Efficient perovskite light-emitting diodes on a flexible substrate.

The surface properties of target substrates are crucial for the in situ crystallization and growth of metal halide perovskite films fabricated by the anti-solvent method. In this work, a high-quality quasi-2D perovskite film with various-n phases is fabricated on the commonly used poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) by introducing a branched polyethylenimine (PEI) modifying layer. PEI suppresses the influence of acidic surface of the PEDOT:PSS and regulates the components of the perovskite film, increasing the proportion of large-n phases. Additionally, PEI reduces the formation of defects in perovskite films, leading to higher photoluminescence quantum efficiency and longer photoluminescence lifetime. Based on this high-quality perovskite film, a flexible light-emitting diode with an ultimate current efficiency of 63.2 cd/A is achieved, nearly twofold higher than that of the device (35.1 cd/A) without a PEI modifying layer.

An advanced furoxan-bridged heat-resistant explosive.

Nowadays, thousands of energetic materials have been synthesized, but only a few compounds meet all the high standards of detonation performance comparable to that of the widely used military explosive RDX, thermal stability comparable to that of the most widely used heat-resistant explosive HNS, and impact sensitivity comparable to that of the traditional explosive TNT. Also, as a goal, a novel and unexpected one-step method for constructing the furoxan-bridged energetic compound 3,4-bis(3,8-dinitropyrazolo[5,1-c][1,2,4]triazin-4-amino-7-yl)-1,2,5-oxadiazole 2-oxide (OTF) has been achieved under the conventional TFA/100% HNO3 nitration reaction system from the acetic acid intermediate. In this work, OTF with a high density of 1.90 g cm-3, the highest decomposition temperature of 310 °C (onset) among furoxan-based high explosives to date, superior detonation velocity (DV: 9109 m s-1), and low sensitivity (IS: 25 J) is reported. This work is a giant step forward in the development of advanced high-energy heat-resistant explosives and could improve future possibilities for the design of furoxan-based energetic materials.

High-Energy and Thermally Stable Energetic Materials Based on an Azo-Fused Dinitramino Compound.

Here, azo-fused dinitramino energetic compound 5,5'-dinitramino-8,8'-azo-1,2,5-oxadiazolo[3,4-e][1,2,4]triazolo[4,3-a]pyrazine (4) and its energetic salts 5-7 have been prepared. Azo-fused dinitramino compound 4 exhibits excellent detonation performance (P = 36.13 GPa, and Dv = 9126 m s-1), which is obviously better than that of RDX (Dv = 8796 m s-1, and P = 33.60 GPa). In addition, the thermal stability of carbonyl compound 2 (Td = 325 °C) is similar to that of HNS (Td = 318 °C) and 2 exhibits better energy properties. These properties indicate that azo-dinitramino compound 4 has potential as a new secondary explosive, while carbonyl compound 2 also has potential as a new type of heat-resistant explosive.

Molecular characterization of histone gene in golden pompano (Trachinotus ovatus) and antimicrobial activity of its derived peptides.

In addition to controlling gene expression, mediating DNA folding into chromatin, and responding to immunological stimuli, histones are also thought to have antimicrobial effects. This study identified the molecular characteristics of core Histone MacroH2A2 (TOMacroH2A2) and Histone H2B 1/2 (TOH2B) from Trachinotus ovatus, and the antimicrobial potential of their derived peptides (To.mh2a and To. h2b). The open reading frames (ORFs) of TOMacroH2A2 and TOH2B from T. ovatus were 1010 bp and 375 bp, encoding polypeptides of 369 and 124 amino acids, respectively. The TOMacroH2A2 included an H2A domain and an A1pp domain, while TOH2B included an H2B domain. The amino acid sequences of TOMacroH2A2 and TOH2B demonstrated high homology with other teleost's sequences of histone macroh2a2 and histone h2b, with homologies exceeding 90 %. Expression analysis showed high expression of TOMacroH2A2 in brain, stomach, heart, and skin tissues and TOH2B in gill, brain, and skin tissues. In addition, the histone-derived peptides To. mh2a and To. h2b, synthesized based on two histone sequences from T. ovatus, exhibited typical physical characteristics of antimicrobial peptides, including positive charges, amphipathicity, hydrophobicity, and rich α-helix structure. Crucially, the vitro antibacterial results demonstrated that To. mh2a and To. h2b can inhibit the growth of various aquatic pathogens including Streptococcus agalactiae, Staphylococcus aureus, Bacillus subtilis, Acinetobacter baumannii, Aeromonas hydrophila, and Escherichia coli to varying degrees. Specifically, To. mh2a and To. h2b were capable of disrupting the cell surface structures of S. aureus and penetrating the cell membrane, leading to the leakage of cellular contents, thereby exerting their antibacterial effects. Furthermore, gel electrophoresis migration assays showed that To. mh2a and To. h2b participated in antimicrobial activity by binding to bacterial genomic DNA and reducing the migration rate of gDNA in a dose-dependent manner. The minimum effective concentration for binding to DNA was approximately 50 µM. In conclusion, our study suggested that To. mh2a and To. h2b can act as antimicrobial peptides, providing a potential strategy for controlling bacterial diseases in T. ovatus.

