Multi-step validation of a Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis prediction system integrating multi-modal data: A multi-center study.

BACKGROUND AND STUDY AIMS: The impact of various categories of information on the prediction of Post Endoscopic Retrograde Cholangiopancreatography Pancreatitis (PEP) remains uncertain. We aimed to comprehensively investigate the risk factors associated with PEP by constructing and validating a model incorporating multi-modal data through multiple steps. PATIENTS AND METHODS: A total of 1,916 cases underwent ERCP were retrospectively collected from multiple centers for model construction. Through literature research, 49 electronic health record (EHR) features and one image feature related to PEP were identified. The EHR features were categorized into baseline, diagnosis, technique, and prevent strategies, covering pre-ERCP, intra-ERCP, and peri-ERCP phases. We first incrementally constructed models 1-4 incorporating these four feature categories, then added the image feature into models 1-4 and developed models 5-8. All models underwent testing and comparison using both internal and external test sets. Once the optimal model was selected, we conducted comparison among multiple machine learning algorithms. RESULTS: Compared with model 2 incorporating baseline and diagnosis features, adding technique and prevent strategies (model 4) greatly improved the sensitivity (63.89% vs 83.33%, p<0.05) and specificity (75.00% vs 85.92%, p<0.001). Similar tendency was observed in internal and external tests. In model 4, the top three features ranked by weight were previous pancreatitis, NSAIDS, and difficult cannulation. The image-based feature has the highest weight in model 5-8. Lastly, model 8 employed Random Forest algorithm showed the best performance. CONCLUSIONS: We firstly developed a multi-modal prediction model for identifying PEP with clinical-acceptable performance. The image and technique features are crucial for PEP prediction.

Facile Synthesis of Diazaanthraquinone Dimers as High-Capacity Organic Cathode Materials for Rechargeable Lithium Batteries.

Organic cathode materials (OCMs) have tremendous potential to construct sustainable and highly efficient batteries beyond conventional Li-ion batteries. Thereinto, quinone/pyrazine hybrids show significant advantages in material availability, energy density, and cycling stability. Herein, we propose a facile method to synthesize quinone/pyrazine hybrids, i.e., the condensation reaction between ortho-diamine and bromoacetyl groups. Based on it, we have successfully synthesized three 1,4-diazaanthraquinone (DAAQ) dimers, including 2,2'-bi(1,4-diazaanthraquinone) (BDAAQ) with an exceptional theoretical capacity of 512 mAh g-1 based on the eight-electron reaction. It can be fully utilized in Li batteries in a wide voltage range of 0.8-3.8 V, at the cost of inferior cycling stability. In an optimal voltage range of 1.4-3.8 V, BDAAQ exhibits one of the best comprehensive electrochemical performances for small-molecule OCMs, including a high specific capacity of 366 mAh g-1, an average discharge voltage of 2.26 V, as well as a respectable capacity retention of 59% after 500 cycles. Moreover, the in-depth investigations reveal the redox reaction mechanisms based on C═O and C═N groups as well as the capacity fading mechanisms based on dissolution-redeposition behaviors. In brief, this work provides an instructive synthesis method and mechanism understanding of high-performance OCMs based on a quinone/pyrazine hybrid structure.

The effect of incorporating domain knowledge with deep learning in identifying benign and malignant gastric whitish lesions: A retrospective study.

