Visualization and standardized quantification of surface charge density for triboelectric materials.

Triboelectric nanogenerator (TENG) operates on the principle of utilizing contact electrification and electrostatic induction. However, visualization and standardized quantification of surface charges for triboelectric materials remain challenging. Here, we report a surface charge visualization and standardized quantification method using electrostatic surface potential measured by Kevin probe and the iterative regularization strategy. Moreover, a tuning strategy on surface charge is demonstrated based on the corona discharge with a three-electrode design. The long-term stability and dissipation mechanisms of the injected negative or positive charges demonstrate high dependence on deep carrier traps in triboelectric materials. Typically, we achieved a 70-fold enhancement on the output voltage (~135.7 V) for the identical polytetrafluoroethylene (PTFE) based TENG (neg-PTFE/PTFE or posi-PTFE/PTFE triboelectric pair) with stable surface charge density (5% decay after 140 days). The charged PTFE was demonstrated as a robot e-skins for non-contact perception of object geometrics. This work provides valuable tools for surface charge visualization and quantification, giving a new strategy for a deeper understanding of contact electrification.

Intracerebral Hemorrhage Prognosis Classification via Joint-Attention Cross-Modal Network.

Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care.

Identifying Frailty in Older Adults Receiving Home Care Assessment Using Machine Learning: Longitudinal Observational Study on the Role of Classifier, Feature Selection, and Sample Size.

BACKGROUND: Machine learning techniques are starting to be used in various health care data sets to identify frail persons who may benefit from interventions. However, evidence about the performance of machine learning techniques compared to conventional regression is mixed. It is also unclear what methodological and database factors are associated with performance. OBJECTIVE: This study aimed to compare the mortality prediction accuracy of various machine learning classifiers for identifying frail older adults in different scenarios. METHODS: We used deidentified data collected from older adults (65 years of age and older) assessed with interRAI-Home Care instrument in New Zealand between January 1, 2012, and December 31, 2016. A total of 138 interRAI assessment items were used to predict 6-month and 12-month mortality, using 3 machine learning classifiers (random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) and regularized logistic regression. We conducted a simulation study comparing the performance of machine learning models with logistic regression and interRAI Home Care Frailty Scale and examined the effects of sample sizes, the number of features, and train-test split ratios. RESULTS: A total of 95,042 older adults (median age 82.66 years, IQR 77.92-88.76; n=37,462, 39.42% male) receiving home care were analyzed. The average area under the curve (AUC) and sensitivities of 6-month mortality prediction showed that machine learning classifiers did not outperform regularized logistic regressions. In terms of AUC, regularized logistic regression had better performance than XGBoost, MLP, and RF when the number of features was ≤80 and the sample size ≤16,000; MLP outperformed regularized logistic regression in terms of sensitivities when the number of features was ≥40 and the sample size ≥4000. Conversely, RF and XGBoost demonstrated higher specificities than regularized logistic regression in all scenarios. CONCLUSIONS: The study revealed that machine learning models exhibited significant variation in prediction performance when evaluated using different metrics. Regularized logistic regression was an effective model for identifying frail older adults receiving home care, as indicated by the AUC, particularly when the number of features and sample sizes were not excessively large. Conversely, MLP displayed superior sensitivity, while RF exhibited superior specificity when the number of features and sample sizes were large.

Augmented reality display with high eyebox uniformity over the full field of view based on a random mask grating.

Ensuring uniform illuminance in waveguide-based augmented reality (AR) display devices is crucial for providing an immersive and comfortable visual experience. However, there is a lack of a straightforward and efficient design method available to achieve illuminance uniformity over the full field of view. To address this issue, we propose a novel design that utilizes random mask gratings (RMGs) as the folding grating and the out-coupling grating. Unlike traditional approaches that modify the grating structure, we control the diffraction efficiency distribution by adjusting the filling factor of the mask while keeping the grating structure unchanged in one RMG. The grating structures are designed and optimized based on rigorous coupled wave analysis and particle swarm optimization. The feasibility of our method is verified by the simulation results in Lighttools. In the FOV range of 20°×15°, the eyebox uniformities of all fields are greater than 0.78, which can provide a good visual experience for users.

Anticancer immune reaction and lymph node sinus macrophages: a review from human and animal studies.

