There is no fusion fascia in the abdomen and extraperitoneal fascia always surrounds the mesentery.

Toldt's fascia has always been described as a fusion fascia formed by two layers of visceral peritoneum when the mesentery attaches to the posterior abdominal wall. However, there is still no consensus about the mesentery and its surrounding fascia based on the current anatomic theories. This study aimed to determine the anatomical structures of the abdomen and provide a correct surgical plane for mesenteric-based surgery. Surgical videos of 121 patients who underwent laparoscopic operations of the digestive tract were reviewed to identify and compare the anatomical structures of the mesentery and associated fascia. Twenty-one postoperative specimens were stained with hematoxylin and eosin to indicate the histological appearance of the mesentery and its surrounding fascia. Furthermore, dynamic models had been established to explain the formation mechanism of the associated histological structures in different regions during the progression of mesenteric attachment. The fasciae surrounding the mesentery, including the submesothelial connective tissue, the subserosal connective tissue, Toldt's fascia, and "angel hair," have the same histological characteristic to extraperitoneal fascia. The general anatomical structure of the abdomen can be divided into three layers (abdominal wall, urogenital system, and digestive system) and two interlayers (transversalis fascia and extraperitoneal fascia). The extraperitoneal fascia surrounds the entire digestive system and is the natural layer separating adjacent structures from each other. Typical histological structures in the regions of posterior attachment include the fascia propria of the mesentery, mesofascial plane, extraperitoneal fascia, retrofascial plane, and anterior renal fascia. The urogenital system is surrounded by similar histological structures. There is no fusion fascia in the abdomen due to retreat of the visceral peritoneum, and all of the fasciae surrounding the mesentery are extraperitoneal fascia. This study demonstrates that the typical histological structures in the regions of attachment and mesofascial plane are the correct anatomic interface for mesenteric-based surgery.

Genotoxicity, oxidative stress and transcriptomic effects of Nitenpyram on human bone marrow mesenchymal stem cells.

Despite of the global contamination and ubiquitous exposure to nitenpyram (NIT), little knowledge is available on the adverse effects to human health, with some evidence referring to its genotoxic potency to non-target organisms and esophageal squamous papilloma in rats. Human bone marrow mesenchymal stem cells (hBMSCs) was employed as an in vitro model more relevant to humans to assess the potential genotoxicity of NIT and to understand the underlying mechanisms at cellular and molecular levels. Noncytotoxic concentrations of NIT, 50-2500 µg/mL, dose-dependently elevated micronucleus (MN) and nuclear bud (NB) frequencies to 8.7-29‰ and 15-35‰, respectively. Additional metabolism by rat liver S9 fraction decreased chromosome impairment by 27-52% on MN frequencies and 63-76% on NB frequencies. A commercial NIT product, containing 20% of NIT and 60% of pymetrozine, caused higher cytotoxicity and chromosome impairment in comparison with NIT alone. Expressions of genes responses to DNA damage, ATM, ATR, p53, p21, Bax, H2AX, and GADD45A were disturbed by NIT treatment. Reactive oxygen species (ROS) amount and superoxide dismutase (SOD) activity were enhanced by NIT. Comet assay showed that lower concentrations of NIT, 12.5-100 µg/mL, induced the DNA damage. Transcriptomic analysis identified 468 differentially expressed genes (p < 0.05, |log2(Foldchange)| ≥ 1), from which 22 pathways were enriched. Multiple affected pathways were related to cancer including viral carcinogenesis and bladder cancer. NIT may produce genotoxicity via inducing oxidative stress and deregulating PI3K/Akt, AMPK and mTOR signaling pathways, associated with carcinogenetic potency. While environmental levels of NIT alone may pose little risk to human health, attention should be paid to the health risk arose from the synergistic or additive effects that may exist among NEOs and other types of pesticides.

Effects of salts on the self-assembly behavior and antibacterial activity of a surfactant-like peptide.

