The Nexus of Innovation: Electrochemically Synthesizing H2O2 and Its Integration with Downstream Reactions

Hydrogen peroxide (H2O2) represents a chemically significant oxidant that is prized for its diverse applicability across various industrial domains. Recent innovations have shed light on the electrosynthesis of H2O2 through two-electron oxygen reduction reactions (2e- ORR) or two-electron water oxidation reactions (2e- WOR), processes that underscore the attractive possibility for the on-site production of this indispensable oxidizing agent. However, the translation of these methods into practical utilization within chemical manufacturing industries remains an aspiration rather than a realized goal. This Perspective intends to furnish a comprehensive overview of the latest advancements in the domain of coupled chemical reactions with H2O2, critically examining emergent strategies that may pave the way for the development of new reaction pathways. These pathways could enable applications that hinge on the availability and reactivity of H2O2, including, but not limited to the chemical synthesis coupled with H2O2 and waste water treatment byFenton-like reactions. Concurrently, the Perspective acknowledges and elucidates some of the salient challenges and opportunities inherent in the coupling of electrochemically generated H2O2, thereby providing a scholarly analysis that might guide future research.

Recent developments, advances and strategies in heterogeneous photocatalysts for water splitting

Photocatalytic water splitting (PWS) is an up-and-coming technology for generating sustainable fuel using light energy. Significant progress has been made in the developing of PWS innovations over recent years. In addition to various water-splitting (WS) systems, the focus has primarily been on one- and two-steps-excitation WS systems. These systems utilize singular or composite photocatalysts for WS, which is a simple, feasible, and cost-effective method for efficiently converting prevalent green energy into sustainable H2 energy on a large commercial scale. The proposed principle of charge confinement and transformation should be implemented dynamically by conjugating and stimulating the photocatalytic process while ensuring no unintentional connection at the interface. This study focuses on overall water splitting (OWS) using one/two-steps excitation and various techniques. It also discusses the current advancements in the development of new light-absorbing materials and provides perspectives and approaches for isolating photoinduced charges. This article explores multiple aspects of advancement, encompassing both chemical and physical changes, environmental factors, different photocatalyst types, and distinct parameters affecting PWS. Significant factors for achieving an efficient photocatalytic process under detrimental conditions, (e.g., strong light absorption, and synthesis of structures with a nanometer scale. Future research will focus on developing novel materials, investigating potential synthesis techniques, and improving existing high-energy raw materials. The endeavors aim is to enhance the efficiency of energy conversion, the absorption of radiation, and the coherence of physiochemical processes.

Compressed cerebellar functional connectome hierarchy in spinocerebellar ataxia type 3

Spinocerebellar ataxia type 3 (SCA3) is an inherited movement disorder characterized by a progressive decline in motor coordination. Despite the extensive functional connectivity (FC) alterations reported in previous SCA3 studies in the cerebellum and cerebellar-cerebral pathways, the influence of these FC disturbances on the hierarchical organization of cerebellar functional regions remains unclear. Here, we compared 35 SCA3 patients with 48 age- and sex-matched healthy controls using a combination of voxel-based morphometry and resting-state functional magnetic resonance imaging to investigate whether cerebellar hierarchical organization is altered in SCA3. Utilizing connectome gradients, we identified the gradient axis of cerebellar hierarchical organization, spanning sensorimotor to transmodal (task-unfocused) regions. Compared to healthy controls, SCA3 patients showed a compressed hierarchical organization in the cerebellum at both voxel-level (p < .05, TFCE corrected) and network-level (p < .05, FDR corrected). This pattern was observed in both intra-cerebellar and cerebellar-cerebral gradients. We observed that decreased intra-cerebellar gradient scores in bilateral Crus I/II both negatively correlated with SARA scores (left/right Crus I/II: r = -.48/-.50, p = .04/.04, FDR corrected), while increased cerebellar-cerebral gradients scores in the vermis showed a positive correlation with disease duration (r = .48, p = .04, FDR corrected). Control analyses of cerebellar gray matter atrophy revealed that gradient alterations were associated with cerebellar volume loss. Further FC analysis showed increased functional connectivity in both unimodal and transmodal areas, potentially supporting the disrupted cerebellar functional hierarchy uncovered by the gradients. Our findings provide novel evidence regarding alterations in the cerebellar functional hierarchy in SCA3.

Gut microbiome biomarkers in adolescent obesity: a regional study

Purpose

This study aimed to characterize the gut microbiota in obese adolescents from Shenzhen (China), and evaluate influence of gender on BMI-related differences in the gut microbiome.

