High-Throughput Screening for Oligonucleotide Detection by ADE-OPI-MS.

Oligonucleotides represent a class of shorter DNA or RNA nucleic acid polymers extensively applied in the biomedical field. Despite progress in detecting and analyzing oligonucleotides, high-throughput analysis of the samples remains challenging. In this work, a high-throughput analysis method for oligonucleotide analysis was developed based on acoustic droplet ejection-open port interface-mass spectrometry (ADE-OPI-MS) technology. This approach was applied to determine the enzymatic activity of terminal deoxynucleotide transferase (TdT) for DNA synthesis, with a rate of 3 s/sample, which enhanced single-sample analysis efficiency approximately 60-fold over the previous gel analysis. After testing approximately 10,000 TdT mutants, we obtained three new variants with higher catalytic activities. Finally, by integrating these mutants, the catalytic activity of TdT was improved about 4 times compared to the starting mutant. Our results successfully established a high-throughput screening method for oligonucleotide analysis, which not only provides a foundation to engineer highly efficient TdT for ab initio synthesis of DNA but also paves the way for the potential application of oligonucleotide analysis in biomedical fields.

Robust Learning-Based Control for Uncertain Nonlinear Systems With Validation on a Soft Robot.

Existing modeling and control methods for real-world systems typically deal with uncertainty and nonlinearity on a case-by-case basis. We present a universal and robust control framework for the general class of uncertain nonlinear systems. Our data-driven deep stochastic Koopman operator (DeSKO) model and robust learning control framework guarantee robust stability. DeSKO learns the uncertainty of dynamical systems by inferring a distribution of observables. The inferred distribution is used in our robust and stabilizing closed-loop controller for dynamical systems. We also develop a model predictive control framework with integral action to compensate for run-time parametric uncertainty, such as manipulating unknown objects. Modeling and control experiments in simulation show that our presented framework is more robust and scalable for robotic systems than state-of-the-art controllers using deep Koopman operators and reinforcement learning (RL) methods. We demonstrate that our method resists previously unseen uncertainties, such as external disturbances, at a magnitude of up to five times the maximum control input. Furthermore, we test our DeSKO-based control framework on a real-world soft robotic arm. It shows that our framework outperforms model-based controllers that have full knowledge of the model parameters, and the controller can conduct object pick-and-place tasks without further training. Our approach opens up new possibilities in robustly managing internal or external uncertainty while controlling high-dimensional nonlinear systems in a learning framework. This approach serves as a foundation to greatly simplify high-level control and decision-making for robots.

Identification of pancreatic lipase inhibitors from Eucommia ulmoides tea by affinity-ultrafiltration combined UPLC-Orbitrap MS and in vitro validation.

Pancreatic lipase inhibitors can reduce blood lipids by inactivating the catalytic activity of human pancreatic lipase, a key enzyme involved in triglyceride hydrolysis, which helps control some dyslipidemic diseases. The ability of Eucommia ulmoides tea to improve fat-related diseases is closely related to the natural inhibitory components of pancreatic lipase contained in the tea. In this study, fifteen pancreatic lipase inhibitors were screened and identified from Eucommia ulmoides tea by affinity-ultrafiltration combined UPLC-Q-Exactive Orbitrap/MS. Four representative components of geniposidic acid, quercetin-3-O-sambuboside, isochlorogenic acid A, and quercetin with high binding degrees were further verified by nanoscale differential scanning fluorimetry (nanoDSF) and enzyme inhibitory assays. The results of flow cytometry showed that they could significantly reduce the activity of pancreatic lipase in AR42J cells induced by palmitic acid in a concentration-dependent manner. Our findings suggest that Eucommia ulmoides tea may be a promising resource for pancreatic lipase inhibitors of natural origin.

Exposure to Commonly Used Drugs and the Risk of Gastric Cancer: An Umbrella Review of Meta-Analyses.

Recently, attention has been paid to some medications and gastric cancer (GC) risk. This review aimed to evaluate associations between commonly used drugs and GC risk and to grade evidence from published systematic reviews and meta-analyses. This umbrella review was registered in PROSPERO (CRD42022320276). The systematic reviews and meta-analyses of observational studies were retrieved by searching Embase, PubMed, and Web of Science. The evidence strength of commonly used drugs and GC risk was categorized into four grades: weak, suggestive, highly suggestive, and strong. Of 19 associations between commonly used drugs and GC risk and its subtypes, none was supported by convincing or highly suggestive evidence. The risk of GC related to non-steroidal anti-inflammatory drugs (NSAIDs), non-aspirin NSAIDs, and acid-suppressive drugs, as well as the risk of non-cardia GC related to NSAIDs and aspirin, was supported by suggestive evidence. The results showed that a reduced GC risk was associated with two drug types (NSAIDs and non-aspirin NSAIDs), and an increased GC risk was associated with acid-suppressing drugs at the suggestive evidence level. Moreover, NSAIDs and aspirin reduced non-cardia GC risk as supported by suggestive evidence. However, the evidence supporting statins or metformin in reducing GC risk was weak, and thus future studies are required to clarify these associations.

Exosomal circRNAs: A key factor of tumor angiogenesis and therapeutic intervention.

Over the last few decades, our understanding of the molecular mechanisms underlying tumor angiogenesis has advanced at a significant pace and the clinical translation of these mechanisms has benefited millions of patients. However, limited efficacy and the rapid expansion of drug resistance remain unresolved issues. Recent studies in both preclinical and clinical settings have revealed that circRNAs, as a novel identified non-coding RNA can mediate intercellular communication and regulate the microenvironment within tumors after being selectively packaged, secreted, and transmitted via exosomes. This review aims to provide a comprehensive understanding of how exosomal circRNAs orchestrate inducers and inhibitors of angiogenesis, including their functions, molecular mechanisms, and potential roles as diagnostic biomarkers and therapeutic targets. Finally, we discuss the technological advances in exosome functionalization and exosome-mimetic nanovesicles intending to improve the clinical translation of exosomal circRNAs.