Self-assembling peptides have become one of the most promising antibacterial agents due to their superior properties, such as simple molecular composition, favorable assembly structures, and rich designability. For maximum application in vivo, their activities in the presence of salts are desirable, however, the potent correlation between peptide nanostructures, antibacterial activity, and salt resistance behavior remains poorly explored. Previously, we have demonstrated that the potent antibacterial activity of a designed surfactant-like peptide Ac-A9K-NH2 benefited from its high self-assembly ability and appropriate size of its self-assembled nanostructures. In this study, we investigated the effect of salts on its self-assembly behavior and antibacterial activity. The results indicated that the flexible and long nanofibrils formed by Ac-A9K-NH2 in the presence of CaCl2 were adverse to its membrane insertion, leading to the reduction of antibacterial activity. Comparatively, Ac-A9K-NH2 maintained its potent antibacterial activity in the presence of NaCl due to its suitable shape and size of nanostructures. The newly formed nanofibers and nanorods facilitated the penetration of peptides into the bacterial membrane, forming nanopores and eventually leading to the lysis of bacteria. The high antibacterial activity and NaCl tolerance of Ac-A9K-NH2 make it a promising antibacterial agent at elevated salt concentrations.

A breast one-patient panel of heterogeneous genomes reveals genetic alterations driving DCIS into invasive lesions.

Aim: Utilize breast cancer samples in the same patient to indicate breast cancer development. Patients & methods: We performed whole-exome analysis of spatially independent ductal carcinoma in situ (DCIS) and invasive ductal carcinoma samples from the same breast. Results: In VEGF pathway, we observed two genes disrupted in DCIS, while another four (including ACTN2) mutated in invasive ductal carcinoma. When looked up TCGA database, we identified seven breast cancer patients with ACTN2 somatic mutations and observed a dramatic decrease in the overall survival time in ACTN2 mutant patients (p = 0.0182). A further finding in the TCGA database shows that breast cancer patients with ≥2 mutated genes in VEGF pathways showed worse prognosis (p = 0.0013). Conclusion: TCGA database and special case could inform each other to reveal DCIS developmental rules.

A nomogram based on mammary ductoscopic indicators for evaluating the risk of breast cancer in intraductal neoplasms with nipple discharge.

Mammary ductoscopy (MD) is commonly used to detect intraductal lesions associated with nipple discharge. This study investigated the relationships between ductoscopic image-based indicators and breast cancer risk, and developed a nomogram for evaluating breast cancer risk in intraductal neoplasms with nipple discharge. A total of 879 consecutive inpatients (916 breasts) with nipple discharge who underwent selective duct excision for intraductal neoplasms detected by MD from June 2008 to April 2014 were analyzed retrospectively. A nomogram was developed using a multivariate logistic regression model based on data from a training set (687 cases) and validated in an independent validation set (229 cases). A Youden-derived cut-off value was assigned to the nomogram for the diagnosis of breast cancer. Color of discharge, location, appearance, and surface of neoplasm, and morphology of ductal wall were independent predictors for breast cancer in multivariate logistic regression analysis. A nomogram based on these predictors performed well. The P value of the Hosmer-Lemeshow test for the prediction model was 0.36. Area under the curve values of 0.812 (95 % confidence interval (CI) 0.763-0.860) and 0.738 (95 % CI 0.635-0.841) was obtained in the training and validation sets, respectively. The accuracies of the nomogram for breast cancer diagnosis were 71.2 % in the training set and 75.5 % in the validation set. We developed a nomogram for evaluating breast cancer risk in intraductal neoplasms with nipple discharge based on MD image findings. This model may aid individual risk assessment and guide treatment in clinical practice.

Retrospective study of prenatal diagnosed pulmonary sequestration.

