their response to treatment. Methods: The gene expression and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The key prognostic genes associated with the mTORC1 pathway were screened by univariate Cox regression analyses. A prognostic signature was then established based on significant factors identified in the multivariate Cox regression analysis. The performance of the multigene signature was evaluated by the Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) analysis. Based on the median risk score, patients were categorized into high- and low-risk groups. Subsequently, a hybrid prognostic nomogram was constructed and estimated by a calibration plot and decision curve analysis. Furthermore, immune cell infiltration and therapeutic responses were compared between the two risk groups. Finally, we measured the expression levels of seven genes by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). Results: The mTORC1 pathway-based signature was constructed using the seven identified genes (SEC11A, CYB5B, HPRT1, SLC2A3, SC5D, CORO1A, and PIK3R3). Patients in the high-risk group exhibited a lower overall survival (OS) rate than those in the low-risk group in both datasets. Through the univariate and multivariate Cox regression analyses, this gene signature was confirmed to be an independent prognostic risk factor for HNSCC. The constructed nomogram based on age, American Joint Committee on Cancer (AJCC) stage, and the risk score exhibited satisfactory performance in predicting the OS. In addition, immune cell infiltration and chemotherapeutic and immunotherapeutic responses differed significantly between the two risk groups. The expression levels of SEC11A and CYB5B were higher in HNSCC tissues than in normal tissues. Conclusion: Our study established and verified an mTORC1 signaling pathway-related gene signature that could be used as a novel prognostic factor for HNSCC.

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.

Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may the benefits of DL be achieved. A fully crossed multireader multicase study was conducted to evaluate DL assistance with pathologists' diagnosis of gastric cancer. A total of 110 whole-slide images (WSI) (50 malignant and 60 benign) were interpreted by 16 board-certified pathologists with or without DL assistance, with a washout period between sessions. DL-assisted pathologists achieved a higher area under receiver operating characteristic curve (ROC-AUC) (0.911 vs. 0.863, P = 0.003) than unassisted in interpreting the 110 WSIs. Pathologists with DL assistance demonstrated higher sensitivity in detection of gastric cancer than without (90.63% vs. 82.75%, P = 0.010). No significant difference was observed in specificity with or without deep learning assistance (78.23% vs. 79.90%, P = 0.468). The average review time per WSI was shortened with DL assistance than without (22.68 vs. 26.37 second, P = 0.033). Our results demonstrated that DL assistance indeed improved pathologists' accuracy and efficiency in gastric cancer diagnosis and further boosted the acceptance of this new technique.

Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics.

BACKGROUND: Histopathologic evaluation after surgery is the gold standard to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). However, it cannot be used to guide organ-preserving strategies due to poor timeliness. PURPOSE: To develop and validate a multiscale model incorporating radiomics and pathomics features for predicting pathological good response (pGR) of down-staging to stage ypT0-1N0 after nCRT. STUDY TYPE: Retrospective. POPULATION: A total of 153 patients (median age, 55 years; 109 men; 107 training group; 46 validation group) with clinicopathologically confirmed LARC. FIELD STRENGTH/SEQUENCE: A 3.0-T; fast spin echo T2 -weighted and single-shot EPI diffusion-weighted images. ASSESSMENT: The differences in clinicoradiological variables between pGR and non-pGR groups were assessed. Pretreatment and posttreatment radiomics signatures, and pathomics signature were constructed. A multiscale pGR prediction model was established. The predictive performance of the model was evaluated and compared to that of the clinicoradiological model. STATISTICAL TESTS: The χ2 test, Fisher's exact test, t-test, the minimum redundancy maximum relevance algorithm, the least absolute shrinkage and selection operator logistic regression algorithm, regression analysis, receiver operating characteristic curve (ROC) analysis, Delong method. P < 0.05 indicated a significant difference. RESULTS: Pretreatment radiomics signature (odds ratio [OR] = 2.53; 95% CI: 1.58-4.66), posttreatment radiomics signature (OR = 9.59; 95% CI: 3.04-41.46), and pathomics signature (OR = 3.14; 95% CI: 1.40-8.31) were independent factors for predicting pGR. The multiscale model presented good predictive performance with areas under the curve (AUC) of 0.93 (95% CI: 0.88-0.98) and 0.90 (95% CI: 0.78-1.00) in the training and validation groups, those were significantly higher than that of the clinicoradiological model with AUCs of 0.69 (95% CI: 0.55-0.82) and 0.68 (95% CI: 0.46-0.91) in both groups. DATA CONCLUSION: A model incorporating radiomics and pathomics features effectively predicted pGR after nCRT in patients with LARC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

