Targeting Properties Research Articles

Reinforcing effect of CNT on the microstructure and creep properties of high-strength lightweight concrete

Demand for precast high-strength lightweight concrete (HSLC) has recently increased for cost benefits. This study aims to explore the reinforcing effect of carbon nanotube (CNT) on the microstructure and creep properties of HSLC. A total of four types of HSLC mixture were applied by securing two target properties (a compressive strength of 120 MPa and a density of 2000 kg/m3; a compressive strength of 80 MPa and a density of 1600 kg/m3) through steam curing with high temperature (90℃), considering a combination of lightweight materials and CNT utilization as variables. Lightweight aggregates operated internal curing (IC) more effectively than hollow microsphere, improving the pore structure of HSLC and leading to the formation of hydration products, especially calcium silicate hydrate (C-S-H). The efficiency of lightweight aggregate on IC was proven through nanoindentation testing, identifying that no unhydrated particles were observed in the specimen incorporating only lightweight aggregate as lightweight material, while unhydrated particles made up 8–19% of the total phase volume with hollow microsphere incorporation. The utilization of CNT enhanced the microstructure of the cement matrix by filling pores and inducing formation of ultra-high-density (UHD) C-S-H, improving the creep resistance of HSLC. Furthermore, CNT bridging suppressed redistribution of C-S-H caused by C-S-H slip, resulting in approximately 49% of the total specific creep compared to conventional high-strength concrete (HSC). The fédération internationale du béton (fib) model is considered to derive the creep properties of HSLC, since it indirectly applies the impact of lightweight material utilizing its oven-dried density.

Plasma-liquid interactions in the presence of organic matter-A perspective.

As investigations in the biomedical applications of plasma advance, a demand for describing safe and efficacious delivery of plasma is emerging. It is quite clear that not all plasmas are "equal" for all applications. This Perspective discusses limitations of the existing parameters used to define plasma in context of the need for the "right plasma" at the "right dose" for each "disease system." The validity of results extrapolated from in vitro studies to preclinical and clinical applications is discussed. We make a case for studying the whole system as a single unit, in situ. Furthermore, we argue that while plasma-generated chemical species are the proposed key effectors in biological systems, the contribution of physical effectors (electric fields, surface charging, dielectric properties of target, changes in gap electric fields, etc.) must not be ignored.

Construction and evaluation of near-infrared fluorescent probes for imaging lipid droplet and lysosomal viscosity

Microenvironmental viscosity is a crucial parameter for biological systems, and its abnormal fluctuations are closely associated with various functional disorders and diseases. However, it is still important and urgent to develop improved near-infrared fluorescent probes for micro-viscosity with dual-organelle targeting properties, low background noise, and high sensitivity. Herein, two BODIPY-based small-molecule fluorescent probes were designed and synthesized, which were explored for their viscosity- and polarity-responsive properties, and were further applied to imaging sub-cellular viscosity in living cells. Interestingly, BSZ-Ph and BSZ-R displayed near-infrared fluorescence (more than 650 nm) and were sensitive to environmental viscosity and polarity due to the introduction of a benzothiazole at the 2-position and electron-rich aniline groups at the 5-position of the BODIPY core, respectively. The fluorescence intensity increased exponentially with the viscosity changes. Furthermore, the probe BSZ-Ph could successfully target lipid droplets and image cellular viscosity changes by treating lipopolysaccharides (LPS) and nystatin. Comparatively, the probe BSZ-R could successfully target the dual organelles of lipid droplets and lysosomes and image cellular viscosity changes by treating LPS and monensin. Therefore, in this work, we reported two new BODIPY-based near-infrared fluorescent probes, BSZ-Ph and BSZ-R, for cellular viscosity imaging, which could target lipid droplets and the dual organelles of lysosomes and lipid droplets, respectively. The study could provide a reference for the future development of fluorescent probes for viscosity in lipid droplets and lysosomes.

Efficient search of molecular interaction parameters for polar liquids

The use of molecular simulation methods to reproduce and predict experimental information has become an excellent option for designing new experiments. The success of these methods depends on the force field used. Their quality depends on the interaction parameters and the target properties used to obtain them. In this work, we review and extend the methodology developed by our group to obtain interaction parameters of polar liquids. In addition, an automated workflow is developed to obtain new force field parameters and the analysis of physical properties. It is applied to small molecules of various functional groups in organic chemistry. The dielectric constant, surface tension, liquid density, self-diffusion coefficient and solubility of a solute in water are used as target properties. The simulations are developed in liquid phase, liquid–vapour and liquid–liquid equilibriums. The procedure can be extended to include other target properties and has the advantage that the parameters can be obtained in shorter times. It also opens the possibility of studying a wide range of problems of scientific and technological interest.

