Novel bibenzyl compound Ae exhibits anti-agiogenic activity in HUVECs in vitro and zebrafish in vivo.

The inhibition of angiogenesis has been considered as an attractive method for the discovery of potential anti-cancer drugs. Herein, we report our new synthesized bibenzyl compound Ae had potent anti-angiogenic activity(the lowest effective concentration is to 0.62-1.25 µM) in zebrafish in vivo and showed a concentration-dependent inhibition of inter-segmental blood vessels (ISVs) compared to control. Further, Ae exhibited the obvious inhibitory activity of proliferation, migration, invasion and tube formation in HUVEC cells in vitro. Moreover, qRT-PCR analysis revealed that the anti-angiogenic activity of compound Ae is connected with the ang-2, tek in ANGPT-TEK pathway and the kdr, kdrl signaling axle in VEGF-VEGFR pathway. Molecular docking studies revealed that compound Ae had an interaction with the angiopoietin-2 receptor(TEK) and VEGFR2. Additionally, analysis of the ADMET prediction data indicated that compound Ae possessed favorable physicochemical properties, drug-likeness, and synthetic accessibility. In conclusion, compound Ae had remarkable anti-angiogenic activity and could be served as an candidate for cancer therapy.

A medical image classification method based on self-regularized adversarial learning.

BACKGROUND: Deep learning (DL) techniques have been extensively applied in medical image classification. The unique characteristics of medical imaging data present challenges, including small labeled datasets, severely imbalanced class distribution, and significant variations in imaging quality. Recently, generative adversarial network (GAN)-based classification methods have gained attention for their ability to enhance classification accuracy by incorporating realistic GAN-generated images as data augmentation. However, the performance of these GAN-based methods often relies on high-quality generated images, while large amounts of training data are required to train GAN models to achieve optimal performance. PURPOSE: In this study, we propose an adversarial learning-based classification framework to achieve better classification performance. Innovatively, GAN models are employed as supplementary regularization terms to support classification, aiming to address the challenges described above. METHODS: The proposed classification framework, GAN-DL, consists of a feature extraction network (F-Net), a classifier, and two adversarial networks, specifically a reconstruction network (R-Net) and a discriminator network (D-Net). The F-Net extracts features from input images, and the classifier uses these features for classification tasks. R-Net and D-Net have been designed following the GAN architecture. R-Net employs the extracted feature to reconstruct the original images, while D-Net is tasked with the discrimination between the reconstructed image and the original images. An iterative adversarial learning strategy is designed to guide model training by incorporating multiple network-specific loss functions. These loss functions, serving as supplementary regularization, are automatically derived during the reconstruction process and require no additional data annotation. RESULTS: To verify the model's effectiveness, we performed experiments on two datasets, including a COVID-19 dataset with 13 958 chest x-ray images and an oropharyngeal squamous cell carcinoma (OPSCC) dataset with 3255 positron emission tomography images. Thirteen classic DL-based classification methods were implemented on the same datasets for comparison. Performance metrics included precision, sensitivity, specificity, and F 1 $F_1$ -score. In addition, we conducted ablation studies to assess the effects of various factors on model performance, including the network depth of F-Net, training image size, training dataset size, and loss function design. Our method achieved superior performance than all comparative methods. On the COVID-19 dataset, our method achieved 95.4 % ± 0.6 % $95.4\%\pm 0.6\%$ , 95.3 % ± 0.9 % $95.3\%\pm 0.9\%$ , 97.7 % ± 0.4 % $97.7\%\pm 0.4\%$ , and 95.3 % ± 0.9 % $95.3\%\pm 0.9\%$ in terms of precision, sensitivity, specificity, and F 1 $F_1$ -score, respectively. It achieved 96.2 % ± 0.7 % $96.2\%\pm 0.7\%$ across all these metrics on the OPSCC dataset. The study to investigate the effects of two adversarial networks highlights the crucial role of D-Net in improving model performance. Ablation studies further provide an in-depth understanding of our methodology. CONCLUSION: Our adversarial-based classification framework leverages GAN-based adversarial networks and an iterative adversarial learning strategy to harness supplementary regularization during training. This design significantly enhances classification accuracy and mitigates overfitting issues in medical image datasets. Moreover, its modular design not only demonstrates flexibility but also indicates its potential applicability to various clinical contexts and medical imaging applications.

Design, synthesis, and biological evaluation of ß-carboline-cinnamic acid derivatives as DYRK1A inhibitors in the treatment of diabetes.