Lymph nodes are secondary lymphoid organs localized throughout the body that typically appear as bean-like nodules. Numerous antigen-presenting cells, including dendritic cells and macrophages, that mediate host defense responses against pathogens, such as bacteria and viruses, reside within lymph nodes. To react to cancer cell-derived antigens in a variety of cancers, antigen-presenting cells induce cytotoxic T lymphocytes (CTLs). In relation to anticancer immune responses, macrophages in the lymph node sinus have been of particular interest because a number of studies involving both human specimens and animal models have reported that lymph node macrophages expressing CD169 play a key role in activating anticancer CTLs. Recent studies have indicated that dysfunction of lymph node macrophages potentially contributes to immune suppression in elderly patients and immunological "cold" tumors. Therefore, in anticancer therapy, the regulation of lymph node macrophages is a potentially promising approach.

Palladium-Catalyzed Triple Suzuki-Miyaura Reactions Using Cyclic (Vinyl Triflate)iodonium Salts.

By using cyclic (vinyl triflate)iodonium salts, a novel triple Suzuki-Miyaura reaction was accomplished for the synthesis of polyaromatic ethylene derivatives in the presence of palladium catalysts. The reaction exhibits extensive compatibility with a wide range of readily available arylboronic acids, giving triaryl-substituted ethylenes in good yields.

Arbitrary engineering of spatial caustics with 3D-printed metasurfaces.

Caustics occur in diverse physical systems, spanning the nano-scale in electron microscopy to astronomical-scale in gravitational lensing. As envelopes of rays, optical caustics result in sharp edges or extended networks. Caustics in structured light, characterized by complex-amplitude distributions, have innovated numerous applications including particle manipulation, high-resolution imaging techniques, and optical communication. However, these applications have encountered limitations due to a major challenge in engineering caustic fields with customizable propagation trajectories and in-plane intensity profiles. Here, we introduce the "compensation phase" via 3D-printed metasurfaces to shape caustic fields with curved trajectories in free space. The in-plane caustic patterns can be preserved or morphed from one structure to another during propagation. Large-scale fabrication of these metasurfaces is enabled by the fast-prototyping and cost-effective two-photon polymerization lithography. Our optical elements with the ultra-thin profile and sub-millimeter extension offer a compact solution to generating caustic structured light for beam shaping, high-resolution microscopy, and light-matter-interaction studies.

Knock down of APE1 suppressed gastric cancer metastasis via improving immune disorders caused by myeloid-derived suppressor cells.

Gastric cancer is a highly immunogenic malignancy. Immune tolerance facilitated by myeloid-derived suppressor cells (MDSCs) has been implicated in gastric cancer resistance mechanisms. The potential role of APE1 in regulating gastric cancer metastasis by targeting MDSCs remains uncertain. In this study, the plasmid Plxpsp-mGM-CSF was used to induce high expression of granulocyte-macrophage colony-stimulating factor (GM-CSF) in GES-1 cells. For tumor transplantation experiments, AGS, AGS+GM-CSF and AGS+GM-CSF-siAPE1 cell lines were established by transfection, followed by subcutaneous implantation of tumor cells. MDSCs, Treg cells, IgG, CD3 and CD8 levels were assessed. Transfection with siAPE1 significantly inhibited tumor growth compared to the AGS+GM-CSF group. APE1 gene knockdown modulated the immune system in gastric cancer mice, characterized by a decrease in MDSCs and an increase in Treg cells, IgG, CD3 and CD8. In addition, APE1 gene knockdown resulted in decreased levels of pro-MDSC cytokines (HGF, CCL5, IL-6, CCL12). Furthermore, APE1 gene knockdown inhibited proliferation, migration and invasion of AGS and MKN45 cells. AGS-GM-CSF cell transplantation increased MDSC levels and accelerated tumor growth, whereas APE1 knockdown reduced MDSC levels, inhibited tumor growth and attenuated inflammatory infiltration in gastric cancer tissues. Strategies targeting the APE1/MDSC axis offer a promising approach to the prevention and treatment of gastric cancer, providing new insights into its management.

Deep-eutectic-solvent modulation of self-assembled multi-responsive films based on polyvinyl alcohol/cellulose nanocrystal and grape skin red for highly sensitive food monitoring.

