Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study.

OBJECTIVES: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). METHODS: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). RESULTS: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). CONCLUSION: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. CRITICAL RELEVANCE STATEMENT: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. KEY POINTS: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.

Bioactive neolignan, iridoid and flavonoid glycosides from the leaves of Vaccinium bracteatum.

Two neolignan glycosides including a new one (1), along with seven iridoid glycosides (3 - 9) and nine flavonoid glycosides (10 - 18), were isolated from the leaves of Vaccinium bracteatum. Their structures were established mainly on the basis of 1D/2D NMR and ESIMS analyses, as well as comparison to known compounds in the literature. The structure of 1 with absolute stereochemistry was also confirmed by chemical degradation and ECD calculation. Selective compounds showed antiradical activity against ABTS and/or DPPH. Moreover, several isolates also suppressed the production of ROS in RAW264.7 cells and exerted neuroprotective effect toward PC12 cells.

Learning how to detect: A deep reinforcement learning method for whole-slide melanoma histopathology images.

Cutaneous melanoma represents one of the most life-threatening malignancies. Histopathological image analysis serves as a vital tool for early melanoma detection. Deep neural network (DNN) models are frequently employed to aid pathologists in enhancing the efficiency and accuracy of diagnoses. However, due to the paucity of well-annotated, high-resolution, whole-slide histopathology image (WSI) datasets, WSIs are typically fragmented into numerous patches during the model training and testing stages. This process disregards the inherent interconnectedness among patches, potentially impeding the models' performance. Additionally, the presence of excess, non-contributing patches extends processing times and introduces substantial computational burdens. To mitigate these issues, we draw inspiration from the clinical decision-making processes of dermatopathologists to propose an innovative, weakly supervised deep reinforcement learning framework, titled Fast medical decision-making in melanoma histopathology images (FastMDP-RL). This framework expedites model inference by reducing the number of irrelevant patches identified within WSIs. FastMDP-RL integrates two DNN-based agents: the search agent (SeAgent) and the decision agent (DeAgent). The SeAgent initiates actions, steered by the image features observed in the current viewing field at various magnifications. Simultaneously, the DeAgent provides labeling probabilities for each patch. We utilize multi-instance learning (MIL) to construct a teacher-guided model (MILTG), serving a dual purpose: rewarding the SeAgent and guiding the DeAgent. Our evaluations were conducted using two melanoma datasets: the publicly accessible TCIA-CM dataset and the proprietary MELSC dataset. Our experimental findings affirm FastMDP-RL's ability to expedite inference and accurately predict WSIs, even in the absence of pixel-level annotations. Moreover, our research investigates the WSI-based interactive environment, encompassing the design of agents, state and reward functions, and feature extractors suitable for melanoma tissue images. This investigation offers valuable insights and references for researchers engaged in related studies. The code is available at: https://github.com/titizheng/FastMDP-RL.

Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions.

BACKGROUND: The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the most discriminative candidate features in the development cohort. Feature's reproducibility was evaluated by intra-class correlation coefficients and inter-class correlation coefficients. Pearson correlation coefficients for normal distribution and Spearman's rank correlation coefficients for non-normal distribution were utilized to remove redundant features. After that, a blending ensemble machine learning model were developed in training cohort. Area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA) were employed to evaluate the performance of the final model in testing cohort. RESULTS: The fusion feature-based machine learning algorithm demonstrated excellent diagnostic performance in external validation dataset (AUC = 0.934, ACC = 0.905). Net benefits presented by DCA are higher than Bosniak-2019 version classification for stratifying patients with CRL to the appropriate surgery procedure. CONCLUSIONS: Fusion feature-based classifier accurately distinguished malignant and benign CRLs which outperformed the Bosniak-2019 version classification and illustrated improved clinical decision-making utility.

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm.

Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature's reproducibility. Pearson correlation coefficients for normal distribution features and Spearman's rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

Automatic CD30 scoring method for whole slide images of primary cutaneous CD30+ lymphoproliferative diseases.