PURPOSE: To describe the prenatal findings, treatments and outcomes of fetuses with pulmonary sequestrations (PS), which were retrospectively studied. METHODS: From May 2010 to January 2013, 292 women were referred to the Guangdong Women and Children Hospital, Guangzhou because obstetric ultrasound had demonstrated fetal lung lesions. In 68 fetuses, the echogenic lung masses were pulmonary sequestrations deriving arterial blood supply from clearly identifiable systemic arteries rather than the pulmonary artery. We examined records of the 68 fetuses and measured the lesions and congenital cystic adenomatoid malformation volume ratio (CVR), provided prenatal counseling and treatment, documented the CVR, location of lesion, gestational age at diagnosis, need for fetal intervention, perinatal clinical course (including the development of hydrops, effusions, and neonatal respiratory distress), gestational age at delivery, postnatal ultrasound and CT, operation treatment, survival and pathology. RESULTS: There were 68 PS in our study. All of 56 cases with CVR ≤ 1.6, survived without prenatal hydrops or postnatal respiratory symptoms. In 12 cases with CVR >1.6, 7 cases (58.3 %, 7/12) had hydrops, 10 cases (83.3 %, 10/12) survived, 2 cases (16.7 %, 2/12) of induced abortion, 8 cases (66 %, 8/12) were postnatal symptomatic. There was statistical significant difference in the incidence rate of hydrops and postnatal respiratory symptoms between the PS with CVR ≤ 1.6 and that with CVR >1.6. 66 cases postnatal survived, two cases of induced abortion. 64 cases had no prenatal treatment, two cases underwent thoracoamniotic shunt. The mean gestational age at birth was 38 weeks (range 34-40 weeks). 21 asymptomatic cases had no surgery. 45 cases (8 symptomatic and 37 asymptomatic) underwent surgical resections, 43 cases without postoperative complications, two cases of postoperative pneumothorax, no wound infection. Postoperative follow-up showed good growth in all surgery cases. CONCLUSION: PS specific absence of hydrop was a congenital disorder with an excellent prognosis. CVR >1.6 was associated with fetal hydrops and postnatal symptoms. However, absolute CVR value cannot be used to select fetuses for fetal treatment before the development of hydrops. Surgery of PS in neonates or infants presented a good outcome.

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach.

Objective: This research aims to develop and assess the performance of interpretable machine learning models for diagnosing three histological subtypes of non-small cell lung cancer (NSCLC) utilizing CT imaging data. Methods: A retrospective cohort of 317 patients diagnosed with NSCLC was included in the study. These individuals were randomly segregated into two groups: a training set comprising 222 patients and a validation set with 95 patients, adhering to a 7:3 ratio. A comprehensive extraction yielded 1,834 radiomic features. For feature selection, statistical methodologies such as the Mann-Whitney U test, Spearman's rank correlation, and one-way logistic regression were employed. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized. The study designed three distinct models to predict adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC). Six different classifiers, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, eXtreme Gradient Boosting (XGB), and LightGBM, were deployed for model training. Model performance was gauged through accuracy metrics and the area under the receiver operating characteristic (ROC) curves (AUC). To interpret the diagnostic process, the Shapley Additive Explanations (SHAP) approach was applied. Results: For the ADC, SCC, and LCC groups, 9, 12, and 8 key radiomic features were selected, respectively. In terms of model performance, the XGB model demonstrated superior performance in predicting SCC and LCC, with AUC values of 0.789 and 0.848, respectively. For ADC prediction, the Random Forest model excelled, showcasing an AUC of 0.748. Conclusion: The constructed machine learning models, leveraging CT imaging, exhibited robust predictive capabilities for SCC, LCC, and ADC subtypes of NSCLC. These interpretable models serve as substantial support for clinical decision-making processes.

Targeted Drug Nanodelivery and Immunotherapy for Combating Tumor Resistance.

Chemotherapy resistance is a common cause of tumor treatment failure. Various molecular responses, such as increased expression of efflux transporter proteins, including Pglycoprotein (P-gp), changes in the tumor microenvironment (TME), the role of platelets, and the effects of cancer stem cells (CSCs), can lead to drug resistance. Through extensive research on the mechanisms of drug resistance, more effective anti-resistance drugs and therapeutic approaches are being developed. This review explores drug resistance mechanisms and summarizes relevant anti-resistance drugs. In addition, due to the therapeutic limitations of the aforementioned treatments, new advances in nanocarrier-based combination immunotherapy to address the challenge of drug resistance have been described. Nanocarriers combined with immunotherapy can not only target tumor sites for targeted drug release but also modulate the autoimmune system and enhance immune efficacy, thereby overcoming tumor drug resistance. This review suggests new strategies for overcoming tumor drug resistance and is expected to inform tumor treatment and prognosis.