Establishment and identification of the head kidney cell line of yellowfin seabream (Acanthopagrus latus) and its application in a virus susceptibility study.

The yellowfin seabream (Acanthopagrus latus) is a crucial marine resource owing to its economic significance. Acanthopagrus latus aquaculture faces numerous challenges from viral diseases, but a robust in-vitro research model to understand and address these threats is lacking. Therefore, we developed a novel A. latus cell line from head kidney cells called ALHK1. This study details the development, characterisation, and viral susceptibility properties of ALHK cells. This cell line primarily comprises fibroblast-like cells and has robust proliferative capacity when cultured at 28 °C in Leibovitz's L-15 medium supplemented with 10-20% foetal bovine serum. It exhibited remarkable stability after more than 60 consecutive passages and validation through cryopreservation techniques. The specificity of the ALHK cell line's origin from A. latus was confirmed via polymerase chain reaction (PCR) amplification of the cytochrome B gene, and a chromosomal karyotype analysis revealed a diploid count of 48 (2n = 48). Furthermore, the lipofection-mediated transfection efficiency using the pEGFP-N3 plasmid was high, at nearly 40%, suggesting that ALHK cells could be used for studies involving exogenous gene manipulation. In addition, ALHK cells displayed heightened sensitivity to the large mouth bass virus (LMBV), substantiated through observations of cytopathic effects, quantitative real-time PCR, and viral titration assays. Finally, the response of ALHK cells to LMBV infection resulted in differentially expressed antiviral genes associated with innate immunity. In conclusion, the ALHK cell line is a dependable in-vitro platform for elucidating the mechanisms of viral diseases in yellowfin seabream. Moreover, this cell line could be valuable for immunology, vaccine development, and host-pathogen interaction studies.

Impact of several sludge dewatering conditioners on municipal sludge pyrolysis properties, kinetics, by-products, and environmental risk assessment.

The pyrolysis characteristics of four different types of conditioned sludge were ascertained, and PAM, CaO, K2FeO4, and K2FeO4-CaO-PAM (KCP) conditioners were employed as sludge dewatering conditioners. The sludge pyrolysis reaction's activation energy (E) dropped with the addition of four conditioners. CaO, PAM, KCP, and K2FeO4 were the sequences of E needed for the pyrolysis of four different types of conditioned sludge. The addition of K2FeO4, CaO, and KCP resulted in an increase in the yields of H2 and CO. Except for the K2FeO4 conditioning sludge carbon, the pyrolytic carbon of the other three groups of samples showed an increase in S contents, while the pyrolytic carbon of the four groups of samples treated with conditioners clearly showed lower C and N contents compared to the raw sludge carbon. Protein-N made up the majority of N in sludge pyrolytic carbon. After adding conditioner, the level of organic sulfur decreased. Organic sulfur could then be broken down by K2FeO4 and CaO. The four conditioners efficiently mitigated the ecological and environmental risks posed by heavy metals. Alkynes were the most abundant result in pyrolytic volatiles of sludge pyrolysis; the other products included acids, alcohols, lipids, furans, ketones, phenols, hydrocarbons, N-components, and so on. All samples' acids, alcohols, and ketones from pyrolysis were decreased once the conditioner was added. The acid reduction rate reached 66.7 %, and the alkynes clearly increased during the KCP conditioned sludge's pyrolysis. The sulfur level of the bio-oil was decreased by all four conditioners. Everything mentioned above indicated that the KCP aided in the subsequent pyrolysis of the sludge, leading to the production of an advantageous pyrolysis bio-oil.

Genome-wide characterization, phylogenetic and expression analysis of Galectin gene family in Golden pompano Trachinotus ovatus.