AIMS: Deep-learning methods for scoring biomarkers are an active research topic. However, the superior performance of many studies relies on large datasets collected from clinical samples. In addition, there are fewer studies on immunohistochemical marker assessment for dermatological diseases. Accordingly, we developed a method for scoring CD30 based on convolutional neural networks for a few primary cutaneous CD30+ lymphoproliferative disorders and used this method to evaluate other biomarkers. METHODS: A multipatch spatial attention mechanism and conditional random field algorithm were used to fully fuse tumour tissue characteristics on immunohistochemical slides and alleviate the few sample feature deficits. We trained and tested 28 CD30+ immunohistochemical whole slide images (WSIs), evaluated them with a performance index, and compared them with the diagnoses of senior dermatologists. Finally, the model's performance was further demonstrated on the publicly available Yale HER2 cohort. RESULTS: Compared with the diagnoses by senior dermatologists, this method can better locate the tumour area and reduce the misdiagnosis rate. The prediction of CD3 and Ki-67 validated the model's ability to identify other biomarkers. CONCLUSIONS: In this study, using a few immunohistochemical WSIs, our model can accurately identify CD30, CD3 and Ki-67 markers. In addition, the model could be applied to additional tumour identification tasks to aid pathologists in diagnosis and benefit clinical evaluation.

Ultralow detection limit and ultrafast response/recovery of the H2 gas sensor based on Pd-doped rGO/ZnO-SnO2 from hydrothermal synthesis.

Hydrogen (H2) sensors are of great significance in hydrogen energy development and hydrogen safety monitoring. However, achieving fast and effective detection of low concentrations of hydrogen is a key problem to be solved in hydrogen sensing. In this work, we combined the excellent gas sensing properties of tin(IV) oxide (SnO2) and zinc oxide (ZnO) with the outstanding electrical properties of reduced graphene oxide (rGO) and prepared palladium (Pd)-doped rGO/ZnO-SnO2 nanocomposites by a hydrothermal method. The crystal structure, structural morphology, and elemental composition of the material were characterized by FE-SEM, TEM, XRD, XPS, Raman spectroscopy, and N2 adsorption-desorption. The results showed that the Pd-doped ZnO-SnO2 composites were successfully synthesized and uniformly coated on the surface of the rGO. The hydrogen gas sensing performance of the sensor prepared in this work was investigated, and the results showed that, compared with the pure Pd-doped ZnO-SnO2 sensor, the Pd-doped rGO/ZnO-SnO2 sensor modified with 3 wt% rGO had better hydrogen (H2)-sensing response of 9.4-100 ppm H2 at 380 °C. In addition, this sensor had extremely low time parameters (the response time and recovery time for 100 ppm H2 at 380 °C were 4 s and 8 s, respectively) and an extremely low detection limit (50 ppb). Moreover, the sensor exhibited outstanding repeatability and restoration. According to the analysis of the sensing mechanism of this nanocomposite, the enhanced sensing performance of the Pd-doped rGO/ZnO-SnO2 sensor is mainly due to the heterostructure of rGO, ZnO, and SnO2, the excellent electrical and physical properties of rGO and the synergy between rGO and Pd.

Hyaluronic acid-decorated carborane-TAT conjugation nanomicelles: A potential boron agent with enhanced selectivity of tumor cellular uptake.

Boron neutron capture therapy (BNCT) has received widespread attention as a new type of radiation therapy. The main problem encountered in BNCT is insufficient tumor cellular uptake of boron agents. In this study, cell-penetrating peptide TAT-conjugated o-carborane was synthesized. The conjugation can self-assemble to form positively charged carborane-TAT micelles, and then adsorb negatively charged hyaluronic acid (HA) to give core-shell structured carborane-TAT@HA micelles. Carborane-TAT@HA micelles exhibits a large amount of boron uptake at the tumor tissue through the enhanced permeability and retention (EPR) effect and the ability of HA to bind to CD44 receptors. Carborane-TAT@HA was wrapped by the HA shell during systemic circulation to avoid non-specific uptake of TAT with normal cells, while tumor microenvironment-responsive shedding of HA shell could expose Carborane-TAT to penetrate the cell membrane into tumor cells. Experiments have proved the enhanced selectivity of tumor cellular uptake of the boron drug, displayed excellent drug delivery potential, and can meet the basic requirements of BNCT.

Functional roles of circular RNAs during epithelial-to-mesenchymal transition.

