Microfluidic particle counter visualizing mucosal antibodies against SARS-CoV-2 in the upper respiratory tract for rapid evaluation of immune protection.

Mucosal antibodies in the upper respiratory tract are the earliest and most critical responders to prevent respiratory infections, providing an indication for the rapid evaluation of immune protection. Here, we report a microfluidic particle counter that directly visualizes mucosal antibody levels in nasal mucus. The mucosal anti-SARS-CoV-2 spike receptor binding domain (RBD) antibodies in nasal secretions first react with magnetic microparticles (MMPs) and polystyrene microparticles (PMPs) that are surface-modified to form a "MMPs-anti-spike RBD IgG-PMPs" complex when RBD is present. After magnetic separation and loading into the microfluidic particle counter, the free PMPs, which are reduced with increasing anti-spike RBD IgG antibody levels, are trapped by a microfluidic particle dam and accumulate in the trapping channel. A sensitive mode [limit of detection (LOD): 14.0 ng mL-1; sample-to-answer time: 70 min] and an equipment-free rapid mode (LOD: 37.4 ng mL-1; sample-to-answer time: 20 min) were achieved. Eighty-seven nasal secretion (NS) samples from vaccinees were analyzed using our microfluidic particle counter, and the results closely resemble those of the gold-standard enzyme-linked immunosorbent assay (ELISA). The analysis shows that higher antibody levels were found in convalescent volunteers compared to noninfected volunteers. Together, we demonstrate a rapid kit that directly indicates immune status, which can guide vaccine strategy for individuals and the government.

General Hyperspectral Image Super-Resolution via Meta-Transfer Learning.

Recent advances in deep learning-based methods have led to significant progress in the hyperspectral super-resolution (SR). However, the scarcity and the high dimension of data have hindered further development since deep models require sufficient data to learn stable patterns. Moreover, the huge domain differences between hyperspectral image (HSI) datasets pose a significant challenge in generalizability. To address these problems, we present a general hyperspectral SR framework via meta-transfer learning (MTL). We randomly sample various spectral ranges for SR tasks during MTL, allowing the model to accumulate diverse task experiences. Additionally, we implement a task schedule to gradually expand the number of bands, bridging the significant domain differences between datasets. By leveraging multiple datasets, we are able to achieve better performance and greater generalizability, making it applicable under various circumstances. Meanwhile, as a general framework, our scheme can be applied to existing methods to obtain performance improvements. In addition, we design an advanced network architecture based on the multifusion features to further improve the performance. Experiments demonstrate that our method not only achieves superior performance in both qualitative and quantitative terms but also can adapt robustly to a new and difficult sample, where few epochs can yield quite considerable results.

RotatedStomataNet: a deep rotated object detection network for directional stomata phenotype analysis.

KEY MESSAGE: Innovatively, we consider stomatal detection as rotated object detection and provide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system, RotatedeStomataNet. Stomata acts as a pathway for air and water vapor in the course of respiration, transpiration, and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high-throughput stoma is a key issue. Nevertheless, currently available methods usually suffer from detection errors or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time, and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired destructively, and the maize stomatal data set acquired in a non-destructive way, enabling the one-stop automatic collection of phenotypic, such as the location, density, length, and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. The experimental results that the prediction results of the method are consistent with those of manual labeling. The test sets, the system code, and their usage are also given ( https://github.com/AITAhenu/RotatedStomataNet ).

Forest management positively reshapes the phyllosphere bacterial community and improves community stability.

Research has shown that forest management can improve the post-drought growth and resilience of Qinghai spruce in the eastern Qilian Mountains, located on the northeastern Tibetan Plateau. However, the impact of such management on the tree-associated phyllosphere microbiome is not yet fully understood. This study provides new evidence of positive forest management effects on the phyllosphere microbiome after extreme drought, from the perspectives of community diversity, structure, network inference, keystone species, and assembly processes. In managed Qinghai spruce forest, the α-diversity of the phyllosphere bacterial communities increased, whereas the ß-diversity decreased. In addition, the phyllosphere bacterial community became more stable and resistant, yet less complex, following forest management. Keystone species inferred from a bacterial network also changed under forest management. Furthermore, forest management mediated changes in community assembly processes, intensifying the influence of determinacy, while diminishing that of stochasticity. These findings support the hypothesis that management can re-assemble the phyllosphere bacterial community, enhance community stability, and ultimately improve tree growth. Overall, the study highlights the importance of forest management on the phyllosphere microbiome and furnishes new insights into forest conservation from the perspective of managing microbial processes and effects.

