The efficacy and safety of vonoprazan in quadruple therapy for Helicobacter pylori eradication: a comparative study.

Background: The efficacy and optimal dose of the new acid-suppressant vonoprazan (VPZ) for quadruple therapy remain uncertain. This study aimed to compare the efficacy and safety of 20 mg VPZ daily (VOD) and 20 mg VPZ twice daily (VTD) with a proton pump inhibitor (PPI) twice daily in quadruple therapy. Methods: We retrospectively analyzed the data of 954 patients treated with quadruple therapy to eradicate Helicobacter pylori. Eradication rates and adverse events were compared between the VOD and VTD groups, and between the VOD and PPI groups. Multivariate analysis was conducted to identify the predictors of eradication failure. Results: Eradication was successful in 875 (91.7%) of the 954 patients. The total, initial, and rescue eradication rates in the VOD group were 92.1%, 93.3%, and 77.8%, respectively. In both the crude and multivariate analyses, the VOD group showed eradication rates comparable to those of the VTD and PPI groups (all P > 0.05). Age > 60 years (odds ratio [OR] = 2.165, P = 0.012) and use of rescue therapy (OR = 3.496, P < 0.001) were independent risk factors for eradication failure, whereas VPZ at a low dosing frequency of 20 mg daily was not. A total of 787 patients (82.5%) were followed up (mean follow-up time, 6.7 ± 2.0 months). Compared with the VOD group, the VTD group was more likely to experience adverse events (OR = 2.073, P = 0.035). Conclusion: VPZ at a low dose of 20 mg daily is an effective and safe component of the quadruple therapy for H.pylori eradication.

Wetting Behavior of Inkjet-Printed Electronic Inks on Patterned Substrates.

In inkjet printing technology, one important factor influencing the printing quality and reliability of printed films is the interaction of the jetted ink with the substrate surface. This short-range interaction determines the wettability and the adhesion of the ink to the solid surface and is hence responsible for the final shape of the deposited ink. Here, we investigate wetting morphologies of inkjet-printed inks on patterned substrates by carefully designed experimental test structures and simulations. The contact angles, the surface properties, and drop shapes, as well as their influence on the device variability, are experimentally and theoretically analyzed. For the simulations, we employ the phase-field method, which is based on the free energy minimization of the two-phase system with the given wetting boundary conditions. Through a systematic investigation of printed drops on patterned substrates consisting of hydrophilic and hydrophobic areas, we report that the printed morphology is related not only to the designed layout and the drop volume but also to the printing strategy and the wettability. Furthermore, we show how one can modify the intrinsic wettability of the patterned substrates to enhance the printing quality and reliability. Based on the present findings, we cast light on the improvement of the fabrication quality of thin film transistors.

High-Performance Pressure Sensors Based on Shaped Gel Droplet Arrays.

Polymer gel-based pressure sensors offer numerous advantages over traditional sensing technologies, including excellent conformability and integration into wearable devices. However, challenges persist in terms of their performance and manufacturing technology. In this study, a method for fabricating gel pressure sensors using a hydrophobic/hydrophilic patterned surface is introduced. By shaping and fine-tuning the droplets of the polymer gel prepolymerization solution on the patterned surface, remarkable sensitivity improvements compared to unshaped hydrogels have been achieved. This also showcased the potential for tailoring gel pressure sensors to different applications. By optimizing the configuration of the sensor array, an uneven conductive gel array is fabricated, which exhibited a high sensitivity of 0.29 kPa-1 in the pressure range of 0-30 kPa, while maintaining a sensitivity of 0.13 kPa-1 from 30 kPa up to 100 kPa. Furthermore, the feasibility of using these sensors for human motion monitoring is explored and a conductive gel array for 2D force detection is successfully developed. This efficient and scalable fabrication method holds promise for advancing pressure sensor technology and offers exciting prospects for various industries and research fields.

Dielectric Gels with Microphase Separation for Wide-Range and Self-Damping Pressure Sensing.

