Implication of Amyloid Precursor-like Protein 2 Expression in Cutaneous Squamous Cell Carcinoma Pathogenesis.

BACKGROUND/AIM: Regulatory functions of amyloid precursor-like protein 2 (APLP2) expression in intracellular trafficking of major histocompatibility complex class I (MHC-I) and biological behavior of tumor cells have been reported in various types of malignancies but not in cutaneous squamous cell carcinoma (CSCC). This study aimed to investigate the role of APLP2 expression in the pathogenesis of CSCC. PATIENTS AND METHODS: The expression of APLP2 and a key modulator of cancer immune escape, MHC-I, were determined in CSCC tissue samples obtained from 141 patients using immunohistochemistry. The regulatory effects of APLP2 expression on the biological behavior and surface expression of MHC-I in CSCC cells were investigated by trypan blue assay, Matrigel invasion assay, and in vivo xenograft analysis. RESULTS: APLP2 immunoreactivity was high in 73 (51.8%) tissue samples from patients with CSCC and was significantly related to subcutaneous fat invasion and poor prognosis in our cohort. Moreover, proliferation of and invasion by CSCC cells were significantly reduced after APLP2 knockdown in CSCC cells both in vitro and in vivo. A significant association was found between APLP2 and membrane MHC-I expression in patients with CSCC. In vivo xenograft analysis showed that APLP2 knockdown increased membrane MHC-I expression in CSCC cells. CONCLUSION: APLP2 not only acts as an oncogene in CSCC progression but also as a possible modulator of cancer immune escape by influencing MHC-I expression on the cell surface. APLP2 may serve as a novel molecular biomarker and therapeutic target for patients with CSCC.

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Human Phenotype Ontology (HPO)-based approaches have gained popularity in recent times as a tool for genomic diagnostics of rare diseases. However, these approaches do not make full use of the available information on disease and patient phenotypes. We present a new method called Phen2Disease, which utilizes the bidirectional maximum matching semantic similarity between two phenotype sets of patients and diseases to prioritize diseases and genes. Our comprehensive experiments have been conducted on six real data cohorts with 2051 cases (Cohort 1, n = 384; Cohort 2, n = 281; Cohort 3, n = 185; Cohort 4, n = 784; Cohort 5, n = 208; and Cohort 6, n = 209) and two simulated data cohorts with 1000 cases. The results of the experiments showed that Phen2Disease outperforms the three state-of-the-art methods when only phenotype information and HPO knowledge base are used, particularly in cohorts with fewer average numbers of HPO terms. We also observed that patients with higher information content scores have more specific information, leading to more accurate predictions. Moreover, Phen2Disease provides high interpretability with ranked diseases and patient HPO terms presented. Our method provides a novel approach to utilizing phenotype data for genomic diagnostics of rare diseases, with potential for clinical impact. Phen2Disease is freely available on GitHub at https://github.com/ZhuLab-Fudan/Phen2Disease.

Bioinformatic analysis confirms differences in circular RNA expression profiles of cumulus cells between patients with ovarian and peritoneal endometriosis-associated infertility.

Endometriosis has a detrimental effect on oocyte quality, and ovarian endometriosis (OEM) and peritoneal endometriosis (PEM) may have different effects on female fertility. Therefore, we conducted a study to explore the circular RNA (circRNA) expression profiles of cumulus cells (CCs) in patients with OEM (n = 3), PEM (n = 3), and tubal factor infertility (TFI, n = 3) using high-throughput sequencing techniques and attempted to identify common and unique circRNAs in the OEM and PEM groups. The CIRCexplorer2 program was used to identify circRNAs. Seven candidate circRNAs were validated in 30 samples using quantitative real-time polymerase chain reaction (qRT-PCR). Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to annotate the function of circRNA-targeted genes, which were verified by sequencing results and constructed circRNA-miRNA-mRNA networks. A total of 11833 circRNAs were identified in nine samples. The numbers of differentially expressed circRNAs between the OEM and TFI groups, PEM and TFI groups, and OEM and PEM groups were 130, 71, and 191, respectively. After taking intersections, 11 circRNAs were considered common circRNAs in the OEM and PEM groups; 39 circRNAs in the OEM group and 17 circRNAs in the PEM group were identified as unique key circRNAs. During qRT-PCR validation, hsa_circ_0003638 was significantly upregulated in the PEM group compared to that in the OEM and TFI groups. Functional analysis of circRNA-targeted genes revealed that apoptosis, PI3K-AKT, and p53 signaling pathways were enriched in the PEM-TFI comparison groups, whereas the functions of target genes involved in the JAK-STAT and TGF-ß signaling pathways were enriched in the PEM-OEM comparison groups. Our findings confirmed differences in circRNA expression profiles of CCs between patients with OEM and PEM infertility and provide new insights into the different effects of various endometriosis phenotypes on oocytes.

