Regulation of secondary metabolites in the endophytic fungus Penicillium sp. KMU18029 by the chemical epigenetic modifier 5-azacitidine.

The chemical epigenetic modifier 5-azacitidine (5-Aza C), a DNA methyltransferase inhibitor, was used to manipulate the endophytic fungus Penicillium sp. KMU18029. From its rice fermentation extract, a new polyketone compound (3S,4R)-3,4,8-trihydroxy-6-methyl-3,4-dihydronaphthalen-1(2H)-one (1), along with 13 known compounds, 3,4,8-trihydroxy-6-(hydroxymethyl)-3,4-dihydronaphthalen-1(2H)-one (2), decaturin B (3), 15-hydroxydecaturin A (4), oxalicine A (5), pileotin A (6), pyrandecarurin A (7), decaturenol A (8), decaturenoid (9), penisarins A (10), oxaline (11), (4E,8E)-N-D-2'-hydroxyocta-decanoyl-1-O-ß-D-glycopy-ranosyl-9-methyl-4,8-sphingadienine (12), ergosterol (13) and stigma-5-en-3-O-ß-glucoside (14), were separated. Among the known compounds, 2, 7, 12 and 14 were not found in our previous research on this strain. The structure of the new compound was identified by spectroscopic techniques such as HR-ESIMS, 1D NMR, 2D NMR and CD. Furthermore, all the isolated compounds were tested for their antimicrobial activities, and only compounds 1, 2 and 11 showed weak activities against S. aureus, with MICs of 128 µg/mL.

Unsupervised machine learning model to predict cognitive impairment in subcortical ischemic vascular disease.

INTRODUCTION: It is challenging to predict which patients who meet criteria for subcortical ischemic vascular disease (SIVD) will ultimately progress to subcortical vascular cognitive impairment (SVCI). METHODS: We collected clinical information, neuropsychological assessments, T1 imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging from 83 patients with SVCI and 53 age-matched patients with SIVD without cognitive impairment. We built an unsupervised machine learning model to isolate patients with SVCI. The model was validated using multimodal data from an external cohort comprising 45 patients with SVCI and 32 patients with SIVD without cognitive impairment. RESULTS: The accuracy, sensitivity, and specificity of the unsupervised machine learning model were 86.03%, 79.52%, and 96.23% and 80.52%, 71.11%, and 93.75% for internal and external cohort, respectively. DISCUSSION: We developed an accurate and accessible clinical tool which requires only data from routine imaging to predict patients at risk of progressing from SIVD to SVCI. HIGHLIGHTS: Our unsupervised machine learning model provides an accurate and accessible clinical tool to predict patients at risk of progressing from subcortical ischemic vascular disease (SIVD) to subcortical vascular cognitive impairment (SVCI) and requires only data from imaging routinely used during the diagnosis of suspected SVCI. The model yields good accuracy, sensitivity, and specificity and is portable to other cohorts and to clinical practice to distinguish patients with SIVD at risk for progressing to SVCI. The model combines assessment of diffusion tensor imaging and functional magnetic resonance imaging measures in patients with SVCI to analyze whether the "disconnection hypothesis" contributes to functional and structural changes and to the clinical presentation of SVCI.

A Meniscus Multifocusing Compound Eye Camera Based on Negative Pressure Forming Technology.

To meet the challenge of preparing a high-resolution compound eye, this paper proposes a multi-focal-length meniscus compound eye based on MEMS negative pressure molding technology. The aperture is increased, a large field of view angle of 101.14° is obtained, and the ommatidia radius of each stage is gradually increased from 250 µm to 440 µm. A meniscus structure is used to improve the imaging quality of the marginal compound eye so that its resolution can reach 36.00 lp/mm. The prepared microlenses have a uniform shape and a smooth surface, and both panoramic image stitching and moving object tracking are achieved. This technology has great potential for application in many fields, including automatic driving, machine vision, and medical endoscopy.

Discovery of novel BRD4-BD2 inhibitors via in silico approaches: QSAR techniques, molecular docking, and molecular dynamics simulations.