Omnipresent vibrations pose a significant challenge to flexible pressure sensors by inducing unstable output signals and curtailing their operational lifespan. Conventional soft sensing materials possess adequate elasticity but prove inadequate in countering vibrations. Moreover, the utilization of conventional highly-damping materials for sensing is challenging due to their substantial hysteresis. To tackle this dilemma, dielectric gels with controlled in situ microphase separation have been developed, leveraging the miscibility disparity between copolymers and solvents. The resulting gels exhibit exceptional compression stress, remarkable dielectric constant, and exceptional damping capabilities. Furthermore, flexible pressure sensors based on these microphase-separated gels show a wide detection range and low detection limit, more importantly, excellent sensing performance on vibrating surfaces. This work offers high potentials for applying flexible pressure sensors in complex practical scenarios and opens up new avenues for applications in soft electronics, biomimetic robots, and intelligent sensing.

Diapers to Thickeners and Pressure-Sensitive Adhesives: Recycling of Superabsorbers via UV Degradation.

Superabsorbers based on crosslinked sodium polyacrylate polymers cannot be easily recycled, resulting in 2 million tons of superabsorbers being landfilled or burned every year. A fast and efficient strategy to recycle superabsorbers would significantly alleviate environmental pollution and promote a sustainable use of these polymers. Herein, the rapid recycling of crosslinked sodium polyacrylate hydrogels based on their inherent UV degradation is demonstrated without the need for chemicals besides water. A quantitative conversion of crosslinked sodium polyacrylate into soluble sodium polyacrylate is achieved in minutes, almost 200 times faster than a previous approach based on de-esterification. The obtained soluble sodium polyacrylate can be used, for example, as a thickener for aqueous dyes or can be esterified with n-butanol or 2-ethylhexanol to serve as a pressure-sensitive adhesive. The UV photodegradation and esterification of superabsorbers is fast, scalable, safe, and economical and yields polymers with controllable molecular weight in the range of 100-400 kg/mol. It thus offers distinct advantages over the chemical de-crosslinking strategies presented previously.

D-1553: A novel KRASG12C inhibitor with potent and selective cellular and in vivo antitumor activity.

D-1553 is a small molecule inhibitor selectively targeting KRASG12C and currently in phase II clinical trials. Here, we report the preclinical data demonstrating antitumor activity of D-1553. Potency and specificity of D-1553 in inhibiting GDP-bound KRASG12C mutation were determined by thermal shift assay and KRASG12C -coupled nucleotide exchange assay. In vitro and in vivo antitumor activity of D-1553 alone or in combination with other therapies were evaluated in KRASG12C mutated cancer cells and xenograft models. D-1553 showed selective and potent activity against mutated GDP-bound KRASG12C protein. D-1553 selectively inhibited ERK phosphorylation in NCI-H358 cells harboring KRASG12C mutation. Compared to the KRAS WT and KRASG12D cell lines, D-1553 selectively inhibited cell viability in multiple KRASG12C cell lines, and the potency was slightly superior to sotorasib and adagrasib. In a panel of xenograft tumor models, D-1553, given orally, showed partial or complete tumor regression. The combination of D-1553 with chemotherapy, MEK inhibitor, or SHP2 inhibitor showed stronger potency on tumor growth inhibition or regression compared to D-1553 alone. These findings support the clinical evaluation of D-1553 as an efficacious drug candidate, both as a single agent or in combination, for patients with solid tumors harboring KRASG12C mutation.

Naturally sourced hydrogels: emerging fundamental materials for next-generation healthcare sensing.

The flourishing development of flexible healthcare sensing systems is inseparable from the fundamental materials with application-oriented mechanical and electrical properties. Thanks to continuous inspiration from our Mother Nature, flexible hydrogels originating from natural biomass are attracting growing attention for their structural and functional designs owing to their unique chemical, physical and biological properties. These highly efficient architectural and functional designs enable them to be the most promising candidates for flexible electronic sensing devices. This comprehensive review focuses on the recent advances in naturally sourced hydrogels for constructing multi-functional flexible sensors and healthcare applications thereof. We first briefly introduce representative natural polymers, including polysaccharides, proteins, and polypeptides, and summarize their unique physicochemical properties. The design principles and fabrication strategies for hydrogel sensors based on these representative natural polymers are outlined after the fundamental material properties required in healthcare sensing applications are presented. We then highlight the various fabrication techniques of natural hydrogels for sensing devices, and illustrate the representative examples of wearable or implantable bioelectronics for pressure, strain, temperature, or biomarker sensing in the field of healthcare systems. Finally, concluding remarks on challenges and prospects in the development of natural hydrogel-based flexible sensors are provided. We hope that this review will provide valuable information for the development of next-generation bioelectronics and build a bridge between the natural hydrogels as fundamental matter and multi-functional healthcare sensing as an applied target to accelerate new material design in the near future.

