Reactive Oxygen-Correlated Photothermal Imaging of Smart COF Nanoreactors for Monitoring Chemodynamic Sterilization and Promoting Wound Healing.

Chemodynamic therapy (CDT) has emerged as a promising approach for treating infected diabetic wounds, while reliable imaging technology for simultaneous monitoring of ROS and therapeutic processes is still a formidable challenge. Herein, smart covalent organic framework (COF) nanoreactors (COF NRs) are constructed by hyaluronic acid (HA) packaged glucose oxidase (GOx) covalently linked Fe-COF for diabetic wound healing. Upon the breakdown of the HA protective layer, GOx consumes glucose to produce gluconic acid and hydrogen peroxide (H2O2), resulting in decreased local pH and H2O2 supplementation. Density functional theory (DFT) calculations show that Fe-COF has high catalytic activity towards H2O2, leading to in situ generation of hydroxyl radicals (·OH) for sterilization, and the localized downregulation of glucose effectively improved the microenvironment of diabetic wounds. Meanwhile, based on the near-infrared photothermal imaging of oxidized 3,3',5,5'-tetramethylbenzidine (oxTMB), the authors showed that TMB can be applied for the point-of-care testing of ·OH and glucose, and assessing the sterilization progress in vivo. More significantly, the facile photothermal signaling strategy can be extended to monitor various ROS-mediated therapeutic systems, enabling accurate prediction of treatment outcomes.

Progress in the Use of Echocardiography in Patients with Tumors.

Advances in cancer treatment have increased patient survival rates, shifting clinical focus towards minimizing treatment-related morbidity, including cardiovascular issues. Since echocardiography allows for a comprehensive non-invasive assessment at all cancer stages, it is well suited to monitor cardiovascular disease secondary to oncology treatment. This has earned it significant attention in the study of cardiac tumors and treatment-induced cardiac alterations. Ultrasound methods-ranging from transthoracic and transesophageal echocardiography to ultrasound diagnostic techniques including myocardial strain imaging, myocardial work indices, three-dimensional cardiac imaging-offer a holistic view of both the tumor and its treatment impact cardiac function. Stress echocardiography, myocardial contrast echocardiography, and myocardial acoustic angiography further augment this capability. Together, these echocardiographic techniques provide clinicians with early detection opportunities for cardiac damage, enabling timely interventions. As such, echocardiography continues to be instrumental in monitoring and managing the cardiovascular health of oncology patients, complementing efforts to optimize their overall treatment and survival outcomes.

Ambient Synthesis of Porphyrin-Based Fe-Covalent Organic Frameworks for Efficient Infected Skin Wound Healing.

Reactive oxygen species (ROS) have emerged as a promising treatment option for antibacterial and biofilm eradication. However, their therapeutic efficacy is significantly hampered by the unique microenvironments of diabetic wounds. In this study, we designed and synthesized porphyrin-based Fe covalent organic frameworks (Fe-COF) through a Schiff base condensation reaction. Subsequently, Fe-COF were encapsulated with hyaluronic acid (HA) through electrostatic adsorption, resulting in a novel formulation named HA-Fe-COF for diabetic wound healing. HA-Fe-COF were engineered to respond to hyaluronidase in the infected wound, leading to the controlled release of Fe-COF. Those released Fe-COF served a dual role as photosensitizers, generating singlet oxygen and localized heating when exposed to dual light sources. Additionally, they acted as peroxidase-like nanozymes, facilitating the production of ROS through enzymatic reactions. This innovative approach enabled a synergistic therapeutic effect combining photodynamic, photothermal, and chemodynamic modalities. Furthermore, the sustained release of HA from HA-Fe-COF promoted angiogenesis, collagen deposition, and re-epithelialization during the diabetic wound healing process. This "all-in-one" strategy offers a novel approach for the development of antimicrobial and biofilm eradication strategies that minimize damage to healthy tissues in vivo.

Alkali metal hydroxide-catalyzed mechanisms of Csp-H silylation of alkynes: a DFT investigation.

