COVID-19 cooling: Nanostrategies targeting cytokine storm for controlling severe and critical symptoms.

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to wreak havoc worldwide, the "Cytokine Storm" (CS, also known as the inflammatory storm) or Cytokine Release Syndrome has reemerged in the public consciousness. CS is a significant contributor to the deterioration of infected individuals. Therefore, CS control is of great significance for the treatment of critically ill patients and the reduction of mortality rates. With the occurrence of variants, concerns regarding the efficacy of vaccines and antiviral drugs with a broad spectrum have grown. We should make an effort to modernize treatment strategies to address the challenges posed by mutations. Thus, in addition to the requirement for additional clinical data to monitor the long-term effects of vaccines and broad-spectrum antiviral drugs, we can use CS as an entry point and therapeutic target to alleviate the severity of the disease in patients. To effectively combat the mutation, new technologies for neutralizing or controlling CS must be developed. In recent years, nanotechnology has been widely applied in the biomedical field, opening up a plethora of opportunities for CS. Here, we put forward the view of cytokine storm as a therapeutic target can be used to treat critically ill patients by expounding the relationship between coronavirus disease 2019 (COVID-19) and CS and the mechanisms associated with CS. We pay special attention to the representative strategies of nanomaterials in current neutral and CS research, as well as their potential chemical design and principles. We hope that the nanostrategies described in this review provide attractive treatment options for severe and critical COVID-19 caused by CS.

Wearable biosensors for human sweat glucose detection based on carbon black nanoparticles.

Wearable glucose biosensors enable noninvasive glucose monitoring, thereby enhancing blood glucose management. In this work, we present a wearable biosensor based on carbon black nanoparticles (CBNPs) for glucose detection in human sweat. The biosensor consists of CBNPs, Prussian blue (PB), glucose oxidase, chitosan, and Nafion. The fabricated biosensor has a linear range of 5 µM to 1250 µM, sensitivity of 14.64 µA mM-1 cm-2, and a low detection potential (-0.05 V, vs. Ag/AgCl). The detection limit for glucose was calculated as 4.83 µM. This reusable biosensor has good selectivity and stability and exhibits a good response to glucose in real sweat. These results demonstrate the potential of our CBNP-based biosensor for monitoring blood glucose in human sweat.

A lava-inspired proteolytic enzyme therapy on cancer with a PEG-based hydrogel enhances tumor distribution and penetration of liposomes.

The enhanced permeability and retention (EPR) effect has become the guiding principle for nanomedicine against cancer for a long time. However, several biological barriers severely resist therapeutic agents' penetration and retention into the deep tumor tissues, resulting in poor EPR effect and high tumor mortality. Inspired by lava, we proposed a proteolytic enzyme therapy to improve the tumor distribution and penetration of nanomedicine. A trypsin-crosslinked hydrogel (Trypsin@PSA Gel) was developed to maintain trypsin's activity. The hydrogel postponed trypsin's self-degradation and sustained the release. Trypsin promoted the cellular uptake of nanoformulations in breast cancer cells, enhanced the penetration through endothelial cells, and degraded total and membrane proteins. Proteomic analysis reveals that trypsin affected ECM components and down-regulated multiple pathways associated with cancer progression. Intratumoral injection of Trypsin@PSA Gel significantly increased the distribution of liposomes in tumors and reduced tumor vasculature. Combination treatment with intravenous injection of gambogic acid-loaded liposomes and intratumoral injection of Trypsin@PSA Gel inhibited tumor growth. The current study provides one of the first investigations into the enhanced tumor distribution of liposomes induced by a novel proteolytic enzyme therapy.

Analysis of pollutant dispersion patterns in rivers under different rainfall based on an integrated water-land model.

