Lanternarene-Based Self-Sorting Double-Network Hydrogels for Flexible Strain Sensors.

Conductive flexible hydrogels have attracted immense attentions recently due to their wide applications in wearable sensors. However, the poor mechanical properties of most conductive polymer limit their utilizations. Herein, a double network hydrogel is fabricated via a self-sorting process with cationic polyacrylamide as the first flexible network and the lantern[33]arene-based hydrogen organic framework nanofibers as the second rigid network. This hydrogel is endowed with good conductivity (0.25 S m-1) and mechanical properties, such as large Young's modulus (31.9 MPa), fracture elongation (487%) and toughness (6.97 MJ m-3). The stretchability of this hydrogel is greatly improved after the kirigami cutting, which makes it can be used as flexible strain sensor for monitoring human motions, such as bending of fingers, wrist and elbows. This study not only provides a valuable strategy for the construction of double network hydrogels by lanternarene, but also expands the application of the macrocycle hydrogels to flexible electronics.

Sulfadiazine proliferated antibiotic resistance genes in the phycosphere of Chlorella pyrenoidosa: Insights from bacterial communities and microalgal metabolites.

The phycosphere is an essential ecological niche for the proliferation of antibiotic resistance genes (ARGs). However, how ARGs' potential hosts change and the driving mechanism of metabolites under antibiotic stress in the phycosphere have seldom been researched. We investigated the response of Chlorella pyrenoidosa and the structure and abundance of free-living (FL) and particle-attached (PA) bacteria, ARGs, and metabolites under sulfadiazine by using real-time quantitative PCR, 16 S rRNA high-throughput. The linkage of key bacterial communities, ARGs, and metabolites through correlations was established. Through analysis of physiological indicators, Chlorella pyrenoidosa displayed a pattern of "low-dose promotion and high-dose inhibition" under antibiotic stress. ARGs were enriched in the PA treatment groups by 117 %. At the phylum level, Proteobacteria, Bacteroidetes, and Actinobacteria as potential hosts for ARGs. At the genus level, potential hosts included Sphingopyxis, SM1A02, Aquimonas, Vitellibacter, and Proteiniphilum. Middle and high antibiotic concentrations induced the secretion of metabolites closely related to potential hosts by algae, such as phytosphingosine, Lysophosphatidylcholine, and α-Linolenic acid. Therefore, changes in bacterial communities indirectly influenced the distribution of ARGs through alterations in metabolic products. These findings offer essential details about the mechanisms behind the spread and proliferation of ARGs in the phycosphere.

Multifunctional Metal-Phenolic Composites Promote Efficient Periodontitis Treatment via Antibacterial and Osteogenic Properties.

Periodontitis, a complex inflammatory disease initiated by bacterial infections, presents a significant challenge in public health. The increased levels of reactive oxygen species and the subsequent exaggerated immune response associated with periodontitis often lead to alveolar bone resorption and tooth loss. Herein, we develop multifunctional metal-phenolic composites (i.e., Au@MPN-BMP2) to address the complex nature of periodontitis, where gold nanoparticles (AuNPs) are coated by metal-phenolic networks (MPNs) and bone morphogenetic protein 2 (BMP2). In this design, MPNs exhibit remarkable antibacterial and antioxidant properties, and AuNPs and BMP2 promote osteogenic differentiation of bone marrow mesenchymal stem cells under inflammatory conditions. In a rat model of periodontitis, treatment with Au@MPN-BMP2 leads to notable therapeutic outcomes, including mitigated oxidative stress, reduced progression of inflammation, and the significant prevention of inflammatory bone loss. These results highlight the multifunctionality of Au@MPN-BMP2 nanoparticles as a promising therapeutic approach for periodontitis, addressing both microbial causative factors and an overactivated immune response. We envision that the rational design of metal-phenolic composites will provide versatile nanoplatforms for tissue regeneration and potential clinical applications.

The influence of depth on object selection and manipulation in visual working memory within a 3D context.