Impact of endometriosis on embryo quality and endometrial receptivity in women undergoing assisted reproductive technology.

ART is an important treatment method for infertile patients with endometriosis. However, the effects of endometriosis on embryo quality and endometrial receptivity remain unclear. Thus, we aimed to simultaneously investigate the impact of endometriosis and its stage on embryo quality and endometrial receptivity in women undergoing ART. We retrospectively analyzed the data from patients with and without endometriosis who underwent oocyte retrieval and/or high-quality embryos transfer between July 2015 and December 2020, including 1312 IVF cycles and 608 IVF or frozen-thawed embryo transfer (FET) cycles, respectively. The endometriosis group had a lower percentage of good cleavage-stage embryos and fertilization rates than those in the control group (p = 0.038 and 0.008, respectively). The number of retrieved oocytes, MII oocytes, cleavage, blastocysts, and blastulation rates was comparable between two groups. We found no significant difference in clinical pregnancy, implantation, live birth, miscarriage, or multiple pregnancy rates between the two groups among patients who transferred high-quality embryos. Stratification analysis showed that patients with stage III-IV endometriosis had fewer retrieved oocytes than those with stage I-II endometriosis (p = 0.012) and marginally fewer retrieved oocytes than the control group (p = 0.051). The stage I-II group had the lowest percentage of good cleavage-stage embryos, which was significantly lower than that of the control group (p = 0.043). In FET cycles, patients with stage III-IV endometriosis had a higher miscarriage rate than those in the control group (p = 0.023). Our results suggest that endometriosis does not alter endometrial receptivity but affects embryo quality, oocyte fertilization ability, and ovarian response.

Multiview Deep Forest for Overall Survival Prediction in Cancer.

Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper. MVDF can learn the features of each view and fuse them with integrated learning and multiple kernel learning. Then, a gradient boost forest based on the information bottleneck theory is proposed to reduce redundant information and avoid overfitting. In addition, a pruning strategy for a cascaded forest is used to limit the impact of outlier data. Comprehensive experiments have been carried out on a data set from West China Hospital of Sichuan University and two public data sets. Results have demonstrated that our method outperforms the compared methods in predicting overall survival.

Bisphenol A exposure induces testicular oxidative damage via FTO/m6A/Nrf2 axis during postnatal development in mice.

Bisphenol A (BPA), a commonly used plasticizer in the production of polycarbonate plastics and epoxy resins, has been shown to induce male reproductive toxicity. However, the effects of BPA exposure on early testicular development have not been thoroughly studied, and the underlying mechanism is yet to be elucidated. In the current study, neonatal male mice were exposed to BPA at 0, 0.1, and 5 mg/kg, respectively, by daily subcutaneous injection during postnatal day (PND) 1-35 to explore its effects on testicular development at PND 36 (the end of the first round of spermatogenesis). Morphological analyses showed that BPA exposure significantly induced apoptosis of testicular cells (p < 0.01 and p < 0.001) and reduced the thickness of seminiferous epithelium (p < 0.01). In addition, BPA exposure significantly decreased the total antioxidant capacity of testes and levels of transcription factor Nrf2 as well as its downstream antioxidant molecules of NQO1 and GPx-1 (p < 0.05 and p < 0.01). Furthermore, global m6A modifications of mRNAs were upregulated accompanied by declined m6A demethylase (FTO) in the testes of BPA groups (p < 0.05 and p < 0.01). MeRIP-quantitative real-time polymerase chain reaction (qPCR) demonstrated that BPA exposure markedly increased the m6A modification of Nrf2 mRNA (p < 0.05 and p < 0.01). These findings suggest that upregulation of m6A induced by inhibited FTO may be involved in BPA-induced testicular oxidative stress and developmental injury during postnatal development, which provides a new idea to reveal the mechanism underlying BPA interfering with testicular development.