Bromodomain-containing protein 4(BRD4) plays an important role in the occurrence and development of various malignant tumors, which has attracted the attention of scientific research institutions and pharmaceutical companies. The structural modification of most currently available BRD4 inhibitors is relatively simple, but the drug effectiveness is limited. Research has found that the inhibition of BD1 may promote the differentiation of oligodendrocyte progenitor cell; however, the inhibition of BD2 will not cause this outcome. Therefore, newly potential drugs which target BRD4-BD2 need further research. Herein, we initially built QSAR models out of 49 compounds using HQSAR, CoMFA, CoMSIA, and Topomer CoMFA technology. All of the models have shown suitable reliabilities (q2 = 0.778, 0.533, 0.640, 0.702, respectively) and predictive abilities (r2pred = 0.716, 0.6289, 0.6153, 0.7968, respectively) for BRD4-BD2 inhibitors. On the basis of QSAR results and the search of the R-group in the topomer search module, we designed 20 new compounds with high activity that showed appropriate docking score and suitable ADMET. Docking studies and MD simulation were carried out to reveal the amino acid residues (Asn351, Cys347, Tyr350, Pro293, and Asp299) at the active site of BRD4-BD2. Free energy calculations and free energy landscapes verified the stable binding results and indicated stable conformations of the complexes. These theoretical studies provide guidance and theoretical basis for designing and developing novel BRD4-BD2 inhibitors.

Soft Fiber Electronics Based on Semiconducting Polymer.

Fibers, originating from nature and mastered by human, have woven their way throughout the entire history of human civilization. Recent developments in semiconducting polymer materials have further endowed fibers and textiles with various electronic functions, which are attractive in applications such as information interfacing, personalized medicine, and clean energy. Owing to their ability to be easily integrated into daily life, soft fiber electronics based on semiconducting polymers have gained popularity recently for wearable and implantable applications. Herein, we present a review of the previous and current progress in semiconducting polymer-based fiber electronics, particularly focusing on smart-wearable and implantable areas. First, we provide a brief overview of semiconducting polymers from the viewpoint of materials based on the basic concepts and functionality requirements of different devices. Then we analyze the existing applications and associated devices such as information interfaces, healthcare and medicine, and energy conversion and storage. The working principle and performance of semiconducting polymer-based fiber devices are summarized. Furthermore, we focus on the fabrication techniques of fiber devices. Based on the continuous fabrication of one-dimensional fiber and yarn, we introduce two- and three-dimensional fabric fabricating methods. Finally, we review challenges and relevant perspectives and potential solutions to address the related problems.

Bioactive sorbicillinoids from a rhizospheric soil-derived Paecilomyces sp. KMU21009.

A new hybrid sorbicillinoid named paeciureallin (1) and a new monomeric sorbicillinoid named paecillyketide (2), along with six known analogues (3-8), were isolated from the rhizospheric soil-derived fungus Paecilomyces sp. KMU21009 associated with Delphinium yunnanense. Their structures were elucidated by extensive spectroscopic analysis and comparison with literature values. Paeciureallin (1) is the first example of hybrid sorbicillinoids possessing a rare sorbicillinoid urea unit and containing a ß-D-ribofuranose functionality. In pharmacological studies, compounds 1 and 2 were evaluated for in vitro anti-inflammatory and cytotoxic activities. Paeciureallin (1) exhibited moderate cytotoxicity against SW480 and A549 cell lines, and the IC50 values were 32.0 ± 0.1 and 34.4 ± 2.0 µM, respectively.

ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data.

Long-read RNA sequencing (RNA-seq) holds great potential for characterizing transcriptome variation and full-length transcript isoforms, but the relatively high error rate of current long-read sequencing platforms poses a major challenge. We present ESPRESSO, a computational tool for robust discovery and quantification of transcript isoforms from error-prone long reads. ESPRESSO jointly considers alignments of all long reads aligned to a gene and uses error profiles of individual reads to improve the identification of splice junctions and the discovery of their corresponding transcript isoforms. On both a synthetic spike-in RNA sample and human RNA samples, ESPRESSO outperforms multiple contemporary tools in not only transcript isoform discovery but also transcript isoform quantification. In total, we generated and analyzed ~1.1 billion nanopore RNA-seq reads covering 30 human tissue samples and three human cell lines. ESPRESSO and its companion dataset provide a useful resource for studying the RNA repertoire of eukaryotic transcriptomes.

Effects of heavy metals on the adsorption of ciprofloxacin on polyethylene microplastics: Mechanism and toxicity evaluation.