Photoswitchable Temperature Nanosensors Based on the Chemical Kinetics of Photochromic Naphthopyran for Live Cell Imaging.

Microscopic temperature imaging holds significant importance in various fields, particularly in the development of nanomaterials for photothermal therapy (PTT). In this study, we present an analytical method to probe cellular temperature based on chemical kinetics and additional luminescence quenching by photoswitchable naphthopyrans. Taking advantage of the rapid ring-closing reaction of naphthopyran, temperature sensing was realized with a linear relationship between the logarithmic decay time constant (ln τ) and the reciprocal temperature (T-1). To create luminescent temperature nanosensors, we harnessed the ability of ring-opened naphthopyran to quench the luminescence of a semiconducting polymer, resulting in a diverse array of probes. Structural modifications on the naphthopyran also provided a way to fine-tune the sensitivity and response window of the nanosensors. The method allowed cellular temperature imaging on a cost-effective fluorescence microscopic setup. As an application, the temperature increase induced by gold nanorods (AuNRs) in cell lysosomes was successfully monitored, laying the foundation for a new class of photoswitchable nanosensors with promising biological applications.

The coupling coordination characteristics of China's health production efficiency and new urbanization and its influencing factors.

Urbanization leads to dramatic changes in habitat quality, which significantly affects population health. Research on the coupling coordination relationship between new urbanization and health production efficiency is conducive to improving residents' well-being and urban sustainable development. In this article, we adopted the super-efficient SBM model and entropy value method separately to evaluate the spatiotemporal variation characteristics of health production efficiency and new urbanization in China. Then, we used the coupling coordination degree model to investigate the interactive coercing relationship between new urbanization and health production efficiency. Finally, the panel Tobit model is used to analyze the factors influencing the coupled coordination of the two systems. The results showed that the new urbanization levels of 31 provinces in China have all steadily increased from 2003 to 2018. Health production efficiency exhibited a fluctuating but increasing trend, and its regional differences are gradually narrowing. Health production efficiency and new urbanization have developed in a more coordinated direction, with a spatial pattern of "high in the southeast and low in the northwest." Meanwhile, the relative development characteristics between the two systems have constantly changed, from the new urbanization lagged type to the two systems synchronized type and the health production efficiency lagged type. Population density, economic development level, government financial investment, and government health investment positively impact the coupling coordination degree of the two systems. In comparison, individual health investment harms the harmonization of the two systems.

SLPA-Net: a real-time recognition network for intelligent stomata localization and phenotypic analysis.

Plant stomatal phenotype traits play an important role in improving crop water use efficiency, stress resistance and yield. However, at present, the acquisition of phenotype traits mainly relies on manual measurement, which is time-consuming and laborious. In order to obtain high-throughput stomatal phenotype traits, we proposed a real-time recognition network SLPA-Net for stomata localization and phenotypic analysis. After locating and identifying stomatal density data, ellipse fitting is used to automatically obtain phenotype data such as apertures. Aiming at the problems of small stomata and high similarity to background, we introduced ECANet to improve the accuracy of stoma and aperture location. In order to effectively alleviate the unbalance problem in bounding box regression, we replaced the Loss function with a more effective Focal EIoU Loss. The experimental results show that SLPA-Net has excellent performance in the migration generalization and robustness of stomata and apertures detection and identification, as well as the correlation between stomata phenotype data obtained and artificial data. For convenience, we developed SLPA-Net into a freely available software, the software can be obtained at https://github.com/AITAhenu/SLPA.

Effect and Safety of Herbal Medicine Foot Baths in Patients with Diabetic Peripheral Neuropathy: A Multicenter Double-Blind Randomized Controlled Trial.