Dual-specificity tyrosine phosphorylation-regulated kinase A (DYRK1A) is a potential drug target for diabetes. The DYRK1A inhibitor can promote ß cells proliferation, increase insulin secretion and reduce blood sugar in diabetes. In this paper, a series ß-carboline-cinnamic acid skeletal derivatives were designed, synthesized and evaluated to inhibit the activity of DYRK1A and promote pancreatic islet ß cell proliferation. Pharmacological activity showed that all of the compounds could effectively promote pancreatic islet ß cell proliferation at a concentration of 1 µM, and the cell viability of compound A1, A4 and B4 reached to 381.5 %, 380.2 % and 378.5 %, respectively. Compound A1, A4 and B4 could also inhibit the expression of DYRK1A better than positive drug harmine. Further mechanistic studies showed that compound A1, A4 and B4 could inhibit DYRK1A protein expression via promoting its degradation and thus enhancing the expression of proliferative proteins PCNA and Ki67. Molecular docking showed that ß-carboline scaffold of these three compounds was fully inserted into the ATP binding site and formed hydrophobic interactions with the active pocket. Besides, these three compounds were predicted to possess better drug-likeness properties using SwissADME. In conclusion, compounds A1, A4 and B4 were potent pancreatic ß cell proliferative agents as DYRK1A inhibitors and might serve as promising candidates for the treatment of diabetes.

Exhaust and evaporative volatile organic compounds emissions from vehicles fueled with ethanol-blended-gasoline.

By 2020, China has implemented the use of 10% ethanol-blended-gasoline (E10), which is expected to notably impact vehicular volatile organic compounds (VOCs) emissions. The adoption of E10 reduced certain emissions but raised concerns with about more reactive oxygenated volatile organic compounds (OVOCs). This study aimed to evaluate the impact of E10 on the total VOCs emissions from both exhaust and evaporative emissions by conducting tests on the CHINA V (or CHINA VI) light-duty gasoline vehicles (LDGVs) using 0% ethanol blended gasoline (E0) and E10. E10 reduces VOCs emissions in the exhaust, and reduces the ozone and secondary organic aerosol generation potential of VOCs in the exhaust, as evidenced by the lower emission factors (EFs), ozone formation potentials (OFPs) and secondary organic aerosol formation potential (SOAFPs) in the CHINA V LDGVs. Evaporative emissions showed differences in emitted VOCs, with lower EFs, OFPs and SOAFPs for the CHINA V LDGVs fueled with E10. The CHINA VI LDGVs also exhibited reduced EFs, OFPs and SOAFPs. These findings highlight the environmental benefits of E10 in the CHINA VI-compliant LDGVs; however, the effectiveness of the earlier CHINA V standard vehicles requires further evaluation.

Metal-Organic Frameworks/Heterojunction Structures for Surface-Enhanced Raman Scattering with Enhanced Sensitivity and Tailorability.

Metal-organic frameworks (MOFs), which are composed of crystalline microporous materials with metal ions, have gained considerable interest as promising substrate materials for surface-enhanced Raman scattering (SERS) detection via charge transfer. Research on MOF-based SERS substrates has advanced rapidly because of the MOFs' excellent structural tunability, functionalizable pore interiors, and ultrahigh surface-to-volume ratios. Compared with traditional noble metal SERS plasmons, MOFs exhibit better biocompatibility, ease of operation, and tailorability. However, MOFs cannot produce a sufficient limit of detection (LOD) for ultrasensitive detection, and therefore, developing an ultrasensitive MOF-based SERS substrate is imperative. To the best of our knowledge, this is the first study to develop an MOFs/heterojunction structure as an SERS enhancing material. We report an in situ ZIF-67/Co(OH)2 heterojunction-based nanocellulose paper (nanopaper) plate (in situ ZIF-67 nanoplate) as a device with an LOD of 0.98 nmol/L for Rhodamine 6G and a Raman enhancement of 1.43 × 107, which is 100 times better than that of the pure ZIF-67-based SERS substrate. Further, we extend this structure to other types of MOFs and develop an in situ HKUST-1 nanoplate (with HKUST-1/Cu(OH)2). In addition, we demonstrate that the formation of heterojunctions facilitates efficient photoinduced charge transfer for SERS detection by applying the Mx(OH)y-assisted (where M = Co, Cu, or other metals) MOFs/heterojunction structure. Finally, we successfully demonstrate the application of medicine screening on our nanoplates, specifically for omeprazole. The nanoplates we developed still maintain the tailorability of MOFs and perform high anti-interference ability. Our approach provides customizing options for MOF-based SERS detection, catering to diverse possibilities in future research and applications.