Leakage Proof, Flame-Retardant, and Electromagnetic Shield Wood Morphology Genetic Composite Phase Change Materials for Solar Thermal Energy Harvesting.

Phase change materials (PCMs) offer a promising solution to address the challenges posed by intermittency and fluctuations in solar thermal utilization. However, for organic solid-liquid PCMs, issues such as leakage, low thermal conductivity, lack of efficient solar-thermal media, and flammability have constrained their broad applications. Herein, we present an innovative class of versatile composite phase change materials (CPCMs) developed through a facile and environmentally friendly synthesis approach, leveraging the inherent anisotropy and unidirectional porosity of wood aerogel (nanowood) to support polyethylene glycol (PEG). The wood modification process involves the incorporation of phytic acid (PA) and MXene hybrid structure through an evaporation-induced assembly method, which could impart non-leaking PEG filling while concurrently facilitating thermal conduction, light absorption, and flame-retardant. Consequently, the as-prepared wood-based CPCMs showcase enhanced thermal conductivity (0.82 W m-1 K-1, about 4.6 times than PEG) as well as high latent heat of 135.5 kJ kg-1 (91.5% encapsulation) with thermal durability and stability throughout at least 200 heating and cooling cycles, featuring dramatic solar-thermal conversion efficiency up to 98.58%. In addition, with the synergistic effect of phytic acid and MXene, the flame-retardant performance of the CPCMs has been significantly enhanced, showing a self-extinguishing behavior. Moreover, the excellent electromagnetic shielding of 44.45 dB was endowed to the CPCMs, relieving contemporary health hazards associated with electromagnetic waves. Overall, we capitalize on the exquisite wood cell structure with unidirectional transport inherent in the development of multifunctional CPCMs, showcasing the operational principle through a proof-of-concept prototype system.

An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer.

Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth.

Dual resolution deep learning network with self-attention mechanism for classification and localisation of colorectal cancer in histopathological images.

AIMS: Microscopic examination is a basic diagnostic technology for colorectal cancer (CRC), but it is very laborious. We developed a dual resolution deep learning network with self-attention mechanism (DRSANet) which combines context and details for CRC binary classification and localisation in whole slide images (WSIs), and as a computer-aided diagnosis (CAD) to improve the sensitivity and specificity of doctors' diagnosis. METHODS: Representative regions of interest (ROI) of each tissue type were manually delineated in WSIs by pathologists. Based on the same coordinates of centre position, patches were extracted at different magnification levels from the ROI. Specifically, patches from low magnification level contain contextual information, while from high magnification level provide important details. A dual-inputs network was designed to learn context and details simultaneously, and self-attention mechanism was used to selectively learn different positions in the images to enhance the performance. RESULTS: In classification task, DRSANet outperformed the benchmark networks which only depended on the high magnification patches on two test set. Furthermore, in localisation task, DRSANet demonstrated a better localisation capability of tumour area in WSI with less areas of misidentification. CONCLUSIONS: We compared DRSANet with benchmark networks which only use the patches from high magnification level. Experimental results reveal that the performance of DRSANet is better than the benchmark networks. Both context and details should be considered in deep learning method.

Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning.

PURPOSE: Rapid diagnosis and risk stratification can provide timely treatment for colorectal cancer (CRC) patients. Deep learning (DL) is not only used to identify tumor regions in histopathological images, but also applied to predict survival and achieve risk stratification. Whereas, most of methods dependent on regions of interest annotated by pathologist and ignore the global information in the image. METHODS: A dual resolution DL network based on weakly supervised learning (WDRNet) was proposed for CRC identification and prognosis. The proposed method was trained and validated on the dataset from The Cancer Genome Atlas (TCGA) and tested on the external dataset from Affiliated Cancer Hospital and Institute of Guangzhou Medical University (ACHIGMU). RESULTS: In identification task, WDRNet accurately identified tumor images with an accuracy of 0.977 in slide level and 0.953 in patch level. Besides, in prognosis task, WDRNet showed an excellent prediction performance in both datasets with the concordance index (C-index) of 0.716 ± 0.037 and 0.598 ± 0.024 respectively. Moreover, the results of risk stratification were statistically significant in univariate analysis (p < 0.001, HR = 7.892 in TCGA-CRC, and p = 0.009, HR = 1.718 in ACHIGMU) and multivariate analysis (p < 0.001, HR = 5.914 in TCGA-CRC, and p = 0.025, HR = 1.674 in ACHIGMU). CONCLUSIONS: We developed a weakly supervised resolution DL network to achieve precise identification and prognosis of CRC patients, which will assist doctors in diagnosis on histopathological images and stratify patients to select appropriate therapeutic schedule.