In the context of rapid urban expansion, the interaction between humanity and nature has become more prominent. Urban land and rivers often exist as distinct entities with limited material exchange. However, during rainfall, these two systems interconnect, resulting in the transfer of land-derived pollutants into rivers. Such transfer significantly increases river pollutant levels, adversely affecting water quality. Therefore, developing a water quality simulation and prediction model is crucial. This model should effectively illustrate pollutant movement and dispersion during rain events. This study proposes a comprehensive model that merges the Storm Water Management Model (SWMM) with the Environmental Fluid Dynamics Code (EFDC). This integrated model assesses the spread and dispersion of pollutants, including Ammonia Nitrogen (NH3-N), Total Phosphorus (TP), Total Nitrogen (TN), and Chemical Oxygen Demand (COD), within urban water cycles for various rainfall conditions, thus offering critical theoretical support for managing the water environment. The application of this model under different rainfall intensities (light, moderate and heavy) provides vital insights. During light rainfall, the river's natural purification process can sustain surface water quality at Class IV. Moderate rainfall causes accumulation of pollutants, reducing water quality to Class V. Conversely, heavy rainfall rapidly increases pollutant concentrations due to higher inflow, pushing the river to a degraded Class V status, which is beyond its natural purification capacity, necessitating engineering solutions to reattain Class IV quality. Furthermore, pollutant accumulation in downstream river sections is more influenced by flow rate than by rainfall intensity. In summary, the SWMM-EFDC integrated model proves highly effective in predicting river water quality, thereby significantly aiding urban water pollution control.

Efficient flexible quantum-dot light-emitting diodes with unipolar charge injection.

The exfoliation between the electrode film and the adjacent functional layer is still a big challenge for the flexible light emitting diodes, especially for the devices dependent on the direct charge injection from the electrodes. To address this issue, we design a flexible quantum-dot light-emitting diodes (QLEDs) with a charge-generation layer (CGL) on the bottom electrode as the electron supplier. The CGL consisting of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)/ZnO can provide sufficient electron injection into the QDs, enabling a balanced charge injection. As a result, the CGL-based QLED exhibits a peak external quantum efficiency 18.6%, over 25% enhancement in comparison with the device with ZnO as the electron transport layer. Moreover, the residual electrons in the ZnO can be pulled back to the PEDOT:PSS/ZnO interface by the storage holes in the CGL, which are released and accelerates the electron injection during the next driving voltage pulse, hence improving the electroluminescence response speed of the QLEDs.

LncRNA KCNQ1OT1 knockdown inhibits ox-LDL-induced inflammatory response and oxidative stress in THP-1 macrophages through the miR-137/TNFAIP1 axis.

Both inflammatory response and oxidative stress are regarded as two critical contributors to atherosclerosis. Kcnq1 overlapping transcript 1 (KCNQ1OT1) is an imprinted antisense long non-coding RNA in the kcnq1 locus. Our previous study has demonstrated that KCNQ1OT1 aggravates atherosclerosis by promoting macrophage lipid accumulation. However, its role in atherogenesis remains to be elucidated. This study aimed to observe the impact of KCNQ1OT1 on oxidized low-density lipoprotein (ox-LDL)-induced inflammatory response and oxidative stress and to explore the underlying mechanism. We found that ox-LDL up-regulated KCNQ1OT1 expression in THP-1 macrophages. Knockdown of KCNQ1OT1 increased miR-137 levels, decreased tumor necrosis factor-α-induced protein 1 (TNFAIP1) expression, and inhibited inflammatory response and alleviated oxidative stress in ox-LDL-treated THP-1 macrophages. A ceRNA regulatory network was identified among KCNQ1OT1, miR-137 and TNFAIP1. The inhibitory effect of KCNQ1OT1 knockdown on inflammatory response and oxidative stress was significantly reversed by miR-137 prevention or TNFAIP1 overexpression. In summary, these findings suggest that silencing of KCNQ1OT1 suppresses inflammatory response and oxidative stress induced by ox-LDL through the miR-137/TNFAIP1 pathway in THP-1 macrophages, thereby providing novel mechanistical insights into its pro-atherosclerotic action.

Fast location and segmentation of high-throughput damaged soybean seeds with invertible neural networks.

BACKGROUND: Fast identification of damaged soybean seeds has undeniable importance in seed sorting and food quality. Mechanical vibration is generally used in soybean seed sorting, but this can seriously damage soybean seeds. The convolutional neural network (CNN) is considered an effective method for location and segmentation tasks. However, a CNN requires a large amount of ground truth data and has high computational cost. RESULTS: First, we propose a self-supervision manner to automatically generate ground truths, which can theoretically create an almost unlimited number of labeled images. Second, instead of using popular CNNs, a novel invertible convolution (involution)-enabled scheme is proposed by using the bottleneck block of the residual networks. Third, a feature selection feature pyramid network (FS-FPN) based on involution is designed, which selects features more flexibly and adaptively. We further merge involution-based backbones and FS-FPN into a unified network, achieving an end-to-end seed location and segmentation model; the best mean average precision of location and segmentation achieved was 85.1% and 81% respectively. CONCLUSION: The experimental results demonstrate that the proposed method greatly improves the performance of the baseline network with faster speed and fewer parameters, enabling it to detect soybean seeds more effectively. © 2022 Society of Chemical Industry.

