Immobilized biocatalytic process to prepare enantiopure pregabalin intermediate using engineered hydantoinase

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Immuno Nanoparticles Integrated Electrical Control of Targeted Cancer Cell Development Using Whole Cell Bioelectronic Device

Electrical properties of cells determine most of the cellular functions, particularly ones which occur in the cell’s membrane. Manipulation of these electrical properties may provide a powerful electrotherapy option for the treatment of cancer as cancerous cells have been shown to be more electronegative than normal proliferating cells. Previously, we used an electrical impedance sensing system (EIS) to explore the responses of cancerous SKOV3 cells and normal HUVEC cells to low intensity (\u3c2 V/cm) AC electric fields, determining that the optimal frequency for SKOV3 proliferation arrest was 200 kHz, without harming the non-cancerous HUVECs. In this study, to determine if these effects are cell type dependant, human breast adenocarcinoma cells (MCF7) were subjected to a range of frequencies (50 kHz–2 MHz) similar to the previously tested SKOV3. For the MCF7, an optimal frequency of 100 kHz was determined using the EIS, indicating a higher sensitivity towards the applied field. Further experiments specifically targeting the two types of cancer cells using HER2 antibody functionalized gold nanoparticles (HER2-AuNPs) were performed to determine if enhanced electric field strength can be induced via the application of nanoparticles, consequently leading to the killing of the cancerous cells without affecting non cancerous HUVECs and MCF10a providing a platform for the development of a non-invasive cancer treatment without any harmful side effects. The EIS was used to monitor the real-time consequences on cellular viability and a noticeable decrease in the growth profile of the MCF7 was observed with the application of the HER2-AuNPs and the electric fields indicating specific inhibitory effects on dividing cells in culture. To further understand the effects of the externally applied field to the cells, an Annexin V/EthD-III assay was performed to determine the cell death mechanism indicating apoptosis. The zeta potential of the SKOV3 and the MCF7 before and after incorporation of the HER2-AuNPs was also obtained indicating a decrease in zeta potential with the incorporation of the nanoparticles. The outcome of this research will improve our fundamental understanding of the behavior of cancer cells and define optimal parameters of electrotherapy for clinical and drug delivery applications

Highly Sensitive Electrochemical Sensor for the Determination of 8-Hydroxy-2 \u27-deoxyguanosine Incorporating SWCNTs-Nafion Composite Film

8-Hydroxy-2\u27-deoxyguanosine (8-OHdG) is a typical biomarker of oxidative DNA damage and has attracted much attention in recent years since the level of 8-OHdG in body fluids is typically associated with various diseases. In this work, a simple and highly sensitive electrochemical sensor for the determination of 8-OHdG was fabricated incorporating single wall carbon nanotubes-(SWCNTs-) Nafion composite film coated on glassy carbon electrode. Nafion was chosen as an optimal adhesive agent from a series of adhesive agents and acted as a binder, enrichment, and exclusion film. Due to the strong cation-exchange ability of Nafion and the outstanding electronic properties ofSWCNTs, the prepared SWCNTs-Nafion film can strongly enhance the electrochemical response to oxidation of 8-OHdG and efficiently alleviate the interferences from uric acid and ascorbic acid. The oxidation peak currents are linear with the concentration of 8-OHdG in the range of 0.03 to 1.25 mu M with a detection limit of 8.0 nM (S/N = 3). This work demonstrates that SWCNTs-Nafion film can improve the sensitivity, selectivity, reproducibility, and stability, making it an ideal candidate for electrochemical detection of 8-OHdG

Nova+^+: Generative Language Models for Binaries

Generative large language models (LLMs) pre-trained on code have shown impressive effectiveness in code generation, program repair, and document analysis. However, existing generative LLMs focus on source code and are not specialized for binaries. There are three main challenges for LLMs to model and learn binary code: hex-decimal values, complex global dependencies, and compiler optimization levels. To bring the benefit of LLMs to the binary domain, we develop Nova and Nova$^+$, which are LLMs pre-trained on binary corpora. Nova is pre-trained with the standard language modeling task, showing significantly better capability on five benchmarks for three downstream tasks: binary code similarity detection (BCSD), binary code translation (BCT), and binary code recovery (BCR), over GPT-3.5 and other existing techniques. We build Nova$^+$ to further boost Nova using two new pre-training tasks, i.e., optimization generation and optimization level prediction, which are designed to learn binary optimization and align equivalent binaries. Nova$^+$ shows overall the best performance for all three downstream tasks on five benchmarks, demonstrating the contributions of the new pre-training tasks

Construction of an interferon regulatory factors-related risk model for predicting prognosis, immune microenvironment and immunotherapy in clear cell renal cell carcinoma