BACKGROUND AND AIM: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the effect of adding domain knowledge in model construction. METHODS: We collected 4558 images from two institutions to train and test models. We first developed two sole DL models (1 and 2) using supervised and semi-supervised algorithms. Then we selected diagnosis-related features through literature research and developed feature-extraction models to determine features including boundary, surface, roundness, depression, and location. Then predictions of the five feature-extraction models and sole DL model were combined and inputted into seven machine-learning (ML) based fitting-diagnosis models. The optimal model was selected as ENDOANGEL-WD (whitish-diagnosis) and compared with endoscopists. RESULTS: Sole DL 2 had higher sensitivity (83.12% vs 68.67%, Bonferroni adjusted P = 0.024) than sole DL 1. Adding domain knowledge, the decision tree performed best among the seven ML models, achieving higher specificity than DL 1 (84.38% vs 72.27%, Bonferroni adjusted P < 0.05) and higher accuracy than DL 2 (80.47%, Bonferroni adjusted P < 0.001) and was selected as ENDOANGEL-WD. ENDOANGEL-WD showed better accuracy compared with 10 endoscopists (75.70%, P < 0.001). CONCLUSIONS: We developed a novel system ENDOANGEL-WD combining domain knowledge and traditional DL to detect gastric whitish neoplasms. Adding domain knowledge improved the performance of traditional DL, which provided a novel solution for establishing diagnostic models for other rare diseases potentially.

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination.

BACKGROUND: Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE. DESIGN AND METHODS: A total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus. RESULTS: For the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts. CONCLUSIONS: We established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.

An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy.

BACKGROUND AND AIMS: The diagnosis and stratification of gastric atrophy (GA) predict patients' gastric cancer progression risk and determine endoscopy surveillance interval. We aimed to construct an artificial intelligence (AI) system for GA endoscopic identification and risk stratification based on the Kimura-Takemoto classification. METHODS: We constructed the system using two trained models and verified its performance. First, we retrospectively collected 869 images and 119 videos to compare its performance with that of endoscopists in identifying GA. Then, we included original image cases of 102 patients to validate the system for stratifying GA and comparing it with endoscopists with different experiences. RESULTS: The sensitivity of model 1 was higher than that of endoscopists (92.72% vs. 76.85 %) at image level and also higher than that of experts (94.87% vs. 85.90 %) at video level. The system outperformed experts in stratifying GA (overall accuracy: 81.37 %, 73.04 %, p = 0.045). The accuracy of this system in classifying non-GA, mild GA, moderate GA, and severe GA was 80.00 %, 77.42 %, 83.33 %, and 85.71 %, comparable to that of experts and better than that of seniors and novices. CONCLUSIONS: We established an expert-level system for GA endoscopic identification and risk stratification. It has great potential for endoscopic assessment and surveillance determinations.

Efficacy and safety of first-line therapy in patients with HER2-positive advanced breast cancer: a network meta-analysis of randomized controlled trials.

PURPOSE: The numerous first-line treatment regimens for human epidermal growth factor receptor 2 (HER2)-positive advanced breast cancer (ABC) necessitate a comprehensive evaluation to inform clinical decision-making. We conducted a Bayesian network meta-analysis (NMA) to compare the efficacy and safety of different interventions. METHODS: We systematically searched for relevant randomized controlled trials (RCTs) in Pubmed, Embase, Cochrane Library and online abstracts from inception to June 1, 2023. NMA was performed to calculate and analyze progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and adverse events of grade 3 or higher (≥ 3 AEs). RESULTS: Out of the 10,313 manuscripts retrieved, we included 28 RCTs involving 11,680 patients. Regarding PFS and ORR, the combination of trastuzumab with tyrosine kinase inhibitors (TKIs) was more favorable than dual-targeted therapy. If only using trastuzumab, combination chemotherapy is superior to monochemotherapy in terms of PFS. It is important to note that the addition of anthracycline did not result in improved PFS. For patients with hormone receptor-positive HER2-positive diseases, dual-targeted combined with endocrine therapy showed better benefit in terms of PFS compared to dual-targeted alone, but it did not reach statistical significance. The comprehensive analysis of PFS and ≥ 3 AEs indicates that monochemotherapy combined with dual-targeted therapy still has the optimal balance between efficacy and safety. CONCLUSION: Monochemotherapy (Docetaxel) plus dual-target (Trastuzumab and Pertuzumab) therapy remains the optimal choice among all first-line treatment options for ABC. The combination of trastuzumab with TKIs (Pyrotinib) demonstrated a significant improvement in PFS and ORR, but further data are warranted to confirm the survival benefit.