Investigating the Effect of Processing and Material Parameters of Alginate Dialdehyde-Gelatin (ADA-GEL)-Based Hydrogels on Stiffness by XGB Machine Learning Model.

To address the limitations of alginate and gelatin as separate hydrogels, partially oxidized alginate, alginate dialdehyde (ADA), is usually combined with gelatin to prepare ADA-GEL hydrogels. These hydrogels offer tunable properties, controllable degradation, and suitable stiffness for 3D bioprinting and tissue engineering applications. Several processing variables affect the final properties of the hydrogel, including degree of oxidation, gelatin content and type of crosslinking agent. In addition, in 3D-printed structures, pore size and the possible addition of a filler to make a hydrogel composite also affect the final physical and biological properties. This study utilized datasets from 13 research papers, encompassing 33 unique combinations of ADA concentration, gelatin concentration, CaCl2 and microbial transglutaminase (mTG) concentrations (as crosslinkers), pore size, bioactive glass (BG) filler content, and one identified target property of the hydrogels, stiffness, utilizing the Extreme Boost (XGB) machine learning algorithm to create a predictive model for understanding the combined influence of these parameters on hydrogel stiffness. The stiffness of ADA-GEL hydrogels is notably affected by the ADA to GEL ratio, and higher gelatin content for different ADA gel concentrations weakens the scaffold, likely due to the presence of unbound gelatin. Pore size and the inclusion of a BG particulate filler also have a significant impact on stiffness; smaller pore sizes and higher BG content lead to increased stiffness. The optimization of ADA-GEL composition and the inclusion of BG fillers are key determinants to tailor the stiffness of these 3D printed hydrogels, as found by the analysis of the available data.

Extractor-attention-predictor network for quantitative photoacoustic tomography

Quantitative photoacoustic tomography (qPAT) holds great potential in estimating chromophore concentrations, whereas the involved optical inverse problem, aiming to recover absorption coefficient distributions from photoacoustic images, remains challenging. To address this problem, we propose an extractor-attention-predictor network architecture (EAPNet), which employs a contracting–expanding structure to capture contextual information alongside a multilayer perceptron to enhance nonlinear modeling capability. A spatial attention module is introduced to facilitate the utilization of important information. We also use a balanced loss function to prevent network parameter updates from being biased towards specific regions. Our method obtains satisfactory quantitative metrics in simulated and real-world validations. Moreover, it demonstrates superior robustness to target properties and yields reliable results for targets with small size, deep location, or relatively low absorption intensity, indicating its broader applicability. The EAPNet, compared to the conventional UNet, exhibits improved efficiency, which significantly enhances performance while maintaining similar network size and computational complexity.

Neuropilin-1-Targeted Nanomedicine for Spatiotemporal Tumor Suppression through Photodynamic Vascular Damage and Antiangiogenesis.

Antiangiogenic therapy is an effective way to disrupt nutrient supply and starve tumors, but it is restricted by poor efficacy and negative feedback-induced tumor relapse. In this study, a neuropilin-1 (NRP-1)-targeted nanomedicine (designated as FPPT@Axi) is reported for spatiotemporal tumor suppression by combining photodynamic therapy (PDT) with antiangiogenesis. In brief, FPPT@Axi is prepared by utilizing an NRP-1-targeting chimeric peptide (Fmoc-K(PpIX)-PEG8-TKPRR) to encapsulate the antiangiogenic drug Axitinib (Axi). Importantly, the NRP-1-mediated targeting property enables FPPT@Axi to selectively concentrate at vascular endothelial and breast cancer cells, facilitating the production of reactive oxygen species (ROS) in situ for specific vascular disruption and enhanced cell apoptosis under light stimulation. Moreover, the codelivered Axi can further inhibit vascular endothelial growth factor receptor (VEGFR) to impair the negative feedback of PDT-induced tumor neovascularization. Consequently, FPPT@Axi spatiotemporally restrains the tumor growth through blocking angiogenesis, destroying tumor vessels, and inducing tumor apoptosis. Such an NRP-1-mediated targeting codelivery system sheds light on constructing an appealing candidate with translational potential by using clinically approved PDT and chemotherapy.

Celine, a long interspersed nuclear element retrotransposon, colonizes in the centromeres of poplar chromosomes.

