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.

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.

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.

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.

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.

Effect of a deep learning-based automatic upper GI endoscopic reporting system: a randomized crossover study (with video).

BACKGROUND AND AIMS: EGD is essential for GI disorders, and reports are pivotal to facilitating postprocedure diagnosis and treatment. Manual report generation lacks sufficient quality and is labor intensive. We reported and validated an artificial intelligence-based endoscopy automatic reporting system (AI-EARS). METHODS: The AI-EARS was designed for automatic report generation, including real-time image capturing, diagnosis, and textual description. It was developed using multicenter datasets from 8 hospitals in China, including 252,111 images for training, 62,706 images, and 950 videos for testing. Twelve endoscopists and 44 endoscopy procedures were consecutively enrolled to evaluate the effect of the AI-EARS in a multireader, multicase, crossover study. The precision and completeness of the reports were compared between endoscopists using the AI-EARS and conventional reporting systems. RESULTS: In video validation, the AI-EARS achieved completeness of 98.59% and 99.69% for esophageal and gastric abnormality records, respectively, accuracies of 87.99% and 88.85% for esophageal and gastric lesion location records, and 73.14% and 85.24% for diagnosis. Compared with the conventional reporting systems, the AI-EARS achieved greater completeness (79.03% vs 51.86%, P < .001) and accuracy (64.47% vs 42.81%, P < .001) of the textual description and completeness of the photo-documents of landmarks (92.23% vs 73.69%, P < .001). The mean reporting time for an individual lesion was significantly reduced (80.13 ± 16.12 seconds vs 46.47 ± 11.68 seconds, P < .001) after the AI-EARS assistance. CONCLUSIONS: The AI-EARS showed its efficacy in improving the accuracy and completeness of EGD reports. It might facilitate the generation of complete endoscopy reports and postendoscopy patient management. (Clinical trial registration number: NCT05479253.).

A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video).

BACKGROUND: White light (WL) and weak-magnifying (WM) endoscopy are both important methods for diagnosing gastric neoplasms. This study constructed a deep-learning system named ENDOANGEL-MM (multi-modal) aimed at real-time diagnosing gastric neoplasms using WL and WM data. METHODS: WL and WM images of a same lesion were combined into image-pairs. A total of 4201 images, 7436 image-pairs, and 162 videos were used for model construction and validation. Models 1-5 including two single-modal models (WL, WM) and three multi-modal models (data fusion on task-level, feature-level, and input-level) were constructed. The models were tested on three levels including images, videos, and prospective patients. The best model was selected for constructing ENDOANGEL-MM. We compared the performance between the models and endoscopists and conducted a diagnostic study to explore the ENDOANGEL-MM's assistance ability. RESULTS: Model 4 (ENDOANGEL-MM) showed the best performance among five models. Model 2 performed better in single-modal models. The accuracy of ENDOANGEL-MM was higher than that of Model 2 in still images, real-time videos, and prospective patients. (86.54 vs 78.85%, P = 0.134; 90.00 vs 85.00%, P = 0.179; 93.55 vs 70.97%, P < 0.001). Model 2 and ENDOANGEL-MM outperformed endoscopists on WM data (85.00 vs 71.67%, P = 0.002) and multi-modal data (90.00 vs 76.17%, P = 0.002), significantly. With the assistance of ENDOANGEL-MM, the accuracy of non-experts improved significantly (85.75 vs 70.75%, P = 0.020), and performed no significant difference from experts (85.75 vs 89.00%, P = 0.159). CONCLUSIONS: The multi-modal model constructed by feature-level fusion showed the best performance. ENDOANGEL-MM identified gastric neoplasms with good accuracy and has a potential role in real-clinic.

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.

Placenta-specific lncRNA 1600012P17Rik is expressed in spongiotrophoblast and glycogen trophoblast cells of mouse placenta.