Microplastics are plastic particles less than 5 mm in diameter and are widely present in water environments. Their unique surface structures can adsorb coexisting pollutants in the surrounding environment, such as antibiotics and metal ions, leading to compound pollution. The adsorption of ciprofloxacin on polyethylene microplastics under different environmental conditions (pH and salinity) was investigated. The Freundlich model fitted well at 25 °C, indicating that the adsorption of ciprofloxacin by polyethylene microplastics was multilayered, and Fourier Transform infrared spectroscopy (FTIR) analysis indicated that the adsorption of ciprofloxacin by polyethylene microplastics was physical. The kinetic adsorption of ciprofloxacin on polyethylene microplastics followed a pseudo-second-order mode. Heavy metals (Cu2+, Cr3+, Cr6+, Cd2+, and Pb2+) affected the adsorption of ciprofloxacin by microplastics, which was related to the type and concentration of metal ions and the valence state of the ions. The acute toxicity of microplastics and the microplastic-ciprofloxacin-Cu2+ complex were evaluated using luminescent Photobacterium phosphoreum, demonstrating the polyethylene toxicity microplastic-ciprofloxacin-Cu2+ complex was mainly caused by Cu2+ and ciprofloxacin rather than microplastics. This study provides theoretical support for the environmental behavior and ecological effects of microplastics in aqueous environments.

Key mutations in the spike protein of SARS-CoV-2 affecting neutralization resistance and viral internalization.

To control the ongoing COVID-19 pandemic, a variety of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines have been developed. However, the rapid mutations of SARS-CoV-2 spike (S) protein may reduce the protective efficacy of the existing vaccines which is mainly determined by the level of neutralizing antibodies targeting S. In this study, we screened prevalent S mutations and constructed 124 pseudotyped lentiviral particles carrying these mutants. We challenged these pseudoviruses with sera vaccinated by Sinovac CoronaVac and ZF2001 vaccines, two popular vaccines designed for the initial strain of SARS-CoV-2, and then systematically assessed the susceptivity of these SARS-CoV-2 variants to the immune sera of vaccines. As a result, 14 S mutants (H146Y, V320I + S477N, V382L, K444R, L455F + S477N, L452M + F486L, F486L, Y508H, P521R, A626S, S477N + S698L, A701V, S477N + T778I, E1144Q) were found to be significantly resistant to neutralization, indicating reduced protective efficacy of the vaccines against these SARS-CoV-2 variants. In addition, F486L and Y508H significantly enhanced the utilization of human angiotensin-converting enzyme 2, suggesting a potentially elevated infectivity of these two mutants. In conclusion, our results show that some prevalent S mutations of SARS-CoV-2 reduced the protective efficacy of current vaccines and enhance the infectivity of the virus, indicating the necessity of vaccine renewal and providing direction for the development of new vaccines.

VirusRecom: an information-theory-based method for recombination detection of viral lineages and its application on SARS-CoV-2.

Genomic recombination is an important driving force for viral evolution, and recombination events have been reported for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the Coronavirus Disease 2019 pandemic, which significantly alter viral infectivity and transmissibility. However, it is difficult to identify viral recombination, especially for low-divergence viruses such as SARS-CoV-2, since it is hard to distinguish recombination from in situ mutation. Herein, we applied information theory to viral recombination analysis and developed VirusRecom, a program for efficiently screening recombination events on viral genome. In principle, we considered a recombination event as a transmission process of ``information'' and introduced weighted information content (WIC) to quantify the contribution of recombination to a certain region on viral genome; then, we identified the recombination regions by comparing WICs of different regions. In the benchmark using simulated data, VirusRecom showed a good balance between precision and recall compared to two competing tools, RDP5 and 3SEQ. In the detection of SARS-CoV-2 XE, XD and XF recombinants, VirusRecom providing more accurate positions of recombination regions than RDP5 and 3SEQ. In addition, we encapsulated the VirusRecom program into a command-line-interface software for convenient operation by users. In summary, we developed a novel approach based on information theory to identify viral recombination within highly similar sequences, providing a useful tool for monitoring viral evolution and epidemic control.

Key mutations in the spike protein of SARS-CoV-2 affecting neutralization resistance and viral internalization.

To control the ongoing COVID-19 pandemic, a variety of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines have been developed. However, the rapid mutations of SARS-CoV-2 spike (S) protein may reduce the protective efficacy of the existing vaccines which is mainly determined by the level of neutralizing antibodies targeting S. In this study, we screened prevalent S mutations and constructed 124 pseudotyped lentiviral particles carrying these mutants. We challenged these pseudoviruses with sera vaccinated by Sinovac CoronaVac and ZF2001 vaccines, two popular vaccines designed for the initial strain of SARS-CoV-2, and then systematically assessed the susceptivity of these SARS-CoV-2 variants to the immune sera of vaccines. As a result, 14 S mutants (H146Y, V320I + S477N, V382L, K444R, L455F + S477N, L452M + F486L, F486L, Y508H, P521R, A626S, S477N + S698L, A701V, S477N + T778I, E1144Q) were found to be significantly resistant to neutralization, indicating reduced protective efficacy of the vaccines against these SARS-CoV-2 variants. In addition, F486L and Y508H significantly enhanced the utilization of human angiotensin-converting enzyme 2, suggesting a potentially elevated infectivity of these two mutants. In conclusion, our results show that some prevalent S mutations of SARS-CoV-2 reduced the protective efficacy of current vaccines and enhance the infectivity of the virus, indicating the necessity of vaccine renewal and providing direction for the development of new vaccines.

