In Situ Synthesis of NH2-MIL-53-Al/PAN Nanofibers for Removal Co(II) through an Electrospinning Process.

In this study, researchers developed a novel composite material called NH2-MIL-53-Al/PAN, which consists of metal-organic frameworks (MOFs) grown on electrospun PAN nanofibers (NFs). The successful formation of the composite was confirmed by X-ray diffraction (XRD) and Fourier transform infrared (FTIR), and the hydrophilicity of NH2-MIL-53-Al/PAN was demonstrated by the water contact angle (WCA). Batch experiments were conducted to investigate the adsorption performance of Co(II) under different conditions. The maximum adsorption capacity reached 58.72 mg/g, and almost 95% of the adsorption was achieved within the first 6 h. The adsorption process was found to be spontaneous and endothermic and followed the pseudo-second-order kinetics and Langmuir models. Chemisorption and molecular layer adsorption are the main mechanisms of adsorption, and X-ray photoelectron spectroscopy (XPS) analysis further reveals that the interaction between the adsorbent and cobalt is a coordination interaction. In this study, NH2-MIL-53-Al was grown in situ on PAN to ensure effective loading of MOFs and prevent agglomeration during the NF mixing process. This approach successfully addressed the challenge of exposing active sites within the embedded MOF crystals. Additionally, it overcame the difficulty of recycling traditional MOF adsorbents. As a result, the exceptional performance of MOF NFs offers a promising solution for the efficient removal of cobalt-containing wastewater.

Point Cloud Instance Segmentation with Inaccurate Bounding-Box Annotations.

Most existing point cloud instance segmentation methods require accurate and dense point-level annotations, which are extremely laborious to collect. While incomplete and inexact supervision has been exploited to reduce labeling efforts, inaccurate supervision remains under-explored. This kind of supervision is almost inevitable in practice, especially in complex 3D point clouds, and it severely degrades the generalization performance of deep networks. To this end, we propose the first weakly supervised point cloud instance segmentation framework with inaccurate box-level labels. A novel self-distillation architecture is presented to boost the generalization ability while leveraging the cheap but noisy bounding-box annotations. Specifically, we employ consistency regularization to distill self-knowledge from data perturbation and historical predictions, which prevents the deep network from overfitting the noisy labels. Moreover, we progressively select reliable samples and correct their labels based on the historical consistency. Extensive experiments on the ScanNet-v2 dataset were used to validate the effectiveness and robustness of our method in dealing with inexact and inaccurate annotations.

Facile construction of ZIF-94/PAN nanofiber by electrospinning for the removal of Co(II) from wastewater.

This study aimed to synthesize a novel nanofiber adsorbent based on metal-organic frameworks (MOFs), ZIF-94-PAN, by incorporating ZIF-94 into polyacrylonitrile (PAN) through electrospinning. The investigation of the adsorption characteristics of ZIF-94-PAN for cobalt ions was undertaken, yielding findings that suggest an optimum ZIF-94 loading content within the ZIF-94-PAN composite of 8%. The adsorption experiments revealed that, under pH 8.3 and 298 K, ZIF-94-PAN-8% attained cobalt ion equilibrium adsorption (139.08 mg/g). Additionally, the adsorption kinetics of cobalt ions exhibited conformity with the pseudo-second-order model, whereas adherence to the Freundlich isotherm model indicated a non-homogeneous, endothermic process. XPS analysis unveiled that the adsorption mechanism was characterized by the coordination of nitrogen and oxygen atoms within ZIF-94-PAN with cobalt ions. This study effectively addressed the challenges of separating and recovering MOFs adsorbents by fabricating them as nanofibers. The remarkable adsorption performance and stability of the ZIF-94-PAN nanofibers highlight their potential for removing cobalt-contaminated wastewater.

Leukemia can be Effectively Early Predicted in Routine Physical Examination with the Assistance of Machine Learning Models.

Objectives: The diagnosis of leukemia relies very much on the results of bone marrow examinations, which is never generally performed in routine physical examination. In many rural areas even community hospitals and primary care clinics, the lack of hematological specialist and facility does not allow a definite diagnosis of leukemia. Thus, there will be a significant benefit if machine learning (ML) models could help early predict leukemia using preliminary blood test data in a routine physical examination in community hospitals to save time before a definite diagnosis. Methods: We collected the routine physical examination data of 1230 newly diagnosed leukemia patients and 1300 healthy people. We trained and tested 3 machine learning (ML) models including linear support vector machine (LSVM), random forest (RF), and XGboost models. We not only examined the accordance between model results and statistical analysis of the input data but also examined the consistency of model accuracy scores and relative importance order of model factors with regard to different input data sets and different model arguments to check the applicability of both the models and the input data. Results: Generally, the RF and XGboost models give more identical, consistent, and robust relative importance order of factors that is also accordant with the statistical analysis, while the LSVM gives much different and nonsense orders for different inputs. Results of the RF and XGboost models show that (1) generally, the models achieve accuracy scores above 0.9, indicating effective identification of leukemia, and (2) the top three factors that contribute most to the identification of leukemia include red blood cell (RBC), hematocrit (HCT), and white blood cell (WBC), while the other factors contribute relatively less. Conclusions: This study shows a feasible case example for early identification of leukemia using routine physical examination data with the assistance of ML models, which can be conveniently, cheaply, and widely applied in community hospitals or primary care clinics to save time before definite diagnosis; however, more studies are still needed to validate the applicability of more ML models to a larger variety of input data sets.