T-cell receptor sequencing reveals hepatocellular carcinoma immune characteristics according to Barcelona Clinic liver cancer stages within liver tissue and peripheral blood.

Analysis of T-cell receptor (TCR) repertoires in different stages of hepatocellular carcinoma (HCC) might help to elucidate its pathogenesis and progression. This study aimed to investigate TCR profiles in liver biopsies and peripheral blood mononuclear cells (PBMCs) in different Barcelona Clinic liver cancer (BCLC) stages of HCC. Ten patients in early stage (BCLC_A), 10 patients in middle stage (BCLC_B), and 10 patients in late stage (BCLC_C) cancer were prospectively enrolled. The liver tumor tissues, adjacent tissues, and PBMCs of each patient were collected and examined by TCR ß sequencing. Based on the ImMunoGeneTics (IMGT) database, we aligned the V, D, J, and C gene segments and identified the frequency of CDR3 sequences and amino acids sequence. Diversity of TCR in PBMCs was higher than in both tumor tissues and adjacent tissues, regardless of BCLC stage and postoperative recurrence. TCR clonality was increased in T cells from peripheral blood in advanced HCC, compared with the early and middle stages. No statistical differences were observed between different BCLC stages, either in tumors or adjacent tissues. TCR clonality revealed no significant difference between recurrent tumor and non-recurrent tumor, therefore PBMCs was better to be representative of TCR characteristics in different stages of HCC compared to tumor tissues. Clonal expansion of T cells was associated with low risk of recurrence in HCC patients.

Preparation of high strength, self-healing conductive hydrogel based on polysaccharide and its application in sensor.

The development of cost-effective, eco-friendly conductive hydrogels with excellent mechanical properties, self-healing capabilities, and non-toxicity holds immense significance in the realm of biosensors. The biosensors demonstrate promising applications in the fields of biomedical engineering and human motion detection. A unique double-network hydrogel was prepared through physical-chemical crosslinking using chitosan (CS), polyacrylic acid (AA), and sodium alginate (SA) as raw materials. The prepared double-network hydrogels exhibited exceptional mechanical properties, as well as self-healing and conductive capabilities. Polyacrylic acid as the first layer network, while chitosan and sodium alginate were incorporated to establish the second layer network through electrostatic interactions, thereby imparting self-healing and self-recovery properties. The hydrogel was subsequently immersed in the salt solution to induce network winding. The mechanical robustness of the hydrogel was significantly enhanced through synergistic coordination of covalent and non-covalent interactions. When the concentration of sodium alginate was 20 g/L, the double-network hydrogel exhibits enhanced mechanical properties, with a tensile fracture stress of up to 1.31 MPa and a strength of 4.17 MPa under 80% compressive deformation. Furthermore, the recovery rate of this double-network hydrogel reached an impressive 89.63% within a span of 30 min. After 24 h without any external forces, the self-healing rate reached 26.11%, demonstrating remarkable capabilities in terms of self-recovery and self-healing. Furthermore, this hydrogel exhibited consistent conductivity properties and was capable of detecting human finger movements. Hence, this study presents a novel approach for designing and synthesizing environmentally friendly conductive hydrogels for biosensors.

Flexible wearable intelligent sensing system for wheelchair sports monitoring.

The application of wearable intelligent systems toward human-computer interaction has received widespread attention. It is still desirable to conveniently promote health and monitor sports skills for disabled people. Here, a wireless intelligent sensing system (WISS) has been developed, which includes two ports of wearable flexible triboelectric nanogenerator (WF-TENG) sensing and an upper computer digital signal receiving intelligent processing. The WF-TENG sensing port is connected by the WF-TENG sensor and flexible printed circuit (FPC). Due to its flexibility, the WF-TENG sensing port can be freely adhered on the surface of human skin. The WISS can be applied to entertainment reaction training based on human-computer interaction, and to the technical judgment and analysis on wheelchair curling sport. This work provides new application opportunities for wearable devices in the fields of sports skills monitoring, sports assistive devices and health promotion for disabled people.

