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.

MicroRNA-19 upregulation attenuates cardiac fibrosis via targeting connective tissue growth factor.

BACKGROUND: Previous studies have shown the role of microRNA (miR)-19 in aging-related heart failure. The present study aimed to verify the effects of miR-19 on cardiac fibrosis and its target. METHODS: Cardiac fibrosis was induced by myocardial infarction (MI)-induced heart failure and angiotensin (Ang) II-treated rats in vivo, and was induced in Ang II-treated cardiac fibroblasts (CFs) in vitro. RESULTS: The expression of miR-19 was reduced in the heart tissue of MI and Ang II-treated rats, and Ang II-treated CFs. The impaired cardiac function in rats was repaired after miR-19 administration. The levels of collagen I, collagen III and transforming growth factor-beta (TGF-ß) increased in the heart tissue of MI and Ang II-treated rats, and Ang II-treated CFs. These increases were reversed by miR-19 agomiR. Moreover, the bioinformatic analysis and luciferase reporter assays demonstrated that connective tissue growth factor (CTGF) was a direct target of miR-19. MiR-19 treatment inhibited CTGF expression in CFs, while CTGF overexpression inhibited miR-19 agomiR to attenuate the Ang II-induced increases of collagen I and collagen III in CFs. The increases of p-ERK, p-JNK and p-p38 in the CFs induced by Ang II were repressed by miR-19 agomiR. CONCLUSIONS: Upregulating miR-19 can improve cardiac function and attenuate cardiac fibrosis by inhibiting the CTGF and MAPK pathways.

Maxillary sinus volumetric changes in jet aircraft pilots: A multislice computed tomography pilot study

Purpose@#This study evaluated maxillary sinus volume changes in military jet aircraft pilot candidates before and after the training program, in comparison with a control group, considering the effects of pressurization, altitude, and total flight hours, through multislice computed tomography. @*Materials and Methods@#Fifteen fighter pilots were evaluated before initiating the training program and after the final approval. The control group consisted of 41 young adults who had not flown during their military career. The volumes of each maxillary sinus were measured individually before and at the end of the training program. @*Results@#When comparing the initial and final volumes in the pilots, a statistically significant increase was observed both in the left and right maxillary sinuses. When evaluating the average total volume of the maxillary sinuses (i.e., the average volume of the right and left maxillary sinuses together), a significant increase in the volume of the maxillary sinuses was observed in the pilot group when compared to the control group. @*Conclusion@#The maxillary sinus volumes in aircraft pilot candidates increased after the 8-month training program. This may be explained by changes in the gravitational force, the expansion of gas, and positive pressure from oxygen masks. This unprecedented investigation among pilots might lead to other investigations considering paranasal sinus alterations in this singular population.

A 6-Year-Old Boy With Cough, Mucositis, and Vesiculobullous Skin Lesions.

A 6-year-old boy taking no regular medications had persistent fever and cough for 15 days. Physical examination revealed eyelid swelling; vesiculobullous lesions on the palms and soles; vesicles and erosions on the face, trunk, and limbs; erosions on the lips and oral mucosa; and blisters on the anal mucosa. Laboratory testing revealed leukocytosis, and lung auscultation revealed bilateral crackles. What is the diagnosis and what would you do next?

Opioid-reduced anesthesia based on esketamine in gynecological day surgery: a randomized double-blind controlled study.

BACKGROUND: Opioid-reduced anesthesia may accelerate postoperative rehabilitation by reducing opioid-related side effects. The objective was to investigate the feasibility of opioid-reduced general anesthesia based on esketamine and to observe postoperative nausea and vomiting (PONV), postoperative pain, hemodynamics and other adverse reactions in gynecological day surgery compared with the traditional opioid-based anesthesia program. METHOD: This study was conducted as a prospective parallel-group randomized controlled trial. A total of 141 adult women undergoing gynecological day surgery were included. Patients were randomly assigned to receive traditional opioid-based anesthesia (Group C) with alfentanil, or opioid-reduced anesthesia (a moderate-opioid group (Group MO) and low-opioid group (Group LO) with esketamine and alfentanil). For anesthesia induction, the three groups received 20, 20, 10 µg/kg alfentanil respectively and Group LO received an additional 0.2 mg/kg esketamine. For maintenance of anesthesia, the patients in Group C received 40 µg/kg/h alfentanil, and those in Group MO and Group LO received 0.5 mg/kg/h esketamine. RESULTS: Patients in the three groups had comparable clinical and surgical data. A total of 33.3% of patients in Group C, 18.4% of patients in Group MO and 43.2% of patients in Group LO met the primary endpoint (p = 0.033), and the incidence of nausea within 24 hours after surgery in Group MO was lower than in Group LO (p < 0.05). The extubation time, median length of stay in the hospital after surgery and visual analog scale (VAS) of postoperative pain were equivalent in the three groups. The frequencies of adverse hemodynamic events in the MO 1(0, 2) and LO 0(0, 1) groups were significantly decreased (p < 0.05). Compared with Group C, the median length of stay in the postanesthesia care unit (PACU) in Group LO was increased, 60.0 (36.25, 88.75) vs. 42.5 (25, 73.75) minutes (p < 0.05). CONCLUSIONS: Opioid-reduced anesthesia based on esketamine is feasible and provides effective analgesia for patients. Esketamine provided a positive analgesic effect and the opioid-reduced groups showed more stable hemodynamics. However, less or no use of opioids did not result in a more comfortable prognosis. TRIAL REGISTRATION: This study was registered at Chictr.org.cn (NO. ChiCTR2100053153 ); November 13, 2021.