Recent studies have examined whether the internal selection mechanism functions similarly for perception and visual working memory (VWM). However, the process of how we access and manipulate object representations distributed in a 3D space remains unclear. In this study, we utilized a memory search task to investigate the effect of depth on object selection and manipulation within VWM. The memory display consisted of colored items half positioned at the near depth plane and the other half at the far plane. During memory maintenance, the participants were instructed to search for a target representation and update its color. The results showed that under object-based attention (Experiments 1, 3, and 5), the update time was faster for targets at the near plane than for those at the far plane. This effect was absent in VWM when deploying spatial attention (Experiment 2) and in visual search regardless of the type of attention deployed (Experiment 4). The differential effects of depth on spatial and object-based attention in VWM suggest that spatial attention primarily relied on 2D location information irrespective of depth, whereas object-based attention seemed to prioritize memory representations at the front plane before shifting to the back. Our findings shed light on the interaction between depth perception and the selection mechanisms within VWM in a 3D context, emphasizing the importance of ordinal, rather than metric, spatial information in guiding object-based attention in VWM.

3D-CAM: a novel context-aware feature extraction framework for neurological disease classification.

In clinical practice and research, the classification and diagnosis of neurological diseases such as Parkinson's Disease (PD) and Multiple System Atrophy (MSA) have long posed a significant challenge. Currently, deep learning, as a cutting-edge technology, has demonstrated immense potential in computer-aided diagnosis of PD and MSA. However, existing methods rely heavily on manually selecting key feature slices and segmenting regions of interest. This not only increases subjectivity and complexity in the classification process but also limits the model's comprehensive analysis of global data features. To address this issue, this paper proposes a novel 3D context-aware modeling framework, named 3D-CAM. It considers 3D contextual information based on an attention mechanism. The framework, utilizing a 2D slicing-based strategy, innovatively integrates a Contextual Information Module and a Location Filtering Module. The Contextual Information Module can be applied to feature maps at any layer, effectively combining features from adjacent slices and utilizing an attention mechanism to focus on crucial features. The Location Filtering Module, on the other hand, is employed in the post-processing phase to filter significant slice segments of classification features. By employing this method in the fully automated classification of PD and MSA, an accuracy of 85.71%, a recall rate of 86.36%, and a precision of 90.48% were achieved. These results not only demonstrates potential for clinical applications, but also provides a novel perspective for medical image diagnosis, thereby offering robust support for accurate diagnosis of neurological diseases.

Sema3A secreted by sensory nerve induces bone formation under mechanical loads.

Bone formation and deposition are initiated by sensory nerve infiltration in adaptive bone remodeling. Here, we focused on the role of Semaphorin 3A (Sema3A), expressed by sensory nerves, in mechanical loads-induced bone formation and nerve withdrawal using orthodontic tooth movement (OTM) model. Firstly, bone formation was activated after the 3rd day of OTM, coinciding with a decrease in sensory nerves and an increase in pain threshold. Sema3A, rather than nerve growth factor (NGF), highly expressed in both trigeminal ganglion and the axons of periodontal ligament following the 3rd day of OTM. Moreover, in vitro mechanical loads upregulated Sema3A in neurons instead of in human periodontal ligament cells (hPDLCs) within 24 hours. Furthermore, exogenous Sema3A restored the suppressed alveolar bone formation and the osteogenic differentiation of hPDLCs induced by mechanical overload. Mechanistically, Sema3A prevented overstretching of F-actin induced by mechanical overload through ROCK2 pathway, maintaining mitochondrial dynamics as mitochondrial fusion. Therefore, Sema3A exhibits dual therapeutic effects in mechanical loads-induced bone formation, both as a pain-sensitive analgesic and a positive regulator for bone formation.

Microneedle-Mediated Cell Therapy.

Microneedles have emerged as a promising platform for transdermal drug delivery with prominent advantages, such as enhanced permeability, mitigated pain, and improved patient adherence. While microneedles have primarily been employed for delivering small molecules, nucleic acids, peptides, and proteins, recent researches have demonstrated their prospect in combination with cell therapy. Cell therapy involving administration or transplantation of living cells (e.g. T cells, stem cells, and pancreatic cells) has gained significant attention in preclinical and clinical applications for various disease treatments. However, the effectiveness of systemic cell delivery may be restricted in localized conditions like solid tumors and skin disorders due to limited penetration and accumulation into the lesions. In this perspective, an overview of recent advances in microneedle-assisted cell delivery for immunotherapy, tissue regeneration, and hormone modulation, with respect to their mechanical property, cell loading capacity, as well as viability and bioactivity of the loaded cells is provided. Potential challenges and future perspectives with microneedle-mediated cell therapy are also discussed.

