A meaningful exploration of ofatumumab in refractory NMOSD: a case report

ObjectiveTo report the case of a patient with refractory neuromyelitis optica spectrum disorder (NMOSD), who, despite showing poor response or intolerance to multiple immunosuppressants, was successfully treated with Ofatumumab.Case presentationA 42-year-old female was diagnosed with NMOSD in the first episode of the disease. Despite treatment with intravenous methylprednisolone, immunoglobulin, rituximab and immunoadsorption, together with oral steroids, azathioprine, mycophenolate mofetil and tacrolimus, she underwent various adverse events, such as abnormal liver function, repeated infections, fever, rashes, hemorrhagic shock, etc., and experienced five relapses over the ensuing four years. Finally, clinicians decided to initiate Ofatumumab to control the disease. The patient received 9 doses of Ofatumumab over the next 10 months at customized intervals. Her symptoms were stable and there was no recurrence or any adverse events.ConclusionOfatumumab might serve as an effective and safe alternative for NMOSD patients who are resistant to other current immunotherapies

An Effective Electrochemical Platform for Chloramphenicol Detection Based on Carbon-Doped Boron Nitride Nanosheets

Currently, accurate quantification of antibiotics is a prerequisite for health care and environmental governance. The present work demonstrated a novel and effective electrochemical strategy for chloramphenicol (CAP) detection using carbon-doped hexagonal boron nitride (C-BN) as the sensing medium. The C-BN nanosheets were synthesized by a molten-salt method and fully characterized using various techniques. The electrochemical performances of C-BN nanosheets were studied using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The results showed that the electrocatalytic activity of h-BN was significantly enhanced by carbon doping. Carbon doping can provide abundant active sites and improve electrical conductivity. Therefore, a C-BN-modified glassy carbon electrode (C-BN/GCE) was employed to determine CAP by differential pulse voltammetry (DPV). The sensor showed convincing analytical performance, such as a wide concentration range (0.1 µM–200 µM, 200 µM–700 µM) and low limit of detection (LOD, 0.035 µM). In addition, the proposed method had high selectivity and desired stability, and can be applied for CAP detection in actual samples. It is believed that defect-engineered h-BN nanomaterials possess a wide range of applications in electrochemical sensors

Self-organized phase segregation between inorganic nanocrystals and PC61BM for hybrid high-efficiency bulk heterojunction photovoltaic cells

We demonstrate a simple approach to generate phase segregation between colloidal PbS nanocrystals (NCs) and organic [6,6]-phenyl C\u2086\u2081 butyric acid methyl ester (PC\u2086\u2081BM). Continuous vertical phase segregation is observed in cross-linked composite films of NCs and PC\u2086\u2081BM. Hybrid bulk heterojunction photovoltaic cells fabricated with the phase segreated composite layer have achieved the state-of-art power conversion efficiency of 3.7% under one sun of simulated Air Mass 1.5 Global solar irradiation. The presented method can be generally applied in other NC/organic systems for the development of hybrid heterojunction photovoltaic cells.Peer reviewed: YesNRC publication: Ye

IL-1β is involved in docetaxel chemoresistance by regulating the formation of polyploid giant cancer cells in non-small cell lung cancer

Abstract Docetaxel (Doc) is a cornerstone of chemotherapy; however, treatment with Doc often and inevitably leads to drug resistance and the formation of polyploid giant cancer cells (PGCCs). In this study, we investigated the effect of Doc on non-small cell lung cancer to explore the role of PGCCs in drug resistance and the molecular mechanisms that regulate this resistance. We found that Doc induced G2/M cell cycle arrest and cell death in A549 and NCI-H1299 cells. However, many cells remained alive and became PGCCs by decreasing the expression of key regulatory proteins related to the cell cycle and proliferation. Notably, the PGCCs showed typical features of senescence, especially upregulation of p21 and p-histone H2A.X expression. Moreover, the mRNA level of IL-1β in the senescence-associated secretory phenotype was increased significantly with the development of PGCCs. Inhibition of IL-1β reduced the expression of p-histone H2A.X and promoted polyploidy to enhance the proapoptotic effect of Doc. Taken together, our results suggested that IL-1β was involved in the formation of PGCCs and regulated the senescence of PGCCs, which contributed to drug resistance to Doc. Therefore, targeting IL-1β in PGCCs may be a novel approach to overcome drug resistance

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

Abstract Background Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information. Results The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study. Conclusions Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet

Impact of the growth conditions of colloidal PbS nanocrystals on photovoltaic device performance

Here, we present a detailed investigation on the influence of the growth conditions of colloidal lead sulfide (PbS) nanocrystals on photovoltaic device performance. The PbS nanocrystals were synthesized in a noncoordinating solvent, 1-octadecene, using oleic acid (OA) as the ligand. It was found that both the feeding molar ratio of OA to Pb and the reactant concentration were critical for controlling the growth rate of nanocrystals. Transient photocurrent (TPC) measurements revealed reduced trap density in thin films using the slow-growth nanocrystals. Solar cells based on the slow-growth nanocrystals showed a high power conversion efficiency (PCE) of 3.8% under simulated Air Mass 1.5 Global (AM 1.5G) irradiation (100 mW/cm2), a 2-fold increase in PCE, compared to the fast-growth nanocrystals, because of the remarkable improvement in the open-circuit voltage and fill factor in the PV devices.Peer reviewed: YesNRC publication: Ye

Low expression of RECQL is associated with poor prognosis in Chinese breast cancer patients

Abstract Background RECQL is a number of the RecQ DNA helicase family and plays an important role in maintaining genome stability. Although several studies have reported that RECQL mutations were correlated with the susceptibility to breast cancer, the effect on prognosis in breast cancer was not yet clarified. Here, we explored the association between RECQL expression level and survival in patients with breast cancer. Methods In the first cohort, the RECQL mRNA expression level was evaluated in 774 primary breast cancer patients using a quantitative real-time PCR assay. Then, in the second independent cohort, the level of RECQL protein expression was detected in 322 patients with breast cancer using immunohistochemistry assay. Survival curves of patients with RECQL expression were compared using the Kaplan-Meier method with log-rank test. Results In the first cohort of 774 breast cancer patients, the low expression level of RECQL mRNA was significantly correlated with aggressive clinicopathological characteristics, including the positive lymph node status (P = 0.026), HER2 overexpression (P < 0.001), ER negative status (P = 0.047) and high tumor grade (P = 0.041). Moreover, the low expression level of RECQL mRNA was significantly associated with poor distant recurrence-free survival (DRFS, unadjusted hazard ratio (HR): 2.77, 95% confidence interval (CI): 1.88–4.09, P < 0.001) and disease-specific survival (DSS, unadjusted HR: 3.10, 95% CI: 1.84–5.20,P < 0.001), and it remained an independent unfavorable factor for DRFS and DSS (DRFS: adjusted HR: 3.04, 95% CI: 1.89–4.87, P < 0.001; DSS: adjusted HR: 4.25, 95% CI: 2.12–8.46, P < 0.001). In the second cohort of 322 breast cancer patients, low expression of RECQL protein was also subject to poor survival in breast cancer, and it was an independent prognosis factor of poor DRFS by multivariate analysis (DRFS: adjusted HR: 2.12, 95% CI: 1.16–3.88, P = 0.015). Conclusions Breast cancer patients with low RECQL expression had a worse survival. The expression level of RECQL may be a potential prognosis factor for breast cancer

Additional file 1 of An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

Additional file 1. Table S1. The deviation obtained by UISNet in 5-fold cross validation and 10-fold cross validation