Long non-coding RNA VAL facilitates PKM2 enzymatic activity to promote glycolysis and malignancy of gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common types of cancer worldwide, which leads to more than 10% of cancer-related deaths. Metabolism reprogramming presents as a pivotal event in cancer initiation and progression through enhancing aerobic glycolysis and anabolic metabolism. However, the underlying regulatory mechanisms in GC remain unknown. METHODS: VAL was identified by bioinformatics analyses in GC. Cell-based assays and mouse model illustrate the role of VAL in GC. RNA pull-down, immunoprecipitation assay and Western blot elucidate the interaction between VAL and PKM2. Pyruvate kinase activity, ECAR and OCR were measured to validate aerobic glycolysis of GC cells. RESULTS: Long non-coding RNA (lncRNA) VAL is significantly upregulated in GCs and indicates poor prognosis. Functional assays showed that VAL promotes GC malignant progression. Mechanistically, VAL strengthens the enzymatic activity of PKM2 and aerobic glycolysis of GC cells through directly binding with PKM2 to abrogate the PKM2-Parkin interaction, and to suppress Parkin-induced polyubiquitination of PKM2. In addition, glucose starvation induces VAL expression to enhance this process. CONCLUSIONS: Our study provides an insight into an lncRNA-dependent regulation on the enzymatic activity of PKM2, and suggests a potential of targeting VAL or PKM2 as promising biomarkers in GC diagnosis and treatment.

Flexible demodulation for rotation-induced phase difference based on a phase-controlled microwave photonic filter.

High-performance demodulation of Sagnac effect is of great importance for rotation rate measurement in inertial navigation system. In this paper, we propose a flexible measurement of rotation rate based on a phase-controlled microwave photonic filter (MPF), which incorporates an orthogonal double-sideband (ODSB) modulator, a Sagnac loop, a linearly chirped fiber Bragg grating (LCFBG), a polarizer, and a photodetector. The ODSB modulator is used to generate optical carrier (OC) and first-order sidebands with mutually orthogonal polarizations. For the MPF, its central frequency can be tuned through changing the phase difference between the OC and first-order sidebands thanks to the dispersion of the LCFBG. Therefore, if the OC and first-order sidebands are separated by a polarization beam splitter and then travel along the Sagnac loop in opposite directions, the rotation-induced phase difference between them will lead to a shift on the frequency response of the MPF. Thus, two ways can be adopted to detect the rotation rate of the Sagnac loop for different applications: monitoring the frequency response shift of the MPF and measuring the power variation at a certain frequency. Besides, the measurement sensitivity can be easily adjusted to satisfy specific requirements by tuning a polarization controller or choosing a different operating frequency. An experiment is performed to validate the proposed scheme. The results show that the maximum frequency shift of the MPF can reach 1.7 GHz at a rotation rate of 1 rad/s, and a scale factor of 0.016 mW/(rad/s) is obtained at 4 GHz.

Detecting multiple lesions of lung cancer-caused metastasis with bone scans using a self-defined object detection model based on SSD framework.

Objective.To facilitate manual diagnosis of lung cancer-caused metastasis, in this work, we propose a deep learning-based method to automatically identify and locate the hotspots in a bone scan image which denote the lesions metastasized from lung cancer.Approach.An end-to-end metastasis lesion detection model is proposed by following the classical object detection framework single shot multibox object detector (SSD). The proposed model casts lesion detection problem into automatically learning the hierarchal representations of lesion features, locating the spatial position of lesion areas, and boxing the detected lesions.Main results.Experimental evaluation conducted on clinical data of retrospective bone scans shows the comparable performance with a mean score of 0.7911 for average precision. A comparative analysis between our network and others including SSD shows the feasibility of the proposed detection network on automatically detecting multiple lesions of metastasis lesions caused by lung cancer.Significance.The proposed method has the potential to be used as an auxiliary tool for improving the accuracy and efficiency of metastasis diagnosis routinely conducted by nuclear medicine physicians.

Hormone Therapy Reduces Recurrence in Stage II-IV Uterine Low-Grade Endometrial Stromal Sarcomas: A Retrospective Cohort Study.

