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Recently, there has been a continuous stream of reports on emerging tick-borne pathogens affecting humans. Qinghai Province, located in the northweastern region, is one of China’s major pastoral areas, providing a suitable environment for ticks' survival and transmitting tick-borne pathogens. Here, we collected 560 free-living and parasitic ticks from 11 locations in Qinghai Province using the flag-drag method or tweezers, identifying them as belonging to 4 species of ticks. The overall positivity rate for tick-borne pathogens was 51.61 %, comprising Rickettsia (34.64 %), Anaplasma (5.00 %), Ehrlichia (2.14 %), Borrelia burgdorferi sensu lato (BBSL) (7.50 %), Babesia (0.18 %), and Theileria (5.89 %). Sequencing revealed the presence of 7 species of Rickettsia, 4 species of Anaplasma, 2 species of Ehrlichia, 2 species of BBSL, 1 species of Babesia, and 3 species of Theileria. Among the ticks, 6.43 % were co-infected with 2 pathogens, while 0.36 % exhibited co-infection with 3 pathogens. Significant correlations (P

Background and Objective: Emerging as a hybrid imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been developed using X-ray-excitable nanoparticles. In contrast to conventional bio-optical imaging techniques like bioluminescence tomography (BLT) and fluorescence molecular tomography (FMT), CB-XLCT offers the advantage of greater imaging depth while significantly reducing interference from autofluorescence and background fluorescence, owing to its utilization of X-ray-excited nanoparticles. However, due to the intricate excitation process and extensive light scattering within biological tissues, the inverse problem of CB-XLCT is fundamentally ill-conditioned. Methods: An end-to-end three-dimensional deep encoder-decoder network, termed DeepCB-XLCT, is introduced to improve the quality of CB-XLCT reconstructions. This network directly establishes a nonlinear mapping between the distribution of internal X-ray-excitable nanoparticles and the corresponding boundary fluorescent signals. To improve the fidelity of target shape restoration, the structural similarity loss (SSIM) was incorporated into the objective function of the DeepCB-XLCT network. Additionally, a loss term specifically for target regions was introduced to improve the network’s emphasis on the areas of interest. As a result, the inaccuracies in reconstruction caused by the simplified linear model used in conventional methods can be effectively minimized by the proposed DeepCB-XLCT method. Results and Conclusions: Numerical simulations, phantom experiments, and in vivo experiments with two targets were performed, revealing that the DeepCB-XLCT network enhances reconstruction accuracy regarding contrast-to-noise ratio and shape similarity when compared to traditional methods. In addition, the findings from the XLCT tomographic images involving three targets demonstrate its potential for multi-target CB-XLCT imaging.

In this study, tobermorite was prepared by hydrothermal method using two kinds of typical solid waste as raw materials, i.e., fly ash aluminum extraction slag (by-product in the process of fly ash hydrochloric acid aluminum extraction) and papermaking white mud. The XRD, SEM, FTIR, TG-DSC and Specific surface area and porosity were used to investigate the influence of Ca/Si ratio, alkalinity, hydrothermal conditions and other factors on the synthesis products, and the formation mechanism of tobermorite. The adsorption capacity of tobermorite was investigated by adsorption experiments. The effects of different dosage, initial concentration of Cd2+ and adsorption time on the adsorption behavior were investigated to understand the adsorption mechanism of tobermorite on Cd2+. The results show that the well crystal tobermorite can be synthesized at 180 °C for 6 h with the Ca∶Si molar ratio of 0.8 and the NaOH addition amount of 2.5%−5.0%. The specific surface area of tobermorite is 80.956 m2/g, the total pore volume is 0.218 cm3/g, the pore size is 1.21 nm, and the crystallization of tobermorite is 97%. The microstructure of tobermorite is dominated by short fibers and has a good thermal stability below 650 °C. The formation process of tobermorite can be described that the CaO in papermaking white clay is converted into Ca(OH)2 and the amorphous silicon dioxide is transformed into sodium silicate. The gel blocks material is formed by calcium hydroxide and sodium silicate solution. Tobermorite whiskers are formed, and the whiskers gradually exfoliate, split and grow to form tobermorite fibrous materials. The Cd2+ removal rate of tobermorite can reach 99.99% at pH value of 3, the dosage of tobermorite is 2.5 g/L, and the concentration of Cd2+ is 100 mg/L. The adsorption of Cd2+ by modified tobermorite fits the quasi-second-order kinetic equation and the Langmuir model, and is controlled by membrane diffusion and intra-particle diffusion, and the chemisorption is the main control step. The study indicates that tobermorite can be prepared from aluminum extraction slag with fly ash and white paper mud. The tobermorite prepared had a good adsorption effect on heavy metals and can be used as a high efficient adsorbent for heavy metal ions. Also, the study realizes the collaborative utilization of two typical solid waste resources in different industries, and provides a potential method for the treatment of wastewater containing Cd2+ and other heavy metal ions.

Abstract Bioactive materials have been extensively developed for the adjuvant therapy of cancer. However, few materials can meet the requirements for the postoperative resection of hepatocellular carcinoma (HCC) due to massive bleeding and high recurrence. In particular, combination therapy for HCC has been highly recommended in clinical practice, including surgical resection, interventional therapy, ablation therapy and chemotherapy. Herein, an injectable magnetic colloidal gel (MCG) was developed by controllable electrostatic attraction between clinically available magnetic montmorillonites and amphoteric gelatin nanoparticles. The optimized MCG exhibited an effective magnetic heating effect, remarkable rheological properties, and high gel network stability, realizing the synergistic treatment of postoperative HCC by stimuli-responsive drug delivery, hemostasis and magnetic hyperthermia. Furthermore, a minimal invasive MCG-induced interventional magnetic hyperthermia therapy (MHT) under ultrasound guidance was realized on hepatic tumor rabbits, providing an alternative therapeutics to treat the postoperative recurrence. Overall, MCG is a clinically available injectable formulation for adjuvant therapy after HCC surgical resection.

Abstract The passive diffusion performance of nanocarriers results in inefficient drug transport across multiple biological barriers and consequently cancer therapy failure. Here, a magnetically driven amoeba‐like nanorobot (amNR) is presented for whole‐process active drug transport. The amNR is actively extravasated from blood vessels and penetrated into deep tumor tissue through a magnetically driven deformation effect. Moreover, the acidic microenvironment of deep tumor tissue uncovers the masked targeting ligand of amNR to achieve active tumor cell uptake. Furthermore, the amNR rapidly releases the encapsulated doxorubicin (DOX) after alternating magnetic field application. The amNRs eventually deliver DOX into ≈92.3% of tumor cells and completely delay tumor growth with an inhibition rate of 96.1%. The deformable amNRs, with the assistance of magnetic field application, provide a facile strategy for whole‐process active drug transport.

Cecropins, originally found in insects, are a group of cationic antimicrobial peptides. Most cecropins have an amphipathic N-terminal segment and a largely hydrophobic C-terminal segment, and normally form a helix-hinge-helix structure. In this study, we developed the novel 32-residue cecropin-like peptide cecropin DH by deleting the hinge region (Alanine-Glycine-Proline) of cecropin B isolated from Chinese oak silk moth, Antheraea pernyi. Cecropin DH possesses effective antibacterial activity, particularly against Gram-negative bacteria, with very low cytotoxicity against mammalian cells. Interactions between cecropin DH and the highly anionic lipopolysaccharide (LPS) component of the Gram-negative bacterial outer membrane indicate that it is capable of dissociating LPS micelles and disrupting LPS aggregates into smaller assemblies, which may play a vital role in its antimicrobial activity. Using LPS-stimulated mouse macrophage RAW264.7 cells, we found that cecropin DH exerted higher potential anti-inflammatory activity than cecropin B, as demonstrated by the inhibition of pro-inflammatory cytokines nitric oxide production and secretion of tumor necrosis factor-α. In conclusion, cecropin DH has potential as a therapeutic agent for both antibacterial and anti-inflammatory applications.

Text plays an important role in daily life because of its rich information, thus automatic text detection in natural scenes has many attractive applications. However, detecting and recognising such text is always a challenging problem. In this study, the authors propose a method which extends the widely‐used stroke width transform by two steps of edge analysis, namely candidate edge recombination and edge classification. A new method that recognises text through candidate edge recombination and candidate edge recognition is also proposed. In the step of candidate edge recombination, they use the idea of over‐segmentation and region merging. To separate text edge from background, the edge of the input image is first divided into small segments. Then, neighbour edge segments are merged, if they have similar stroke width and colour. Through this step, each character is described by one candidate boundary. In the step of boundary classification, candidate boundaries are aggregated into text chains, followed by chain classification using character‐based and chain‐based features. To recognise text, the grey image is extracted based on the location of each candidate edge after the step of candidate edge recombination. Then, histogram of gradient features and a classifier are used to recognise each character. To evaluate the effectiveness of their method, the algorithm is run on the ICDAR competition dataset and Street View Text database. The experimental results show that the proposed method provides promising performance in comparison with the existing methods.