The biomarkers of key miRNAs and gene targets associated with extranodal NK/T-cell lymphoma.

Gene expression profiling studies have shown the pathogenetic role of oncogenic pathways in extranodal natural killer/T-cell lymphoma (ENKL). In this study, we aimed to identify the microRNAs (miRNAs) playing potential roles in ENKL, and to evaluate the genes and biological pathways associated to them. Gene expression profiles of ENKL patients were acquired from the gene expression omnibus (GEO) database. Most differentially expressed (DE)-miRNAs were identified in ENKL patients using limma package. Gene targets of the DE-miRNAs were collected from online databases (miRDB, miRWalk, miRDIP, and TargetScan), and used in Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses on Database for annotation, visualization, and integrated discovery database, and then used in protein-protein interaction (PPI) analysis on STRING database. Hub genes of the PPI network were identified in cytoHubba, and were evaluated in Biological networks gene ontology. According to the series GSE31377 and GSE43958 from GEO database, four DE-miRNAs were screened out: hsa-miR-363-3p, hsa-miR-296-5p, hsa-miR-155-5p, and hsa-miR-221-3p. Totally 164 gene targets were collected from the online databases, and used in the GO and KEGG pathway analyses and PPI network analysis. Ten hub genes of the PPI network were identified: AURKA, TP53, CDK1, CDK2, CCNB1, PLK1, CUL1, ESR1, CDC20, and PIK3CA. Those hub genes, as well as their correlative pathways, may be of diagnostic or therapeutic potential for ENKL, but further clinical evidence is still expected.

Collective outbreak of severe acute histoplasmosis in immunocompetent Chinese in South America: the clinical characteristics and continuous monitoring of serum cytokines/chemokines.

BACKGROUND: Acute histoplasmosis is a rare fungal disease in China. This study is aimed to summarize the clinical characteristics of the first large-scale outbreak of imported acute histoplasmosis in Chinese, so as to provide suggestions for clinical diagnosis and treatment. METHODS: We collected the symptoms, signs, laboratory examination and imaging data of 10 patients in so far the biggest outbreak of imported acute histoplasmosis in immunocompetent Chinese. Their clinical characteristics and time-varying cytokine/chemokine levels were analyzed, and rank correlation analysis between these markers was utilized to show their condition. RESULTS: The 10 patients of imported acute histoplasmosis were working without any respiratory protection in an abandoned mine tunnel in Guyana. The most common symptoms were fever and cough. Their chest CT imaging showed multiple nodular shadows in lungs. Laboratory examination showed that at admission the CRP, PCT, LDH, CysC, G-test, ß2-MG were all increased in at least 9 patients, and the CD4/CD8 was decreased to < 1 in all patients. Most cytokines/chemokines (other than IL-4, IL-12, INF-α, TNF-α) varied widely with patients and time, but their overall trend is higher at admission and decreasing gradually during hospitalization, especially for the IL-6, IL-8, IL-10 and IFN-γ. The LDH, CysC, G-test, ß2-MG, N/L, IL-6, IL-8, IL-10, IFN-γ, IL-27 are in positive associations to both CRP and PCT. CONCLUSIONS: The diagnosis of acute histoplasmosis needs a comprehensive analysis of epidemiological history, clinical symptoms and signs, and results of imaging, laboratory, microbiological and pathological examinations. Although none of the CRP, PCT, G-test, N/L, LDH, CysC, ß2-MG, IL-6, IL-8, IL-10, IFN-γ shows specificity in the diagnosis of acute histoplasmosis, there is possibility that the above factors might help in the inflammation and prognosis estimation. However, more studies and further investigation are still required for the verification.

"I Am Like a Lost Child": L2 Writers' Linguistic Metaphors as a Window Into Their Writer Identity.

The past two decades have witnessed a burgeoning literature on L2 writers' identities, especially their discoursal identities. In contrast, little attention is paid to the writers' felt sense of self when they write in an L2, which is an integral dimension of their autobiographical self. In this article, we provide empirical evidence of the nature of this aspect of L2 writer identity. To illustrate, we analyzed linguistic metaphors elicited from three groups of L2 writers (N = 83), majoring respectively in Thai, Japanese, and English in a Chinese university. Descriptive analysis shows that, due to challenges in content, language, organization, and cultural differences, a majority of L2 writers, especial Thai and Japanese L2 writers, experience a diminishing sense of self when they write in L2. In contrast, some L2 writers, especially English L2 writers, find writing in an L2 liberating, revealing the impact of their individual learning trajectories and pedagogical practices on L2 writers' felt sense of self. Findings suggest that L2 writers' identity work is both complex and dynamic. L2 writing teachers can utilize the metaphor questionnaire as a tool to facilitate their learner needs analysis and to raise L2 writers' metacognition.