Centromeres in most multicellular eukaryotes are composed of long arrays of repetitive DNA sequences. Interestingly, several transposable elements, including the well-known long terminal repeat centromeric retrotransposon of maize (CRM), were found to be enriched in functional centromeres marked by the centromeric histone H3 (CENH3). Here, we report a centromeric long interspersed nuclear element (LINE), Celine, in Populus species. Celine has colonized preferentially in the CENH3-associated chromatin of every poplar chromosome, with 84% of the Celine elements localized in the CENH3-binding domains. In contrast, only 51% of the CRM elements were bound to CENH3 domains in Populus trichocarpa. These results suggest different centromere targeting mechanisms employed by Celine and CRM elements. Nevertheless, the high target specificity seems to be detrimental to further amplification of the Celine elements, leading to a shorter life span and patchy distribution among plant species compared with the CRM elements. Using a phylogenetically guided approach, we were able to identify Celine-like LINE elements in tea plant (Camellia sinensis) and green ash tree (Fraxinus pennsylvanica). The centromeric localization of these Celine-like LINEs was confirmed in both species. We demonstrate that the centromere targeting property of Celine-like LINEs is of primitive origin and has been conserved among distantly related plant species.

A Dynamic Ultrasound Phantom with Tissue-Mimicking Mechanical and Acoustic Properties.

Tissue-mimicking phantoms are valuable tools that aid in improving the equipment and training available to medical professionals. However, current phantoms possess limited utility due to their inability to precisely simulate multiple physical properties simultaneously, which is crucial for achieving a system understanding of dynamic human tissues. In this work, novel materials design and fabrication processes to produce various tissue-mimicking materials (TMMs) for skin, adipose, muscle, and soft tissue at a human scale are developed. Target properties (Young's modulus, density, speed of sound, and acoustic attenuation) are first definedfor each TMM based on literature. Each TMM recipe is developed, associated mechanical and acoustic properties are characterized, and the TMMs are confirmed to have comparable mechanical and acoustic properties with the corresponding human tissues. Furthermore, a novel sacrificial core to fabricate a hollow, ellipsoid-shaped bladder phantom complete with inlet and outlet tubes, which allow liquids to flow through and expand this phantom, is adopted. This dynamic bladder phantom with realistic mechanical and acoustic properties to human tissues in combination with the developed skin, soft tissue, and subcutaneous adipose tissue TMMs, culminates in a human scale torso tank and electro-mechanical system that can be systematically utilized for characterizing various medical imaging devices.

Supramolecular self‐assembled nanoparticles for targeted therapy of myocardial infarction by enhancing cardiomyocyte mitophagy

AbstractMyocardial infarction accompanied by diabetes mellitus is accepted as the most serious type of coronary heart disease, and among the current treatment strategies, the precise delivery of protective drugs for inhibiting cardiomyocyte apoptosis is still a challenge. In this study, we developed a biodegradable nanoparticles‐based delivery system with excellent macrophage escape, cardiac targeting, and drug release properties to achieve targeted therapy of myocardial infarction. Specifically, a copolymer of p(DMA–MPC–CD) combining self‐adhesion, hydration lubrication, and targeting peptide binding site was successfully prepared by free radical copolymerization, and it was self‐assembled on the surface of melatonin‐loaded dendritic mesoporous silica nanoparticles (bMSNs) following the integration of adamantane‐modified cardiac homing peptide (CHP) based on supramolecular host–guest interaction. Importantly, a hydration layer formed around the zwitterionic phosphorylcholine groups of the multifunctional nanoparticles, which was confirmed by the enhancement in hydration lubrication and reduction in coefficient of friction, prevented the nanoparticles from phagocytosis by the macrophages. The in vivo bioluminescence imaging test indicated that the nanoparticles were endowed with satisfied cardiac targeting capability, and the in vivo mice study demonstrated that the intravenous injection of drug‐loaded nanoparticles (namely bMSNs–Mel@PDMC–CHP) effectively reduced cardiomyocyte apoptosis, alleviated myocardial interstitial fibrosis, and enhanced cardiac function.

Fast and effective molecular property prediction with transferability map.

Effective transfer learning for molecular property prediction has shown considerable strength in addressing insufficient labeled molecules. Many existing methods either disregard the quantitative relationship between source and target properties, risking negative transfer, or require intensive training on target tasks. To quantify transferability concerning task-relatedness, we propose Principal Gradient-based Measurement (PGM) for transferring molecular property prediction ability. First, we design an optimization-free scheme to calculate a principal gradient for approximating the direction of model optimization on a molecular property prediction dataset. We have analyzed the close connection between the principal gradient and model optimization through mathematical proof. PGM measures the transferability as the distance between the principal gradient obtained from the source dataset and that derived from the target dataset. Then, we perform PGM on various molecular property prediction datasets to build a quantitative transferability map for source dataset selection. Finally, we evaluate PGM on multiple combinations of transfer learning tasks across 12 benchmark molecular property prediction datasets and demonstrate that it can serve as fast and effective guidance to improve the performance of a target task. This work contributes to more efficient discovery of drugs, materials, and catalysts by offering a task-relatedness quantification prior to transfer learning and understanding the relationship between chemical properties.