Neoadjuvant therapy in triple-negative breast cancer: A systematic review and network meta-analysis.

BACKGROUND: Evidence for the preferred neoadjuvant therapy regimen in triple-negative breast cancer (TNBC) is not yet established. METHODS: Literature search was conducted from inception to February 12, 2022. Phase 2 and 3 randomized controlled trials (RCTs) investigating neoadjuvant therapy for TNBC were eligible. The primary outcome was pathologic complete response (pCR); the secondary outcomes were all-cause treatment discontinuation, disease-free survival or event-free survival (DFS/EFS), and overall survival. Odd ratios (OR) with 95% credible intervals (CrI) were used to estimate binary outcomes; hazard ratios (HR) with 95% CrI were used to estimate time-to-event outcomes. Bayesian network meta-analysis was implemented for each endpoint. Sensitivity analysis and network meta-regression were done. RESULTS: 41 RCTs (N = 7109 TNBC patients) were eligible. Compared with anthracycline- and taxane-based chemotherapy (ChT), PD-1 inhibitor plus platinum plus anthracycline- and taxane-based ChT was associated with a significant increased pCR rate (OR 3.95; 95% CrI 1.81-9.44) and a higher risk of premature treatment discontinuation (3.25; 1.26-8.29). Compared with dose-dense anthracycline- and taxane-based ChT, the combined treatment was not associated with significantly improved pCR (OR 2.57; 95% CrI 0.69-9.92). In terms of time-to-event outcomes, PD-1 inhibitor plus platinum plus anthracycline- and taxane-based ChT was associated with significantly improved DFS/EFS (HR 0.42; 95% CrI 0.19-0.81). CONCLUSIONS: PD-1 inhibitor plus platinum and anthracycline- and taxane-based ChT was currently the most efficacious regimen for pCR and DFS/EFS improvement in TNBC. The choice of chemotherapy backbone, optimization of patient selection with close follow-up and proactive symptomatic managements are essential to the antitumor activity of PD-1 inhibitor.

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study.

INTRODUCTION: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs. MATERIALS AND METHODS: We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs. RESULTS: The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80). CONCLUSION: Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.

Prevention of postoperative nausea and vomiting after gynaecological day surgery under remimazolam general anesthesia: a randomized double-blind controlled study.

PURPOSE: To observe the effect of different antiemetic drugs for the prevention of postoperative nausea and vomiting (PONV) after gynaecological day surgery under remimazolam general anesthesia. METHODS: One hundred ninety-two patients were selected for gynaecological day surgery and randomly divided into three groups: droperidol group (DD group), tropisetron group (DT group) and control group (DC group). Flurbiprofen axetil 50 mg and dexamethasone 5 mg were given intravenously before induction of anesthesia, and 2 min later droperidol 1 mg was given intravenously to the DD group, tropisetron 5 mg to the DT group and saline (5 ml) to the DC group. Induction of anesthesia: remimazolam 6 mg/kg/h was continuously infused until sleep, mivacurium 0.2 mg/kg and alfentanil 20ug/kg were slowly pushed, 3 min later intubation was performed to control breathing. Maintenance of anesthesia: 40ug/kg/h of alfentanil, 1 mg/kg/h of remimazolam continuous infusion. After awakening and extubation, the patient was transferred to the PACU. PONV were recorded in the PACU and an electronic questionnaire was pushed 24 h after surgery. RESULTS: The incidence of PONV within the PACU was significantly lower in the DD (14.5%)and DT(26.7%) groups than in the DC(50%) group (p < 0.01), there was no significantly difference between the DT and DD groups. There were no significant difference in the incidence of PONV in 24 h after surgery between the three groups(DD:DT:DC = 44.5%:45.1%:63.8%,p > 0.05). CONCLUSIONS: Droperidol or tropisetron combined with dexamethasone is superior to dexamethasone alone for the prevention of PONV in the PACU after remimazolam combined with alfentanil anesthesia, with no significant difference in the incidence of PONV in 24 h after surgery.