OBJECTIVE: To evaluate the effect and safety of foot baths with Tangbi Waixi Decoction (TW) in treating patients with diabetic peripheral neuropathy (DPN). METHODS: It is a multicenter double-blinded randomized controlled trial. Participants with DPN were recruited between November 18, 2016 and May 30, 2018 from 8 hospitals in China. All patients received basic treatments for glycemic management. Patients received foot baths with TW herbal granules either 66.9 g (intervention group) or 6.69 g (control group) for 30 min once a day for 2 weeks and followed by a 2-week rest, as a therapeutic course. If the Toronto Clinical Scoring System total score (TCSS-TS) ⩾6 points, the patients received a total of 3 therapeutic courses (for 12 weeks) and were followed up for 12 weeks. The primary outcome was change in TCSS-TS score at 12 and 24 weeks. Secondary outcomes included changes in bilateral motor nerve conduction velocity (MNCV) and sensory nerve conduction velocity (SNCV) of the median and common peroneal nerve. Safety was also assessed. RESULTS: Totally 632 patients were enrolled, and 317 and 315 were randomized to the intervention and control groups, respectively. After the 12-week intervention, patients in both groups showed significant declines in TCSSTS scores, and significant increases in MNCV and SNCV of the median and common peroneal nerves compared with pre-treatment (P<0.05). The reduction of TCSS-TS score at 12 weeks and the increase of SNCV of median nerve at 24 weeks in the control group were greater than those in the intervention group (P<0.05). The number of adverse events did not differ significantly between groups (P>0.05), and no serious adverse event was related with treatment. CONCLUSION: Treatment of TW foot baths was safe and significantly benefitted patients with DPN. A low dose of TW appeared to be more effective than a high dose. (Registry No. ChiCTR-IOR-16009331).

Space-time cube uncovers spatiotemporal patterns of basin ecological quality and their relationship with water eutrophication.

Maintaining an optimal eco-environment is important for sustainable regional development. However, existing methods are inadequate for examining both spatial and temporal dimensions. Here, we propose a systematic procedure for spatiotemporal examination of the eco-environment using the space-time cube (STC) model and describe a preliminary investigation of the coupling relationships between basin ecological quality and water eutrophication in upstream of the Han River basin between 2000 and 2020. The STC model considers the temporal dimension as the third dimension in calculations. We first categorized the basin into three sub-watershed types: forest, cultivated land, and artificial surface. Subsequently, the ecological quality and driving factors were assessed and identified using the remote sensing ecological index (RSEI) and Geodetector method, respectively. The findings indicated that the forest basin and artificial surface basin had the highest and lowest ecological quality, respectively. The spatiotemporal cold spots of ecological quality during the past 20 years were mostly located in the vicinity of reservoirs, rivers, and artificial surface areas. Human activity, precipitation, and the percentage of cultivated land were other important driving factors in the artificial surface, forest, and cultivated land sub-watersheds, respectively, in addition to the dominant factors of elevation and temperature. The results also indicated that when the ecological quality degraded to a certain extent, water eutrophication was significantly coupled with the ecological quality of the catchments. The findings of this study are useful for ecological restoration and sustainable river basin development.

Ionic Transistors.

Biological voltage-gated ion channels, which behave as life's transistors, regulate ion transport precisely and selectively through atomic-scale selectivity filters to sustain important life activities. By this inspiration, voltage-adaptable ionic transistors that use ions as signal carriers may provide an alternative information processing unit beyond solid-state electronic devices. This review provides a comprehensive overview of the first generation of biomimetic ionic transistors, including their operating mechanisms, device architecture development, and property characterizations. Despite its infancy, significant progress has been made in the applications of ionic transistors in fields such as DNA detection, drug delivery, and ionic circuits. Challenges and prospects of full exploitation of ionic transistors for a broad spectrum of practical applications are also discussed.

Biodegradable nanofibrillated microcellular PBS/PLA foams for selective oil absorption.