Cancer has become a major health issue worldwide, contributing to a high mortality rate. Tumor metastasis is attributed to the death of most patients. Epithelial-to-mesenchymal transition (EMT) plays a vital role in inducing metastasis. During EMT, epithelial cells lose their characteristics, such as cell-to-cell adhesion and cell polarity, and cells gain motility, migratory potential, and invasive properties to become mesenchymal stem cells. Circular RNAs (circRNAs) are closely associated with tumor metastasis and patient prognosis, as revealed by increasing lines of evidence. CircRNA is a type of single-stranded RNA that forms a covalently closed continuous loop. CircRNAs are insensitive to ribonucleases and are widespread in body fluids. This work is the first review on EMT-related circRNAs. In this review, we briefly discuss the characteristics and functions of circRNAs. The correlation of circRNAs with EMT has been reported, and we discuss the ways circRNAs can regulate EMT progression through EMT transcription factors, EMT-related signaling pathways, and other mechanisms. This work summarizes current studies on EMT-related circRNAs in various cancers and provides a theoretical basis for the use of EMT-related circRNAs in targeted management and therapy.

The roles of Wnt/ß-catenin signaling pathway related lncRNAs in cancer.

Long noncoding RNAs (lncRNAs), with length of more than 200 nucleotides, are not translated into proteins but involved in multiple diverse diseases, especially tumorigenesis. The dysregulation of lncRNAs greatly contributes to the progression of various tumors through specific signaling pathways, including Wnt/ß-catenin signaling pathway, which is associated with malignant features of tumors. The interactions between lncRNAs, which have specific expression characteristics in diverse cancer tissues, and Wnt/ß-catenin signaling pathway, exhibit potential as novel biomarkers and therapeutic targets. In this review, we aim to present research findings on the roles of Wnt pathway-related lncRNAs and their effects on Wnt/ß-catenin signaling to regulate tumorigenesis in different cancer types. Results may be used as basis to develop or improve strategies for treatment of different carcinomas.

Pulmonary toxicity induced by intratracheal instillation of Asian yellow dust (Kosa) in mice.

Asian yellow dust (Kosa) causes adverse respiratory health effects in humans. The objective of this study was to clarify the lung toxicity of Kosa. ICR mice (5 weeks of age) were administered intratracheally with Kosa samples-two samples from Maowusu desert and Shapotou desert, one sample consisted of Shapotou Kosa plus sulfate, and natural Asian dust (NAD) from the atmosphere of Beijing-at doses of 0.05, 0.10 or 0.20mg/mouse at four weekly intervals. The four Kosa samples tested had similar compositions of minerals and concentrations of elements. Instillation of dust particles caused bronchitis and alveolitis in treated mice. The magnitude of inflammation was much greater in NAD-treated mice than in the other particles tested. Increased neutrophils, lymphocytes or eosinophils in bronchoalveolar lavage fluids (BALF) of treated mice were dose dependent. The number of neutrophils in BALF at the 0.2mg level was parallel to the content of ß-glucan in each particle. The numbers of lymphocytes and eosinophils in BALF at the 0.2mg level were parallel to the concentration of SO(4)(2-) in each particle. Pro-inflammatory mediators-such as interleukin (IL)-12, tumor necrosis factor-(TNF)-α, keratinocyte chemoattractant (KC), monocyte chemotactic protein (MCP)-l and macrophage inflammatory protein-(MIP)-lα in BALF-were greater in the treated mice. Specifically, NAD considerably increased pro-inflammatory mediators at a 0.2mg dose. The increased amounts of MlP-lα and TNF-α at 0.2mg dose corresponded to the amount of ß-glucan in each particle. The amounts of MCP-l or IL-12 corresponded to the concentration of sulfate (SO(4)(2-)) at a 0.2mg dose. These results suggest that inflammatory lung injury was mediated by ß-glucan or SO(4)(2-), which was adsorbed into the particles, via the expression of these pro-inflammatory mediators. The results also suggest that the variations in the magnitude of inflammation of the tested Kosa samples depend on the amounts of these toxic materials.

[Removal efficiency of dioxins in flue gas from MSW incineration by using bag house and activated carban filter/adsorbor].

This paper study the removal efficiency of dioxins in the flue gas from small-scale MSW incinerator, by using bag house, activated carban filter/adsorbor, and the combined unit of the bag house and activated carban filter/adsorbor. The removal efficiencies of the above three units respectively were 39.7%, 61.9%, 93.4% at 850-900 degrees C. It was shown that the combined unit of the bg house and activated carban filter/adsobor could reduce the operation cost, as well as meet the national criterion.