Dihydrotrichodimerol Purified from the Marine Fungus Acremonium citrinum Prevents NAFLD by Targeting PPARα.

The pathogenesis of nonalcoholic fatty liver disease (NAFLD) is closely linked to the imbalance of lipid and glucose metabolism, in which peroxisome proliferator-activated receptors (PPARs) play essential roles. The clinical trials have shown the beneficial effects of the PPARs' ligands on NAFLD. In this study, we screen the extracts from the marine fungus Acremonium citrinum and identify the natural compounds dihydrotrichodimerol (L1A) and trichodimerol (L1B) as the ligands of PPARs, of which L1A is a dual PPARα/γ agonist, whereas L1B is a selective PPARγ agonist. L1A but not L1B significantly prevents hepatic lipid accumulation in an oleic acid-induced NAFLD cell model as well as in a high-fat-diet-induced NAFLD mouse model. Moreover, L1A potently inhibits hepatic steatosis in a PPARα-dependent manner in another NAFLD mouse model constructed by using a choline-deficient and amino acid-defined diet. Mechanistically, L1A transcriptionally up-regulates the expression of SIRT1 in a PPARα-dependent manner, followed by the activation of AMPK and inactivation of ACC, resulting in the inhibition of lipid anabolism and the increase of lipid catabolism. Taken together, our study reveals a dual ligand of PPARα/γ with a distinct structure and therapeutic effect on NAFLD, providing a potential drug candidate bridging the currently urgent need for the management of NAFLD.

Tough, Self-Healing, and Conductive Elastomer ─Ionic PEGgel.

Ionically conductive elastomers are necessary for realizing human-machine interfaces, bioelectronic applications, or durable wearable sensors. Current design strategies, however, often suffer from solvent leakage and evaporation, or from poor mechanical properties. Here, we report a strategy to fabricate ionic elastomers (IHPs) demonstrating high conductivity (0.04 S m-1), excellent electrochemical stability (>60,000 cycles), ultra-stretchability (up to 1400%), high toughness (7.16 MJ m-3), and fast self-healing properties, enabling the restoration of ionic conductivity within seconds, as well as no solvent leakage. The ionic elastomer is composed of in situ formed physically cross-linked poly(2-hydroxyethyl methacrylate) networks and poly(ethylene glycol) (PEG). The long molecular chains of PEG serve as a solvent for dissolving electrolytes, improve its long-term stability, reduce solvent leakage, and ensure the outstanding mechanical properties of the IHP. Surprisingly, the incorporation of ions into PEG simultaneously enhances the strength and toughness of the elastomer. The strengthening and toughening mechanisms were further revealed by molecular simulation. We demonstrate an application of the IHPs as (a) flexible sensors for strain or temperature sensing, (b) skin electrodes for recording electrocardiograms, and (c) a tough and sensing material for pneumatic artificial muscles. The proposed strategy is simple and easily scalable and can further inspire the design of novel ionic elastomers for ionotronics applications.

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection.

At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

Inverse Vulcanization of Norbornenylsilanes: Soluble Polymers with Controllable Molecular Properties via Siloxane Bonds.

The inverse vulcanization produces high sulfur content polymers from alkenes and elemental sulfur. Control over properties such as the molar mass or the solubility of polymers is not well established, and existing strategies lack predictability or require large variations of the composition. Systematic design principles are sought to allow for a targeted design of materials. Herein, we report on the inverse vulcanization of norbornenylsilanes (NBS), with a different number of hydrolysable groups at the silicon atom. Inverse vulcanization of mixtures of NBS followed by polycondensation yielded soluble high sulfur content copolymers (50 wt % S) with controllable weight average molar mass (MW ), polydispersity (D), glass transition temperature (TG ), or zero-shear viscosity (η0 ). Polycondensation was conducted in the melt with HCl as a catalyst, abolishing the need for a solvent. Purification by precipitation afforded polymers with a greatly reduced amount of low molar mass species.