SARS-CoV-2 induces blood-brain barrier and choroid plexus barrier impairments and vascular inflammation in mice.

The coronavirus disease of 2019 (COVID-19) pandemic has led to more than 700 million confirmed cases and nearly 7 million deaths. Although severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus mainly infects the respiratory system, neurological complications are widely reported in both acute infection and long-COVID cases. Despite the success of vaccines and antiviral treatments, neuroinvasiveness of SARS-CoV-2 remains an important question, which is also centered on the mystery of whether the virus is capable of breaching the barriers into the central nervous system. By studying the K18-hACE2 infection model, we observed clear evidence of microvascular damage and breakdown of the blood-brain barrier (BBB). Mechanistically, SARS-CoV-2 infection caused pericyte damage, tight junction loss, endothelial activation and vascular inflammation, which together drive microvascular injury and BBB impairment. In addition, the blood-cerebrospinal fluid barrier at the choroid plexus was also impaired after infection. Therefore, cerebrovascular and choroid plexus dysfunctions are important aspects of COVID-19 and may contribute to neurological complications both acutely and in long COVID.

Heterogeneous Photocatalytic C(sp2)-H Activation of Formate for Hydrocarboxylation of Alkenes.

Light-driven carboxylation offers a promising approach for synthesizing valuable fine chemicals under mild conditions. Here we disclose a heterogeneous photocatalytic strategy of C(sp2)-H activation of formate for hydrocarboxylation of alkenes over zinc indium sulfide (ZnIn2S4) under visible light. This protocol functions well with a variety of substituted styrenes with good to excellent yields; it also works for unactivated alkenes albeit with lower yields. Mechanistic studies confirm the existence of CO2⋅- as a key intermediate. It was found that C(sp2)-H activation of formate is induced by S⋅ species on the surface of ZnIn2S4 via hydrogen atom transfer (HAT) instead of a photogenerated hole oxidation mechanism. Moreover, both cleavage of the C(sp2)-H of HCOO- and formation of a benzylic anion were found to be involved in the rate-determining step for the hydrocarboxylation of styrene.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (µPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on µPADs can further facilitate the realization of smartphone µPADs platforms for efficient disease detection. RESULTS: This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on µPADs. Our platform successfully applied sandwich c-ELISA for detecting the ß-amyloid peptide 1-42 (Aß 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. SIGNIFICANCE: This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aß 1-42, particularly in areas with low resources and limited communication infrastructure.

Assessing the Impact of PM2.5-Bound Arsenic on Cardiovascular Risk among Workers in a Non-ferrous Metal Smelting Area: Insights from Chemical Speciation and Bioavailability.

Inhalation of fine particulate matter PM2.5-bound arsenic (PM2.5-As) may cause significant cardiovascular damage, due to its high concentration, long transmission range, and good absorption efficiency in organisms. However, both the contribution and the effect of the arsenic exposure pathway, with PM2.5 as the medium, on cardiovascular system damage in nonferrous smelting sites remain to be studied. In this work, a one-year site sample collection and analysis work showed that the annual concentration of PM2.5-As reached 0.74 µg/m3, which was 120 times the national standard. The predominant species in the PM2.5 samples were As (V) and As (III). A panel study among workers revealed that PM2.5-As exposure dominantly contributed to human absorption of As. After exposure of mice to PM2.5-As for 8 weeks, the accumulation of As in the high exposure group reached equilibrium, and its bioavailability was 24.5%. A series of animal experiments revealed that PM2.5-As exposure induced cardiac injury and dysfunction at the environmental relevant concentration and speciation. By integrating environmental and animal exposure assessments, more accurate health risk assessment models exposed to PM2.5-As were established for metal smelting areas. Therefore, our research provides an important scientific basis for relevant departments to formulate industry supervision, prevention and control policies.

Uncovering the selectivity mechanism of phosphodiesterase 7A/8A inhibitors through computational studies.