To address the challenges posed by spilled oil and oily wastewater, the development of clean oil-adsorption materials is crucial. However, traditional oil-adsorption materials suffer from the issue of secondary pollution. Herein, fully biodegradable nanofibrillated poly(butylene succinate)/poly(lactic acid) (PBS/PLA) foams with outstanding selective oil-adsorption performance were successfully fabricated via an eco-friendly supercritical CO2 foaming technology. The PBS/PLA composites, featuring nanofibrils with a diameter of approximately 100 nm, were prepared through a hot-stretching method subsequent to extrusion. Substantial improvements were observed in the crystallization rate and rheological properties of the fibrillated PBS/PLA composites. Furthermore, PLA nanofibrils enhanced foamability of the composite, achieving an impressive expansion ratio of up to 38.0, resulting in an outstanding oil-absorption performance (19.2-50.4 g/g) of the F-1 %-95 foam. Additionally, 20 adsorption-desorption cycles illustrated the prepared F-1 %-95 foam displayed recyclable oil-absorption characteristics. This work provides an eco-friendly strategy for preparing fully biodegradable foams intended for application as oil-adsorption materials.

HostNet: improved sequence representation in deep neural networks for virus-host prediction.

BACKGROUND: The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand. RESULTS: To overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of "Rabies lyssavirus" and an in-house dataset of "Flavivirus". Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability. CONCLUSION: HostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.

Lattice Thermal Conductivity in XMg2Sb2(X = Ca or Mg) Compounds: Temperature and High-Order Anharmonicity Effect.

The binary compound Mg3Sb2 (also written as MgMg2Sb2) exhibits a much lower lattice thermal conductivity (κL) than its ternary analog CaMg2Sb2, despite its relatively low mass density and simple crystalline structure. Here, we perform a comparative first-principles study of the lattice dynamics in MgMg2Sb2 and CaMg2Sb2 based on the density functional theory, together with the self-consistent phonon theory and the Boltzmann transport theory. We show that the modest anharmonicity of CaMg2Sb2 renders the three-phonon processes dominant, and the temperature dependence of κL approximately follows the T-1 relationship. In contrast, the strong quartic anharmonicity of MgMg2Sb2 leads to the ultralow κL and weak temperature dependence, in agreement with the experimental observations. A comprehensive analysis reveals that the κLs in the two compounds are mainly carried by the acoustic phonons associated with the Sb atoms, and the different behaviors of κL result from the chemical bond changes around Sb atoms, which bond more covalently with the Mg atoms than the Ca atoms and thus lead to high-order anharmonicity in MgMg2Sb2. These results give us insights into the understanding of the anomalous thermal transport in thermoelectric materials.

Development of Eco-Friendly and High-Strength Foam Sensors Based on Segregated Elastomer Composites with a Large Work Range and High Sensitivity.

Achieving a high-strength piezoresistive foam with high sensitivity and a large workable range remains a major challenge. To realize these goals, we developed a facile, novel, and eco-friendly strategy for constructing segregated microcellular structures fabricated using coating, heat compression molding, and supercritical CO2 (ScCO2) foaming. The segregated poly(ether block amide) (PEBA)/carbon nanostructure (CNS) composites were fabricated via compression molding. This effectively improved the foamability and cell morphology of PEBA/CNS composites. Moreover, compared with the randomly distributed structure, the segregated structure also endowed the foams with better conductivity and sensing capability. Subsequently, the ScCO2 foaming was employed to fabricate segregated PEBA/CNS composite foams. The foaming gave composites a large compressibility and reduced their percolation threshold. Under 1 wt % CNS loading, via tuning the expansion ratio of foam from ∼2.1 to 4.1, the compression stress at 50% compression strain of foam varied from ∼3.3 to 0.5 MPa, and the conductivity changed from 4.89 × 10-3 to 1.93 × 10-6 s/m, implying a tunable conductivity. Additionally, the adjustable conductivity enabled the sensitivity of segregated composite foams to be regulated. The segregated PEBA/CNS foam (FCNS1-4.1) exhibited a good combination of high sensitivity (GF = 3.5), large work range (80% strain), and high compression strength (∼0.5 MPa at 50% strain) as well as a stable, reproducible, and durable sensing response under a low CNS content (∼0.11 vol %). Furthermore, the ΔI/I0 of FCNS1-4.1 (75.6% porosity) reached a high value of ∼810 and exhibited an ultrahigh sensitivity of ∼3706 (ΔI/I0ε) from 60 to 80% strain. Moreover, the foam sensor could be used as a sensing function sole for monitoring diverse human motions. Therefore, the segregated PEBA/CNS composite foams with outstanding piezoresistive performances show promising potential applications in monitoring human motions as wearable electronics and provides a new design strategy for a new generation of foam sensors with high performance.

In Vitro Detection of S100B and Severity Evaluation of Traumatic Brain Injury Based on Biomimetic Peptide-Modified Nanochannels.

The diagnosis and evaluation of traumatic brain injury (TBI) are crucial steps toward the treatment and prognosis of patients. A common question remains as to whether it is possible to introduce an ideal device for signal detection and evaluation that can directly connect digital signals with TBI, thereby enabling prompt response of the evaluation signal and sensitive and specific functioning of the detection process. Herein, a method is presented utilizing polymetric porous membranes with TRTK-12 peptide-modified nanochannels for the detection of S100B (a TBI biomarker) and assessment of TBI severity. The method leverages the specific bonding force between TRTK-12 peptide and S100B protein, along with the nanoconfinement effect of nanochannels, to achieve high sensitivity (LOD: 0.002 ng mL-1 ) and specificity (∆I/I0 : 44.7%), utilizing ionic current change as an indicator. The proposed method, which is both sensitive and specific, offers a simple yet responsive approach for real-time evaluation of TBI severity. This innovative technique provides valuable scientific insights into the advancement of future diagnostic and therapeutic integration devices.

Effects of environmental factors on the river water quality on the Tibetan Plateau: a case study of the Xoirong River, China.

Climate, topography, and landscape patterns affect river water quality through processes that influence non-point source pollution. However, little is known about the response of the water quality of rivers on China's Tibetan Plateau to these environmental factors. Based on the water quality parameters data of the Xoirong River on the Tibetan Plateau in western China, the redundancy analysis and variation partitioning analysis were adopted to determine the main influencing factors affecting river water quality and their spatial scale effects. The major water pollutants were further analyzed using the partial least square-structural equation modeling (PLS-SEM). Another mountainous river with a similar latitude, the same stream order, and low anthropogenic disturbance in central China, the Jinshui River, was also selected for comparative discussion. The results indicated that the overall river water quality on the Tibetan Plateau was superior to that of the Jinshui River. At the catchment scale, the cumulative explanatory powers of the influencing factors of both rivers were greatest. Landscape composition and configuration were the determinant factors for the overall water quality of the two rivers, while the river on the Tibetan Plateau was also significantly affected by climatic and topographical factors. Regarding the main water quality issue, i.e., total nitrogen, agricultural production activities might be the main cause of the river on the Tibetan Plateau. This study unveiled that the river water quality on the Tibetan Plateau is sensitive to climate and topography through comparative studies.

Comparative genomics reveals environmental adaptability and antimicrobial activity of a novel Streptomyces isolated from soil under black Gobi rocks.

A novel Streptomyces strain, designated 3_2T, was isolated from soil under the black Gobi rock sample of Northwest China. The taxonomic position of this strain was revealed by a polyphasic approach. Comparative analysis of the 16S rRNA gene sequences indicated that 3_2T was closely related to the members of the genus Streptomyces, with the highest similarity to Streptomyces rimosus subsp. rimosus CGMCC 4.1438 (99.17%), Streptomyces sioyaensis DSM 40032 (98.97%). Strain 3_2T can grow in media up to 13% NaCl. The genomic DNA G + C content of strain 3_2T was 69.9%. We obtained the genomes of 22 Streptomyces strains similar to strain 3_2T, compared the average nucleotide similarity, dDDH and average amino acid identity, and found that the genomic similarity of the new isolate 3_2T to all strains was below the threshold for interspecies classification. Chemotaxonomic data revealed that strain 3_2T possessed MK-9 (H6) and MK-9 (H8) as the major menaquinones. The cell wall contained LL-diaminopimelic acid (LL-DAP) and the whole-cell sugars were ribose and glucose. The major fatty acid methyl esters were iso-C16:0 (23.6%) and anteiso-C15:0 (10.4%). The fermentation products of strain 3_2T were inhibitory to Staphylococcus aureus and Bacillus thuringiensi. The genome of 3_2T was further predicted using anti-smash and the strain was found to encode the production of 41 secondary metabolites, and these gene clusters may be key to the good inhibitory activity exhibited by the strain. Genomic analysis revealed that strain 3_2T can encode genes that produce a variety of genes in response to environmental stresses, including cold shock, detoxification, heat shock, osmotic stress, oxidative stress, and these genes may play a key role in the harsh environment in which the strain can survive. Therefore, this strain represents a novel Streptomyces species, for which the name Streptomyces halobius sp. nov. is proposed. The type strain is 3_2T (= JCM 34935T = GDMCC 4.217T).