Radiofrequency ablation combined with toripalimab for recurrent hepatocellular carcinoma: A prospective controlled trial.

OBJECTIVE: The effectiveness and security of radiofrequency ablation (RFA) in combination with toripalimab (anti-PD-1) for the treatment of recurrent hepatocellular carcinoma (HCC) was studied in this article. METHODS: Total of 40 patients were enrolled in the study between September 2019 and November 2021. Data follow-up ends in April 2022. The study's main focus is on recurrence free survival (RFS), while the secondary objectives was safety. Chi-square tests, Kaplan-Meier, and Cox proportional hazards models were utilized to analyze the data. RESULTS: The median follow-up period was 21.40 months, and the median RFS was 15.40 months in the group that received combination therapy, which was statistically significantly different (HR: 0.44, p = 0.04) compared with the RFA group (8.2 months). RFS rates (RFSr) at 6, 12 and 18 months in the combination therapy groups and RFA groups were 80% vs 65%, 62.7% vs 35% and 48.7% vs 18.8%, respectively. Between the two groups, significant difference of RFSr was found at 18 months (p = 0.04). No statistical differences were observed between the two groups in terms of safeness (p > 0.05). The subgroup analysis indicated that the combination of RFA and anti-PD-1 led to better RFS than RFA alone. Moreover, patients benefited more from combination therapy in the groups younger than 60 years (HR: 0.26, p = 0.018), male (HR: 0.32, p = 0.028) and Child-Pugh grade A (HR: 0.38, p = 0.032). CONCLUSIONS: Combining RFA with anti-PD-1 showed improved RFS and was deemed safe for patients with recurrent HCC who had previously undergone RFA treatment alone.

Development and Evaluation of a Surveillance System for Follow-Up After Colorectal Polypectomy.

Importance: The adherence of physicians and patients to published colorectal postpolypectomy surveillance guidelines varies greatly, and patient follow-up is critical but time consuming. Objectives: To evaluate the accuracy of an automatic surveillance (AS) system in identifying patients after polypectomy, assigning surveillance intervals for different risks of patients, and proactively following up with patients on time. Design, Setting, and Participants: In this diagnostic/prognostic study, endoscopic and pathological reports of 47 544 patients undergoing colonoscopy at 3 hospitals between January 1, 2017, and June 30, 2022, were collected to develop an AS system based on natural language processing. The performance of the AS system was fully evaluated in internal and external tests according to 5 guidelines worldwide and compared with that of physicians. A multireader, multicase (MRMC) trial was conducted to evaluate use of the AS system and physician guideline adherence, and prospective data were collected to evaluate the success rate in contacting patients and the association with reduced human workload. Data analysis was conducted from July to September 2022. Exposures: Assistance of the AS system. Main Outcomes and Measures: The accuracy of the system in identifying patients after polypectomy, stratifying patient risk levels, and assigning surveillance intervals in internal (Renmin Hospital of Wuhan University), external 1 (Wenzhou Central Hospital), and external 2 (The First People's Hospital of Yichang) test sets; the accuracy of physicians and their time burden with and without system assistance; and the rate of successfully informed patients of the system were evaluated. Results: Test sets for 16 106 patients undergoing colonoscopy (mean [SD] age, 51.90 [13.40] years; 7690 females [47.75%]) were evaluated. In internal, external 1, and external 2 test sets, the system had an overall accuracy of 99.91% (95% CI, 99.83%-99.95%), 99.54% (95% CI, 99.30%-99.70%), and 99.77% (95% CI, 99.41%-99.91%), respectively, for identifying types of patients and achieved an overall accuracy of at least 99.30% (95% CI, 98.67%-99.63%) in the internal test set, 98.89% (95% CI, 98.33%-99.27%) in external test set 1, and 98.56% (95% CI, 95.86%-99.51%) in external test set 2 for stratifying patient risk levels and assigning surveillance intervals according to 5 guidelines. The system was associated with increased mean (SD) accuracy among physicians vs no AS system in 105 patients (98.67% [1.28%] vs 78.10% [18.01%]; P = .04) in the MRMC trial. In a prospective trial, the AS system successfully informed 82 of 88 patients (93.18%) and was associated with reduced burden of follow-up time vs no AS system (0 vs 2.86 h). Conclusions and Relevance: This study found that an AS system was associated with improved adherence to guidelines among physicians and reduced workload among physicians and nurses.

Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions.

BACKGROUND: Artificial intelligence (AI) performed variously among test sets with different diversity due to sample selection bias, which can be stumbling block for AI applications. We previously tested AI named ENDOANGEL, diagnosing early gastric cancer (EGC) on single-center videos in man-machine competition. We aimed to re-test ENDOANGEL on multi-center videos to explore challenges applying AI in multiple centers, then upgrade ENDOANGEL and explore solutions to the challenge. METHODS: ENDOANGEL was re-tested on multi-center videos retrospectively collected from 12 institutions and compared with performance in previously reported single-center videos. We then upgraded ENDOANGEL to ENDOANGEL-2022 with more training samples and novel algorithms and conducted competition between ENDOANGEL-2022 and endoscopists. ENDOANGEL-2022 was then tested on single-center videos and compared with performance in multi-center videos; the two AI systems were also compared with each other and endoscopists. RESULTS: Forty-six EGCs and 54 non-cancers were included in multi-center video cohort. On diagnosing EGCs, compared with single-center videos, ENDOANGEL showed stable sensitivity (97.83% vs. 100.00%) while sharply decreased specificity (61.11% vs. 82.54%); ENDOANGEL-2022 showed similar tendency while achieving significantly higher specificity (79.63%, p < 0.01) making fewer mistakes on typical lesions than ENDOANGEL. On detecting gastric neoplasms, both AI showed stable sensitivity while sharply decreased specificity. Nevertheless, both AI outperformed endoscopists in the two competitions. CONCLUSIONS: Great increase of false positives is a prominent challenge for applying EGC diagnostic AI in multiple centers due to high heterogeneity of negative cases. Optimizing AI by adding samples and using novel algorithms is promising to overcome this challenge.

Human-Like Artificial Intelligent System for Predicting Invasion Depth of Esophageal Squamous Cell Carcinoma Using Magnifying Narrow-Band Imaging Endoscopy: A Retrospective Multicenter Study.

INTRODUCTION: Endoscopic evaluation is crucial for predicting the invasion depth of esophagus squamous cell carcinoma (ESCC) and selecting appropriate treatment strategies. Our study aimed to develop and validate an interpretable artificial intelligence-based invasion depth prediction system (AI-IDPS) for ESCC. METHODS: We reviewed the PubMed for eligible studies and collected potential visual feature indices associated with invasion depth. Multicenter data comprising 5,119 narrow-band imaging magnifying endoscopy images from 581 patients with ESCC were collected from 4 hospitals between April 2016 and November 2021. Thirteen models for feature extraction and 1 model for feature fitting were developed for AI-IDPS. The efficiency of AI-IDPS was evaluated on 196 images and 33 consecutively collected videos and compared with a pure deep learning model and performance of endoscopists. A crossover study and a questionnaire survey were conducted to investigate the system's impact on endoscopists' understanding of the AI predictions. RESULTS: AI-IDPS demonstrated the sensitivity, specificity, and accuracy of 85.7%, 86.3%, and 86.2% in image validation and 87.5%, 84%, and 84.9% in consecutively collected videos, respectively, for differentiating SM2-3 lesions. The pure deep learning model showed significantly lower sensitivity, specificity, and accuracy (83.7%, 52.1% and 60.0%, respectively). The endoscopists had significantly improved accuracy (from 79.7% to 84.9% on average, P = 0.03) and comparable sensitivity (from 37.5% to 55.4% on average, P = 0.27) and specificity (from 93.1% to 94.3% on average, P = 0.75) after AI-IDPS assistance. DISCUSSION: Based on domain knowledge, we developed an interpretable system for predicting ESCC invasion depth. The anthropopathic approach demonstrates the potential to outperform deep learning architecture in practice.