Construction of Z-scheme Ti/Ga co-doped ZnO heterostructure photocatalyst with graphitic carbon nitride for efficient visible-light-driven dye degradation.

Innovative solar-driven heterostructure photocatalysts are promising for removing the organic contaminants in the water environment. In this work, a sequence of well-defined Z-scheme Ti-Ga co-doped ZnO/g-C3N4 (TGZ/CN) heterostructure photocatalysts were developed via a simple sol-gel method and the single-phase dispersion method in order to realize the cooperative improvement from the Ti/Ga co-doping and construction of heterostructure. The synthesized samples were analyzed by a variety of characterization techniques, and the photocatalytic activity was assessed by photodegradation of methylene blue (MB) under visible light irradiation. Compared to the ZnO and g-C3N4, the TGZ/CN composite demonstrated higher photocatalytic performance for the degradation of MB indicating an efficient photocatalytic degradation rate of 95.4% in 105 min under visible light. Moreover, the TGZ/CN photocatalyst exhibited excellent stability after five cycles of MB photodegradation. Furthermore, the as-prepared composites' possible photocatalytic mechanism was discussed in detail. The improved photocatalytic performance primarily resulted from the effectively reduced band gap of ZnO after Ti/Ga co-doping and the facilitated separation of photoexcited e-/h+ pairs caused by the construction of Z-scheme heterojunction. This work offers novel insights in developing hybrids with highly efficient photocatalytic activity towards future environmental applications.

An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images.

Introduction: Deep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images. However, these methods often require a large number of expert annotations, which are both time-consuming and labor-intensive to obtain. Several scholars have proposed generative models to augment labeled data, but these often result in label uncertainty due to incomplete learning of the data distribution. Methods: To alleviate these issues, a method called InceptionV3-SMSG-GAN has been proposed to enhance classification performance by generating high-quality images. Specifically, images synthesized by Multi-Scale Gradients Generative Adversarial Network (MSG-GAN) are selectively added to the training set through a selection mechanism utilizing a trained model to choose generated images with higher class probabilities. The selection mechanism filters the synthetic images that contain ambiguous category information, thus alleviating label uncertainty. Results: Experimental results show that compared with the baseline method which uses InceptionV3, the proposed method can significantly improve the performance of pathological image classification from 86.87% to 89.54% for overall accuracy. Additionally, the quality of generated images is evaluated quantitatively using various commonly used evaluation metrics. Discussion: The proposed InceptionV3-SMSG-GAN method exhibited good classification ability, where histological image could be divided into nine categories. Future work could focus on further refining the image generation and selection processes to optimize classification performance.

Reproducibility and usefulness of quantitative apparent diffusion coefficient measurements for predicting program death-ligand 1 expression in nasopharyngeal carcinoma.