VirusRecom: an information-theory-based method for recombination detection of viral lineages and its application on SARS-CoV-2.

Genomic recombination is an important driving force for viral evolution, and recombination events have been reported for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the Coronavirus Disease 2019 pandemic, which significantly alter viral infectivity and transmissibility. However, it is difficult to identify viral recombination, especially for low-divergence viruses such as SARS-CoV-2, since it is hard to distinguish recombination from in situ mutation. Herein, we applied information theory to viral recombination analysis and developed VirusRecom, a program for efficiently screening recombination events on viral genome. In principle, we considered a recombination event as a transmission process of ``information'' and introduced weighted information content (WIC) to quantify the contribution of recombination to a certain region on viral genome; then, we identified the recombination regions by comparing WICs of different regions. In the benchmark using simulated data, VirusRecom showed a good balance between precision and recall compared to two competing tools, RDP5 and 3SEQ. In the detection of SARS-CoV-2 XE, XD and XF recombinants, VirusRecom providing more accurate positions of recombination regions than RDP5 and 3SEQ. In addition, we encapsulated the VirusRecom program into a command-line-interface software for convenient operation by users. In summary, we developed a novel approach based on information theory to identify viral recombination within highly similar sequences, providing a useful tool for monitoring viral evolution and epidemic control.

Progress of catalytic oxidation of typical chlorined volatile organic compounds (CVOCs): A review.

Chlorinated volatile organic compounds (CVOCs) are still a part of the current atmospheric environmental problems that cannot be ignored, but unlike conventional VOCs, the presence of Cl causes various catalyst deactivations in the catalytic process. In this paper, we focus on six common CVOCs and discuss various behavioral mechanisms of the whole catalytic process from six aspects: catalyst selection, factors affecting the catalytic effect, changes in catalytic behavior in the presence of different gases, catalyst poisoning deactivation behavior, degradation products and degradation mechanisms to provide guidance for further development of low-temperature and efficient CVOCs catalysts.

Challenges in the Management of Laryngo-Tracheo-Bronchial Stenosis Secondary to Tuberculosis.

Laryngo-tracheo-bronchial tuberculosis may be primary or secondary to pulmonary tuberculosis. It causes stenosis of the airway, leading to life threatening airway obstruction. We herein describe the challenges in managing a case of laryngo-tracheo-bronchial stenosis in a 22-year-old patient who presented with stridor post antituberculous therapy secondary to a malacic airway.

Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring.

Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various small-molecule metabolites.

The Landscape of Nucleic-Acid-Based Aptamers for Treatment of Hematologic Malignancies: Challenges and Future Directions.

Hematologic malignancies, including leukemia, lymphoma, myeloproliferative disorder and plasma cell neoplasia, are genetically heterogeneous and characterized by an uncontrolled proliferation of their corresponding cell lineages in the bone marrow, peripheral blood, tissues or plasma. Although there are many types of therapeutic drugs (e.g., TKIs, chemotherapy drugs) available for treatment of different malignancies, the relapse, drug resistance and severe side effects due to the lack of selectivity seriously limit their clinical application. Currently, although antibody-drug conjugates have been well established as able to target and deliver highly potent chemotherapy agents into cancer cells for the reduction of damage to healthy cells and have achieved success in leukemia treatment, they still also have shortcomings such as high cost, high immunogenicity and low stability. Aptamers are ssDNA or RNA oligonucleotides that can also precisely deliver therapeutic agents into cancer cells through specifically recognizing the membrane protein on cancer cells, which is similar to the capabilities of monoclonal antibodies. Aptamers exhibit higher binding affinity, lower immunogenicity and higher thermal stability than antibodies. Therefore, in this review we comprehensively describe recent advances in the development of aptamer-drug conjugates (ApDCs) with cytotoxic payload through chemical linkers or direct incorporation, as well as further introduce the latest promising aptamers-based therapeutic strategies such as aptamer-T cell therapy and aptamer-PROTAC, clarifying their bright application, development direction and challenges in the treatment of hematologic malignancies.

Influence of Parental and Offspring Dietary Behaviors on the Association of Overweight and Obesity between Two Generations: Results from a Cross-Sectional Analysis of Parent-Offspring Trios in China.