Mechanisms for the Csp-H silylation between prop-2-yn-1-ylcyclohexane and triethylsilane, catalyzed by MOH/MH (M = Na or K), were investigated at the M06-L-D3/ma-def2-TZVP level. The SMD model was applied to simulate the solvent effect of 1,2-dimethoxyethane (DME). Computational results suggested that the Csp-H activation of prop-2-yn-1-ylcyclohexane could be achieved by MOH to generate R-CC-M compounds, which continued to react with triethylsilane to yield the final product: (3-cyclohexylprop-1-yn-1-yl) triethylsilane. Moreover, analysis of the Gibbs free energy surface of the three reactions suggested that a path with the participation of LiOH had the highest energy barrier, which was consistent with experimental results showing that only a small amount of product had been formed. The obtained KH could interact readily with the H2O molecule with a much lower energy barrier (0.6 kcal mol-1) than that using the path with prop-2-yn-1-ylcyclohexane. Furthermore, compared to MOH, MH could catalyze the reaction with lower energy barriers, and the reactions became exothermic, thereby benefiting the reaction. Finally, the mechanism for obtaining the byproduct (prop-1-yn-1-ylcyclohexane) was posited: it had a higher energy barrier than the path to yield the main product. Frontier orbital, noncovalent interactions (NCI), Fukui function and dual descriptor analyses could be used to analyze the structure and reveal the reaction substances.

Grappling with the trade-offs of carbon emission trading and green certificate: Achieving carbon neutrality in China.

Although our knowledge of national carbon emission trading system and green certificate trading system are powerful incentive instruments that can deliver on increasingly ambitious climate targets in China, there remains an uncertainty of systems' structural reforms. This study builds on and extends a well-established dynamic computable general equilibrium (CGE) model to incorporate carbon trading system and green certificate trading system into the modeling framework, simulating a diverse of system development pathways further allows an exploration of the many possible policy effect. Then, using total factor productivity as a comprehensive indicator to asses policy effectiveness, the evolutionary trend of comprehensive effects under different paths are separately evaluated to discover the reforms' optimal range. Our work offers main results: First, these instruments provide a price signal. The introduction of a carbon allowance auction drive up carbon prices, while the implementation of a green certificate punishment and the expansion of the trading scope promote an increase in green certificate prices. Second, all policy scenarios that help reduce carbon emission intensity and optimize the power supply structure. However, in achieving the net-zero goal, the green certificate policy incurs more economic costs than the carbon trading policy. Indeed, the combination of multiple policy tools alleviates the decline of social welfare levels. Third, synergism design among policy tools: the focus should be on carbon trading policy from 2021 to 2030, green certificate trading policy from 2030 to 2050, and strengthened policy from 2050 to 2060. Reform measures within policies may need to be introduced in a timely manner. This study offers specific insights and tailored policy proposals to support policymakers in balancing environmental goals with economic and social needs in light of the aforementioned findings.

Increasing coordination and responsivity of emotion-related brain regions with a heart rate variability biofeedback randomized trial.

Heart rate variability is a robust biomarker of emotional well-being, consistent with the shared brain networks regulating emotion regulation and heart rate. While high heart rate oscillatory activity clearly indicates healthy regulatory brain systems, can increasing this oscillatory activity also enhance brain function? To test this possibility, we randomly assigned 106 young adult participants to one of two 5-week interventions involving daily biofeedback that either increased heart rate oscillations (Osc+ condition) or had little effect on heart rate oscillations (Osc- condition) and examined effects on brain activity during rest and during regulating emotion. While there were no significant changes in the right amygdala-medial prefrontal cortex (MPFC) functional connectivity (our primary outcome), the Osc+ intervention increased left amygdala-MPFC functional connectivity and functional connectivity in emotion-related resting-state networks during rest. It also increased down-regulation of activity in somatosensory brain regions during an emotion regulation task. The Osc- intervention did not have these effects. In this healthy cohort, the two conditions did not differentially affect anxiety, depression, or mood. These findings indicate that modulating heart rate oscillatory activity changes emotion network coordination in the brain.

Effects of a Randomised Trial of 5-Week Heart Rate Variability Biofeedback Intervention on Cognitive Function: Possible Benefits for Inhibitory Control.

Previous research suggests that higher heart rate variability (HRV) is associated with better cognitive function. However, since most previous findings on the relationship between HRV and cognitive function were correlational in nature, it is unclear whether individual differences in HRV play a causal role in cognitive performance. To investigate whether there are causal relationships, we used a simple breathing manipulation that increases HRV through a 5-week HRV biofeedback intervention and examined whether this manipulation improves cognitive performance in younger and older adults (N = 165). The 5-week HRV biofeedback intervention did not significantly improve inhibitory control, working memory and processing speed across age groups. However, improvement in the Flanker score (a measure of inhibition) was associated with the amplitude of heart rate oscillations during practice sessions in the younger and older intervention groups. Our results suggest that daily practice to increase heart rate oscillations may improve inhibitory control, but future studies using longer intervention periods are warranted to replicate the present finding.