BackgroundInterferon regulatory factors (IRFs) played complex and essential roles in progression, prognosis, and immune microenvironment in clear cell renal cell carcinoma (ccRCC). The purpose of this study was to construct a novel IRFs-related risk model to predict prognosis, tumor microenvironment (TME) and immunotherapy response in ccRCC.MethodsMulti-omics analysis of IRFs in ccRCC was performed based on bulk RNA sequencing and single cell RNA sequencing data. According to the expression profiles of IRFs, the ccRCC samples were clustered by non-negative matrix factorization (NMF) algorithm. Then, least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were applied to construct a risk model to predict prognosis, immune cells infiltration, immunotherapy response and targeted drug sensitivity in ccRCC. Furthermore, a nomogram comprising the risk model and clinical characteristics was established.ResultsTwo molecular subtypes with different prognosis, clinical characteristics and infiltration levels of immune cells were identified in ccRCC. The IRFs-related risk model was developed as an independent prognostic indicator in the TCGA-KIRC cohort and validated in the E-MTAB-1980 cohort. The overall survival of patients in the low-risk group was better than that in the high-risk group. The risk model was superior to clinical characteristics and the ClearCode34 model in predicting the prognosis. In addition, a nomogram was developed to improve the clinical utility of the risk model. Moreover, the high-risk group had higher infiltration levels of CD8+ T cell, macrophages, T follicular helper cells and T helper (Th1) cells and activity score of type I IFN response but lower infiltration levels of mast cells and activity score of type II IFN response. Cancer immunity cycle showed that the immune activity score of most steps was remarkably higher in the high-risk group. TIDE scores indicated that patients in the low-risk group were more likely responsive to immunotherapy. Patients in different risk groups showed diverse drug sensitivity to axitinib, sorafenib, gefitinib, erlotinib, dasatinib and rapamycin.ConclusionsIn brief, a robust and effective risk model was developed to predict prognosis, TME characteristics and responses to immunotherapy and targeted drugs in ccRCC, which might provide new insights into personalized and precise therapeutic strategies

Ag-Mg antisite defect induced high thermoelectric performance of α-MgAgSb

Engineering atomic-scale native point defects has become an attractive strategy to improve the performance of thermoelectric materials. Here, we theoretically predict that Ag-Mg antisite defects as shallow acceptors can be more stable than other intrinsic defects under Mg-poor-Ag/Sb-rich conditions. Under more Mg-rich conditions, Ag vacancy dominates the intrinsic defects. The p-type conduction behavior of experimentally synthesized ¿-MgAgSb mainly comes from Ag vacancies and Ag antisites (Ag on Mg sites), which act as shallow acceptors. Ag-Mg antisite defects significantly increase the thermoelectric performance of ¿-MgAgSb by increasing the number of band valleys near the Fermi level. For Li-doped ¿-MgAgSb, under more Mg-rich conditions, Li will substitute on Ag sites rather than on Mg sites and may achieve high thermoelectric performance. A secondary valence band is revealed in ¿-MgAgSb with 14 conducting carrier pockets

Assembly strategies for rubber-degrading microbial consortia based on omics tools

Numerous microorganisms, including bacteria and fungus, have been identified as capable of degrading rubber. Rubber biodegradation is still understudied due to its high stability and the lack of well-defined pathways and efficient enzymes involved in microorganism metabolism. However, rubber products manufacture and usage cause substantial environmental issues, and present physical-chemical methods involve dangerous chemical solvents, massive energy, and trash with health hazards. Eco-friendly solutions are required in this context, and biotechnological rubber treatment offers considerable promise. The structural and functional enzymes involved in poly (cis-1,4-isoprene) rubber and their cleavage mechanisms have been extensively studied. Similarly, novel bacterial strains capable of degrading polymers have been investigated. In contrast, relatively few studies have been conducted to establish natural rubber (NR) degrading bacterial consortia based on metagenomics, considering process optimization, cost effective approaches and larger scale experiments seeking practical and realistic applications. In light of the obstacles encountered during the constructing NR-degrading consortia, this study proposes the utilization of multi-omics tools to discern the underlying mechanisms and metabolites of rubber degradation, as well as associated enzymes and effective synthesized microbial consortia. In addition, the utilization of omics tool-based methods is suggested as a primary research direction for the development of synthesized microbial consortia in the future

Assembly strategies for polyethylene-degrading microbial consortia based on the combination of omics tools and the “Plastisphere”

Numerous microorganisms and other invertebrates that are able to degrade polyethylene (PE) have been reported. However, studies on PE biodegradation are still limited due to its extreme stability and the lack of explicit insights into the mechanisms and efficient enzymes involved in its metabolism by microorganisms. In this review, current studies of PE biodegradation, including the fundamental stages, important microorganisms and enzymes, and functional microbial consortia, were examined. Considering the bottlenecks in the construction of PE-degrading consortia, a combination of top-down and bottom-up approaches is proposed to identify the mechanisms and metabolites of PE degradation, related enzymes, and efficient synthetic microbial consortia. In addition, the exploration of the plastisphere based on omics tools is proposed as a future principal research direction for the construction of synthetic microbial consortia for PE degradation. Combining chemical and biological upcycling processes for PE waste could be widely applied in various fields to promote a sustainable environment

Applications of Nanomaterials in Electrogenerated Chemiluminescence Biosensors

Electrogenerated chemiluminescence (also called electrochemiluminescence and abbreviated ECL) involves the generation of species at electrode surfaces that then undergo electron-transfer reactions to form excited states that emit light. ECL biosensor, combining advantages offered by the selectivity of the biological recognition elements and the sensitivity of ECL technique, is a powerful device for ultrasensitive biomolecule detection and quantification. Nanomaterials are of considerable interest in the biosensor field owing to their unique physical and chemical properties, which have led to novel biosensors that have exhibited high sensitivity and stability. Nanomaterials including nanoparticles and nanotubes, prepared from metals, semiconductor, carbon or polymeric species, have been widely investigated for their ability to enhance the efficiencies of ECL biosensors, such as taking as modification electrode materials, or as carrier of ECL labels and ECL-emitting species. Particularly useful application of nanomaterials in ECL biosensors with emphasis on the years 2004-2008 is reviewed. Remarks on application of nanomaterials in ECL biosensors are also surveyed