To enhance the mechanics performance, sensitivity and response range of multi-responsive photonic films, herein, a facile method for fabricating multi-responsive films is demonstrated using the evaporative self-assembly of a mixture of grape skin red (GSR), cellulose nanocrystal (CNC), polyvinyl alcohol (PVA) and deep eutectic solvent (DES). The prepared materials exhibited excellent thermal stability, strain properties, solvent resistance, ultraviolet (UV) resistance and antioxidant activity. Compared to a pure PVA film, the presence of GSR strengthened the antioxidant property of the film by 240.1 % and provided excellent UV barrier capability. The additional cross-linking of DES and CNC promoted more efficient phase fusion, yielding a film strain of 41.5 %. The addition of hydrophilic compound GSR, wetting and swelling due to the DES and the surface inhomogeneity of the films rendered the multi-responsive films high sensitivity, wide response range and multi-cyclic stability in environments with varying pH and humidity. A sample application showed that a PVA/CNC/DES film has the potential to differentiate between fresh, sub-fresh and fully spoiled shrimps. The above results help in designing intelligent thin film materials that integrate antioxidant properties, which help in monitoring the changes in food freshness and food packaging.

Kaempferol attenuates particle-induced osteogenic impairment by regulating ER stress via the IRE1α-XBP1s pathway.

Periprosthetic osteolysis and subsequent aseptic loosening are the primary causes of failure following total joint arthroplasty. Wear particle-induced osteogenic impairment is recognized as an important contributing factor in the development of osteolysis, with endoplasmic reticulum (ER) stress emerging as a pivotal underlying mechanism. Hence, searching for potential therapeutic targets and agents capable of modulating ER stress in osteoblasts is crucial for preventing aseptic loosening. Kaempferol (KAE), a natural flavonol compound, has shown promising osteoprotective effects and anti-ER stress properties in diverse diseases. However, the influence of KAE on ER stress-mediated osteogenic impairment induced by wear particles remains unclear. In this study, we observed that KAE effectively relieved TiAl6V4 particles-induced osteolysis by improving osteogenesis in a mouse calvarial model. Furthermore, we demonstrated that KAE could attenuate ER stress-mediated apoptosis in osteoblasts exposed to TiAl6V4 particles, both in vitro and in vivo. Mechanistically, our results revealed that KAE mitigated ER stress-mediated apoptosis by upregulating the IRE1α-XBP1s pathway while concurrently partially inhibiting the IRE1α-regulated RIDD and JNK activation. Collectively, our findings suggest that KAE is a prospective therapeutic agent for treating wear particle-induced osteolysis and highlight the IRE1α-XBP1s pathway as a potential therapeutic target for preventing aseptic loosening.

Development and Validation of a Prognostic Nomogram for HR+ HER- Breast Cancer.

Purpose: We aimed to develop a nomogram to predict prognosis of HR+ HER2- breast cancer patients and guide the application of postoperative adjuvant chemotherapy. Methods: We identified 310 eligible HR+ HER- breast cancer patients and randomly divided the database into a training group and a validation group. The endpoint was disease free survival (DFS). Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluate predictive accuracy and discriminative ability of the nomogram. We also compared the predictive accuracy and discriminative ability of our nomogram with the eighth AJCC staging system using overall data. Results: According to the training group, platelet-to-lymphocyte ratio (PLR), tumor size, positive lymph nodes and Ki-67 index were used to construct the nomogram of DFS. The C-index of DFS was 0.708 (95% CI: 0.623-0.793) in the training group and 0.67 (95% CI: 0.544-0.796) in the validation group. The calibration curves revealed great consistencies in both groups. Conclusion: We have developed and validated a novel and practical nomogram that can provide individual prediction of DFS for patients with HR+ HER- breast cancer. This nomogram may help clinicians in risk consulting and guiding the application of postoperative adjuvant chemotherapy.

Enhanced systemic antitumor efficacy of PD-1/PD-L1 blockade with immunological response induced by photodynamic therapy.

BACKGROUND: Photodynamic therapy (PDT) is an antitumor therapy and has traditionally been regarded as a localized therapy in itself. However, recent reports have shown that it not only exerts a direct cytotoxic effect on cancer cells but also enhances body's tumor immunity. We hypothesized that the immunological response induced by PDT could potentially enhance the efficacy of programmed death-1 (PD-1) / programmed death-ligand 1 (PD-L1) blockade. METHODS: The cytotoxic effects of PDT on colon 26 cells were investigated in vitro using the WST assay. We investigated whether the antitumor effect of anti-PD-1 antibodies could be amplified by the addition of PDT. We performed combination therapy by randomly allocating tumor-bearing mice to four treatment groups: control, anti-PD-1 antibodies, PDT, and a combination of anti-PD-1 antibodies and PDT. To analyze the tumor microenvironment after treatment, the tumors were resected and pathologically evaluated. RESULTS: The viability rate of colon 26 cells decreased proportionally with the laser dose. In vivo experiments for combined PDT and anti-PD-1 antibody treatment, combination therapy showed an enhanced antitumor effect compared with the control. Immunohistochemical findings of the tumor microenvironment 10 days after PDT indicated that the number of CD8+ cells, the area of Iba-1+ cells and the area expressing PD-L1 were significantly higher in tumors treated with combination therapy than in tumors treated with anti-PD-1 antibody alone, PDT alone, or the control. CONCLUSIONS: PDT increased immune cell infiltration into the tumor microenvironment. The immunological response induced by PDT may enhance the efficacy of PD-1/PD-L1 blockade.