Low-grade endometrial stromal sarcoma (LG-ESS) is a rare and indolent malignancy. Hormone therapy has been reported as an adjuvant treatment for LG-ESS, although its effectiveness is controversial. Here we aimed to investigate the effects of postoperative hormone therapy on recurrence in patients with uterine LG-ESS. Between January 2010 and December 2019, a total of 152 patients (23 with and 129 without fertility-sparing) with a diagnosis of primary uterine LG-ESS confirmed by pathologists were enrolled in this study. In the cohort without fertility-sparing, 22 (17.7%) patients had recurrence, and the median disease-free survival (DFS) was 47 (2-130) months; only one of these patients died of LG-ESS. No significant difference was found in recurrence between the groups with and without hormone therapy (p=0.802). However, subgroup analysis showed that hormone therapy decreased the recurrence rate in stage II-IV (p=0.001, HR 0.144, 95% CI: 0.038-0.548), but not in stage I disease (p=0.256). High-dose progestins notably reduced recurrence (p=0.012, HR 0.154, 95% CI: 0.036-0.660), whereas non-progestin therapy marginally influenced recurrence (p=0.054) compared with no hormone therapy in stage II-IV disease. Moreover, hormone therapy within 12 months was effective in reducing recurrence (p=0.038, HR 0.241, 95% CI: 0.063-0.922). Ovarian preservation (p=0.004, HR 6.250, 95% CI: 1.786-21.874) and negative expression of ER/PR (p=0.000, HR 23.249, 95% CI: 4.912-110.026) were high-risk factors for recurrence in patients without fertility-sparing. In the fertility-sparing cohort, 15 (65.2%) patients experienced recurrence, and the median DFS was 24 (3-107) months. Six patients successfully delivered healthy fetuses, and five received hormone therapy. Twelve patients finally accepted hysterectomy after repeated recurrence, and only two of them had given birth before surgery. Patients who received hormone therapy showed longer DFS, although this difference was not statistically significant (p=0.466). In conclusion, postoperative hormone therapy reduces recurrence in patients with stage II-IV uterine LG-ESS without fertility-sparing, and high-dose treatment with progestins within 12 months is recommended. Bilateral oophorectomy can also reduce the risk of recurrence. Patients with fertility-sparing have a high risk of recurrence and poor pregnancy outcomes, and hormone therapy may be a reasonable choice in postoperative management.

DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system.

Examination of pathological images is the golden standard for diagnosing and screening many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released in recent years, resulting in significant improvements in computer-aided diagnosis (CAD) of related diseases. However, few existing works focus on the digestive system. We released two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). This paper first introduces the two released datasets, i.e., signet ring cell detection and colonoscopy tissue segmentation, with the descriptions of data collection, annotation, and potential uses. We also report the set-up, evaluation metrics, and top-performing methods and results of two challenge tasks for cell detection and tissue segmentation. In particular, the challenge received 234 effective submissions from 32 participating teams, where top-performing teams developed advancing approaches and tools for the CAD of digestive pathology. To the best of our knowledge, these are the first released publicly available datasets with corresponding challenges for the digestive-system pathological detection and segmentation. The related datasets and results provide new opportunities for the research and application of digestive pathology.

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage.

PURPOSE: We develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH). METHOD: We applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups - 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC). RESULTS: The incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739-0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694-0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762-0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774-0.794]. CONCLUSION: ICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.

Dimethyl fumarate ameliorates autoimmune hepatitis in mice by blocking NLRP3 inflammasome activation.

Dimethyl fumarate (DMF) is a fumaric acid derivative clinically approved for the treatment of some inflammatory diseases, but the underlying mechanism for its therapeutic effects remains incompletely understood. NLR family pyrin domain containing 3 (NLRP3) inflammasome activation has critical roles in innate immune responses to various infections and sterile inflammations. In this study, we aimed to explore whether DMF affects auto-immune hepatitis (AIH) in mice induced by concanavalin A (Con A) by modulating NLRP3 inflammasome activation. The results showed that DMF suppressed the activation of NLRP3 inflammasome activation in lipopolysaccharide-primed murine bone marrow-derived macrophages upon ATP or nigericin treatment, as evidenced by reduced cleavage of pro-caspase-1, release of mature interleukin-1ß (IL-1ß) and generation of gasdermin D N-terminal fragment (GSDMD-NT). DMF also greatly reduced ASC speck formation upon the stimulation of nigericin or ATP, indicating its inhibitory effect on NLRP3 inflammasome assembly. Consistent with reduced generation of GSDMD-NT, ATP or nigericin-induced pyroptosis was markedly suppressed by DMF. Moreover, DMF treatment alleviated mitochondrial damage induced by ATP or nigericin. Interestingly, all these effects were reversed by the protein kinase A (PKA) pathway inhibitors (H89 and MDL-12330A). Mechanistically, DMF enhanced PKA signaling and thus increased NLRP3 phosphorylation at PKA-specific sites to attenuate its activation. Importantly, DMF decreased serum levels of inflammatory cytokines and ameliorated liver injury in Con A-induced AIH of mice, concomitant with reduced the generation of caspase-1p10 and GSDMD-NT and alleviating mitochondrial aggregation in the liver. Collectively, DMF displayed anti-inflammatory effects by inhibiting NLRP3 inflammasome activation likely through regulating PKA signaling, highlighting its potential application in treating AIH.