Methods

Evaluation of physical examination, blood pressure measurement, serological assay and body composition were conducted in 205 adolescent subjects at Shenzhen. Fecal microbiome composition was profiled via high-throughput sequencing of the V3-V4 regions of the 16S rRNA gene. A Random Forest (RF) classifier model was built to distinguish the BMI categories based on the gut bacterial composition.

Results

Fifty-six taxa consisting mainly of Firmicutes were identified that having significant associations with BMI; 2 OTUs belonging to Ruminococcaceae and 1 belonging to Lachnospiraceae had relatively strong positive correlations with body fate rate, waistline and most of serum biochemical properties. Based on the 56 BMI-associated OTUs, the RF model showed a robust classification accuracy (AUC 0.96) for predicting the obese phenotype. Gender-specific differences in the gut microbiome composition was obtained, and a lower relative abundance of Odoribacter genus was particularly found in obese boys. Functional analysis revealed a deficiency in bacterial gene contents related to peroxisome and PPAR signaling pathway in the obese subjects for both genders.

Conclusions

This study reveals unique features of gut microbiome in terms of microbial composition and metabolic functions in obese adolescents, and provides a baseline for reference and comparison studies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00236-9.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis

The dynamic changes of key biological characteristics from gastric low-grade intraepithelial neoplasia (LGIN) to high-grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC) are still unclear, which greatly affect the accurate diagnosis and treatment of EGC and prognosis evaluation of gastric cancer (GC). In this study, bioinformatics methods/tools are applied to quantitatively analyze molecular characteristics evolution of GC progression, and a prognosis model is constructed. This study finds that some dysregulated differentially expressed mRNAs (DEmRNAs) in the LGIN stage may continue to promote the occurrence and development of EGC. Among the LGIN, HGIN, and EGC stages, there are differences and relevance in the transcription expression patterns of DEmRNAs, and the activation related to immune cells is very different. The biological functions continuously changed during the progression from LGIN to HGIN to EGC. The COX model constructed based on the three EGC-related DEmRNAs has GC prognostic risk prediction ability. The evolution of biological characteristics during the development of EGC mined by the authors provides new insight into understanding the molecular mechanism of EGC occurrence and development. The three-gene prognostic risk model provides a new method for assisting GC clinical treatment decisions.

Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI).

Methods

In this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD.

Results

The experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age.

Discussion

Our study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.

Kinetically Controlled Synthesis of Highly Emissive Au18SG14 Clusters and Their Phase Transfer: Tips and Tricks

Glutathione (GSH) protected gold nanoclusters (Au _n SG _m NCs) have been attractive because of their novel properties such as enhanced luminescence and band gap tunability at their quantum confinement region (below ∼2 nm). Initial synthetic routes of mixed-size clusters and size-based separation techniques had latter evolved toward atomically precise nanoclusters via thermodynamic and kinetic control routes. One such exemplary synthesis taking the advantages of a kinetically controlled approach is producing highly red-emissive Au₁₈SG₁₄ NCs (where SG = thiolate of glutathione), thanks to the slow reduction kinetics provided by the mild reducing agent NaBH₃CN. Despite the developments in the direct synthesis of Au₁₈SG₁₄, several meticulous reaction conditions still need to be understood for the highly adaptable synthesis of atomically pure NCs irrespective of the laboratory conditions. Herein, we have systematically studied a series of reaction steps involved in this kinetically controlled approach starting from the role of the antisolvent, formation of precursors to Au-SG thiolates, growth of Au-SG thiolates as a function of aging time, and exploring an optimal reaction temperature to optimize the desired nucleation under slow reduction kinetics. The crucial parameters derived in our studies guide the successful and large-scale production of Au₁₈SG₁₄ at any laboratory condition. Next, we investigated the effect of pH on the NCs to study the stability and the best suitable condition for the phase transfer of Au₁₈SG₁₄ clusters. The commonly implemented method of phase transfer at the basic conditions (pH > 9) is not successful in this case. However, we developed a feasible method for the phase transfer by diluting the aqueous NC solution to enhance the negative charges on the NCs' surface by increasing the degree of dissociation at the carboxylic acid group. It is interesting to note that after the phase transfer, the Au₁₈SG₁₄-TOA NCs in toluene as well as in other organic solvents exhibited enhanced luminescence quantum yields from 9 to 3 times and increased average photoluminescence lifetimes by 1.5-2.5 times, respectively.