The expression of phosphodiesterase 7A (PDE7A) and phosphodiesterase 8A (PDE8) genes is integral to human signaling pathways, and the inhibition of PDE7A has been associated with the onset of various diseases, including effects on the immune system and nervous system. The development of PDE7 selective inhibitors can promote research on immune and nervous system diseases, such as multiple sclerosis, chronic inflammation, and autoimmune responses. PDE8A is expressed alongside PDE8B, and its inhibitory mechanism is still unclear. Studying the mechanisms of selective inhibitors against different PDE subtypes is crucial to prevent potential side effects, such as nausea and cardiac toxicity, and the sequence similarity of the two protein subtypes was 55.9%. Therefore, it is necessary to investigate the differences of both subtypes' ligand binding sites. Selective inhibitors of two proteins were chosen to summarize the reason for their selectivity through molecular docking, molecular dynamics simulation, alanine scanning mutagenesis, and MM-GBSA calculation. We found that Phe384PDE7A, Leu401PDE7A, Gln413PDE7A, Tyr419PDE7A, and Phe416PDE7A in the active site positively contribute to the selectivity towards PDE7A. Additionally, Asn729PDE8A, Phe767PDE8A, Gln778PDE8A, and Phe781PDE8A positively contribute to the selectivity towards PDE8A.

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

High-resolution Power Doppler Using Null Subtraction Imaging.

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of 39 µm was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

Rainfall's impact on agricultural production and government poverty reduction efficiency in China.

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China's 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Captivity Shifts Gut Microbiota Communities in Plateau Zokor (Eospalax baileyi).

The gut microbiota in animals is a dynamic ecosystem influenced by both the host itself and the environment it inhabits. It is known that short-term captivity can significantly impact the gut microbiota of plateau zokors, leading to substantial inter-individual variation. However, the specific changes in the assembly process of the gut microbiota in plateau zokors during captivity remain unclear. In this study, we conducted a comparative analysis on the assembly process of the gut microbiota in 22 male plateau zokors from the same location in Qinglin Township, Datong County, Qinghai Province, before (W) and after (L) laboratory rearing. We performed a single-factor correlation network analysis on the top 50 genera with relative abundance in each group. The results revealed that captivity increased the complexity of the gut microbiota in plateau zokors, indicating a higher number of interactions between different microbial species. However, this increase in complexity was accompanied by a decrease in stability, suggesting a higher degree of variability and potential disruption in the microbial community. According to the results of the neutral community model, the gut microbiota of plateau zokors in the W had a higher Nm value (Nm = 48,135) compared to the L (Nm = 39,671), indicating that species dispersal of the gut microbiota was greater in the wild than in captivity. In the wild, the modified stochasticity ratio (MST) was less than 0.5, suggesting that deterministic processes dominated. However, after 15 days of laboratory rearing, the MST became greater than 0.5, indicating a shift toward stochastic processes, and this difference was highly significant (p < 0.001). This differs from research related to aboveground animals. This study provides theoretical support for the application of gut microbiota in subterranean endangered species.

ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging.

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms.

Maintaining the native gut microbiota of bharal (Pseudois nayaur) is crucial in ex situ conservation.

As wildlife protection continue to strengthen, research on the gut microbiota of wildlife is increasing. Carrying out conservation and research on endangered species in the Qinghai Tibet Plateau plays an important role in global biodiversity conservation. This study utilized 16S rRNA sequencing of fecal samples to investigate the composition, function, and changes of the gut microbiota of bharal in different environments, seasons, and genders. The results showed that Firmicutes and Bacteroidota were the dominant phyla and UCG-005, Bacteroides, UCG-010 were the dominant genera of bharal. In the wild, the abundance of Firmicutes increased which was conducive to the decomposition and utilization of cellulose, hemicellulose, and carbohydrate. Due to the variety of food types and nutrition in different seasons, the composition and function of gut microbiota were obviously different between genders. Compared with zoo, higher alpha diversity, a more complex gut microbiota network structure, and stronger metabolic function were conducive bharal to adapting to the wild environment. In the zoo, captive bharals were fed foods rich in high fat and protein, which increased the abundance of Bacteroidota and reduced the alpha diversity of gut microbiota. A fixed diet unified the gut microbiota between genders of bharal. It is very important to pay attention to the impact of captive environments and maintain the native gut microbiota of wildlife.

Insights into the selective mechanism of PDE2/9a inhibitors from silico aspects.