COVID-WideNet-A capsule network for COVID-19 detection.

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.

CCT5 induces epithelial-mesenchymal transition to promote gastric cancer lymph node metastasis by activating the Wnt/ß-catenin signalling pathway.

BACKGROUND: Lymph node (LN) metastasis confers gastric cancer (GC) progression, poor survival and cancer-related death. Aberrant activation of Wnt/ß-catenin promotes epithelial-mesenchymal transition (EMT) and LN metastasis, whereas the constitutive activation mutation of Wnt/ß-catenin is rare in GC, suggesting that the underlying mechanisms enhancing Wnt/ß-catenin activation need to be further investigated and understood. METHODS: Bioinformatics analyses and immunohistochemistry (IHC) were used to identify and detect LN metastasis-related genes in GC. Cellular functional assays and footpad inoculation mouse model illustrate the biological function of CCT5. Co-immunoprecipitation assays, western blot and qPCR elucidate the interaction between CCT5 and E-cadherin, and the regulation on ß-catenin activity. RESULTS: CCT5 is upregulated in LN metastatic GCs and correlates with poor prognosis. In vitro assays prove that CCT5 markedly promotes GC cell proliferation, anti-anoikis, invasion and lymphatic tube formation. Moreover, CCT5 enhances xenograft GC growth and popliteal lymph node metastasis in vivo. Furthermore, CCT5 binds the cytoplasmic domain of E-cadherin and abrogates the interaction between E-cadherin and ß-catenin, thereby releasing ß-catenin to the nucleus and enhancing Wnt/ß-catenin signalling activity and EMT. CONCLUSION: CCT5 promotes GC progression and LN metastasis by enhancing wnt/ß-catenin activation, suggesting a great potential of CCT5 as a biomarker for GC diagnosis and therapy.

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning.

Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.

Enhancing Removal of Cr(VI), Pb2+, and Cu2+ from Aqueous Solutions Using Amino-Functionalized Cellulose Nanocrystal.

In this work, the amino-functionalized cellulose nanocrystal (ACNC) was prepared using a green route and applied as a biosorbent for adsorption of Cr(VI), Pb2+, and Cu2+ from aqueous solutions. CNC was firstly oxidized by sodium periodate to yield the dialdehyde nanocellulose (DACNC). Then, DACNC reacted with diethylenetriamine (DETA) to obtain amino-functionalized nanocellulose (ACNC) through a Schiff base reaction. The properties of DACNC and ACNC were characterized by using elemental analysis, Fourier transform infrared spectroscopy (FT-IR), Kaiser test, atomic force microscopy (AFM), X-ray diffraction (XRD), and zeta potential measurement. The presence of free amino groups was evidenced by the FT-IR results and Kaiser test. ACNCs exhibited an amphoteric nature with isoelectric points between pH 8 and 9. After the chemical modification, the cellulose I polymorph of nanocellulose remained, while the crystallinity decreased. The adsorption behavior of ACNC was investigated for the removal of Cr(VI), Pb2+, and Cu2+ in aqueous solutions. The maximum adsorption capacities were obtained at pH 2 for Cr(VI) and pH 6 for Cu2+ and Pb2+, respectively. The adsorption all followed pseudo second-order kinetics and Sips adsorption isotherms. The estimated adsorption capacities for Cr(VI), Pb2+, and Cu2+ were 70.503, 54.115, and 49.600 mg/g, respectively.

Low-Power Memristive Logic Device Enabled by Controllable Oxidation of 2D HfSe2 for In-Memory Computing.