Emissions and meteorological impacts on PM2.5 species concentrations in Southern California using generalized additive modeling.

The chemical composition of PM2.5 has a significant impact on human health and air quality, and its accurate knowledge can be used to identify contributing emission sources. Assessing and quantifying the impacts of various factors (e.g., emissions, meteorology, and large-scale climate patterns) on the main PM2.5 chemical components can give guidance for implementing effective regulations to improve air quality in the future. In this study, we developed generalized additive models (GAMs) to assess how emissions, meteorological factors, and large-scale climate indices affected ammonium, sulfate, nitrate, elemental carbon, and organic carbon from 2002 to 2019 in the South Coast Air Basin (SoCAB). Concentration trends from three sites in the SoCAB are studied. The statistical results showed that GAMs can capture the variability of these species' daily concentrations (R2 = 0.6 to 0.7) and annual concentrations (R2 = 0.93 to 0.99). Precursor emissions most significantly affect PM2.5 species production, though meteorological factors like maximum temperature, relative humidity, wind speed, and boundary layer height, also influence PM2.5 composition. In the future, these meteorological factors will become more significant in affecting PM2.5 speciation, although emissions will continue to strongly affect formation. Results show that the composition of most PM2.5 species will decrease in the future except for OC, which will become the largest contributor to PM2.5.

Machine learning with spatial interpolation techniques for constructing 2-dimensional ozone concentrations in Southern California during the COVID-19 shutdown.

In this study, we combine machine learning and geospatial interpolations to create a two-dimensional high-resolution ozone concentration fields over the South Coast Air Basin for the entire year of 2020. Three spatial interpolation methods (bicubic, IDW, and ordinary kriging) were employed. The predicted ozone concentration fields were constructed using 15 building sites, and random forest regression was employed to test predictability of 2020 data based on input data from past years. Spatially interpolated ozone concentrations were evaluated at twelve sites that were independent of the actual spatial interpolations to find the most suitable method for SoCAB. Ordinary kriging interpolation had the best performance overall for 2020: concentrations were overestimated for Anaheim, Compton, LA North Main Street, LAX, Rubidoux, and San Gabriel sites and underestimated for Banning, Glendora, Lake Elsinore, and Mira Loma sites. The model performance improved from the West to the East, exhibiting better predictions for inland sites. The model is best at interpolating ozone concentrations inside the sampling region (bounded by the building sites), with R2 ranging from 0.56 to 0.85 for those sites, as prediction deficiencies occurred at the periphery of the sampling region, with the lowest R2 of 0.39 for Winchester. All the interpolation methods poorly predicted and underestimated ozone concentrations in Crestline during summer (up to 19 ppb). Poor performance for Crestline indicates that the site has a distribution air pollution levels independent from all other sites. Therefore, historical data from coastal and inland sites should not be used to predict ozone in Crestline using data-driven spatial interpolation approaches. The study demonstrates the utility of machine learning and geospatial techniques for evaluating air pollution levels during anomalous periods.

The Impact of a Nursing-Led Intervention Bundle With a Bedside Checklist to Reduce Mortality During the Initial COVID-19 Pandemic and Implications for Future Emergencies.

OBJECTIVE: The study objective was to examine the effectiveness of a bedside checklist enforcing nursing-led interventions in hospitalized COVID-19 patients early in the pandemic. BACKGROUND: The absence of treatment guidelines for COVID-19 presented challenges to reducing mortality rates early in the pandemic. A bedside checklist and a bundle of nursing-led interventions named "Nursing Back to Basics (NB2B)" were assembled for patient care after a scoping review of evidence. METHODS: A retrospective analysis was conducted to investigate the impact of the evidence-based interventions randomly implemented based on patient bed assignment. Electronic data (patient demographics, bed assignment and ICU transfers, length of stay, and patient discharge disposition) were extracted and calculated using descriptive statistics, t tests, and linear regression. RESULTS: Patients receiving the NB2B intervention enforced with a bedside checklist had significantly lower mortality rates (12.3%) compared with those receiving standard nursing care (26.9%). CONCLUSIONS: Bedside checklists enforcing evidence-based nursing-led interventions may be beneficial as a 1st-line public health emergency response.

Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates.

Annual fine particulate matter (PM2.5) mass concentrations in the South Coast Air Basin (SoCAB) of California decreased from around 30 µg/m3 to 11 µg/m3 between 2000 and 2013 but rose from 11 µg/m3 to 13 µg/m3 between 2014 and 2018, raising important questions about the effectiveness of ongoing emission control policies. A two-step generalized additive model (GAM)-least squares approach was developed to explore the effects of emissions, large-scale climate events and meteorological factors on daily PM2.5 mass concentrations from 2000 to 2019 to quantitatively link impacts of emissions and meteorological on PM2.5 and to assess factors leading to the increase. The GAM had an R2 = 0.99 and root mean square error (RMSE) = 0.7 µg/m3 for the annual average PM2.5 concentrations. The two-step method had an R2 = 0.93 and RMSE = 4.07 µg/m3 for the 98th percentile 24-hr average PM2.5 concentrations. Variations in both emissions and relative humidity were of high importance compared with other included factors. Interactions of NH3 emissions with NOx and SO2 emissions, which lead to ammonium nitrate and sulfate aerosol formation, were the most important factors. Meteorological effects on PM2.5 explained the majority of the daily PM2.5 fluctuations. Emission changes (increases in SO2 and PM2.5) led to increases in predicted PM2.5 between 2014 and 2018. Predicted future PM2.5, using projected emissions and meteorological data from model simulations of representative concentration pathway (RCP) scenarios, are around 12 µg/m3 (annual) and 30 µg/m3 (98th percentile daily), which are both close to the current National Ambient Air Quality Standards (NAAQS) for PM2.5. Meteorological impacts on the predicted PM2.5 in future years lead to variations of ±2 µg/m3 for the annual average and ±5 µg/m3 for the 98th percentile daily level. Future climate changes lead to a probable year-to-year variation that will let PM2.5 levels in some years exceed the standard.

Hierarchical graph learning for protein-protein interaction.

Protein-Protein Interactions (PPIs) are fundamental means of functions and signalings in biological systems. The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and understanding of PPIs. Despite recent progress, in silico methods remain inadequate in modeling the natural PPI hierarchy. Here we present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved. In this model, we create a hierarchical graph, in which a node in the PPI network (top outside-of-protein view) is a protein graph (bottom inside-of-protein view). In the bottom view, a group of chemically relevant descriptors, instead of the protein sequences, are used to better capture the structure-function relationship of the protein. HIGH-PPI examines both outside-of-protein and inside-of-protein of the human interactome to establish a robust machine understanding of PPIs. This model demonstrates high accuracy and robustness in predicting PPIs. Moreover, HIGH-PPI can interpret the modes of action of PPIs by identifying important binding and catalytic sites precisely. Overall, "HIGH-PPI [ https://github.com/zqgao22/HIGH-PPI ]" is a domain-knowledge-driven and interpretable framework for PPI prediction studies.

Extendible ghost imaging with high reconstruction quality in strong scattering medium.

Ghost imaging (GI) possesses significant application prospects in scattering imaging, which is a classic example of underdetermined conversion problem in optical field. However, even under the framework of single-pixel imaging (SPI), a challenge remains unresolved, i.e., structured patterns may be damaged by scattering media in both the emissive and receiving optical paths. In this study, an extendible ghost imaging, a numerical reproduction of the qualitative process using deep learning (DL)-based GI is presented. First, we propose and experimentally verify a brief degradation-guided reconstruction (DR) approach with a neural network to demonstrate the degradation principle of scattering, including realistic dataset simulations and a new training structure in the form of a convolutional neural network (CNN). Then, a novel photon contribution model (PCM) with redundant parameters is proposed to generate intensity sequences from the forward direction through volumetric scattering media; the redundant parameters are constructed and relate to the special output configuration in a lightweight CNN with two branches, based on a reformulated atmospheric scattering model. The proposed scheme recovers the semantics of targets and suppresses the imaging noise in the strong scattering medium, and the obtained results are very satisfactory for applications to scattering media of more practical scenarios and are available for various scattering coefficients and work distances of an imaging prototype. After using DL methods in computational imaging, we conclude that strategies embedded in optics or broader physical factors can result in solutions with better effects for unanalyzable processes.