A few long noncoding RNAs (long ncRNAs, lncRNAs) exhibit trophoblast cell type-specific expression patterns and functional roles in mouse placenta. However, the cell- and stage-specific expression patterns and functions of most placenta-derived lncRNAs remain unclear. In this study, we explored mouse placenta-associated lncRNAs using a combined bioinformatic and experimental approach. We used the FANTOM5 database to survey lncRNA expression in mouse placenta and found that 1600012P17Rik (MGI: 1919275, designated P17Rik), a long intergenic ncRNA, was the most highly expressed lncRNA at gestational day 17. Polymerase chain reaction analysis confirmed that P17Rik was exclusively expressed in placenta and not in any of the adult organs examined in this study. In situ hybridization analysis revealed that it was highly expressed in spongiotrophoblast cells and to a lesser extent in glycogen trophoblast cells, including migratory glycogen trophoblast cells invading the decidua. Moreover, we found that it is a polyadenylated lncRNA localized mainly to the cytoplasm of these trophoblast cells. As these trophoblast cells also expressed the neighboring protein-coding gene, pappalysin 2 (Pappa2), we investigated the effects of P17Rik on Pappa2 expression using Pappa2-expressing MC3T3-E1 cells and found that P17Rik transfection increased the messenger RNA (mRNA) and protein levels of Pappa2. These results indicate that mouse placenta-specific lncRNA P17Rik modulates the expression of the neighboring protein-coding gene Pappa2 in spongiotrophoblast and glycogen trophoblast cells of mouse placenta during late gestation.

Farmland quality assessment using deep fully convolutional neural networks.

Farmland is the cornerstone of agriculture and is important for food security and social production. Farmland assessment is essential but traditional methods are usually expensive and slow. Deep learning methods have been developed and widely applied recently in image recognition, semantic understanding, and many other application domains. In this research, we used fully convolutional networks (FCN) as the deep learning model to evaluate farmland grades. Normalized difference vegetation index (NDVI) derived from Landsat images was used as the input data, and the China National Cultivated Land Grade Database within Jiangsu Province was used to train the model on cloud computing. We also applied an image segmentation method to improve the original results from the FCN and compared the results with classical machine learning (ML) methods. Our research found that the FCN can predict farmland grades with an overall F1 score (the harmonic mean of precision and recall) of 0.719 and F1 score of 0.909, 0.590, 0.740, 0.642, and 0.023 for non-farmland, level I, II, III, and IV farmland, respectively. Combining the FCN and image segmentation method can further improve prediction accuracy with results of fewer noise pixels and more realistic edges. Compared with conventional ML, at least in farmland evaluation, FCN provides better results with higher precision, recall, and F1 score. Our research indicates that by using remote sensing NDVI data, the deep learning method can provide acceptable farmland assessment without fieldwork and can be used as a novel supplement to traditional methods. The method used in this research will save a lot of time and cost compared with traditional means.

Investigations on the Photochemical Reaction Mechanisms of Selected Dibenzoylmethane Compounds.

In this work, combined time-resolved spectroscopies of femtosecond transient absorption, nanosecond transient absorption, and DFT calculations were performed to unravel the photocyclization reaction mechanisms of selected dibenzoylmethane (DBM) derivatives, including 2-chloro-1,3-diphenylpropan-1,3-dione (1a), 2-chloro-1-(3,5-dimethoxyphenyl)-3-phenylpropan-1,3-dione (1b), 2-chloro-2-fluoro-1,3-diphenylpropan-1,3-dione (1c), and 2-chloro-2-fluoro-1,3-di(4-methoxyphenyl)propan-1,3-dione (1d). Photocyclization reaction mechanisms for 1a and 1b are similar, where a C-Cl heterolysis occurs yielding an α-ketocation intermediate, followed by cyclization to generate the cation species. On the other hand, 1c and 1d undergo dechlorination primarily producing a radical species, which further experiences cyclization yielding cyclized radical species. The dominant factor leading to the different reaction mechanisms is the involvement of a fluorine atom bonded at α-C. Due to the meta-effect, the p-methoxy substitution on the benzene ring inhibits the photocyclization reaction and reduces the yield of photocyclization.