Memristive logic device is a promising unit for beyond von Neumann computing systems and 2D materials are widely used because of their controllable interfacial properties. Most of these 2D memristive devices, however, are made from semiconducting chalcogenides which fail to gate the off-state current. To this end, a crossbar device using 2D HfSe2 is fabricated, and then the top layers are oxidized into "high-k" dielectric HfSex Oy via oxygen plasma treatment, so that the cell resistance can be remarkably increased. This two-terminal Ti/HfSex Oy /HfSe2 /Au device exhibits excellent forming-free resistive switching performance with high switching speed (<50 ns), low operation voltage (<3 V), large switching window (103 ), and good data retention. Most importantly, the operation current and the power consumption reach 100 pA and 0.1 fJ to 0.1 pJ, much lower than other HfO based memristors. A functionally complete low-power Boolean logic is experimentally demonstrated using the memristive device, allowing it in the application of energy-efficient in-memory computing.

NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information.

With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a problem of large-scale multi-label classification. Under the learning to rank (LTR) framework, our previous NetGO tool integrated massive networks and multi-type information about protein sequences to achieve good performance by dealing with all possible GO terms (>44 000). In this work, we propose the updated version as NetGO 2.0, which further improves the performance of large-scale AFP. NetGO 2.0 also incorporates literature information by logistic regression and deep sequence information by recurrent neural network (RNN) into the framework. We generate datasets following the critical assessment of functional annotation (CAFA) protocol. Experiment results show that NetGO 2.0 outperformed NetGO significantly in biological process ontology (BPO) and cellular component ontology (CCO). In particular, NetGO 2.0 achieved a 12.6% improvement over NetGO in terms of area under precision-recall curve (AUPR) in BPO and around 2.6% in terms of $\mathbf {F_{max}}$ in CCO. These results demonstrate the benefits of incorporating text and deep sequence information for the functional annotation of BPO and CCO. The NetGO 2.0 web server is freely available at http://issubmission.sjtu.edu.cn/ng2/.

Curcumin inhibits the growth of liver cancer by impairing myeloid-derived suppressor cells in murine tumor tissues.

Curcumin, one of the active ingredients of Curcuma longa (Jianghuang), has been reported to exert multiple bioactivities, including pro-apoptotic and anti-inflammatory activities. In recent years, curcumin has been extensively studied, and it has been revealed that curcumin inhibits the growth of numerous types of cancer. However, to the best of our knowledge, the inhibitory effects of curcumin on the activation or expansion of myeloid-derived suppressor cells (MDSCs) in liver cancer and the underlying mechanism have not yet been determined. Therefore, the present study aimed to investigate the inhibitory effect of curcumin on MDSC activity and the associated anti-neoplastic mechanism in a HepG2 ×enograft mouse model. The effect of curcumin on the viability of Huh-7, MHCC-97H and HepG2 cells in vitro was analyzed using a Cell Counting Kit-8 assay. The effects of curcumin on tumor growth, numbers of MDSCs, expression levels of proteins involved in the toll-like receptor 4 (TLR4)/NF-κB signaling pathway, levels of related inflammatory factors and angiogenesis were determined in HepG2 ×enograft model mice, which were given different doses of curcumin via intragastrical administration. The results of the present study revealed that curcumin inhibited the viability of Huh-7, MHCC-97H and HepG2 cells and the growth of HepG2 ×enograft tumors in mice. Flow cytometric analysis indicated that curcumin reduced the number of MDSCs in mouse xenograft tumors. In addition, the results demonstrated that curcumin inhibited the TLR4/NF-κB signaling pathway and the expression of inflammatory factors, including IL-6, IL-1ß, prostaglandin E2 and cyclooxygenase-2, in mouse xenograft tumors. Furthermore, curcumin suppressed the secretion of granulocyte-macrophage colony-stimulating factor (GM-CSF) and granulocyte-colony stimulating factor (G-CSF), which are essential factors for MDSCs modulation, in tumor tissues. Additionally, curcumin was revealed to inhibit angiogenesis, which was demonstrated by the downregulation of the expression levels of vascular endothelial growth factor, CD31 and α-smooth muscle actin in western blotting, immunohistochemistry and immunofluorescence experiments. In conclusion, the findings of the present study identified a novel mechanism via which curcumin may suppress the growth of liver cancer by reducing the numbers of MDSCs and subsequently disrupting the process of angiogenesis. These conclusions were supported by the observed inactivation of the TLR4/NF-κB signaling pathway-mediated inflammatory response and the downregulation of GM-CSF and G-CSF secretion in xenograft tissues.