AgriPest: A Large-Scale Domain-Specific Benchmark Dataset for Practical Agricultural Pest Detection in the Wild.

The recent explosion of large volume of standard dataset of annotated images has offered promising opportunities for deep learning techniques in effective and efficient object detection applications. However, due to a huge difference of quality between these standardized dataset and practical raw data, it is still a critical problem on how to maximize utilization of deep learning techniques in practical agriculture applications. Here, we introduce a domain-specific benchmark dataset, called AgriPest, in tiny wild pest recognition and detection, providing the researchers and communities with a standard large-scale dataset of practically wild pest images and annotations, as well as evaluation procedures. During the past seven years, AgriPest captures 49.7K images of four crops containing 14 species of pests by our designed image collection equipment in the field environment. All of the images are manually annotated by agricultural experts with up to 264.7K bounding boxes of locating pests. This paper also offers a detailed analysis of AgriPest where the validation set is split into four types of scenes that are common in practical pest monitoring applications. We explore and evaluate the performance of state-of-the-art deep learning techniques over AgriPest. We believe that the scale, accuracy, and diversity of AgriPest can offer great opportunities to researchers in computer vision as well as pest monitoring applications.

Occurrence, risk and influencing factors of polycyclic aromatic hydrocarbons in surface soils from a large-scale coal mine, Huainan, China.

Coal is one of the most important fossil fuels for energy, but it can cause serious polycyclic aromatic hydrocarbon (PAH) pollution to the environment. In this work, the distribution, sources, influencing factors, and risk assessment of PAHs were studied in a soil of typical coal resource city, Huainan, China. The total concentration of 16 PAHs classified by USEPA in 47 soils ranged from 109.94 to 1105.30 ng/g with a mean concentration of 528.06 ng/g. The PAH concentration was higher in soil of this area than most of the agricultural, urban and industrial soils and lower than some coal mine and coal-fired power plant areas in the world. The principal component analysis (PCA) and diagnostic ratios demonstrated that PAHs in soils were mainly from the coal combustion and refined petroleum products. The total organic carbon (TOC, p < 0.01) and black carbon (BC, p < 0.01) can significantly influence PAH inventories in soils, particularly for PAHs with high molecular weight. In addition, the significantly positive correlations between PAHs in feed coal (p < 0.05), fly ash (p < 0.01), particulate matter (PM1-2.5 and PM2.5-10, p < 0.01) and PAHs in soils revealed that the emission sources and deposition processes were also the main factors affecting PAH contents in soils. The estimated values of incremental lifetime cancer risk (ILCR) for children and adults were higher than 10-4 at all sampling sites, suggesting high carcinogenic risks for local residents, and the most important exposure route for PAHs was dermal absorption. These findings are valuable for assessing the health risk of PAHs in soils around typical coal mine and coal-fired power plants and highlight the urgency of taking actions to control and reduce the carcinogenic risks for local residents.

A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network.

Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized the frames into video. In the still-image detector, we used faster-RCNN as the framework. We used image-training models to detect relatively blurry videos. Additionally, a set of video-based evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet-50, ResNet-101 backbone system and YOLOv3 with our experimental environment.

Simultaneous measurement of refractive index and temperature for prism-based surface plasmon resonance sensors.

A detailed theoretical model is provided to analyze the effects of temperature on prism-based surface plasmon resonance (SPR) sensors, including temperature dependence of the metal and prism. A complete sensitivity matrix simultaneously measures variations in refractive index (RI) and temperatures using measurements at two wavelengths for the angular-interrogation mode, or at two angles of incidence for the wavelength-interrogation mode. Correction of matrix coefficients improves accuracy of the two modes. Validation is performed using a self-designed wavelength SPR system with an adjustable incident angle perform. This method provides a new way to detect the RI and may lead to the better design and fabrication of prism-based SPR sensors.

Univariate and multivariate analyses of strontium and vanadium in soil by laser-induced breakdown spectroscopy.