Ortho-ester-substituted diaryliodonium salts enabled regioselective arylocyclization of naphthols toward 3,4-benzocoumarins.

Cyclic annulation involving diaryliodonium salts is an efficient tool for the construction of two or more chemical bonds in a one-pot process. Ortho-functionalized diaryliodonium salts have showcased distinct reactivity in the exploration of benzocyclization or arylocyclization. With this strategy of ortho-ester-substituted diaryliodonium salts, herein, we utilized a copper catalyst to activate the C-I bond of diaryliodonium salts in the generation of aryl radicals, thus resulting in an annulation reaction with naphthols and substituted phenols. This approach yielded a diverse array of 3,4-benzocoumarin derivatives bearing various substituents.

Design, Manufacture, and Characterization of a Critical-Sized Gradient Porosity Dual-Material Tibial Defect Scaffold.

This study proposed a composite tibia defect scaffold with radial gradient porosity, utilizing finite element analysis to assess stress in the tibial region with significant critical-sized defects. Simulations for scaffolds with different porosities were conducted, designing an optimal tibia defect scaffold with radial gradient porosity for repairing and replacing critical bone defects. Radial gradient porosity scaffolds resulted in a more uniform stress distribution, reducing titanium alloy stiffness and alleviating stress shielding effects. The scaffold was manufactured using selective laser melting (SLM) technology with stress relief annealing to simplify porous structure fabrication. The study used New Zealand white rabbits' tibia defect sites as simulation parameters, reconstructing the 3D model and implanting the composite scaffold. Finite element analysis in ANSYS-Workbench simulated forces under high-activity conditions, analyzing stress distribution and strain. In the simulation, the titanium alloy scaffold bore a maximum stress of 122.8626 MPa, while the centrally encapsulated HAp material delivered 27.92 MPa. The design demonstrated superior structural strength, thereby reducing stress concentration. The scaffold was manufactured using SLM, and the uniform design method was used to determine a collection of optimum annealing parameters. Nanoindentation and compression tests were used to determine the influence of annealing on the elastic modulus, hardness, and strain energy of the scaffold.

Proteomic Analysis of Salivary Secretions from the Tea Green Leafhopper, Empoasca flavescens Fabrecius.

Saliva plays a crucial role in shaping the compatibility of piercing-sucking insects with their host plants. Understanding the complex composition of leafhopper saliva is important for developing effective and eco-friendly control strategies for the tea green leafhopper, Empoasca flavescens Fabrecius, a major piercing-sucking pest in Chinese tea plantations. This study explored the saliva proteins of tea green leafhopper adults using a custom collection device, consisting of two layers of Parafilm stretched over a sucrose diet. A total of 152 proteins were identified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) following the filter-aided sample preparation (FASP). These proteins were categorized into six groups based on their functions, including enzymes, transport proteins, regulatory proteins, cell structure proteins, other proteins, and unknown proteins. Bioinformatics analyses predicted 16 secreted proteins, which were successfully cloned and transcriptionally analyzed across various tissues and developmental stages. Genes encoding putative salivary secretory proteins, including Efmucin1, EfOBP1, EfOBP2, EfOBP3, Efmucin2, low-density lipoprotein receptor-related protein (EfLRP), EFVg1, and EFVg2, exhibited high expressions in salivary gland (SG) tissues and feeding-associated expressions at different developmental stages. These findings shed light on the potential elicitors or effectors mediating the leafhopper feeding and defense responses in tea plants, providing insights into the coevolution of tea plants and leafhoppers. The study's conclusions open avenues for the development of innovative leafhopper control technologies that reduce the reliance on pesticides in the tea industry.

Age-associated reduction of sinus macrophages in human mesenteric lymph nodes.