Electrospun Nanofibrous Membranes for Controlling Airborne Viruses: Present Status, Standardization of Aerosol Filtration Tests, and Future Development.

The global COVID-19 pandemic has raised great public concern about the airborne transmission of viral pathogens. Virus-laden aerosols with small size could be suspended in the air for a long duration and remain infectious. Among a series of measures implemented to mitigate the airborne spread of infectious diseases, filtration by face masks, respirators, and air filters is a potent nonpharmacologic intervention. Compared with conventional air filtration media, nanofibrous membranes fabricated via electrospinning are promising candidates for controlling airborne viruses due to their desired characteristics, i.e., a reduced pore size (submicrometers to several micrometers), a larger specific surface area and porosity, and retained surface and volume charges. So far, a wide variety of electrospun nanofibrous membranes have been developed for aerosol filtration, and they have shown excellent filtration performance. However, current studies using electrospinning for controlling airborne viruses vary significantly in the practice of aerosol filtration tests, including setup configurations and operations. The discrepancy among various studies makes it difficult, if not impossible, to compare filtration performance. Therefore, there is a pressing need to establish a standardized protocol for evaluating the electrospun nanofibrous membranes' performance for removing viral aerosols. In this perspective, we first reviewed the properties and performance of diverse filter media, including electrospun nanofibrous membranes, for removing viral aerosols. Next, aerosol filtration protocols for electrospun nanofibrous membranes were discussed with respect to the aerosol generation, filtration, collection, and detection. Thereafter, standardizing the aerosol filtration test system for electrospun nanofibrous membranes was proposed. In the end, the future advancement of electrospun nanofibrous membranes for enhanced air filtration was discussed. This perspective provides a comprehensive understanding of status and challenges of electrospinning for air filtration, and it sheds light on future nanomaterial and protocol development for controlling airborne viruses, preventing the spread of infectious diseases, and beyond.

Photosensitized Electrospun Nanofibrous Filters for Capturing and Killing Airborne Coronaviruses under Visible Light Irradiation.

To address the challenge of the airborne transmission of SARS-CoV-2, photosensitized electrospun nanofibrous membranes were fabricated to effectively capture and inactivate coronavirus aerosols. With an ultrafine fiber diameter (∼200 nm) and a small pore size (∼1.5 µm), optimized membranes caught 99.2% of the aerosols of the murine hepatitis virus A59 (MHV-A59), a coronavirus surrogate for SARS-CoV-2. In addition, rose bengal was used as the photosensitizer for membranes because of its excellent reactivity in generating virucidal singlet oxygen, and the membranes rapidly inactivated 97.1% of MHV-A59 in virus-laden droplets only after 15 min irradiation of simulated reading light. Singlet oxygen damaged the virus genome and impaired virus binding to host cells, which elucidated the mechanism of disinfection at a molecular level. Membrane robustness was also evaluated, and in general, the performance of virus filtration and disinfection was maintained in artificial saliva and for long-term use. Only sunlight exposure photobleached membranes, reduced singlet oxygen production, and compromised the performance of virus disinfection. In summary, photosensitized electrospun nanofibrous membranes have been developed to capture and kill airborne environmental pathogens under ambient conditions, and they hold promise for broad applications as personal protective equipment and indoor air filters.

Safe Reinforcement Learning With Stability Guarantee for Motion Planning of Autonomous Vehicles.

Reinforcement learning with safety constraints is promising for autonomous vehicles, of which various failures may result in disastrous losses. In general, a safe policy is trained by constrained optimization algorithms, in which the average constraint return as a function of states and actions should be lower than a predefined bound. However, most existing safe learning-based algorithms capture states via multiple high-precision sensors, which complicates the hardware systems and is power-consuming. This article is focused on safe motion planning with the stability guarantee for autonomous vehicles with limited size and power. To this end, the risk-identification method and the Lyapunov function are integrated with the well-known soft actor-critic (SAC) algorithm. By borrowing the concept of Lyapunov functions in the control theory, the learned policy can theoretically guarantee that the state trajectory always stays in a safe area. A novel risk-sensitive learning-based algorithm with the stability guarantee is proposed to train policies for the motion planning of autonomous vehicles. The learned policy is implemented on a differential drive vehicle in a simulation environment. The experimental results show that the proposed algorithm achieves a higher success rate than the SAC.

Development of Electrospun Nanofibrous Filters for Controlling Coronavirus Aerosols.

Airborne transmission of SARS-CoV-2 plays a critical role in spreading COVID-19. To protect public health, we designed and fabricated electrospun nanofibrous air filters that hold promise for applications in personal protective equipment (PPE) and the indoor environment. Due to ultrafine nanofibers (∼300 nm), the electrospun air filters had a much smaller pore size in comparison to the surgical mask and cloth masks (a couple of micrometers versus tens to hundreds of micrometers). A coronavirus strain served as a SARS-CoV-2 surrogate and was used to generate aerosols for filtration efficiency tests, which can better represent SARS-CoV-2 in comparison to other agents used for aerosol generation in previous studies. The electrospun air filters showed excellent performance by capturing up to 99.9% of coronavirus aerosols, which outperformed many commercial face masks. In addition, we observed that the same electrospun air filter or face mask removed NaCl aerosols equivalently or less effectively in comparison to the coronavirus aerosols when both aerosols were generated from the same system. Our work paves a new avenue for advancing air filtration by developing electrospun nanofibrous air filters for controlling SARS-CoV-2 airborne transmission.