LncRNA FOXD3-AS1 aggravates myocardial ischemia/reperfusion injury by inactivating the Redd1/AKT/GSK3ß/Nrf2 signaling pathway via the miR-128/TXNIP axis.

Long noncoding RNA forkhead box D3-antisense RNA 1 (FOXD3-AS1) is associated with cardiovascular diseases, but its roles in myocardial ischemia/reperfusion (I/R) injury and the related signaling pathway have not been fully reported. We aimed to investigate the roles and mechanism of action of FOXD3-AS1 in myocardial I/R injury. An in vivo myocardial I/R injury mouse model and an in vitro hypoxia/reoxygenation (H/R) cardiomyocyte model was established. Quantitative reverse transcription-polymerase chain reaction, western blotting, and immunofluorescent assays were performed to examine the expression levels of FOXD3-AS1, microRNA (miR)-128, thioredoxin-interacting protein/regulation of development and DNA damage response 1/protein kinase B/glycogen synthase kinase 3ß/nuclear factor erythroid 2-related factor 2 (TXNIP/Redd1/AKT/GSK3ß/Nrf2) pathway-related proteins and apoptosis-related proteins. The interactions between FOXD3-AS1 and miR-128 and miR-128 and TXNIP were analyzed by Spearman's correlation test, predicted by ENCORI, and verified by dual-luciferase reporter assay. In addition, the levels of cardiac injury markers and oxidative stress markers were evaluated by corresponding kits. Cell Counting Kit-8 assays and flow cytometry were performed to assess cell viability and apoptosis. Hematoxylin and eosin staining was applied to observe the effect of FOXD3-AS1 on the morphology of myocardial I/R injured tissues. The results showed that the FOXD3-AS1 and TXNIP were highly expressed, whereas miR-128 was expressed at low levels in I/R myocardial tissues and H/R-induced H9c2 cells. FOXD3-AS1 directly targeted miR-128 to reduce its expression. TXNIP was confirmed as a downstream target of miR-128. Knockdown of FOXD3-AS1 led to the alleviation of I/R injury in vivo and in vitro. FOXD3-AS1 enhanced the expression of TXNIP by sponging miR-128, which inhibited the Redd1/AKT/GSK3ß/Nrf2 pathway. Both inhibition of miR-128 and overexpression of TXNIP reversed the cardioprotective effect of FOXD3-AS1 small interfering RNA in H/R-induced H9c2 cells.

C-C bonded bis-5,6 fused triazole-triazine compound: an advanced heat-resistant explosive with high energy and low sensitivity.

It is still an urgent problem in the field of energetic materials to explore the synthesis of heat-resistant compounds with balanced energy and thermal stability through simple synthetic routes. Recently, fused compounds are considered to provide a promising framework for the construction of ideal heat-resistant compounds. In this study, three novel C-C bonded bis-5,6 fused triazole-triazine compounds, 3,3'-dinitro-[7,7'-bi[1,2,4]triazolo[5,1-c][1,2,4]triazine]-4,4'-diamine (2), 4,4'-diamino-[7,7'-bi[1,2,4]triazolo[5,1-c][1,2,4]triazine]-3,3'-dicarbonitrile (3), and 3,3'-di(1H-tetrazol-5-yl)-[7,7'-bi[1,2,4]triazolo[5,1-c][1,2,4]triazine]-4,4'-diamine (4), were synthesized by a simple method. Compound 2 exhibited an approaching detonation velocity of 8837 m s-1 compared with that of the traditional high energy explosive RDX velocity of 8795 m s-1, while its thermal stability (Td = 327 °C) was comparable to that of the heat-resistant explosive HNS (Td = 318 °C). At the same time, the double fused compound 2 also realized high density (1.90 g cm-3) and extremely low sensitivity (FS > 360 N, IS > 40 J). The above good comprehensive properties prove that compound 2 can be used as a potential insensitive high-energy heat-resistant explosive. In addition, the effects of the crystal structure on the sensitivity and thermal stability were studied using the quantum chemical methods. These results imply that the formation of double fused ring compounds by the ring closing reaction at symmetrical positions is an ideal strategy for the development of advanced heat-resistant explosives.