There are numerous macrophages and dendritic cells in lymph nodes (LNs). Recent studies have highlighted that sinus macrophages (SMs) in LNs possess antigen-presenting capabilities and are related to anti-cancer immune responses. In this study, we assessed the distribution of SMs in mesenteric LNs removed during surgery for colorectal cancer. A marked reduction of SMs was noted in elderly patients, particularly those over 80 years old. We observed a disappearance of CD169-positive cells in LNs where SMs were reduced. In silico analysis of publicly available single-cell RNA sequencing data from LNs revealed that CD169-positive macrophages express numerous genes associated with antigen presentation and lymphocyte proliferation, similar to dendritic cells' functions. In conclusion, our study demonstrates that SMs, potentially crucial for immune activation, diminish in the LNs of elderly patients. This reduction of SMs may contribute to the immune dysfunction observed in the elderly.

Rainfall-runoff partitioning in small watersheds of different vegetation types in the loess area based on hydrogen and oxygen isotope tracing.

Recognizing watershed runoff process and its component sources is a prerequisite for the rational use of water resources. To elucidate the effects and quantitative contributions of various vegetation types on the components of watershed runoff, we centered on the Caijiachuan main channel watershed in Jixian, Shanxi and five sub-watersheds with distinct vegetation types. By tracking the hydrological responses to two representative rainfall events and assessing the spatiotemporal variations in hydrogen and oxygen isotope signatures, we aimed to discern disparities in the runoff processes across these sub-watersheds and pinpoint their constituent origins. The results showed that under medium rainfall condition, the contribution rates of event water to the river flow of each watershed were in an order of protected forest (94.3%) > Caijiachuan main channel (83.1%) > agro-pastoral composite (64.3%) > plantation-secondary forest (52.4%) > cropland (0.3%) > secondary forest (0.0%); under light rainfall condition, plantation-secondary forest (52.4%) > protected forest (58.5%) > cropland (40.6%) > secondary forest (15.8%) > agro-pastoral composite (12.5%) > Caijiachuan main channel (9.3%). The event water contribution rate of secondary forest and protected forest watersheds to runoff was higher than that of plantation watersheds. The secondary forests watersheds had a stronger runoff storage capacity. The event water contribution rate of protected forest and agro-pastoral composite watersheds under medium rainfall intensity condition was greater than that under light rainfall intensity condition, while the event water contribution rate of cropland, plantation-secondary forest, and secondary forest watersheds was in adverse. The event water contribution to the runoff of forested watersheds was greater than that of cropland watersheds, which may be related to the presence of silt dams at the mouth of agricultural watershed channels. This study can provide a scientific basis for the analysis of water conservation and runoff change attribution in the loess area of west Shanxi.

A novel growth-friendly system alleviates pulmonary dysplasia in early-onset scoliosis combined with thoracic insufficiency syndrome: Radiological, pathological, and transcriptomic assessments.

Background: The posterior procedure utilizing growth-friendly techniques is the golden standard for patients with early-onset scoliosis combined with thoracic insufficiency syndrome (EOS + TIS). Pulmonary hypoplasia is the main cause of dying prematurely in the EOS + TIS. This study assessed the therapeutic impact of a novel growth-friendly system on the pulmonary development of piglet's EOS + TIS model. Methods: The animal procedure period lasts 12 weeks, of which the construction of the EOS + TIS was performed at 0-8 weeks, and implantation of a novel growth-friendly system was applied at 8-12 weeks. During the animal procedure, X-rays and CT were performed to observe scoliosis, thorax, and lungs. After 12 weeks, pathological changes in lung tissue were assessed using HE and IHC staining. RNA-seq characterized novel growth-friendly system-associated differentially expressed genes (DEGs) and validated using RT-qPCR, western blotting, and IHC. Results: Implantation of the novel growth-friendly system increased body weight, body length, and total lung volume, as well as decreased the coronal and sagittal Cobb angles for the EOS + TIS model. It also ameliorated EOS + TIS-induced thickening of the alveolar wall, increased alveolar spaces, and decreased alveolar number and diameter. In lung tissue, a total of 790 novel growth-friendly system-associated DEGs were identified, and they were mainly involved in the regulation of immune, inflammatory, calcium transport, and vascular development. Among these DEGs, BDKRB1, THBS1, DUSP1, IDO1, and SPINK5 were hub genes, and their differential expression was consistent with RNA-seq results in lung tissues. Conclusion: The novel growth-friendly system has mitigated scoliosis and pulmonary hypoplasia in the EOS + TIS model. We further elucidate the molecular mechanisms underlying the amelioration of pulmonary hypoplasia.

Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging.

The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study's findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases.