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The prognosis of patients with locally advanced esophageal squamous cell carcinoma with different recurrence backgrounds is highly heterogeneous. This study aims to explore the effects of recurrence patterns on prognosis.The phase III, multicenter, prospective NEOCRTEC5010 trial enrolled 451 patients with stage IIB-III esophageal squamous cell carcinoma randomly assigned to neoadjuvant chemoradiotherapy combined with surgery (NCRT group) or surgery alone (S group) and followed them long-term. We investigated the effects of recurrence patterns on survival in patients undergoing radical esophagectomy.In total, 353 patients were included in the study. The 5-year overall survival of patients with different recurrence patterns was significantly different: recurrence versus recurrence-free (17.8% vs 89.2%; P .001), early recurrence versus late recurrence (4.6% vs 51.2%; P .001), and distant metastasis versus locoregional recurrence (17.0% vs 20.0%; P = .666). Patients with early recurrence had significantly shorter survival after recurrence than those with late recurrence (hazard ratio, 1.541; 95% confidence interval, 1.047-2.268, P = .028). There was no significant difference in postrecurrence survival between patients with distant metastasis and locoregional recurrence (hazard ratio, 1.181; 95% confidence interval, 0.804-1.734; P = .396). Multivariate logistic analysis showed that pN1 stage, lymph node dissection20, and lack of response to NCRT were independent risk factors for postoperative early recurrence. Multivariate Cox regression suggested that NCRT, age ≥60 years, early recurrence, and the pN1 stage were independent risk factors for shortened survival after recurrence.Prerecurrence primary tumor stage is inaccurate in predicting postrecurrence survival. In contrast, recurrence patterns can guide follow-up while also predicting postrecurrence survival. NCRT prolongs disease-free survival but is associated with a worse prognosis in patients with recurrence, especially early recurrence.

Deep learning has recently attracted great attention in many application fields, especially for big data analysis in the field of edge computing. Federated learning, as a promising machine learning technology, applies training data on distributed edge nodes to design shared learning systems to protect data privacy. Due to the system update in federated learning is at the expense of parameter exchange between edge nodes, it is extremely bandwidth consuming. A novel distributed hierarchical tensor depth optimization algorithm is proposed, which compresses the model parameters from the high-dimensional tensor space to a union of low-dimensional subspaces to reduce bandwidth consumption and storage demands of federated learning. In addition, an update method based on hierarchical tensor back propagation is developed by directly calculating the gradient of low-dimensional parameters to reduce the memory requirement and improve the training efficiency caused by edge node training. Finally, a large number of simulation experiments were performed to evaluate performance based on classical data sets with different local data distributions. Experimental results show that the proposed algorithm reduces the burden of communication bandwidth and the energy consumption of edge nodes.

The triple cation mixed perovskites (CsFAMA) are known as one of the most efficient candidates for perovskite solar cells (PSCs). It is found that the power conversion efficiency (PCE) of triple-cation based devices would increase with the test time extending, and the maximum efficiency is normally obtained after several days aging storage. Here, the relationship between enhanced device performance, excess PbI2 and its evolution in triple cation perovskite films of initial days was systematically explored. The CsFAMA-PSCs are prepared by two-step methods under two environmental conditions, including in the glove box and the ambient air (30% humidity). After 7 days testing, the maximum PCE of PSCs under two conditions dramatically increased 12.4% and 12.2%, reached 21.68% and 21.89%, respectively. At initial days, the XRD peak intensities of perovskite phase gradually decreased and those corresponding to PbI2 increased. Along with time-resolved photoluminescence (TRPL) and kelvin probe force microscopy (KPFM), it was found that the defects were passivated with the evolution of PbI2. This work reveals the excess PbI2 and its evolution in perovskite film, which can further supplement the understanding of PbI2 defect passivation.

Graphical abstract, Introduction The COVID-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for early predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies evolved with disease progression and severity in COIVD-19 patients. Objectives We assumed that antibodies may serve as biomarkers for predicting the clinical outcome of hospitalized COVID-19 patients on admission. Methods By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgG responses against 20 proteins of SARS-CoV-2 in 1,034 hospitalized COVID-19 patients on admission and followed till 66 days. The microarray results were further correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality. Results Nonsurvivors (n=955) induced higher levels of IgG responses against most of non-structural proteins than survivors (n=79) on admission. In particular, the magnitude of IgG antibodies against 8 non-structural proteins (NSP1, NSP4, NSP7, NSP8, NSP9, NSP10, RdRp, and NSP14) and 2 accessory proteins (ORF3b and ORF9b) possessed significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory biomarkers for disease severity (all with p trend < 0.05). Additionally, IgG responses to all of these 10 non-structural/accessory proteins were also associated with the severity of disease, and differential kinetics and serum positive rate of these IgG responses were confirmed in COVID-19 patients of varying severities within 20 days after symptoms onset. The area under curves (AUCs) for these IgG responses, determined by computational cross-validations, were between 0.62 and 0.71. Conclusions Our findings might have important implications for improving clinical management of COVID-19 patients.

In this work, a new type of solid amine sorbent was developed for low concentration CO2 capture at ambient temperature. Polyacrylonitrile(PAN)-poly(methyl methacrylate)(PMMA) porous hollow fibers were firstly prepared by a wet-spinning process. Tetraethylenepentamine(TEPA) was then grafted onto the porous structure of the hollow fibers. The amine modified hollow fibers denoted as TEPA@PAN-PMMA were characterized by the Fourier transform infrared spectroscopy (FT-IR), thermo-gravimetric analysis(TGA), Brunauer-Emmett-Teller(BET), and scanning electron microscopy(SEM) methods. Performance of the TEPA@PAN-PMMA for CO2 capture was evaluated by passing a 0.3% CO2-N2 gas mixture through the porous matrix of the hollow fibers at ambient temperature. The highest CO2 adsorption capacity reached up to 1.50 mmol g−1 for dry feed, and 3.00 mmol g−1 for wet feed, respectively. Cyclic CO2 adsorption-desorption test indicated that the TEPA@PAN-PMMA sorbents are stable and regenerable.

Background: HHLA2 has been recently demonstrated to play multifaceted roles in several types of cancers. However, its underlying mechanism in the progression of human ovarian cancer (OC) remains largely unexplored. In the present study, we aimed to determine whether downregulation of HHLA2 inhibited malignant phenotypes of human OC cells and explore its specific mechanism. Methods: In this study, lentivirus was used to establish individual stable cell lines. In vitro tumorigenesis assays, including cell proliferation, invasion, and wound healing assays were performed to evaluate the biological functions of the target genes in ovarian carcinoma cells. Dual-Luciferase assay was performed to identify the CA9 promoter activity. Animal studies were performed to analyze the effect of HHLA2/CA9 on ovarian cancer progression. Results: Downregulation of HHLA2 significantly suppressed the viability, invasion, and migration of OC cells. Downregulation of HHLA2 in OC cells reduced the expression of CA9 and increased the expressions of p-IKKβ and p-RelA. Conversely, the viability, invasion, and migration of HHLA2-depleted OC cells were increased when CA9 was upregulated. In vivo, we found that downregulation of HHLA2 significantly inhibited tumor growth, which was reversed by CA9 overexpression. In addition, downregulation of HHLA2 inhibited the OC progression via activating the NF-κB signaling pathway and decreasing the expression of CA9. Conclusion: Our data suggested a link between HHLA2 and NF-κB axis in the pathogenesis of OC, and these findings might provide valuable insights into the development of novel potential therapeutic targets for OC.