A microfluidic demonstration of "cluster-sprout-infiltrating" mode for hypoxic mesenchymal stem cell guided cancer cell migration.

Mesenchymal stem cells (MSCs) play a critical role in tumor metastasis. However, the dynamic process of MSCs-mediated cancer cell invasion remains inconclusive. In breast cancer mouse models, we observed that MSCs promoted lung metastasis. We constructed a microfluidic-based 3D co-culture device to monitor MSCs-mediated cancer cell invasion in a nutrient-deficient hypoxic microenvironment. On biomimetic microfluidic devices, MSCs guided cancer cell migration in a "cluster-sprout-infiltrating" mode. Importantly, hypoxic conditions significantly promoted MSCs migration at the infiltration stage, leading to accelerated breast cancer cell invasion. Moreover, hypoxia related LncRNA analysis showed that H19 was dramatically upregulated in response to hypoxic conditions. Conversely, H19 depletion impaired MSCs-directed breast cancer cell invasion. Mechanistically, H19 functions as a competitive endogenous RNA (ceRNA) which sequesters miRNA let-7 to release its target matrix metalloproteinase-1 (MMP1). Intriguingly, aspirin dramatically suppressed H19 and MMP1 expression and blocked MSCs infiltration under hypoxic conditions, resulting in alleviated breast cancer cell invasion. These findings point to the metastatic promoting role of MSCs in tumor stroma and suggest that MSCs might be a therapeutic target for metastatic breast cancer.

Effect of acupuncture at the sphenopalatine ganglion for the treatment of moderate to severe seasonal allergic rhinitis: Study protocol for a three-armed randomized controlled trial.

Introduction: Seasonal allergic rhinitis (SAR) is a major health problem with a relatively high worldwide prevalence that severely limits the quality of life for sufferers. Acupuncture is widely used for SAR treatment in China; however, the evidence on the efficacy of acupuncture at the sphenopalatine ganglion (SPG) for SAR is inconclusive. Therefore, this study aims to investigate the efficacy and safety of acupuncture at the SPG acupoint for the treatment of SAR. Methods and analysis: A total of 120 participants with SAR will be recruited and randomly assigned to the acupuncture group, placebo acupuncture (PA) group, or rescue medication (RM) group with a 1:1:1 allocation ratio. Participants in the acupuncture group and PA group will receive 8 sessions of acupuncture stimulus at the SPG plus RM or 8 sessions of shallow needling at the SPG acupoint plus RM for 4 weeks with a 4-week follow-up in the first year and a 1-week follow-up in the second year. Participants in the RM group will only receive RM throughout the study. The primary outcome is the change from baseline in the average daily combined symptoms and medication score (CSMS) over weeks 1-4. All analysis will be based on an intention-to-treat principle. All statistical tests will be two-sided and a p-value < 0.05 will be considered to be statistically significant.

Regulable Supporting Baths for Embedded Printing of Soft Biomaterials with Variable Stiffness.

Three-dimensional (3D) embedded printing is emerging as a potential solution for the fabrication of complex biological structures and with ultrasoft biomaterials. For the supporting medium, bulk gels can support a wide range of bioinks with higher printing resolution as well as better finishing surfaces than granular microgel baths. However, the difficulties of regulating the physical properties of existing bulk gel supporting baths limit the further development of this method. This work has developed a bulk gel supporting bath with easily regulable physical properties to facilitate soft-material fabrication. The proposed bath is composed based on the hydrophobic association between a hydrophobically modified hydroxypropylmethyl cellulose (H-HPMC) and Pluronic F-127 (PF-127). Its rheological properties can be easily regulated; in the preprinting stage by varying the relative concentration of components, during printing by changing the temperature, and postprinting by adding additives with strong hydrophobicity or hydrophilicity. This has made the supporting bath not only available for various bioinks with a range of printing windows but also easy to be removed. Also, the removal strategy is independent of printing conditions like temperature and ions, which empowers the bath to hold great potential for the embedded printing of commonly used biomaterials. The adjustable rheological properties of the bath were leveraged to characterize the embedded printing quantitatively, involving the disturbance during the printing, filament cross-sectional shape, printing resolution, continuity, and the coalescence between adjacent filaments. The match between the bioink and the bath was also explored. Furthermore, low-viscosity bioinks (with 0.008-2.4 Pa s viscosity) were patterned into various 3D complex delicate soft structures (with a 0.5-5 kPa compressive modulus). It is believed that such an easily regulable assembled bath could serve as an available tool to support the complex biological structure fabrication and open unique prospects for personalized medicine.