Allogeneic Hematopoietic Stem Cell Transplantation in Extranodal Natural Killer/T-cell Lymphoma

Objective: Extranodal NK/T-cell lymphoma (ENKL) is aggressive and resistant to chemotherapy and radiotherapy. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative treatment for high-risk lymphomas owing to its associated graft-versus-lymphoma (GVL) effect. However, its application to ENKL is limited. We aim to summarize the characteristics of allo-HSCT for ENKL and, more importantly, evaluate whether allo-HSCT could offer any benefits for ENKL. Materials and Methods: A systematic review and data analysis were performed to evaluate the performance of allo-HSCT in the treatment of ENKL using studies obtained from PubMed, Medline, and Embase from January 2000 to December 2019 in the English language. Results: A total of 136 cases from 17 eligible publications were included in this study. It was found that after allo-HSCT, with an average follow-up time of 34 months (range: 1-121 months), 37.5% (52) of 136 patients had acute graft-versus-host disease (GVHD) and 31.6% (43) had chronic GVHD. Furthermore, 35.3% (48) of the patients were reported to have relapsed, but 2 of those relapsed only locally and achieved complete remission (CR) again with additional irradiation, chemotherapy, and donor lymphocyte infusions for one and rapid tapering and discontinuation of cyclosporine for the other, earning more than one year of extra survival. Finally, of the 136 patients, 51.5% (70) died because of primary disease progression (42.9%), infection (20.0%), GVHD (11.4%), organ failure (7.1%), hemorrhage (4.3%), and other causes (not specified/unknown) (14.3%). Conclusion: Allo-HSCT may be a treatment option for advanced or relapsed/refractory ENKL, but its role still requires more rigorous future studies.

Electrokinetic Translocation of a Deformable Nanoparticle through a Nanopore.

The nanopore-based biosensing technology is built up on the fluctuation of the ionic current induced by the electrokinetic translation of a particle penetrating the nanopore. It is expected that the current change of a deformable bioparticle is dissimilar from that of a rigid one. This study theoretically investigated the transient translocation process of a deformable particle through a nanopore for the first time. The mathematical model considers the Poisson equation for the electric potential, the Nernst-Planck equations for the ionic transport, the Navier-Stokes equations for the flow field, and the stress-strain equation for the dynamics of the deformable bioparticle. The arbitrary Lagrangian-Eulerian method is used for the fully coupled particle-fluid dynamic interaction. Results show that the deformation degree of the particle, the velocity deviation, and the current is different from the rigid particle. The deformation degree of the particle will reach the maximum when the particle passes a nanopore. Because of the deformation of particles, the total force applied on deformable particles is larger than that of rigid particles, resulting in larger velocity deviation and current deviation. The influences of the ratio of the nanoparticle radius to the Debye length and surface charge density of the nanopore are also studied. The research results illustrate the translocation mechanism of a deformable nanoparticle in the nanopore, which can provide theoretical guidance for the biosensing technology based on the nanopore.

Silencing of rhomboid domain containing 1 to inhibit the metastasis of human breast cancer cells in vitro.

OBJECTIVES: A growing body of evidence indicates that rhomboid domain containing 1 (RHBDD1) plays an important role in a variety of physiological and pathological processes, including tumorigenesis. We aimed to determine the function of RHBDD1 in breast cancer cells. MATERIALS AND METHODS: In this study, we used the Oncomine™ database to determine the expression patterns of RHBDD1 in normal and breast cancer tissues. We performed lentiviral transfection of RHBDD1-specific small interfering RNA into the breast cancer cell lines ZR-75-30 and MDA-MB-231 in order to investigate the effects of RHBDD1 deficiency on breast cancer metastasis. RESULTS: We found that knockdown of RHBDD1 inhibited breast cancer cell migration and invasion in vitro. Moreover, knockdown of RHBDD1 promoted epithelial-mesenchymal transition (EMT) by suppressing the expression of MPP2, MPP9, fibronectin 1, vimentin, SRY-box 2, zinc finger E-box binding homeobox 1, and snail family transcriptional repressor 1, and promoting the expression of cadherin 1. Additionally, knockdown of RHBDD1 inhibited the protein expression and phosphorylation of Akt. CONCLUSION: Our data indicate that RHBDD1 overexpression may promote breast cancer metastasis via the regulation of EMT, suggesting that RHBDD1 may be an important regulator of breast cancer metastasis.