Univariate and multivariate analyses of strontium (Sr) and vanadium (V) elements in soil have been performed using laser-induced breakdown spectroscopy technology. Thirty-three samples were used as a calibration set, and 11 samples were used as a prediction set. The results demonstrated that the correlation coefficients of the calibration curves method were poor due to the matrix effect. Then, the multivariate models of partial least-squares regression and least squares support vector regression (LS-SVR) were used to construct models. The analysis accuracy was improved effectively by the LS-SVR method, and the correlation coefficient is 0.999 for Sr and 0.983 for V. The average relative errors for the prediction set are lower than 7.45% and 2.88% for Sr and V, respectively. The results indicated that the LIBS technique coupled with LS-SVR could be a reliable and accurate method in the quantitative determination of elemental Sr and V in complex matrices like soil.

Historical Pollution and Source Contributions of PAHs in Sediment Cores from the Middle Reach of Huai River, China.

To investigate the spatial and historical distributions, and source contributions of polycyclic aromatic hydrocarbon (PAHs) from the middle reach of Huai River, 15 surface sediments and two sediment cores were analyzed. The Σ16 PAHs levels in surface sediments varied from 533.15 to 1422.83 ng/g dw, and from 413.27 to 43951.56 ng/g dw in individual sediment layer of sediment cores. The temporal trends of PAHs in sediment cores are the good indicators of the anthropogenic emissions over the last 60 years. The stable carbon isotope ratios of PAHs indicate the primary PAHs sources were the combustion of wood and coal during 1950s-1970s, and automobile exhausts and the coal combustion emissions in recent decades.

CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization.

SUMMARY: Identifying molecular cancer subtypes from multi-omics data is an important step in the personalized medicine. We introduce CancerSubtypes, an R package for identifying cancer subtypes using multi-omics data, including gene expression, miRNA expression and DNA methylation data. CancerSubtypes integrates four main computational methods which are highly cited for cancer subtype identification and provides a standardized framework for data pre-processing, feature selection, and result follow-up analyses, including results computing, biology validation and visualization. The input and output of each step in the framework are packaged in the same data format, making it convenience to compare different methods. The package is useful for inferring cancer subtypes from an input genomic dataset, comparing the predictions from different well-known methods and testing new subtype discovery methods, as shown with different application scenarios in the Supplementary Material. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available under GPL-2 license from the Bioconductor website (http://bioconductor.org/packages/CancerSubtypes/). CONTACT: thuc.le@unisa.edu.au or jiuyong.li@unisa.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy.

Accurate information of soil macronutrient contents and fertilizer macronutrient contents is the precondition of precision fertilization; however, how to detect soil and fertilizer information rapidly, reliably, and inexpensively remains a great challenge. Visible and near-infrared (VIS/NIR) diffuse reflectance spectroscopy proves to be an effective tool for extensive investigation of soil and fertilizer properties. This study first collected many soil and chemical fertilizer samples and performed both spectral scanning and chemical analysis. During the correlation between the collected VIS/NIR spectra and the measured data, different spectral pretreatment, sample selection, and wavelength optimization methods were applied for improving the accuracy and robustness of the prediction models. After appropriate spectral processing and selection of representative samples, both principal component regression and genetic algorithm (GA) can adequately reduce the number of variables and pick out the characteristic variables, which not only enhanced prediction speed but also greatly improved prediction accuracy. In particular, using GA-based models, organic matter content (OMC), total N and pH value in soil and N, P, and K contents in fertilizer can all be accurately predicted.

Correlation between Quality and Geographical Origins of Poria cocos Revealed by Qualitative Fingerprint Profiling and Quantitative Determination of Triterpenoid Acids.

Poria cocos (Schw.) Wolf (PC) is a well-known saprophytic fungus, and its sclerotium without the epidermis (PCS) is widely used in traditional Chinese medicine and as a functional food in many countries. PCS is normally collected from multiple geographical regions, but whether and how the quality of PCS correlates with where it grows have not been determined. This correlation could be significant both for quality control and optimum utilization of PCS as a natural resource. In this study, a qualitative fingerprint profiling method performed by ultra-performance liquid chromatography (UHPLC) with diode array detection (DAD) combining quadrupole time-of-flight-mass spectrometry (QTOF-MS/MS) and a quantitative UHPLC coupled with triple quadrupole mass spectrometry (QqQ-MS/MS) approach were established to investigate whether and how the quality of PCS correlates with its collection location. A standard fingerprint of PCS was generated by median simulation of 25 tested samples collected from four main producing areas of China, and similarity analysis was applied to evaluate the similarities between the fingerprints of samples and the standard fingerprint. Twenty three common peaks occurring in the fingerprint were unequivocally or tentatively identified by UHPLC-QTOF-MS/MS. Meanwhile, principal component analysis (PCA), supervised orthogonal partial least squares-discriminate analysis (OPLS-DA) and hierarchical cluster analysis (HCA) were employed to classify 25 batches of PCS samples into four groups, which were highly consistent with the four geographical regions. Ten compounds were screened out as potential markers to distinguish the quality of PCS. Nine triterpene acids, including five compounds that played important roles in the clusters between different samples collected from the four collection locations, were simultaneously quantified by using the multiple reaction monitoring (MRM) mode of UHPLC-QqQ-MS/MS. The current strategy not only clearly expounded the correlation between quality and geographical origins of PCS, but also provided a fast, accurate and comprehensive qualitative and quantitative method for assessing the quality of PCS.