Galectins (Gals) are a type of S-type lectin that are widespread and evolutionarily conserved among metazoans, and can act as pattern recognition receptors (PRRs) to recognize pathogen-associated molecular patterns (PAMPs). In this study, 10 Gals (ToGals) were identified in the Golden pompano (Trachinotus ovatus), and their conserved domains, motifs, and collinearity relationships were analyzed. The expression of ToGals was regulated following infection to Cryptocaryon irritans and Streptococcus agalactiae, indicating that ToGals participate in immune responses against microbial pathogens. Further analysis was conducted on one important member, Galectin-3, subcellular localization showing that ToGal-3like protein is expressed both in the nucleus and cytoplasm. Recombinant protein obtained through prokaryotic expression showed that rToGal-3like can agglutinate red blood cells of rabbit, carp and golden pompano and also agglutinate and kill Staphylococcus aureus, Bacillus subtilis, Vibrio vulnificus, S. agalactiae, Pseudomonas aeruginosa, and Aeromonas hydrophila. This study lays the foundation for further research on the immune roles of Gals in teleosts.

Advanced tetracyclic heat-resistant energetic materials based on bis(4-nitropyrazole) bridged 1,2,4-triazole.

In recent years, with the development of deep coal mines and petroleum resources and the expansion of the aerospace industry, the pursuit of heat-resistant energetic materials with high thermal stability and high energy has been increasing. Bis(4-nitropyrazole) was employed as an energy bridge to link 1,2,4-triazole, thereby constructing a sophisticated tetracyclic framework in this study. A tetracyclic heat-resistant explosive 5,5'-(4,4'-dinitro-2H,2'H-[3,3'-bipyrazole]-5,5'-diyl)bis(4H-1,2,4-triazole-3,4-diamine) (3) and its derivatives 6-8 with excellent comprehensive performance have been successfully prepared. Particularly noteworthy is that compound 3 has a detonation velocity of 8604 m s-1, which exceeds that of the conventional heat-resistant explosive HNS with a velocity of 7164 m s-1. Furthermore, compound 3 has higher thermal stability (Td = 340 °C) than HNS (Td = 318 °C). In addition, the tetracyclic compound 3 also exhibited extraordinarily low sensitivity (IS > 40 J; FS > 360 N). These unique characteristics make it a potential candidate for novel heat-resistant and insensitive energetic materials.

A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial.

BACKGROUND: This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer. METHODS: We included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann-Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC. RESULTS: This study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05). CONCLUSIONS: The stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists. TRIAL REGISTRATION NUMBER: The clinical trial numbers were NCT03154749 and NCT04858529.

Clinical efficacy of tumor organoid-guided cancer therapy for locally advanced unresectable or metastatic breast cancer.

Patient-derived organoids (PDOs) may facilitate treatment selection. This retrospective cohort study evaluated the feasibility and clinical benefit of using PDOs to guide personalized treatment in metastatic breast cancer (MBC). Patients diagnosed with MBC were recruited between January 2019 and August 2022. PDOs were established and the efficacy of customized drug panels was determined by measuring cell mortality after drug exposure. Patients receiving organoid-guided treatment (OGT) were matched 1:2 by nearest neighbor propensity scores with patients receiving treatment of physician's choice (TPC). The primary outcome was progression-free survival. Secondary outcomes included objective response rate and disease control rate. Targeted gene sequencing and pathway enrichment analysis were performed. Forty-six PDOs (46 of 51, 90.2%) were generated from 45 MBC patients. PDO drug screening showed an accuracy of 78.4% (95% CI 64.9%-91.9%) in predicting clinical responses. Thirty-six OGT patients were matched to 69 TPC patients. OGT was associated with prolonged median progression-free survival (11.0 months vs. 5.0 months; hazard ratio 0.53 [95% CI 0.33-0.85]; p = .01) and improved disease control (88.9% vs. 63.8%; odd ratio 4.26 [1.44-18.62]) compared with TPC. The objective response rate of both groups was similar. Pathway enrichment analysis in hormone receptor-positive, human epidermal growth factor receptor 2-negative patients demonstrated differentially modulated pathways implicated in DNA repair and transcriptional regulation in those with reduced response to capecitabine/gemcitabine, and pathways associated with cell cycle regulation in those with reduced response to palbociclib. Our study shows that PDO-based functional precision medicine is a feasible and effective strategy for MBC treatment optimization and customization.

Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden during Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. SUMMARY BACKGROUND DATA: RCB III indicates drug resistance in breast cancer, and early detection methods are lacking. METHODS: This study enrolled 1048 patients with breast cancer from four institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre- and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into three groups (RCB 0-I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U- test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed followed by model integration. The AI system was validated in three external validation cohorts. (EVCs, n=713). RESULTS: Among the patients, 442 (42.18%) were RCB 0-I, 462 (44.08%) were RCB II and 144 (13.74%) were RCB III. Model-I achieved an area under the curve (AUC) of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0-II. Model-II distinguished RCB 0-I from RCB II-III, with an AUC of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes. CONCLUSIONS: The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.

