Utilizing Ni(II) complex for metal drug-gel particles in cervical cancer treatment and designing novel drugs through machine learning methods.

In the present study, a novel coordination polymer (CP) based on Ni(II), namely, [Ni(L)(D-CAM)(H2O)]n (1) (H2D-CAM = (1R,3S)-1,2,2-trimethylcyclopentane-1,3-dicarboxylic acid and L = 3,6-bis(benzimidazol-1-yl)pyridazine), has been produced successfully through applying a mixed ligand synthesis method via reacting Ni(NO3)2·6H2O with 3,6-bis(benzimidazol-1-yl)pyridazine ligand in the presence of a carboxylic acid co-ligand. Hyaluronic acid (HA) and carboxymethyl chitosan (CMCS) are representatives of natural polysaccharides and have good biocompatibility. Based on the chemical synthesis method, HA/CMCS hydrogel was successfully prepared. SEM showed that the lyophilized gel presented a typical macroporous structure with three-dimensional connected pores, which had unique advantages as a drug carrier. Using paclitaxel as a drug model, we further synthesized a novel paclitaxel-loaded metal gel and evaluated its therapeutic effect on cervical cancer. Finally, novel drugs from the reinforcement learning simulation are suggested to have better biological activity against ovarian cancer due to low affinity energy and stronger interaction strength towards the protein receptor.

Effect of waiting time for radiotherapy after last induction chemotherapy on prognosis of locally advanced nasopharyngeal carcinoma.

BACKGROUND: The effect of radiotherapy waiting time after last induction chemotherapy (IC-RT) on prognosis of patients with locally advanced nasopharyngeal carcinoma (LANPC) needs further discussion. METHODS: Three hundred and six patients with LANPC diagnosed pathologically by induction chemotherapy (IC) and radiotherapy (RT) from 2013 to 2018 were selected for this study. RESULTS: The IC-RT was a risk factor for the post-treatment progression of LANPC (OR = 1.017 95%CI: 1.003-1.031), For patients with LANPC, the IC-RT > 40 days significantly reduced 5-year PFS (70% vs. 55%; p = 0.0012), 5-year OS (84% vs. 73%; p = 0.028), 5-year DMFS (80% vs. 66%; p = 0.003), 5-year LRFS (77% vs. 67%; p = 0.012). Indicating that patients with stage IVa who IC-RT > 40 days were found to be a significant predictor of aggravated PFS (HR = 2.69; 95%CI: 1.57-4.6), OS (HR = 2.55; 95%CI: 1.29-5.03), DMFS (HR = 3.07; 95%CI: 1.64-5.76) and LRFS (HR = 2.26; 95%CI: 1.21-4.21). CONCLUSION: The prognosis of patients will be adversely affected if the IC-RT exceeds 40 days, especially for stage IVa patients.

Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women.

OBJECTIVE: To investigate the correlation between the risk of breast cancer for high-risk females and the density and background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM). METHODS: Females at high-risk, without breast cancer history and received CESM from July 2016 to December 2017 were retrospectively enrolled. The longest follow-up time was 4.5 years, and patients who developed breast cancer with maximized follow-up time were classified as cancer cohort, while females who did not develop breast cancer were categorized as control cohort. These two cohorts were one-to-one matched in age, family and/or genetic history of breast cancer, menopausal status and BRCA status. The density and BPE at CESM imaging were assessed. Conditional logistic regression was applied to evaluate the relationship between imaging features and breast cancer risk. RESULTS: During the follow-up interval, 90 women at high-risk without history of breast cancer were newly diagnosed. Compared with minimal BPE, increasing BPE levels were associated with the risk of breast cancer among high-risk females in a time interval of 4.5 years (mild: odds ratio [OR]=3.2, p = 0.001; moderate: OR = 4.0, p = 0.002; marked: OR = 11.2, p < 0.001). In addition, females with mild, moderate or marked BPE were four times more likely to be diagnosed with breast cancer than females with minimal BPE in a time interval of 4.5 years (OR = 4.0, p < 0.001). CONCLUSION: Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk females. ADVANCES IN KNOWLEDGE: • Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk women during the follow-up period of 4.5 years. • The significance of breast density as an independent risk factor is not fully established for high-risk women during the follow-up period of 4.5 years.

Protein function annotation based on heterogeneous biological networks.

BACKGROUND: Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein-protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data. RESULTS: In this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively. CONCLUSIONS: We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

A tensor-based bi-random walks model for protein function prediction.

BACKGROUND: The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein-protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. RESULTS: In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. CONCLUSIONS: The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations.

An efficient strategy for identifying essential proteins based on homology, subcellular location and protein-protein interaction information.