A versatile embedding medium for freeform bioprinting with multi-crosslinking methods.

Embedded freeform writing addresses the contradiction between the material printability and biocompatibility for conventional extrusion-based bioprinting. However, the existing embedding mediums have limitations concerning the restricted printing temperature window, compatibility with bioinks or crosslinkers, and difficulties on medium removal. This work demonstrates a new embedding medium to meet the above demands, which composes of hydrophobically modified hydroxypropylmethyl cellulose and Pluronic F-127. The adjustable hydrophobic and hydrophilic associations between the components permit tunable thermoresponsive rheological properties, providing a programmable printing window. These associations are hardly compromised by additives without strong hydrophilic groups, which means it is compatible with the majority of bioink choices. We use polyethylene glycol 400, a strong hydrophilic polymer, to facilitate easy medium removal. The proposed medium enables freeform writing of the millimetric complex tubular structures with great shape fidelity and cell viability. Moreover, five bioinks with up to five different crosslinking methods are patterned into arbitrary geometries in one single medium, demonstrating its potential in heterogeneous tissue regeneration. Utilizing the rheological properties of the medium, an enhanced adhesion writing method is developed to optimize the structure's strand-to-strand adhesion. In summary, this versatile embedding medium provides excellent compatibility with multi-crosslinking methods and a tunable printing window, opening new opportunities for heterogeneous tissue regeneration.

Separating emissions and meteorological impacts on peak ozone concentrations in Southern California using generalized additive modeling.

Ozone levels have been declining in the Los Angeles, CA, USA area for the last four decades, but there was a recent uptick in the 4th highest daily maximum 8-h (MDA8) ozone concentrations from 2014 to 2018 despite continued reductions in the estimated precursor emissions. In this study, we assess the emissions and meteorological impacts on the 4th highest MDA8 ozone concentrations to better understand the factors affecting the observed MDA8 ozone using a two-step generalized additive model (GAM)/least squares approach applied to the South Coast Air Basin (SoCAB) for the 1990 to 2019 period. The GAM model includes emissions, meteorological factors, large-scale climate variables, date, and the interactions between meteorology and emissions. A least squares method was applied to the GAM output to better capture the 4th highest MDA8 ozone. The resulting two-step model had an R2 of 0.98 and a slope of 1 between the observed and predicted 4th highest MDA8 ozone. Emissions and the interactions between the maximum temperature and emissions explain most of the variation in the peak MDA8 ozone concentrations. Declining emissions have lowered the 4th highest MDA8 ozone concentration. Meteorology explains the higher than expected 4th-high, ozone levels observed in 2014-2018, indicating that meteorology was a stronger forcer than the continued reductions in emissions during that time period. The model was applied to estimate future ozone levels. Meteorology developed from climate modeling of the representative concentration pathway (RCP) scenarios, and two sets of emissions were used in the application. The modeling results indicated climate trends will push ozone levels slightly higher if no further emissions reductions are realized and that of two emissions trajectories modeled, the more stringent is required to reliably meet the federal ozone standard given annual meteorological variability.

Recent Advances in Intelligent Wearable Medical Devices Integrating Biosensing and Drug Delivery.

The primary roles of precision medicine are to perform real-time examination, administer on-demand medication, and apply instruments continuously. However, most current therapeutic systems implement these processes separately, leading to treatment interruption and limited recovery in patients. Personalized healthcare and smart medical treatment have greatly promoted research on and development of biosensing and drug-delivery integrated systems, with intelligent wearable medical devices (IWMDs) as typical systems, which have received increasing attention because of their non-invasive and customizable nature. Here, the latest progress in research on IWMDs is reviewed, including their mechanisms of integrating biosensing and on-demand drug delivery. The current challenges and future development directions of IWMDs are also discussed.