Ultrasensitive Detection of Aqueous Uranyl Based on Uranyl-Triggered Protein Photocleavage.

The detection of environmental uranyl is attracting increasing attention. However, the available detection strategies mainly depend on the selective recognition of uranyl, which is subject to severe interference by coexisting metal ions. Herein, based on the unique uranyl-triggered photocleavage property, the protein BSA is labelled with fluorescent molecules that exhibit an aggregation-induced emission effect for uranyl detection. Uranyl-triggered photocleavage causes the separation of the fluorescent-molecule-labelled protein fragments, leading to attenuation of the emission fluorescence, which is used as a signal for uranyl detection. This detection strategy shows high selectivity for uranyl and an ultralow detection limit of 24 pM with a broad detection range covering five orders of magnitude. The detection method also shows high reliability and stability, making it a promising technique for practical applications in diverse environments.

In Situ Synthesis of Uranyl-Imprinted Nanocage for Selective Uranium Recovery from Seawater.

An adaptive coordination structure is vital for selective uranium extraction from seawater. By strategy of molecular imprinting, uranyl is introduced into a multivariate metal-organic framework (MOF) during the synthesis process to guide the in situ construction of proper nanocage structure for targeting uranyl binding. Except for the coordination between uranium with four oxygen from the materials, the axial oxygen of uranyl also forms hydrogen bonds with hydrogen from the phenolic hydroxyl group, which enhances the binding affinity of the material to uranyl. Attributing to the high binding affinity, the adsorbent shows high uranium binding selectivity to uranyl against not only the interfering metal ions, but also the carbonate group that coordinates with uranyl to form [UO2 (CO)3 ]4- in seawater. In natural seawater, the adsorbent realizes a high uranium adsorption capacity of 7.35 mg g-1 , together with an 18.38 times higher selectivity to vanadium.

Comprehensive identification, fragmentation pattern, and metabolic pathways of gefitinib metabolites via UHPLC-Q-TOF-MS/MS: in vivo study of rat plasma, urine, bile, and faeces.

Gefitinib, the first approved inhibitor for oral epidermal growth factor receptor (EGFR), has been proved to be effective in non-small cell lung cancer with EGFR mutation. However, there are many metabolites of gefitinib that have not been identified in vivo. This study aims to identify the metabolites of gefitinib and its metabolic pathways in rats using ultra-high-performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS) detector. Protein precipitation, solid-phase and ultrasonic extraction were used for the pre-treatment of plasma, urine, bile and faeces samples. In this study, a total of 28 compounds were identified in rat plasma, 29 in bile, 20 in urine and 16 in faeces. 20 new compounds were firstly reported as metabolites of gefitinib. Reduction, hydroxylation, dealkylation and dehalogenation were the major metabolic pathways in phase I. For phase II, the main pathways were sulphate and glucuronide conjugation. The fragment ions of gefitinib and its metabolites were usually generated via the fracture of C1-O bond of propoxy on the C6 position of aniline quinazoline ring. The results may be valuable and important for understanding the metabolic process of gefitinib in clinical application and drug safety.

Spidroin-Inspired, High-Strength, Loofah-Shaped Protein Fiber for Capturing Uranium from Seawater.

The unique three-dimensional structure of spidrion determines the outstanding mechanical properties of the spider silk fiber. Inspired by the similarity of the three-dimensional structure of superb-uranyl binding protein (SUP) to that of spidroin, a dual-SUP (DSUP) chimeric protein fiber with high tensile strength is designed. The DSUP hydrogel fiber exhibits a loofah-shape structure by the cross-interaction of the protein nanofiber. Full exposure of abundant functional uranyl-binding sites in the stretchable loofah-shape hydrogel protein fiber give the DSUP fiber a groundbreaking uranium extraction capacity of 17.45 mg g-1 with an ultrashort saturation time of 3 days in natural seawater. This work reports the design of an adsorbent with ultrahigh uranium extraction capacity and explores a strategy for fabricating artificial high-strength functional non-spidroin protein fiber.