High throughput biological experiments are expensive and time consuming. For the past few years, many computational methods based on biological information have been proposed and widely used to understand the biological background. However, the processing of biological information data inevitably produces false positive and false negative data, such as the noise in the Protein-Protein Interaction (PPI) networks and the noise generated by the integration of a variety of biological information. How to solve these noise problems is the key role in essential protein predictions. An Identifying Essential Proteins model based on non-negative Matrix Symmetric tri-Factorization and multiple biological information (IEPMSF) is proposed in this paper, which utilizes only the PPI network proteins common neighbor characters to develop a weighted network, and uses the non-negative matrix symmetric tri-factorization method to find more potential interactions between proteins in the network so as to optimize the weighted network. Then, using the subcellular location and lineal homology information, the starting score of proteins is determined, and the random walk algorithm with restart mode is applied to the optimized network to mark and rank each protein. We tested the suggested forecasting model against current representative approaches using a public database. Experiment shows high efficiency of new method in essential proteins identification. The effectiveness of this method shows that it can dramatically solve the noise problems that existing in the multi-source biological information itself and cased by integrating them.

Deep Learning-Based Diffusion-Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction.

The prefiltered image was imported into the local higher-order singular value decomposition (HOSVD) denoising algorithm (GL-HOSVD)-optimized diffusion-weighted imaging (DWI) image, which was compared with the deviation correction nonlocal mean (NL mean) and low-level edge algorithm (LR + edge). Regarding the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), sensitivity, specificity, accuracy, and consistency, the application effect of the GL-HOSVD algorithm in DWI was investigated, and its adoption effect in the examination of ischemic penumbra (IP) of early acute cerebral infarction (ACI) patients was evaluated. A total of 210 patients with ACI were selected as the research subjects, who were randomly rolled into two groups. Those who were checked by conventional DWI were set as the control group, and those who used DWI based on the GL-HOSVD denoising algorithm were set as the observation group, with 105 people in each. Positron emission tomography (PET) test results were set as the gold standard to evaluate the application value of the two examination methods. It was found that under different noise levels, the peak signal-to-noise ratio (PSNR) of the GL-HOSVD algorithm and the root mean square error (RMSE) of the FA parameter were better than those of the nonlocal means (NL-means) of deviation correction and low-rank edge algorithm (LR + edge). The sensitivity, specificity, accuracy, and consistency (8.76%, 81.25%, 87.62%, and 0.52) of the observation group were higher than those of the control group (57.78%, 53.33%, 57.14%, and 0.35) (P < 0.05). Moreover, the apparent diffusion coefficient (ADC) of the DWI images of the observation group was basically consistent with that of the PET images, while the control group had a poor display effect and low definition. In summary, under different noise levels, the GL-HOSVD algorithm had a good denoising effect and greatly reduced fringe artifacts. DWI after denoising had high sensitivity, specificity, accuracy, and consistency in the detection of IP, which was worthy of clinical application and promotion.

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer.

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant (P < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance (P < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.

Evaluation of the sphericity index of the fetal heart during middle and late pregnancy using fetalHQ.

OBJECTIVE: To explore the feasibility of the fetal heart quantitative technique (fetalHQ) for evaluating the sphericity index (SI) of the fetal heart during middle and late pregnancy. METHODS: Ninety-six normal fetuses in middle and late gestation who underwent systemic ultrasound examination in the Department of Ultrasound of the Maternal and Child Health Care Hospital of Hunan Province in November 2020 were enrolled, and dynamic images of the four-chamber view of the fetal heart were collected. The correlation between the global sphericity index (GSI), ventricular 24-segment SI, and gestational age (GA) was analyzed, and the differences between the left and right ventricular 24-segment SI were compared. RESULTS: The success rate of fetalHQ analysis was 93.75%. There was no significant linear correlation between GSI and ventricular 24-segment SI and GA (all ps > .05). The differences in SI between segments 1 and 9 and 15 and 24 in the left and right ventricles were statistically significant (all ps < .05), while the differences in SI between segments 10 and 14 were not statistically significant (all ps > .05). In segments 1-9, the SI of the right ventricle was smaller than that of the left ventricle, indicating that the right ventricle was significantly more spherical than the left ventricle. In segments 15-24, the opposite was true. CONCLUSION: FetalHQ is a simple and reliable method for evaluating the GSI and 24-segment SI of the left and right ventricles. It can provide some theoretical basis for the clinical quantitative evaluation of fetal heart geometry and lay a foundation for the quantitative evaluation of fetal heart function in cases of structural and functional abnormalities.

Correlation between Parturients' Uterine Artery Blood Flow Spectra in the First and Second Trimesters of Pregnancy and Fetal Growth Restriction.