Study on the radiation and self-absorption characteristics of plasma under various background gases.

The self-absorption effect is a primary factor responsible for the decline in the precision of quantitative analysis techniques using plasma emission spectroscopy, such as laser-induced breakdown spectroscopy (LIBS). In this study, based on the thermal ablation and hydrodynamics models, the radiation characteristics and self-absorption of laser-induced plasmas under different background gases were theoretically simulated and experimentally verified to investigate ways of weakening the self-absorption effect in plasma. The results reveal that the plasma temperature and density increase with higher molecular weight and pressure of the background gas, leading to stronger species emission line intensity. To reduce the self-absorption effect in the later stages of plasma evolution, we can decrease the gas pressure or substitute the background gas with a lower molecular weight. As the excitation energy of the species increases, the impact of the background gas type on the spectral line intensity becomes more pronounced. Moreover, we accurately calculated the optically thin moments under various conditions using theoretical models, which are consistent with the experimental results. From the temporal evolution of the doublet intensity ratio of species, it is deduced that the optically thin moment appears later with higher molecular weight and pressure of the background gas and lower upper energy of the species. This theoretical research is essential in selecting the appropriate background gas type and pressure and doublets in self-absorption-free LIBS (SAF-LIBS) experiments to weaken the self-absorption effect.

The effectiveness of a computer-aided system in improving the detection rate of gastric neoplasm and early gastric cancer: study protocol for a multi-centre, randomized controlled trial.

BACKGROUND: This protocol is for a multi-centre randomised controlled trial to determine whether the computer-aided system ENDOANGEL-GC improves the detection rates of gastric neoplasms and early gastric cancer (EGC) in routine oesophagogastroduodenoscopy (EGD). METHODS: Study design: Prospective, single-blind, parallel-group, multi-centre randomised controlled trial. SETTINGS: The computer-aided system ENDOANGEL-GC was used to monitor blind spots, detect gastric abnormalities, and identify gastric neoplasms during EGD. PARTICIPANTS: Adults who underwent screening, diagnosis, or surveillance EGD. Randomisation groups: 1. Experiment group, EGD examinations with the assistance of the ENDOANGEL-GC; 2. Control group, EGD examinations without the assistance of the ENDOANGEL-GC. RANDOMISATION: Block randomisation, stratified by centre. PRIMARY OUTCOMES: Detection rates of gastric neoplasms and EGC. SECONDARY OUTCOMES: Detection rate of premalignant gastric lesions, biopsy rate, observation time, and number of blind spots on EGD. BLINDING: Outcomes are undertaken by blinded assessors. SAMPLE SIZE: Based on the previously published findings and our pilot study, the detection rate of gastric neoplasms in the control group is estimated to be 2.5%, and that of the experimental group is expected to be 4.0%. With a two-sided α level of 0.05 and power of 80%, allowing for a 10% drop-out rate, the sample size is calculated as 4858. The detection rate of EGC in the control group is estimated to be 20%, and that of the experiment group is expected to be 35%. With a two-sided α level of 0.05 and power of 80%, a total of 270 cases of gastric cancer are needed. Assuming the proportion of gastric cancer to be 1% in patients undergoing EGD and allowing for a 10% dropout rate, the sample size is calculated as 30,000. Considering the larger sample size calculated from the two primary endpoints, the required sample size is determined to be 30,000. DISCUSSION: The results of this trial will help determine the effectiveness of the ENDOANGEL-GC in clinical settings. TRIAL REGISTRATION: ChiCTR (Chinese Clinical Trial Registry), ChiCTR2100054449, registered 17 December 2021.

Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes.

Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients. Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 breast cancer samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differentially expression genes (DEGs) from cGAS-STING-related genes were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model. Results: We successfully developed a risk model to predict the prognostic value of breast cancer patients and its performance acquired effective validation. The results derived from Kaplan-Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and had good validity in predicting the overall survival of breast cancer patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The cGAS-STING-related genes risk score was also relevant to a series of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in breast cancer patients. Conclusion: cGAS-STING-related genes risk model provides a new credible risk stratification method to improve the clinical prognostic assessment for breast cancer.

Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy.

White light endoscopy is the most pivotal tool for detecting early gastric neoplasms. Previous artificial intelligence (AI) systems were primarily unexplainable, affecting their clinical credibility and acceptability. We aimed to develop an explainable AI named ENDOANGEL-ED (explainable diagnosis) to solve this problem. A total of 4482 images and 296 videos with focal lesions from 3279 patients from eight hospitals were used for training, validating, and testing ENDOANGEL-ED. A traditional sole deep learning (DL) model was trained using the same dataset. The performance of ENDOANGEL-ED and sole DL was evaluated on six levels: internal and external images, internal and external videos, consecutive videos, and man-machine comparison with 77 endoscopists in videos. Furthermore, a multi-reader, multi-case study was conducted to evaluate the ENDOANGEL-ED's effectiveness. A scale was used to compare the overall acceptance of endoscopists to traditional and explainable AI systems. The ENDOANGEL-ED showed high performance in the image and video tests. In man-machine comparison, the accuracy of ENDOANGEL-ED was significantly higher than that of all endoscopists in internal (81.10% vs. 70.61%, p < 0.001) and external videos (88.24% vs. 78.49%, p < 0.001). With ENDOANGEL-ED's assistance, the accuracy of endoscopists significantly improved (70.61% vs. 79.63%, p < 0.001). Compared with the traditional AI, the explainable AI increased the endoscopists' trust and acceptance (4.42 vs. 3.74, p < 0.001; 4.52 vs. 4.00, p < 0.001). In conclusion, we developed a real-time explainable AI that showed high performance, higher clinical credibility, and acceptance than traditional DL models and greatly improved the diagnostic ability of endoscopists.

Total areola approach for endoscopic thyroidectomy: Six years of experience with the same surgeon.

Background: Scarless endoscopic thyroidectomy (ET) is increasingly accepted by the growing amount of surgeons. The target of this study is to assess the efficacy and summarise the experiences of total areola approach for ET (TAAET). Subjects and Methods: TAAET was performed on 529 patients between January 2016 and October 2021. All operated patients were divided into two groups according to the chronological order. Demographic data, perioperative data and post-operative complications were collected to assess the effectiveness of TAAET. Results: Five hundred and twenty-eight patients were successfully treated with TAAET, while 1 case was converted to open surgery due to bleeding. The surgical approach consists of lobectomy or total thyroidectomy with or without central lymph node dissection. The post-operative pathology of 433 (81.9%) patients was diagnosed with T1 ~2N0M0. The average number of unilateral lymph node dissection was 7.72 ± 2.44 while the bilateral lymph node was 10.70 ± 3.72. In terms of complications, 38 cases had transient hoarseness, 28 cases had tetany and numbness, 3 cases had post-operative bleeding, 1 case had infection and 33 cases had subcutaneous fluid. There were statistically significant differences between the two groups with respect to transient hoarseness (P < 0.001), tetany and numbness (P = 0.005), intraoperative blood loss (P = 0.003) and operation time for malignant tumour (P < 0.001) because of the accumulation of surgical experience and the maturation of technology. Conclusions: TAAET which conforms to the anatomical pathway of open thyroidectomy is a safe, effective and feasible technique and is highly suitable for novices.