BACKGROUND: Accurate assessment of programmed death-ligand 1 (PD-L1) expression status in nasopharyngeal carcinoma (NPC) before immunotherapy is crucial. We aimed to explore the reproducibility and usefulness of the quantitative apparent diffusion coefficient (ADC) measurements for predicting PD-L1expression status in NPC. METHODS: We retrospectively recruited 134 NPC patients who underwent MRI scans and PD-L1 detection. A PD-L1 combined positive score (CPS) ≥ 20 was identified as high expression status. Patients were divide into two cohorts based on the MRI scanning devices, including a 1.5-T MRI cohort (n = 85, 44 PD-L1 high expression) and a 3.0-T MRI cohort (n = 49, 24 PD-L1 high expression). The mean ADC (ADCmean), minimum ADC (ADCmin) and maximal ADC (ADCmax) values were independently measured by two observers. The ADC measurement reproducibility was assessed by interclass correlation coefficients (ICC). The correlations between ADC parameters and CPS were analyzed by spearman's correlation coefficient (r), and the performance for PD-L1expression status prediction was assessed by the area under receiver operating characteristic curve (AUC). RESULTS: The measurement reproducibility of ADCmean, ADCmin and ADCmax was good in the 1.5-T MRI cohort (ICC: 0.843-0.930) and 3.0-T MRI cohort (ICC: 0.929-0.960). The ADCmean, ADCmin, and ADCmax tended to inversely correlate with the CPS (r:-0.37 - -0.52 in the 1.5-T MRI cohort, and - 0.52 - -0.60 in the 3.0-T MRI cohort; P all < 0.01). The ADCmean, ADCmin and ADCmax yielded the AUC of 0.756 (95% CI: 0.651, 0.861), 0.689 (95% CI: 0.576, 0.802), and 0.733 (95%CI: 0.626, 0.839) in the 1.5-T MRI cohort and 0.820 (95%CI: 0.703, 0.937), 0.755 (95% CI: 0.616, 0.894), and 0.760 (95%CI: 0.627, 0.893) in the 3.0-T MRI cohort for predicting PD-L1 high expression status, respectively. CONCLUSION: ADC measurements may act as a reproducible and feasible method to predict PD-L1 expression status in NPC.

Pyroptosis relates to tumor microenvironment remodeling and prognosis: A pan-cancer perspective.

Background and aim: Pyroptosis is an inflammatory form of programmed cell death implicated in inflammation and disease. Moreover, inducing pyroptosis has been appreciated as anti-cancer therapy for its ability to unleash anti-cancer immune responses. Methods: Utilizing the data available in The Cancer Genome Atlas (TCGA), pyroptosis-related genes' (PRGs) expression, genomic aberrations, and clinical significance were systematically analyzed in pan-cancer. A GSVA score was obtained to rate pyroptosis level and divide the cancers into pyroptosis-low and pyroptosis-high groups. Immunohistochemistry (IHC) was used to evaluate the differential expression of major PRGs (GSDMC, GSDMD, GSDME, NLRP3, NLRC4, IL1B) in selected tumor types (COAD, HNSC, KIRC, LIHC, LUAD, LUSC). Selection of tumors for immunohistochemistry (IHC) was based on their expression pattern in TCGA cancers, clinical relevance, tumor epidemiology, and sample availability. Results: Differential expression of PRGs was evident in various cancers and associated with prognosis which was driven by genomic variations and epigenetic abnormalities, such as single nucleotide variations (SNVs), copy number variation (CNV) and DNA methylation level. For example, methylation of PRGs in lower grade glioma (LGG), uveal melanoma (UVM) and kidney renal clear cell carcinoma (KIRC) were predictive of improved survival as upregulation of PRGs was risky in these cancers. Pyroptosis level significantly differentiated tumor from normal samples in 15 types of cancers, exhibited a progressive trend with cancer stage, observed variation among cancer subtypes, and showed a significant association with cancer prognosis. Higher pyroptosis level was associated with worst prognosis in majority of the cancers in terms of OS (KIRC, LGG, and UVM), PFS (GBM, KIRC, LGG, PRAD, THCA, and THYM) and DSS (KIRC and LGG) as estimated by Kaplan-Meier survival curves. Moreover, Pyroptosis level was strongly indicative of a hot tumor immune microenvironment with high presence of CD8+ T cell and other T cell subtypes. Several oncogenic pathways, such as P53 pathway, DNA repair, KRAS signaling, epithelial-mesenchymal transition (EMT), IL6 JAK STAT3 signaling, IL2 STAT5 signaling, PI3K AKT MTOR signaling and angiogenesis, were enriched in pyroptosis-hi subgroups across cancers. Conclusions: Genetic alterations in PRGs greatly influence the pyroptosis level and cancer prognosis. A relatively hot tumor immune microenvironment was associated with pyroptosis irrespective of the cancer prognosis. Overall, our study reveals the critical role of pyroptosis in cancer and highlights pyroptosis-based therapeutic vulnerabilities.