Tsc1-dependent transcriptional programming of dendritic cell homeostasis and function.

Dendritic cells (DCs) are pivotal to initiating adaptive immune response. Emerging evidence highlights important roles of tuberous sclerosis complex 1 (Tsc1) in DC development and activation. Our previous study also showed that Tsc1 expression in DCs was required to promote T-cell homeostasis and response partially through inhibiting mammalian target of rapamycin complex1 (mTORC1). However, the molecular mechanism of transcriptional regulation by which Tsc1 control DC homeostasis and function remains largely unknown. Here we globally identified the Tsc1-regulated genes by comparing the transcriptional profiling of Tsc1-deficient DCs with wild-type DCs. It showed that Tsc1 specifically regulated the expression of groups of gene sets critically involved in DC survival, proliferation, metabolism and antigen presentation. The impacts of Tsc1 on DC gene expression were partially dependent on inhibition of mTORC1 signal. Our study thus provides a comprehensive molecular basis for understanding how Tsc1 programs the homeostasis and function of DCs through transcriptional regulation.

Multimodal Human and Environmental Sensing for Longitudinal Behavioral Studies in Naturalistic Settings: Framework for Sensor Selection, Deployment, and Management.

BACKGROUND: Recent advances in mobile technologies for sensing human biosignals are empowering researchers to collect real-world data outside of the laboratory, in natural settings where participants can perform their daily activities with minimal disruption. These new sensing opportunities usher a host of challenges and constraints for both researchers and participants. OBJECTIVE: This viewpoint paper aims to provide a comprehensive guide to aid research teams in the selection and management of sensors before beginning and while conducting human behavior studies in the wild. The guide aims to help researchers achieve satisfactory participant compliance and minimize the number of unexpected procedural outcomes. METHODS: This paper presents a collection of challenges, consideration criteria, and potential solutions for enabling researchers to select and manage appropriate sensors for their research studies. It explains a general data collection framework suitable for use with modern consumer sensors, enabling researchers to address many of the described challenges. In addition, it provides a description of the criteria affecting sensor selection, management, and integration that researchers should consider before beginning human behavior studies involving sensors. On the basis of a survey conducted in mid-2018, this paper further illustrates an organized snapshot of consumer-grade human sensing technologies that can be used for human behavior research in natural settings. RESULTS: The research team applied the collection of methods and criteria to a case study aimed at predicting the well-being of nurses and other staff in a hospital. Average daily compliance for sensor usage measured by the presence of data exceeding half the total possible hours each day was about 65%, yielding over 355,000 hours of usable sensor data across 212 participants. A total of 6 notable unexpected events occurred during the data collection period, all of which had minimal impact on the research project. CONCLUSIONS: The satisfactory compliance rates and minimal impact of unexpected events during the case study suggest that the challenges, criteria, methods, and mitigation strategies presented as a guide for researchers are helpful for sensor selection and management in longitudinal human behavior studies in the wild.

Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking.

Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.

Socioeconomic Drivers of Greenhouse Gas Emissions in the United States.

Existing studies examined the U.S.'s direct GHG emitters and final consumers driving upstream GHG emissions, but overlooked the U.S.'s primary suppliers enabling downstream GHG emissions and relative contributions of socioeconomic factors to GHG emission changes from the supply side. This study investigates GHG emissions of sectors in the U.S. from production-based (direct emissions), consumption-based (upstream emissions driven by final consumption of products), and income-based (downstream emissions enabled by primary inputs of sectors) viewpoints. We also quantify relative contributions of socioeconomic factors to the US's GHG emission changes during 1995-2009 from both the consumption and supply sides, using structural decomposition analysis (SDA). Results show that income-based method can identify new critical sectors leading to GHG emissions (e.g., Renting of Machinery & Equipment and Other Business Activities and Financial Intermediation sectors) which are unidentifiable by production-based and consumption-based methods. Moreover, the supply side SDA reveals new factors for GHG emission changes: mainly production output structure representing product allocation pattern and primary input structure indicating sectoral shares in primary inputs. In addition to production-side and consumption-side GHG reduction measures, the U.S. should also pay attention to supply side measures such as influencing the behaviors of product allocation and primary inputs.

Should diabetic ketosis without acidosis be included in ketosis-prone type 2 diabetes mellitus?