Tough, Transparent, 3D-Printable, and Self-Healing Poly(ethylene glycol)-Gel (PEGgel).

Polymer gels, such as hydrogels, have been widely used in biomedical applications, flexible electronics, and soft machines. Polymer network design and its contribution to the performance of gels has been extensively studied. In this study, the critical influence of the solvent nature on the mechanical properties and performance of soft polymer gels is demonstrated. A polymer gel platform based on poly(ethylene glycol) (PEG) as solvent is reported (PEGgel). Compared to the corresponding hydrogel or ethylene glycol gel, the PEGgel with physically cross-linked poly(hydroxyethyl methacrylate-co-acrylic acid) demonstrates high stretchability and toughness, rapid self-healing, and long-term stability. Depending on the molecular weight and fraction of PEG, the tensile strength of the PEGgels varies from 0.22 to 41.3 MPa, fracture strain from 12% to 4336%, modulus from 0.08 to 352 MPa, and toughness from 2.89 to 56.23 MJ m-3 . Finally, rapid self-healing of the PEGgel is demonstrated and a self-healing pneumatic actuator is fabricated by 3D-printing. The enhanced mechanical properties of the PEGgel system may be extended to other polymer networks (both chemically and physically cross-linked). Such a simple 3D-printable, self-healing, and tough soft material holds promise for broad applications in wearable electronics, soft actuators and robotics.

A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.

Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions and mechanisms of lncRNAs. Existing computational methods have been effectively applied to LPI prediction. However, the majority of them were evaluated only on one LPI dataset, thereby resulting in prediction bias. More importantly, part of models did not discover possible LPIs for new lncRNAs (or proteins). In addition, the prediction performance remains limited. To solve with the above problems, in this study, we develop a Deep Forest-based LPI prediction method (LPIDF). First, five LPI datasets are obtained and the corresponding sequence information of lncRNAs and proteins are collected. Second, features of lncRNAs and proteins are constructed based on four-nucleotide composition and BioSeq2vec with encoder-decoder structure, respectively. Finally, a deep forest model with cascade forest structure is developed to find new LPIs. We compare LPIDF with four classical association prediction models based on three fivefold cross validations on lncRNAs, proteins, and LPIs. LPIDF obtains better average AUCs of 0.9012, 0.6937 and 0.9457, and the best average AUPRs of 0.9022, 0.6860, and 0.9382, respectively, for the three CVs, significantly outperforming other methods. The results show that the lncRNA FTX may interact with the protein P35637 and needs further validation.

A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization.

Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.

Facile Approach to Conductive Polymer Microelectrodes for Flexible Electronics.

Conductive polymers have been intensively investigated as materials for electrodes in flexible electronics due to their favorable biocompatibility and reliable electrochemical stability. Nevertheless, patterning of conductive polymers for the fabrication of devices and in various electronics applications confronts multifarious limitations and challenges. Here, we present a simple but efficient strategy to obtain conductive polymer microelectrodes via utilization of surface-tension-confined liquid patterns. This method shows universality for various oxidizers and conductive polymers, high resolution, stability, and favorable compatibility with different surfaces and materials. The developed method has been demonstrated for creating conductive polymer microelectrodes with a customized reaction process, defined geometry, and flexible substrates. The obtained microelectrodes were assembled into flexible capacitive sensors. Thus, the method realizes a facile approach to conductive polymer microelectrodes for flexible electronics, biomedical applications, human activity monitors, and electronic skin.

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification.

Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.

Highly Sensitive Pressure and Strain Sensors Based on Stretchable and Recoverable Ion-Conductive Physically Cross-Linked Double-Network Hydrogels.