Acoustic Attenuation and Dispersion in Fatty Tissues and Tissue Phantoms Influencing Ultrasound Biomedical Imaging

The development of ultrasonic imaging techniques is optimized using artificial tissue phantoms before the practical applications. However, due to the strong attenuation and dispersion, accumulated fatty tissues can significantly impact the resolution and even feasibility of certain ultrasonic imaging modalities. An appropriate characterization of the acoustic properties on fatty phantoms can help the community to overcome the limitations. Some of the existing methods heavily overestimate attenuation coefficients by including the reflection loss and dispersion effects. Hence, in this study, we use numerical simulation-based comparison between two major attenuation measurement configurations. We further pointed out the pulse dispersion in viscoelastic tissue phantoms by simulations, which barely attracted attention in the existing studies. Using the selected attenuation and dispersion testing methods that were selected from the numerical simulation, we experimentally characterized the acoustic properties of common fatty tissue phantoms and compared the acoustic properties with the natural porcine fatty tissue samples. Furthermore, we selected one of the tissue phantoms to construct ultrasound imaging samples with some biomasses. With the known attenuation and dispersion of the tissue phantom, we showed the clarity enhancement of ultrasound imaging by signal post-processing to weaken the attenuation and dispersion effects.

Mitochondria-targeting Polydopamine-coated Nanodrugs for Effective Photothermal- and Chemo- Synergistic therapies Against Lung Cancer

Targeting mitochondria via nano platform emerged as an attractive anti-tumor pathway due to the central regulation role in cellar apoptosis and drug resistance. Here, a mitochondria-targeting nanoparticle (TOS-PDA-PEG-TPP) was designed to precisely deliver polydopamine (PDA) as the photothermal agent and alpha-tocopherol succinate (α-TOS) as the chemotherapeutic drug to the mitochondria of the tumor cells, which inhibits the tumor growth through chemo- and photothermal- synergistic therapies. TOS-PDA-PEG-TPP was constructed by coating PDA on the surface of TOS NPs self-assembled by α-TOS, followed by grafting PEG and triphenylphosphonium (TPP) on their surface to prolong the blood circulation time and target delivery of TOS and PDA to the mitochondria of tumor cells. In vitro studies showed that TOS-PDA-PEG-TPP could be efficiently internalized by tumor cells and accumulated at mitochondria, resulting in cellular apoptosis and synergistic inhibition of tumor cell proliferation. In vivo studies demonstrated that TOS-PDA-PEG-TPP could be efficiently localized at tumor sites and significantly restrain the tumor growth under NIR irradiation without apparent toxicity or deleterious effects. Conclusively, the combination strategy adopted for functional nanodrugs construction aimed at target-delivering therapeutic agents with different action mechanisms to the same intracellular organelles can be extended to other nanodrugs-dependent therapeutic systems.

A programmable and skin temperature-activated electromechanical synergistic dressing for effective wound healing

Mechanical regulation and electric stimulation hold great promise in skin tissue engineering for manipulating wound healing. However, the complexity of equipment operation and stimulation implementation remains an ongoing challenge in clinical applications. Here, we propose a programmable and skin temperature-activated electromechanical synergistic wound dressing composed of a shape memory alloy-based mechanical metamaterial for wound contraction and an antibacterial electret thin film for electric field generation. This strategy is successfully demonstrated on rats to achieve effective wound healing in as short as 4 and 8 days for linear and circular wounds, respectively, with a statistically significant over 50% improvement in wound closure rate versus the blank control group. The optimally designed electromechanical synergistic stimulation could regulate the wound microenvironment to accelerate healing metabolism, promote wound closure, and inhibit infection. This work provided an effective wound healing strategy in the context of a programmable temperature-responsive, battery-free electromechanical synergistic biomedical device.

Room-temperature on-chip orbital angular momentum single-photon sources

On-chip photon sources carrying orbital angular momentum (OAM) are in demand for high-capacity optical information processing in both classical and quantum regimes. However, currently exploited integrated OAM sources have been primarily limited to the classical regime. Here, we demonstrate a room-temperature on-chip integrated OAM source that emits well-collimated single photons, with a single-photon purity of g⁽²⁾(0) ≈ 0.22, carrying entangled spin and OAM states and forming two spatially separated entangled radiation channels with different polarization properties. The OAM-encoded single photons are generated by efficiently outcoupling diverging surface plasmon polaritons excited with a deterministically positioned quantum emitter via Archimedean spiral gratings. Our OAM single-photon source platform bridges the gap between conventional OAM manipulation and nonclassical light sources, enabling high-dimensional and large-scale photonic quantum systems for quantum information processing.