BACKGROUND: The incidence of ketosis-prone type 2 diabetes is very low except for people of sub-Saharan African origin and African Americans. However, there also are some type 2 diabetes patients with diabetic ketosis without acidosis (DKWA). We question whether DKWA should be included as a subtype of ketosis-prone type 2 diabetes mellitus and compared the clinical characteristics of DKWA and diabetic ketoacidosis (DKA) patients. METHODS: The study population consisted of 594 consecutive unrelated Chinese inpatients with newly diagnosed type 2 diabetes. Demographic and clinical characteristics (age, gender, family history of diabetes, body mass index, blood pressure and plasma lipid parameters) were recorded. The patients were divided into ketosis-resistant diabetes (KRD), DKWA and DKA groups on the basis of urinary ketones, blood pH and bicarbonate levels. The blood glucose and c-peptide levels of the patients were also evaluated. RESULTS: The prevalence of KRD, DKWA and DKA were 78.33%, 19.72% and 1.95%, respectively, in the study population. The clinical characteristics of patients with DKWA group patients were similar to those with DKA, except that DKA patients had higher blood glucose and deteriorated ß cell function. CONCLUSIONS: Diabetic ketosis without acidosis and DKA patients share similar clinical characteristics; DKWA should be considered ketosis-prone type 2 diabetes. Therefore, the prevalence of ketosis-prone type 2 diabetes might be underestimated.

PROTOCOL: Effectiveness of behavioral interventions for smoking cessation among homeless persons: A systematic review and meta-analysis.

This is the protocol for an updated Campbell systematic review. The objectives are as follows: To evaluate the effect of behavioral interventions on smoking cessation among homeless individuals.

Nitrogen-doped activated carbon composite electrode for deionization of phosphate removal and DFT model adsorption of phosphates.

Phosphate discharge in sewage can result in water eutrophication, posing a threat to aquatic ecosystems. Membrane capacitive deionization (MCDI) has demonstrated outstanding performance and significant potential for salt removal and nutrient recovery. In this study, a nitrogen-doped activated carbon electrode material (NAC) was synthesized through one-step pyrolysis to selectively remove phosphate from MCDI. At a voltage of 1.2V, a flow rate of 20 mL/min, and a pH of 6.51, the phosphate adsorption capacity of the NAC electrode was determined to be 1.60 mg/g. The study revealed that NAC pHpzc increased from 4.14 to 6.44, effectively broadening the pH range for phosphate removal. In the presence of competing ions (NO3-, Cl-, and SO42-) at a concentration of 0.5 M, the electroadsorption capacity of phosphate decreased to 1.21 mg/g, 1.14 mg/g, and 1.02 mg/g, respectively. The kinetic parameters of adsorption indicated that NAC electroadsorbed phosphate through physical adsorption, with the maximum adsorption capacity achieved at 303K. Data from the Freundlich isothermal model suggested that phosphate adsorption by the NAC electrode involves a multilayer adsorption process. A carbon structure model of density functional theory (DFT), incorporating doped nitrogen, was constructed based on XPS analysis. Following nitrogen doping, the electrostatic potential (ESP) of unsaturated carbon atoms became more positive, enhancing the ability of nitrogen-doped activated carbon to adsorb phosphate. This study provides compelling evidence that nitrogen doping facilitates the adsorption of phosphate by carbon materials.

Scaling Representation Learning From Ubiquitous ECG With State-Space Models.

Ubiquitous sensing from wearable devices in the wild holds promise for enhancing human well-being, from diagnosing clinical conditions and measuring stress to building adaptive health promoting scaffolds. But the large volumes of data therein across heterogeneous contexts pose challenges for conventional supervised learning approaches. Representation Learning from biological signals is an emerging realm catalyzed by the recent advances in computational modeling and the abundance of publicly shared databases. The electrocardiogram (ECG) is the primary researched modality in this context, with applications in health monitoring, stress and affect estimation. Yet, most studies are limited by small-scale controlled data collection and over-parameterized architecture choices. We introduce WildECG, a pre-trained state-space model for representation learning from ECG signals. We train this model in a self-supervised manner with 275 000 10 s ECG recordings collected in the wild and evaluate it on a range of downstream tasks. The proposed model is a robust backbone for ECG analysis, providing competitive performance on most of the tasks considered, while demonstrating efficacy in low-resource regimes.