A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.

Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EBVaGC from histopathology. The EBVNet yields an averaged area under the receiver operating curve (AUROC) of 0.969 from the internal cross validation, an AUROC of 0.941 on an external dataset from multiple institutes and an AUROC of 0.895 on The Cancer Genome Atlas dataset. The human-machine fusion significantly improves the diagnostic performance of both the EBVNet and the pathologist. This finding suggests that our EBVNet could provide an innovative approach for the identification of EBVaGC and may help effectively select patients with gastric cancer for immunotherapy.

Bilateral primary breast neuroendocrine carcinoma in a young woman: report of a case.

Bilateral breast carcinoma accounts for approximately 5% of all patients with breast cancer, while neuroendocrine breast carcinomas comprise less than 5% of invasive breast carcinomas. In addition, most patients with breast neuroendocrine carcinomas are older. Therefore, bilateral primary breast neuroendocrine carcinoma at a young age is extremely rare. We herein report bilateral neuroendocrine carcinoma of the breast in a 29-year-old woman who underwent bilateral lumpectomy with the initial symptom of bilateral nipple discharge. Grossly, the lesions in both breasts were masses with infinite margins. Histologically, this case was consistent with primary neuroendocrine carcinoma arising in bilateral breasts. Cells from both breast tumors were positive for chromogranin A, neuron-specific enolase, synaptophysin, cytokeratin 7, estrogen receptor, and progesterone receptor, and negative for Her2, cytokeratin 34ß12, cytokeratin 5/6, smooth muscle actin, p63, S-100 protein, and p53. The Ki67 and NE proliferative indices were below 1%. To the best of our knowledge, this is the first reported case in China of bilateral primary neuroendocrine carcinoma presenting in a young woman.

The Prognostic Impact of Combined Tumor-Infiltrating Lymphocytes and Pretreatment Blood Lymphocyte Percentage in Locally Advanced Nasopharyngeal Carcinoma.

PURPOSE: To explore the prognostic impact of combined tumor-infiltrating lymphocytes (TILs) and pretreatment peripheral lymphocyte percentage (LYM%) among patients with locally advanced nasopharyngeal carcinoma (LA-NPC). PATIENTS AND METHODS: TILs and pretreatment LYM% were retrospectively assessed in 253 LA-NPC patients who underwent chemoradiation therapy between January 2012 and December 2017. According to TILs and LYM% status, the patients were divided into three groups: high-risk group (HRG) (TILs-LYM% score = 0), middle-risk group (MRG) (TILs-LYM% score = 1), and low-risk group (LRG) (TILs-LYM% score = 2). The relationship between TILs level and LYM%, and also the associations of TILs-LYM% status with clinicopathological factors and survival, were evaluated. RESULTS: As a continuous variable, LYM% was significantly higher in TILs-high group. High TILs or high LYM% alone was significantly related to better 3-year disease-free survival (DFS), overall survival (OS), distant metastasis-free survival (DMFS) and locoregional relapse-free survival (LRRFS), respectively. Kaplan-Meier analysis and log-rank tests also revealed significant decreases in DFS, OS, DMFS, and LRRFS among LA-NPC patients with TILs-LYM% score of 0, 1, and 2 (all P <0.05). Further multivariate analyses showed that TILs-LYM% score was an independent factor affecting survival of the patients, and HRG (TILs-LYM% score = 0) had increased hazard ratios (HRs) for disease (HR = 6.89, P <0.001), death (HR = 8.08, P = 0.008), distant metastasis (HR = 7.66, P = 0.001), and local relapse (HR = 5.18, P = 0.013) compared with LRG (TILs-LYM% score = 2). In receiver operating characteristics (ROC) analyses, TILs-LYM% score had a higher area under the ROC curve (AUC) for the prediction of DFS than did TILs or LYM% alone. CONCLUSIONS: A positive correlation was found between TILs level and pretreatment blood lymphocyte percentage. Moreover, TILs-LYM% score can be considered as a novel independent prognostic indicator of survival outcome among patients with LA-NPC.