SPOCK1 promotes metastasis in pancreatic cancer via NF-κB-dependent epithelial-mesenchymal transition by interacting with IκB-α.

BACKGROUND: Sparc/osteonectin, cwcv and kazal-like domain proteoglycan 1 (SPOCK1) has been reported to function as an oncogene in a variety of cancer types. Increasing evidence suggests that SPOCK1 contributes to the metastatic cascade, including invasion, epithelial-mesenchymal transition (EMT) and micro-metastasis formation. As yet, however, the underlying mechanism is not clearly understood. Here, we evaluated the expression and clinicopathological significance of SPOCK1 in primary pancreatic cancer (PC) specimens and explored the mechanisms underlying SPOCK1-mediated PC cell growth and metastasis. METHODS: The clinical relevance of SPOCK1 was evaluated in 81 patients with PC. The effect of SPOCK1 on proliferation, cell cycle progression, EMT and metastasis was examined in vitro and in vivo. The molecular mechanisms involved in SPOCK1-mediated regulation of NF-κB-dependent EMT were assessed in PC cell lines. RESULTS: We found that SPOCK1 expression was increased in PC tissues and was associated with lymph node metastasis. Silencing or exogenous overexpression of SPOCK1 markedly altered the proliferation of PC cells through cell cycle transition. Overexpression of SPOCK1 promoted PC cell migration and invasion by regulating EMT progression. Moreover, we found that SPOCK1 contributes to EMT and metastasis by activating the NF-κB signalling pathway via direct interaction with IκBα. After NF-κB pathway inhibition by BAY11-7082, we found that PC cell motility and EMT induced by SPOCK1 were reversed. CONCLUSION: From our data we conclude that SPOCK1 promotes PC metastasis via NF-κB-dependent EMT by interacting with IκBα. This newly identified mechanism may provide novel clues for the (targeted) treatment of PC patients.

Homing peptide combined with DNAzyme-based ELISA-like assay for highly specific and sensitive detection of fibrin.

A highly sensitive and specific ELISA-like chemiluminescence method for detection of fibrin has been developed. In the sensing platform, the homing peptide (CREKA), as recognition molecule, which can specially recognize the fibrin on microtiter plate, combined with G-quadruplex-based DNAzyme to form the probe of G-quadruplex-hemin DNAzyme-CREKA. After the sample solution was coated on the plates, the probe was crosslinked with fibrin through the interaction of CREKA and fibrin. Finally, luminol-H2O2 chemiluminesecence (CL) reaction was exploited for quantitative analysis of fibrin. The liner range for fibrin detection was from 0.112 pmol L-1 to 5.6 pmol L-1 with the detection limit of fibrin as low as 0.04 pmol L-1, based on a signal-to-noise ratio (S/N) of 3. Furthermore, on the basis of the high amplification efficiency of the rolling circle amplification (RCA) reaction, the method enabled to analyze fibrin with a detection limit corresponding to 0.06 fmol L-1, whose sensitivity increased 3 orders of magnitude than that of above method in the absence of RCA reaction. In particular, combined with the separation and washing steps of ELISA, the proposed method possessed higher selectivity, high-throughput and low cost, which shows promise for applications in clinical diagnosis.

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm.

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.