[Studies on chemical constituents of Valeriana plants and their biological activities].

The recent progresses on chemical components and pharmacological activities of the genus Valerianawere summarized.Besides-essential oil, the chemical composition of Valerianais mainly focused on monoterpenoids,sesquiterpenoids,lignans, flavonoids, alkaloids, etc. Iridoids are the main chemical components ofmonoterpenoids. There are two types ofiridoidson the basis of the cyclopentane open or not. The Valerianahas been drawmuch attention for their significant sedation,spasmolysis,antidepression,antitumor, against adenosine A1 receptors and cytotoxicityactivity,and had certain function for cardiovascular disease treatment. Given to the fact of the lack of systematic review and summary of studies on the Valeriana, we summarized and analyze the study literatures on the pharmacological activity of Valerianain recent years, and providedsome basisfor further study.

TSAF-Net: a rotated two-stage Cnaphalocrocis medinalis damage detection method based on anchor-free arbitrary-oriented proposal network.

BACKGROUND: Cnaphalocrocis medinalis (C.medinalis) is an agricultural pest with recurrent outbreaks. The investigation into automated pest and disease detection technology holds significant value for in-field surveys. Current generic detection methods are inadequate due to arbitrary orientations and a wide range of aspect ratios in damage symptoms. To tackle these issues, we put forward a rotated two-stage detection method for in-field C.medinalis surveys. This method relies on an anchor-free rotated region proposal network (AF-R2PN), bypassing the need for hyper-parameter optimization induced by predefined anchor boxes. An in-field C.medinalis dataset is constructed during on-site pest surveys to validate the effectiveness of our method. RESULTS: The experimental results show that our method can accomplish 80% average precision (AP), surpassing the corresponding horizontal detector by 2.3%. The visualization results of our work showcase its exceptional localization capability over generic detection methods, facilitating inspection by plant protectors. Meanwhile, our proposed method outperforms other state-of-the-art rotated detection algorithms. The AF-R2PN module can generate superior arbitrary-oriented proposals even with a decreased number of proposals, balancing inference speed and detection performance among other rotated two-stage methods. CONCLUSION: The proposed method exhibits superiority in detecting C. medinalis damage under complex field conditions. It provides greater practical applicability during in-field surveys, enhancing their efficiency and coverage. The findings hold significance for pest and disease monitoring, providing important technical support for agricultural production. © 2024 Society of Chemical Industry.

Discrimination of internal crack for rice seeds using near infrared spectroscopy.

It is an important thing to identify internal crack in seeds from normal seeds for evaluating the quality of rice seeds (Oryza sativa L.). In this study, non-destructive discrimination of internal crack in rice seeds using near infrared spectroscopy and chemometrics is proposed. Principal component analysis (PCA) was used to analyze the rice seeds spectra. Four supervised classification techniques(partial least squares discriminate analysis (PLS-DA), support vector machines (SVM), k-nearest neighbors (KNN) and random forest (RF)) with four different pre-processing techniques (standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative with Savitzky-Golay (SG) smoothing) were applied. The best results (Sn = 0.8824, Sp = 0.9429, Acc = 0.913) were achieved by PLS-DA with the raw spectral data. The performance of the best SVM model was inferior to that of PLS-DA, but superior to that of RF and KNN. Except for PLS-DA, four different preprocessing techniques were improved the performance of the developed models. The important variables for discriminating internal cracks in rice seeds were related to the amylose. Overall, the all results demonstrated the feasibility of non-destructive discrimination of internal crack for rice seeds (Oryza sativa L.) using near infrared spectroscopy and chemometrics.