The selective design of competitive enzyme inhibitors is an extremely difficult task but necessary work for certain types of systems, such as the phosphodiesterase (PDE) system addressed in this article. In the PDE family, PDE2A and PDE9 respectively target the central nervous system and heart failure, and share many conserved amino acids at their binding sites. Therefore, gaining a deep understanding of the selective mechanisms of PDE2A/9A is crucial for designing highly selective drugs. In this study, various computer-aided drug design (CADD) methods, including molecular docking, molecular dynamics simulations (MD), and binding free energy calculations, are employed to explore the selective mechanisms of PDE2A/9A. Overall, our research results indicate a selective design strategy for PDE2A, which involves incorporating hydrophobic or aromatic moieties into the molecular structure to better accommodate the hydrophobic pocket of PDE2A. Additionally, it is recommended to introduce functional groups capable of forming connections with selective residues, such as Phe830 and Gln812 for PDE2A, or Ala452 and Tyr424 for PDE9A, to enhance the selectivity of inhibitors targeting PDE2A/9A. This achievement is anticipated to pave the way for the development of innovative and selective small molecules targeting PDE2A/9A.Communicated by Ramaswamy H. Sarma.

A SERS nanocellulose-paper-based analytical device for ultrasensitive detection of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD), one of the most prevalent neurodegenerative diseases, results in severe cognitive decline and irreversible memory loss. Early detection of AD is significant to patients for personalized intervention since effective cure and treatment methods for AD are still lacking. Despite the severity of the disease, existing highly sensitive AD detection methods, including neuroimaging and brain deposit-positive lesion tests, are not suitable for screening purposes due to their high cost and complicated operation. Therefore, these methods are unsuitable for early detection, especially in low-resource settings. Although regular paper-based microfluidics are cost-efficient for AD detection, they are restricted by a poor limit of detection (LOD). RESULTS: To address the above limitations, we report the ultrasensitive and low-cost nanocellulose paper (nanopaper)-based analytical microfluidic devices (NanoPADs) for detecting one of the promising AD blood biomarkers (glial fibrillary acidic protein, GFAP) using Surface-enhanced Raman scattering (SERS) immunoassay. Nanopaper offers advantages as a SERS substrate, such as an ultrasmooth surface, high optical transparency, and tunable chemical properties. We detected the target GFAP in artificial serum, achieving a LOD of 150 fg mL-1. SIGNIFICANCE: The developed NanoPADs are distinguished by their cost-efficiency and ease of implementation, presenting a promising avenue for effective early detection of AD's GFAP biomarker with ultrahigh sensitivity. More importantly, our work provides the experimental routes for SERS-based immunoassay of biomarkers on NanoPADs for various diseases in the future.

Estimating the direction of arrival of spatially spread sources using block-sparse Bayesian learning with an extended dictionary.

Estimating the direction of arrival (DOA) of spatially spread sources is a significant challenge in array signal processing. This work introduces an effective method within the sparse Bayesian framework to tackle this issue. A spatially spread source is modeled using a multi-dimensional Slepian signal subspace that expands the dictionary and results in a block-sparse structured solution. By taking advantage of block-sparse Bayesian learning, parameter estimation becomes feasible. A complex Gaussian posterior is derived under a multi-snapshot block-sparse framework with a complex Gaussian prior and varying noise conditions. The hyperparameters are estimated using the expectation-maximization algorithm. Through numerical tests and sea test data evaluations, the proposed method shows superior energy focusing for spatially spread signals. Under limited snapshots and challenging signal-to-noise ratios, the current method can still offer precise DOA determination for spatially spread sources.

Cellulose Nanostructures as Tunable Substrates for Nanocellulose-Metal Hybrid Flexible Composites.

Nanocomposite represents the backbone of many industrial fabrication applications and exerts a substantial social impact. Among these composites, metal nanostructures are often employed as the active constituents, thanks to their various chemical and physical properties, which offer the ability to tune the application scenarios in thermal management, energy storage, and biostable materials, respectively. Nanocellulose, as an emerging polymer substrate, possesses unique properties of abundance, mechanical flexibility, environmental friendliness, and biocompatibility. Based on the combination of flexible nanocellulose with specific metal fillers, the essential parameters involving mechanical strength, flexibility, anisotropic thermal resistance, and conductivity can be enhanced. Nowadays, the approach has found extensive applications in thermal management, energy storage, biostable electronic materials, and piezoelectric devices. Therefore, it is essential to thoroughly correlate cellulose nanocomposites' properties with different metallic fillers. This review summarizes the extraction of nanocellulose and preparation of metal modified cellulose nanocomposites, including their wide and particular applications in modern advanced devices. Moreover, we also discuss the challenges in the synthesis, the emerging designs, and unique structures, promising directions for future research. We wish this review can give a valuable overview of the unique combination and inspire the research directions of the multifunctional nanocomposites using proper cellulose and metallic fillers.