Radiomics nomogram for prediction of glypican-3 positive hepatocellular carcinoma based on hepatobiliary phase imaging.

Introduction: The hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature. Methods: We retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC). Results: In the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively. Conclusion: HBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC.

Performance and mechanism of co-culture of Monascus purpureus, Lacticaseibacillus casei, and Saccharomyces cerevisiae to enhance lovastatin production and lipid-lowering effects.

To facilitate lipid-lowering effects, a lovastatin-producing microbial co-culture system (LPMCS) was constituted with a novel strain Monascus purpureus R5 in combination with Lacticaseibacillus casei S5 and Saccharomyces cerevisiae J7, which increased lovastatin production by 54.21% compared with the single strain R5. Response Surface Methodology (RSM) optimization indicated lovastatin yield peaked at 7.43 mg/g with a fermentation time of 13.88 d, water content of 50.5%, and inoculum ratio of 10.27%. Meanwhile, lovastatin in LPMCS co-fermentation extracts (LFE) was qualitatively and quantitatively analyzed by Thin-Layer Chromatography (TLC) and High-Performance Liquid Chromatography (HPLC). Cellular experiments demonstrated that LFE exhibited no obvious cytotoxicity to L-02 cells and exhibited excellent biosafety. Most notably, high-dose LFE (100 mg/L) exhibited the highest reduction of lipid accumulation, total cholesterol, and triglycerides simultaneously in oleic acid-induced L-02 cells, which decreased by 71.59%, 38.64%, and 58.85% than untreated cells, respectively. Overall, LPMCS provides a potential approach to upgrade the lipid-lowering activity of Monascus-fermented products with higher health-beneficial effects.

Fe/sponge structure peanut shell carbon composite preparation for efficient Fenton oxidation crystal violet.

In order to obtain super synergy effect between adsorption and Fenton oxidation for crystal violet (CV) removement from water, in this study, Fe modified on a sponge structure peanut shell carbon (Fe/SPSC) nanocomposite was successfully synthesized by a wet impregnation method. In the Fe/SPSC sample, the prepared peanut shell carbon had a sponge-like structure, (002) crystal plane of graphite crystallite, and Fe/SPSC composite coexisted Fe2O3 and Fe3O4 crystalline, which could adsorb and enrich crystal violet molecule, decrease the concentration of CV solution rapidly. And also SPSC could do better for electrons transfer and further promote CV oxidation degradation. The removal efficiency results showed that the 7% Fe/SPSC (500 °C, 2 h) had the best CV removal activity. The composite prepared under the optimum conditions is 2.0 g/L, 0.1 mL 30% H2O2, pH = 7.0, 300 mg/L crystal violet water solution, and the CV degradation rate can reach 95.5%, and the CV degradation amount for Fe/SPSC was 143.25 mg/g. It was confirmed that hydroxyl radicals (•OH) is the active center of Fenton oxidation degradation reaction. XPS results showed that Fe, O, and C elements coexist in the 7% Fe/SPSC composite, and N element content increases after the reaction. Remarkable synergies between adsorption and Fenton oxidation, which could make Fe/SPSC, have quick CV abatement ability. The possible systematic effect mechanism of adsorption and Fenton-oxidation CV was also supplied. The present system has advantages on high CV dye degradation performance, no other Fe sludge formation, short reaction time, and better catalyst reusability.