Objective: To explore the correlation between parturients' uterine artery blood flow spectra in the first and second trimesters of pregnancy and fetal growth restriction (FGR). Methods: The data of parturients treated in our hospital from February 2018 to February 2020 were retrospectively analyzed, 50 parturients with FGR were selected as the FGR group, and other 50 healthy cases were selected as the control group. In the first trimester (11-12 weeks of gestation) and the second trimester of pregnancy (13-24 weeks of gestation), the parturients of the two groups accepted the color Doppler ultrasonography (CDS), their hemodynamics indicators of uterine artery were recorded, and the correlation between their uterine artery blood flow spectra in the two periods and FGR was analyzed with the Receiver Operating Characteristic (ROC) curve. Results: No statistical differences in the parturients' general information including age, gestational weeks, gravidity, and parity between the two groups were observed (P > 0.05); the newborn's body weight, Apgar scores, number of preterm infants, and the number of infants transferring to the neonatal intensive care unit (NICU) were significantly different between the two groups (P < 0.05); in the first and second trimesters of pregnancy, the uterine artery pulsatility index (UtA-PI), uterine artery resistance index (UtA-RI), maximal systolic flow velocity, and systolic/diastolic (UtA-S/D) ratio were significantly higher in the FGR group than in the control group (P < 0.05), and the time-averaged maximal velocity (TAMX) was significantly lower in the FGR group than in the control group (P < 0.001); in early pregnancy, the incidence of early diastolic notch at bilateral uterine arteries between the two groups was not significantly different (P > 0.05), and the unilateral and total incidence in the first trimester as well as the unilateral, bilateral, and total incidence in the second trimester were significantly higher in the FGR group than in the control group (P < 0.05); in the first trimester, the sensitivity of detecting FGR with a uterine artery blood flow spectrum was 0.820, AUC (95% CI) = 0.840 (0.757-0.923), and in the second trimester, it was 0.860, AUC (95% CI) = 0.900 (0.832-0.968). Conclusion: There is a correlation between uterine artery blood flow spectra in the first and second trimesters of pregnancy and FGR, and the sensitivity of spectrum in the first trimester is higher than that in the second trimester, presenting a better clinical application value.

Prognostic value of serological markers of hepatitis B virus infection in squamous cell cervical cancer.

Objective: The current study aimed to investigate the prognostic value of serological markers of hepatitis B virus (HBV) infection in squamous cell cervical cancer. Methods: Squamous cell cervical cancer patients treated by concurrent chemoradiotherapy from January 2013 to December 2015 at Yunnan Cancer Hospital were retrospectively reviewed. Results: Of a total of 277 patients, 12 (4.33%), 93 (33.57%), 2 (0.72%), 25 (9.02%), and 36 patients (13.00%) were seropositive for hepatitis B surface antigen (HBsAg), anti-hepatitis B surface antibodies (anti-HBs), hepatitis B envelope antigen (HBeAg), anti-hepatitis B envelope antibodies (anti-HBe), and anti-hepatitis B core antibodies (anti-HBc), respectively. No patients experienced more than mild hepatic adverse events during treatment. The five-year overall survival (OS) rates for patients with anti-HBs positive or negative status were 85.8% and 66.2% (p = 0.039), respectively. No statistically significant difference in the five-year OS rates was observed in HBsAg positive and negative, HBeAg positive and negative, anti-HBe positive and negative, anti-HBc positive and negative patients. The multivariable analysis revealed that anti-HBs positivity was an independent favorable prognostic factor for OS (HR= 0.279; 95%CI: 0.083-0.936; p = 0.039) in patients younger than 50 years. Conclusions: The presence of anti-HBs predicts a superior OS for squamous cell cervical cancer patients aged younger than 50 years.

A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization.

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein-protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein-protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein-protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein-protein interaction networks.

Prenatal case of Simpson-Golabi-Behmel syndrome with a de novo 370Kb-sized microdeletion of Xq26.2 compassing partial GPC3 gene and review.

BACKGROUND: Simpson-Golabi-Behmel syndrome type 1 (SGBS1) is a rare X-linked recessive disorder characterized by pre- and postnatal overgrowth and a broad spectrum of anomalies including craniofacial dysmorphism, heart defects, renal, and genital anomalies. Due to the ultrasound findings are not pathognomonic for this syndrome, most clinical diagnosis of SGBS1 are made postnatally. METHODS: A pregnant woman with abnormal prenatal sonographic findings was advised to perform molecular diagnosis. Single nucleotide polymorphism array (SNP array) was performed in the fetus, and the result was validated with multiplex ligation-dependent probe amplification (MLPA) and real-time quantitative PCR (qPCR). RESULTS: The prenatal sonographic presented with increased nuchal translucency at 13 gestational weeks, and later at 21 weeks with cleft lip and palate, heart defect, increased amniotic fluid index and over growth. A de novo 370Kb-deletion covering the 5'-UTR and exon 1 of GPC3 gene was detected in the fetus by SNP array, which was subsequently confirmed by MLPA and qPCR. CONCLUSION: The de novo 370Kb hemizygous deletion of 5'-UTR and exon 1 of GPC3 results in the SGBS1 of this Chinese family. Combination of ultrasound and genetics tests helped us effectively to diagnose the prenatal cases of SGBS1. Our findings also enlarge the spectrum of mutations in GPC3 gene.