Ion-conductive hydrogel sensors have attracted great research interests for applications in wearable devices, electronic skins, and implantable sensors, but most such sensors are fragile, with low conductivity and sensitivity. This study reports on novel ion-conductive double network hydrogels with a cross-linked helical structure, hydrophobic association, and metal-ion coordination. The helical κ-carrageenan first network and the second network cross-linked by Pluronic F127 diacrylate micelles and tridentate Fe3+-COO- coordination work synergistically to show the tensile strength of 2.7 MPa, fracture strain of 1400%, and tensile toughness of 9.82 MJ m-3 and fatigue resistance against cyclic loadings with high strains. The hydrogels show an ion conductivity of 1.15 S m-1, a strain sensitivity of up to 2.8, and a pressure sensitivity of 0.33 kPa-1. Sensor arrays fabricated from the conductive hydrogels provide an in-plane detection of pressures less than 200 Pa. Such hydrogel sensors have potential applications to electron skins and implantable sensors.

Stretchable and tough conductive hydrogels for flexible pressure and strain sensors.

Flexible pressure and strain sensors have great potential for applications in wearable and implantable devices, soft robotics and artificial skin. Compared to flexible sensors based on filler/elastomer composites, conductive hydrogels are advantageous due to their biomimetic structures and properties, as well as biocompatibility. Numerous chemical and structural designs provide unlimited opportunities to tune the properties and performance of conductive hydrogels to match various demands for practical applications. Many electronically and ionically conductive hydrogels have been developed to fabricate pressure and strain sensors with different configurations, including resistance type and capacitance type. The sensitivity, reliability and stability of hydrogel sensors are dependent on their network structures and mechanical properties. This review focuses on tough conductive hydrogels for flexible sensors. Representative strategies to prepare stretchable, strong, tough and self-healing hydrogels are briefly reviewed since these strategies are illuminating for the development of tough conductive hydrogels. Then, a general account on various conductive hydrogels is presented and discussed. Recent advances in tough conductive hydrogels with well designed network structures and their sensory performance are discussed in detail. A series of conductive hydrogel sensors and their application in wearable devices are reviewed. Some perspectives on flexible conductive hydrogel sensors and their applications are presented at the end.

Quantifying aircraft emissions of Shanghai Pudong International Airport with aircraft ground operational data.

The air traffic growth at Shanghai Pudong International Airport (PVG) has attracted much concern over the potential impacts on local air quality and human health; however, the emission contributions due to aircraft activities, impact on air quality and health effects remain unclear. In this study, the ground operational data derived from the Aircraft Communication Addressing and Reporting System (ACARS) dataset are newly utilized to obtain the PVG-specific emission parameters of 10 distinct aircraft-engine combinations during the taxi-in and taxi-out phases of the landing and take-off (LTO) cycle. The resulting emission parameters, together with PVG-specific operational conditions, are applied to quantify the annual emissions in 2017 for main engines and auxiliary power units (APUs) at PVG, emission variations caused by mixing layer height, sensitivity of black carbon (BC) emissions to the estimation method and sensitivity of PM2.5 emissions to the fuel sulfur content (FSC). The results show noticeable discrepancies between the corrected fuel flows and NOx emission indices (EIs) and those certified by the International Civil Aviation Organization (ICAO). The annual emissions of hydrocarbons (HC), CO, NOx, NO, NO2, HONO, HNO3, NOy, SO2, SO42-, BC, organic carbon (OC) and PM2.5 with corrected emission parameters are 3.82 × 105 kg, 4.35 × 106 kg, 5.36 × 106 kg, 4.40 × 106 kg, 9.58 × 105 kg, 1.03 × 105 kg, 3.83 × 103 kg, 5.47 × 106 kg, 3.56 × 105 kg, 1.31 × 104 kg, 5.43 × 104 kg, 4.73 × 103 kg and 7.22 × 104 kg, respectively, while the application of the maximum height of the mixing layer contributes to emission increases as high as 16.9% (NOx). An alternative estimation of BC emissions leads to an increase of 50% compared with first-order approximation 3 (FOA3), while a reduction in PM2.5 emissions can be expected by minimizing the FSC.

Flexible and wearable strain sensors based on tough and self-adhesive ion conducting hydrogels.

Inspired by biosystem, ionic hydrogels have been extensively studied as promising materials for wearable or implantable devices. Herein, we report novel ionic hydrogels that comprise dynamically crosslinked polyzwitterion and physically crosslinked polyvinyl alcohol, which demonstrate excellent mechanical properties, repeatable self-adhesion, and high and linear strain sensitivity. The obtained hydrogels can be directly attached to human skin as sensors to detect or monitor physiological signals.