The simple regularities in the dynamics of online news impact

Online news can quickly reach and affect millions of people, yet we do not know yet whether there exist potential dynamical regularities that govern their impact on the public. We use data from two major news outlets, BBC and New York Times, where the number of user comments can be used as a proxy of news impact. We find that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems. In particular, we find that a simple exponential distribution yields a better fit to the empirical news impact distributions than a power-law distribution. This observation is explained by the lack or limited influence of the otherwise omnipresent rich-get-richer mechanism in the analyzed data. The temporal dynamics of the news impact exhibits a universal exponential decay which allows us to collapse individual news trajectories into an elementary single curve. We also show how daily variations of user activity directly influence the dynamics of the article impact. Our findings challenge the universal applicability of popularity dynamics patterns found in other social contexts.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42001-021-00140-w.

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

Nanogenerator-based self-powered sensors for data collection

Self-powered sensors can provide energy and environmental data for applications regarding the Internet of Things, big data, and artificial intelligence. Nanogenerators provide excellent material compatibility, which also leads to a rich variety of nanogenerator-based self-powered sensors. This article reviews the development of nanogenerator-based self-powered sensors for the collection of human physiological data and external environmental data. Nanogenerator-based self-powered sensors can be designed to detect physiological data as wearable and implantable devices. Nanogenerator-based self-powered sensors are a solution for collecting data and expanding data dimensions in a future intelligent society. The future key challenges and potential solutions regarding nanogenerator-based self-powered sensors are discussed.

The effects of diagnosis-related groups payment on hospital healthcare in China: a systematic review

BACKGROUND

There has been a growing interest in using diagnosis-related groups (DRGs) payment to reimburse inpatient care worldwide. But its effects on healthcare and health outcomes are controversial, and the evidence from low- and middle- income countries (LMICs) is especially scarce. The objective of this study is to evaluate the effects of DRGs payment on healthcare and health outcomes in China.

METHOD

A systematic review was conducted. We searched literature databases of PubMed, Cochrane Library, EMBASE, Web of Science, Chinese National Knowledge Infrastructure and SinoMed for empirical studies examining the effects of DRGs payment on healthcare in mainland China. We performed a narrative synthesis of outcomes regarding expenditure, efficiency, quality and equity of healthcare, and assessed the quality of evidence.

RESULTS

Twenty-three publications representing thirteen DRGs payment studies were included, including six controlled before after studies, two interrupted time series studies and five uncontrolled before-after studies. All studies compared DRGs payment to fee-for-service, with or without an overall budget, in settings of tertiary (7), secondary (7) and primary care (1). The involved participants varied from specific groups to all inpatients. DRGs payment mildly reduced the length of stay. Impairment of equity of healthcare was consistently reported, especially for patients exempted from DRGs payment, including: patient selection, cost-shifting and inferior quality of healthcare. However, findings on total expenditure, out of pocket payment (OOP) and quality of healthcare were inconsistent. The quality of the evidence was generally low or very low due to the study design and potential risk of bias of included studies.

CONCLUSION

DRGs payment may mildly improve the efficiency but impair the equity and quality of healthcare, especially for patients exempted from this payment scheme, and may cause up-coding of medical records. However, DRGs payment may or may not contain the total expenditure or OOP, depending on the components design of the payment. Policymakers should very carefully consider each component of DRGs payment design against policy goals. Well-designed randomised trials or comparative studies are warranted to consolidate the evidence of the effects of DRGs payment on healthcare and health outcomes in LMICs to inform policymaking.

Epidemic spreading on time-varying multiplex networks

Social interactions are stratified in multiple contexts and are subject to complex temporal dynamics. The systematic study of these two features of social systems has started only very recently, mainly thanks to the development of multiplex and time-varying networks. However, these two advancements have progressed almost in parallel with very little overlap. Thus, the interplay between multiplexity and the temporal nature of connectivity patterns is poorly understood. Here, we aim to tackle this limitation by introducing a time-varying model of multiplex networks. We are interested in characterizing how these two properties affect contagion processes. To this end, we study susceptible-infected-susceptible epidemic models unfolding at comparable timescale with respect to the evolution of the multiplex network. We study both analytically and numerically the epidemic threshold as a function of the multiplexity and the features of each layer. We found that higher values of multiplexity significantly reduce the epidemic threshold especially when the temporal activation patterns of nodes present on multiple layers are positively correlated. Furthermore, when the average connectivity across layers is very different, the contagion dynamics is driven by the features of the more densely connected layer. Here, the epidemic threshold is equivalent to that of a single layered graph and the impact of the disease, in the layer driving the contagion, is independent of the multiplexity. However, this is not the case in the other layers where the spreading dynamics is sharply influenced by it. The results presented provide another step towards the characterization of the properties of real networks and their effects on contagion phenomena.