NPF:network propagation for protein function prediction.

BACKGROUND: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. RESULTS: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. CONCLUSIONS: We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

Nanoscale Photovoltaic Responses in 3D Radial Junction Solar Cells Revealed by High Spatial Resolution Laser Excitation Photoelectric Microscopy.

The actual light absorption photovoltaic responses realized in three-dimensional (3D) radial junction (RJ) units can be rather different from their planar counterparts and remain largely unexplored. We here adopt a laser excitation photoelectric microscope (LEPM) technology to probe the local light harvesting and photoelectric signals of 3D hydrogenated amorphous silicon (a-Si:H) RJ thin film solar cells constructed over a Si nanowire (SiNW) matrix, with a high spatial resolution of 600 nm thanks to the use of a high numerical aperture objective. The LEPM scan can help to resolve clearly the impacts of local structural damages, which are invisible to optical and SEM observations. More importantly, the high-resolution photoelectric mapping establishes a straightforward link between the local 3D geometry of RJ units and their light conversion performance. Surprisingly, it is found that the maximal photoelectric signals are usually recorded in the void locations among the standing SiNW RJs, instead of the overhead positions above the RJs. This phenomenon can be well explained and reproduced by finite element simulation analysis, which highlights unambiguously the dominant contribution of inter-RJ-unit scattering against direct mode incoupling in the 3D solar cell architecture. This LEPM mapping technology and the results help to achieve a straightforward and high-resolution evaluation of the local photovoltaic responses among the 3D RJ units, providing a solid basis for further structural optimization and performance improvement.

Nanoinstabilities of Cu2O porous nanostructured films as driven by nanocurvature effect and thermal activation effect.

In this work, the instabilities at the nanoscale (i.e. nanoinstabilities) of triangular pyramids-like Cu2O porous nanostructured films (PNFs) are studied by heating treatments under different atmosphere and temperature. It is found that the nanoscale building triangular pyramids turn round preferentially at the sharp angles and/or coalesce with their contacting ones by directional diffusion and plastic flow of atoms, which are driven by the nonuniformly-distributed surface nanocurvature. As a result, the triangular pyramids become quasi-sphere shape and the PNF evolves into a big, dense particles film. It is also observed that the heating or thermal activation effect efficiently promotes the reduction or oxidation of Cu2O pyramids and the crystallization or growth of the as-achieved Cu or CuO grains. The above physical and chemical instabilities or changes at the nanoscale of Cu2O PNFs can be well accounted for by the combined mechanism of nanocurvature effect and thermal activation effect. The nanocurvature effect can lower the energy barrier for the atom diffusion or plastic flow and lower the activation energy for the chemical reactions, while the thermal activation effect can supply the required kinetic energy or activation energy and make the atomic transportations and reactions kinetically possible. The findings reveal the evolution laws of morphology, crystal structure and composition of triangular pyramids-like Cu2O PNF during heating treatments, which can further be extended to other types of Cu2O PNFs. Also, the findings have important implications for the nanoinstabilities of Cu2O PNFs-based devices, especially those working at a high temperature.

Cu2O porous nanostructured films fabricated by positive bias sputtering deposition.

In this work, the authors fabricated Cu2O porous nanostructured films (PNFs) on glass slide substrates by the newly developed positive bias deposition approach in a balanced magnetron sputtering (MS) system. It was found that the surface morphology, crystal structure and optical property of the as-deposited products were greatly dependent on the applied positive substrate bias. In particular, when the substrate was biased at +50 and +150 V, both of the as-prepared Cu2O PNFs exhibited a unique triangular pyramids-like structure with obvious edges and corners and little gluing, a preferred orientation of (111) and a blue shift of energy band gap at 2.35 eV. Quantitative calculation results indicated that the traditional bombardment effects of electrons and sputtering argon ions were both negligible during the bias deposition in the balanced MS system. Instead, a new model of tip charging effect was further proposed to account for the controllable formation of PNFs by the balanced bias sputtering deposition.

The role of autophagy in pulmonary hypertension: a double-edge sword.

Autophagy is a recycling process that degrades damaged or unneeded cellular components for renewal. Accumulating evidence suggests that dysregulation of autophagy is involved in pulmonary hypertension (PH). PH is a progressive disease characterized by persistent proliferation of apoptosis-resistant pulmonary vascular cells. However, reports on the role of autophagy in the development of PH are often conflicting. In this review, we discuss recent development in the field with emphasis on pulmonary arterial endothelial cells, pulmonary smooth muscle cells, right ventricular myocyte, as well as pharmacological strategies targeting the autophagic signaling pathway.