Preparation and evaluation of microcapsules of sodium alginate based on microfluidic technology

Many active ingredients in food products are destroyed by gastric acids and enzymes as they pass through the stomach, thereby reducing the desired effect of the target ingredient. In addition, traditional microcapsule preparation methods usually break up the dispersion by mechanical stirring or oscillation, which generates droplets of uneven size and inconsistent inclusion content, and mechanical external forces may cause denaturation and inactivation of active substances. This study aimed to use microfluidics to generate Oil-in-water-in-oil (O/W/O) droplet structures with good monodispersity and highly tunable properties, and to further prepare microcapsules with high encapsulation rate and targeted intestinal release by taking advantage of the cross-linking properties of alginate that reacts with Ca2+ under acidic conditions. Finally, the particle size distribution, release effect, encapsulation effect and oxidative stability of the core-shell microcapsules were evaluated. In this study, two types of microcapsules, homogeneous and core-shell microcapsules, were prepared by selecting tea polyphenols and vitamin E as models for hydrophilic and lipophilic bioactive compounds, respectively. Thin shell microcapsules with a shell thickness of about 25 μm were prepared using PDMS chip conditioning. Large microcapsules with a particle size of 230 μm were generated by using capillary glass tube chips, which had an excellent antioxidant property with a retention rate of 88.66 ± 1.25% of VE after 20 days of storage at 4 °C, while all of the VE was oxidized in the bare environment. In addition, in simulated gastric fluid, the release of VE was only about 20%, while in artificial intestinal fluid, the release was close to 100%, so the microcapsules also had intestinal targeting properties.

AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers

Artificial intelligence (AI) promotes the polymer design paradigm from a traditional trial-and-error approach to a data-driven style. Achieving high thermal conductivity (TC) for intrinsic polymers is urgent because of their importance in the thermal management of many industrial applications such as microelectronic devices and integrated circuits. In this work, we have proposed a robust AI-assisted workflow for the inverse design of high TC polymers. By using 1144 polymers with known computational TCs, we construct a surrogate deep neural network model for TC prediction and extract a polymer-unit library with 32 sequences. Two state-of-the-art multi-objective optimization algorithms of unified non-dominated sorting genetic algorithm III (U-NSGA-III) and q-noisy expected hypervolume improvement (qNEHVI) are employed for sequence-ordered polymer design with both high TC and synthetic possibility. For triblock polymer design, the result indicates that qNHEVI is capable of exploring a diversity of optimal polymers at the Pareto front, but the uncertainty in Quasi-Monte Carlo sampling makes the trials costly. The performance of U-NSGA-III is affected by the initial random structures and usually falls into a locally optimal solution, but it takes fewer attempts with lower costs. 20 parallel U-NSGA-III runs are conducted to design the pentablock polymers with high TC, and half of the candidates among 1921 generated polymers achieve the targets (TC > 0.4 W m−1K−1 and SA < 3.0). Ultimately, we check the TC of 50 promising polymers through molecular dynamics simulations and reveal the intrinsic connections between microstructures and TCs. Our developed AI-assisted inverse design approach for polymers is flexible and universal, and can be extended to the design of polymers with other target properties.

Comparison of approaches for increasing affinity of affibody molecules for imaging of B7-H3: dimerization and affinity maturation