Limited evidence exists on the influence of dietary behaviors on the association of overweight/obesity between parents and offspring. This study aimed to investigate the influence of dietary behaviors on the association of overweight/obesity between two generations, and puts forward strategies for preventing childhood obesity. Data were obtained from a cross-sectional survey conducted in China; a total of 40,197 parent-offspring pairs were included. Overweight/obesity was defined based on the body mass index and waist circumstance; the association of overweight/obesity between two generations was evaluated by multivariate and binary logistic regression and stratified analyses. Compared with the offspring who were free of parental overweight/obesity, the ORs of offspring with both parental overweight/obesity reached 2.66, 1.72 and 4.04 for general, simple abdominal and compound obesity. The offset effect of dietary behaviors was observed on the association between parental obesity and the offspring's general or simple abdominal obesity, with non-significant ORs when parents or/and offspring had healthy dietary behaviors. It was difficult for a healthy diet alone to offset the high heritability and intergenerational transmission of childhood obesity caused by parental obesity. Multifaceted improvement of lifestyle behaviors, and a combination of individual and family engagement, could be targeted measures to control childhood obesity.

Overexpression of lncRNA TUG1 enhances the efficacy of DC-CIK immunotherapy in neuroblastoma in vitro and in vivo.

BACKGROUND: Long non-coding RNA (LncRNA) TUG1 plays a critical role in the development of human cancers. This study explored whether TUG1 is involved in the cytotoxicity of dendritic cells and cytokine-induced killer cells (DCs-CIK), an immunotherapy approach, in neuroblastoma. METHODS: A TUG1 expression plasmid was transfected into DCs. Neuroblastoma SK-N-SH cells were incubated with CIK cells, DCs-CIK cells, and TUG1-overexpressing DCs-CIK cells, with or without irradiation. SK-N-SH cell viability, colony formation, migration, and apoptosis were analyzed using CCK-8, colony formation assay, transwell assay, and flow cytometry, respectively. Production of IL-12, IL-2 and IFN-γ in the supernatants was determined using ELISA. A dual luciferase activity assay was performed to confirm the molecular interactions between TUG1 and miR-204. Tumor-bearing mice were established by injection of SK-N-SH cells followed by stimulation with CIK cells, DC-CIK cells, and TUG1-overexpressing DCs-CIK cells. RESULTS: Compared to CIK alone or DC-CIK therapy, overexpression of TUG1 significantly suppressed tumor cell proliferation, colony formation, and migration of neuroblastoma cells. Moreover, upregulation of TUG1 robustly induced apoptosis and altered key molecules associated with apoptosis and epithelial-mesenchymal transition. Contents of IL-12, IL-2 and IFN-γ were dramatically elevated in the supernatants in the coculturing system upon transfection with TUG1. In addition, TUG1 was found to be act as miR-204 sponge. Furthermore, in vivo experiments demonstrated that upregulation of TUG1 potentiated the antitumor activity of DC-CIK immunotherapy. CONCLUSION: Overexpression of TUG1 promotes DC maturation and enhances CIK cytotoxicity, suggesting that TUG1 may be a novel target for enhancing DC-CIK based immunotherapy for neuroblastoma.

Temperature-Regulated Core Swelling and Asymmetric Shrinkage for Tunable Yolk@Shell Polydopamine@Mesoporous Silica Nanostructures.

Facile and controllable synthesis of functional yolk@shell structured nanospheres with a tunable inner core ('yolk') and mesoporous shell is highly desirable, yet it remains a great challenge. Herein, xx developed a strategy based on temperature-regulated swelling and restricted asymmetric shrinkage of polydopamine (PDA) nanospheres, combined with heterogeneous interface self-assembly growth. This method allows a simple and versatile preparation of PDA@mesoporous silica (MS) nanospheres exhibiting tunable yolk@shell architectures and shell pore sizes. Through reaction temperature-regulated swelling degree and confined shrinkage of PDA nanospheres, the volume ratio of the hollow cavity that the PDA core occupies can easily be tuned from ca. 2/3 to ca. 1/2, then to ca. 2/5, finally to ca. 1/3. Owing to the presence of PDA with excellent photothermal conversion capacity, the PDA@MS nanocomposites with asymmetric yolk distributions can become a colloidal nanomotor propelled by near-infrared (NIR) light. Noteworthily, the PDA@MS with half PDA yolk and microcracks in silica shell reaches 2.18 µm2 s-1 of effective diffusion coefficient (De) in the presence of 1.0 W cm-2 NIR light. This temperature-controlled swelling approach may provide new insight into the design and facile preparation of functional PDA-based yolk@shell structured nanocomposites for wide applications in biology and medicine.

vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data.

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.