Incidence, clinical features, and survival outcomes of primary malignant lacrimal gland tumors: A population-based analysis.

BACKGROUND: Studies on the epidemiological information and prognosis of primary malignant lacrimal gland tumors (MLGTs) are rare for its low occurrence. The goal of our research was to investigate the epidemiological characteristics and survival outcomes of patients with MLGTs. METHODS: Incidence and demographic information of patients with MLGTs were collected from the Surveillance, Epidemiology, and End Results (SEER) database. To identify independent prognostic factors for disease-specific survival (DSS) and overall survival (OS), univariate and multivariate Cox regression analysis were performed. RESULTS: The overall incidence of primary MLGTs from 1975 to 2020 was 0.413/1,000,000 (according to the 2000 American standard population), with a steadily increasing incidence over years. A total of 964 patients with primary MLGTs were diagnosed, with an average age of 59.3 years. Of these, 53.2% were aged ≥60 years, 57.4% were female, and 77.1% were whites. Multivariate Cox regression analysis demonstrated that year of diagnosis, age, sex, histological type, SEER stage, surgery, and chemotherapy were independent prognostic factors of DSS or OS. CONCLUSIONS: Although primary MLGT is rare, its incidence has steadily increased in the past 46 years, and surgery was related to a better prognosis.

Research progress on the immune microenvironment and immunotherapy in gastric cancer.

The tumor microenvironment, particularly the immune microenvironment, plays an indispensable role in the malignant progression and metastasis of gastric cancer (GC). As our understanding of the GC microenvironment continues to evolve, we are gaining deeper insights into the biological mechanisms at the single-cell level. This, in turn, has offered fresh perspectives on GC therapy. Encouragingly, there are various monotherapy and combination therapies in use, such as immune checkpoint inhibitors, adoptive cell transfer therapy, chimeric antigen receptor T cell therapy, antibody-drug conjugates, and cancer vaccines. In this paper, we review the current research progress regarding the GC microenvironment and summarize promising immunotherapy research and targeted therapies.

18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer.

BACKGROUND: This study aimed to assess whether a combined model incorporating radiomic and depth features extracted from PET/CT can predict disease-free survival (DFS) in patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy. RESULTS: This study retrospectively included one hundred and five non-pCR patients. After a median follow-up of 71 months, 15 and 7 patients experienced recurrence and death, respectively. The primary tumor volume underwent feature extraction, yielding a total of 3644 radiomic features and 4096 depth features. The modeling procedure employed Cox regression for feature selection and utilized Cox proportional-hazards models to make predictions on DFS. Time-dependent receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to evaluate and compare the predictive performance of different models. 2 clinical features (RCB, cT), 4 radiomic features, and 7 depth features were significant predictors of DFS and were included to develop models. The integrated model incorporating RCB, cT, and radiomic and depth features extracted from PET/CT images exhibited the highest accuracy for predicting 5-year DFS in the training (AUC 0.943) and the validation cohort (AUC 0.938). CONCLUSION: The integrated model combining radiomic and depth features extracted from PET/CT images can accurately predict 5-year DFS in non-pCR patients. It can help identify patients with a high risk of recurrence and strengthen adjuvant therapy to improve survival.

Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study.

BACKGROUND: The high false negative rate (FNR) associated with sentinel lymph node biopsy often leads to unnecessary axillary lymph node dissection following neoadjuvant chemotherapy (NAC) in breast cancer. The authors aimed to develop a multifactor artificial intelligence (AI) model to aid in axillary lymph node surgery. MATERIALS AND METHODS: A total of 1038 patients were enrolled, comprising 234 patients in the primary cohort, 723 patients in three external validation cohorts, and 81 patients in the prospective cohort. For predicting axillary lymph node response to NAC, robust longitudinal radiomics features were extracted from pre-NAC and post-NAC magnetic resonance images. The U test, the least absolute shrinkage and selection operator, and the spearman analysis were used to select the most significant features. A machine learning stacking model was constructed to detect ALN metastasis after NAC. By integrating the significant predictors, we developed a multifactor AI-assisted surgery pipeline and compared its performance and false negative rate with that of sentinel lymph node biopsy alone. RESULTS: The machine learning stacking model achieved excellent performance in detecting ALN metastasis, with an area under the curve (AUC) of 0.958 in the primary cohort, 0.881 in the external validation cohorts, and 0.882 in the prospective cohort. Furthermore, the introduction of AI-assisted surgery reduced the FNRs from 14.88 (18/121) to 4.13% (5/121) in the primary cohort, from 16.55 (49/296) to 4.05% (12/296) in the external validation cohorts, and from 13.64 (3/22) to 4.55% (1/22) in the prospective cohort. Notably, when more than two SLNs were removed, the FNRs further decreased to 2.78% (2/72) in the primary cohort, 2.38% (4/168) in the external validation cohorts, and 0% (0/15) in the prospective cohort. CONCLUSION: Our study highlights the potential of AI-assisted surgery as a valuable tool for evaluating ALN response to NAC, leading to a reduction in unnecessary axillary lymph node dissection procedures.

Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study.

Background: Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer. Methods: From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively). Findings: A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts. Interpretation: Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer. Funding: This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.

Study on the effectiveness and safety of ciprofol in anesthesia in gynecological day surgery: a randomized double-blind controlled study.

BACKGROUD: ciprofol is a new type of intravenous anesthetic, which is a tautomer of propofol, with the characteristics of less injection pain, less respiratory depression and higher potency, but little clinical experience. The aim of this study was to observe the efficacy and safety of the application of ciprofol in ambulatory surgery anesthesia in gynecology. METHODS: 128 patients were selected to undergo gynecological day surgery under general anesthesia, and the patients were randomly divided into the ciprofol group and the propofol group, with 64 cases in each group. During anesthesia induction, the ciprofol group was infused at a time limit of 0.5 mg/kg for one minute, and the propofol group was infused at a time limit of 2 mg/kg for 1 min. The overall incidence of adverse events was the primary outcome for this study, while secondary outcomes included the success rate of anesthesia induction, the time of loss of consciousness, the time of awakening,top-up dose and frequency of use of rescue drugs. RESULTS: The overall incidence of adverse events was significantly lower in the ciprofol group compared with the propofol group (56.2% vs. 92.2%,P < 0.05). The success rate of anesthesia induction of ciprofol and propofol group was 100.0%. The time of loss of consciousness of the ciprofol group was longer than that of the propofol group (1.6 ± 0.4 min vs. 1.4 ± 0.2 min, P < 0.05). The time of awakening was not statistically significant (5.4 ± 2.8 min vs. 4.6 ± 1.6 min, P > 0.05). The number of drug additions and resuscitation drugs used were not statistically significant. CONCLUSIONS: Compared with propofol, ciprofol had a similar anesthetic effect in gynecological ambulatory surgery, and the incidence of adverse events in the ciprofol group was lower.

Adjuvant icotinib versus observation in patients with completely resected EGFR-mutated stage IB NSCLC (GASTO1003, CORIN): a randomised, open-label, phase 2 trial.

Background: This phase 2 trial aimed to compare adjuvant icotinib with observation in patients with epidermal growth factor receptor (EGFR) mutation-positive resected stage IB non-small cell lung cancer (NSCLC). Methods: We performed a randomised, open-label, phase 2 trial from May 1, 2015 to December 29, 2020 at Sun Yat-sen University Cancer Center in China. Patients with completely resected, EGFR-mutant, stage IB (the 7th edition of TNM staging) NSCLC without adjuvant chemotherapy were randomised (1:1) to receive adjuvant therapy with icotinib (125 mg, three times daily) for 12 months or to undergo observation until disease progression or intolerable toxicity occurred. The primary endpoint was 3-year disease-free survival (DFS). CORIN (GASTO1003) was registered with Clinicaltrials.gov, with the number NCT02264210. Findings: A total of 128 patients were randomised, with 63 patients in the icotinib group and 65 patients in the observation group. The median duration of follow-up was 39.9 months. The three-year DFS was significantly higher in the icotinib group (96.1%, 95% confidence interval [CI], 91.3-99.9) than in the observation group (84.0%, 95% CI, 75.1-92.9; P = 0.041). The DFS was significantly longer in the icotinib group than in the observation group, with a hazard ratio (HR) of 0.23 (95% CI, 0.07-0.81; P = 0.013). The OS data were immature, with three deaths in the observation arm. In the icotinib group, adverse events (AEs) of any grade were reported in 49 patients (77.8%), and grade 3 or greater AEs occurred in four patients (6.3%). No treatment-related deaths occurred. Interpretation: Our findings suggested that adjuvant icotinib improved the 3-year DFS in patients with completely resected EGFR-mutated stage IB NSCLC with a manageable safety profile. Funding: This study was sponsored by Betta Pharmaceutical Co., Ltd.