Dimeric Tetrahydroanthracene Regioisomers and Their Monomeric Precursor Produced by Streptomyces fumigatiscleroticus HDN10255.

Four new tetrahydroanthracene derivatives (1, 3-5) and a known antibiotic, A-39183A (2), were discovered from the marine-sponge-derived actinomycete Streptomyces fumigatiscleroticus HDN10255. Their structures including absolute configurations were elucidated based upon MS and NMR spectroscopic data, ECD calculations, and biogenetic considerations. Compounds 2 and 4 showed considerable cytotoxicity with the best IC50 value of 1.8 µM against HeLa cells.

New metabolites from a Mariana Trench-derived actinomycete Nocardiopsis sp. HDN 17-237.

One new ß,γ-butenoate derivative phenylbutenote (1), and one new α-pyrone nocapyrone T (2) were isolated from the deep-sea derived actinomycete Nocardiopsis sp. HDN 17-237. Their structures were elucidated by extensive HRMS, IR and NMR analyses. Among them, compound 1 is the first microbial natural products bearing a rare ß,γ-butenoate moiety, and compound 2 is the first α-pyrone isolated from strain of Mariana Trench. Compounds 1 and 2 were tested for antioxidant and antibacterial activities, while none of them showed significant activity.

Investigation of different photochemical reactions of avobenzone derivatives by ultrafast transient absorption spectroscopy.

Avobenzone (AB) is one of the most widely used UVA sunscreens, and it is viewed as a model compound for studying the photoisomerization process. In recent years, Miranda and co-workers studied photophysical and photochemical reactions of several AB derivatives. However, there is still a gap in the data of these compounds in the ultrafast time region. To get a better understanding of the photophysical and photochemical reaction mechanisms, selected AB derivatives of AB-Me, AB-Pr, AB-Br and AB-Cl were studied using ultrafast transient absorption spectroscopy and density functional theory calculations in the present study. It is unravelled that alkylated substituted AB compounds of AB-Me and AB-Pr exhibit an efficient intersystem crossing with the generation of the corresponding triplet state species, which further leads to the Norrish type II reaction for AB-Pr. On the other hand, AB-Br and AB-Cl prefer photochemical reactions via the singlet state surface. Based on the DFT calculations, the spin-orbit coupling constant between the singlet and triplet states, the energy difference between the singlet and triplet states and the natural transition orbital separations of the studied AB compounds were found to be the leading reasons accounting for their corresponding photochemical activities via singlet and triplet states.

New Method for Improving Spatial Allocation Accuracy of Industrial Energy Consumption and Implications for Polycyclic Aromatic Hydrocarbon Emissions in China.

The variety of spatial allocation methods for industrial sources can significantly affect the distribution of a gridded pollutant emission inventory. Although uncertainties in current emissions inventories have been discussed extensively, uncertainties resulting from improper spatial proxies have rarely been evaluated. Here, a new subnational fuel data disaggregation method using points-of-interest (POI) data (DPOI) for industrial sources was developed. We compared the accuracies of DPOI and six other spatial allocation methods at the city scale and within the city and found that DPOI had the highest accuracy. Using a population proxy may over-estimate the industrial energy consumption in urban centers or other densely populated areas. We further applied the DPOI to establish a 0.05° × 0.05° gridded industrial polycyclic aromatic hydrocarbon (PAH) emissions inventory in 2016. There are obvious spatial differences in industrial PAH emissions, and high industrial PAH emissions are mainly concentrated in North China and East China. Although some limitations exist, we believe that POI data and the DPOI method have great potential in the field of gridded pollutant emissions inventories and that they can further reduce the spatial allocation uncertainty of gridded emissions inventories.

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.

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.

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.

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.

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.