BackgroundRadionuclide molecular imaging can be used to visualize the expression levels of molecular targets. Affibody molecules, small and high affinity non-immunoglobulin scaffold-based proteins, have demonstrated promising properties as targeting vectors for radionuclide tumour imaging of different molecular targets. B7-H3 (CD276), an immune checkpoint protein belonging to the B7 family, is overexpressed in different types of human malignancies. Visualization of overexpression of B7-H3 in malignancies enables stratification of patients for personalized therapies. Affinity maturation of anti-B7-H3 Affibody molecules as an approach to improve the binding affinity and targeting properties was recently investigated. In this study, we tested the hypothesis that a dimeric format may be an alternative option to increase the apparent affinity of Affibody molecules to B7-H3 and accordingly improve imaging contrast.ResultsTwo dimeric variants of anti-B7-H3 Affibody molecules were produced (designated ZAC12*-ZAC12*-GGGC and ZAC12*-ZTaq_3-GGGC). Both variants were labelled with Tc-99m (99mTc) and demonstrated specific binding to B7-H3-expressing cells in vitro. [99mTc]Tc-ZAC12*-ZAC12*-GGGC showed subnanomolar affinity (KD1=0.28 ± 0.10 nM, weight = 68%), which was 7.6-fold higher than for [99mTc]Tc-ZAC12*-ZTaq_3-GGGC (KD=2.1 ± 0.9 nM). Head-to-head biodistribution of both dimeric variants of Affibody molecules compared with monomeric affinity matured SYNT-179 (all labelled with 99mTc) in mice bearing B7-H3-expressing SKOV-3 xenografts demonstrates that both dimers have lower tumour uptake and lower tumour-to-organ ratios compared to the SYNT-179 Affibody molecule.ConclusionThe improved functional affinity by dimerization does not compensate the disadvantage of increased molecular size for imaging purposes.

Customizable Porphyrin Platform Enables Folate Receptor PET Imaging Using Copper-64.

Folate receptors including folate receptor α (FRα) are overexpressed in up to 90% of ovarian cancers. Ovarian cancers overexpressing FRα often exhibit high degrees of drug resistance and poor outcomes. A porphyrin chassis has been developed that is readily customizable according to the desired targeting properties. Thus, compound O5 includes a free base porphyrin, two water-solubilizing groups that project above and below the macrocycle plane, and a folate targeting moiety. Compound O5 was synthesized (>95% purity) and exhibited aqueous solubility of at least 0.48 mM (1 mg/mL). Radiolabeling of O5 with 64Cu in HEPES buffer at 37 °C gave a molar activity of 1000 μCi/μg (88 MBq/nmol). [64Cu]Cu-O5 was stable in human serum for 24 h. Cell uptake studies showed 535 ± 12% bound/mg [64Cu]Cu-O5 in FRα-positive IGROV1 cells when incubated at 0.04 nM. Subcellular fractionation showed that most radioactivity was associated with the cytoplasmic (39.4 ± 2.7%) and chromatin-bound nuclear (53.0 ± 4.2%) fractions. In mice bearing IGROV1 xenografts, PET imaging studies showed clear tumor uptake of [64Cu]Cu-O5 from 1 to 24 h post injection with a low degree of liver uptake. The tumor standardized uptake value at 24 h post injection was 0.34 ± 0.16 versus 0.06 ± 0.07 in the blocking group. In summary, [64Cu]Cu-O5 was synthesized at high molar activity, was stable in serum, exhibited high binding to FRα-overexpressing cells with high nuclear translocation, and gave uptake that was clearly visible in mouse tumor xenografts.

Magnetic hybrid nanomaterial based on a natural polymer and an amino acid as pH/temperature dual-responsive nanoplatform for the delivery of tamoxifen

Magnetic hybrid nanomaterials offer promising properties for the advancement of nanoplatforms in cancer nanomedicine, particularly in drug delivery applications. These nanoplatforms can effectively respond to various stimuli present at the tumor site, such as pH and temperature fluctuations, allowing for controlled and triggered release of therapeutic payloads. In this study, we present a straightforward methodology for the synthesis of stable hybrid magnetic nanoplatforms (HMNP) based on Fe3O4 nanoparticles, L-cysteine (L-Cys), and hyaluronic acid (HA) as key constituents for the delivery of tamoxifen (TMX). The synthesized superparamagnetic HMNP, Fe3O4-L-Cys-HA, with a size of 11 nm, was successfully loaded with TMX. The incorporation of L-Cys showed superior interaction with the surface of Fe3O4 nanoparticles compared to other L-Cys derivatives explored as ligands. Consequently, L-Cys was selected for further functionalization with HA, providing the HMNP with active targeting properties toward CD44-overexpressed receptors. High loading efficiency of TMX (75%) was achieved via electrostatic interaction between the carboxylate groups exposed by the HMNP and the ammonium group of the TMX side chain. Efficient control in the TMX release towards different receptor media was observed. Notably, the release of TMX from HMNP-TMX was triggered under acidic pH and hyperthermia conditions, showcasing its responsiveness to both stimuli. Furthermore, enhanced anticancer activity of TMX against MDA-MB-231 breast cancer cells was observed when loaded into HMNP (IC50 almost 3-fold lower for HMNP-TMX compared to free TMX), indicating improved cell uptake of TMX-loaded HMNP in comparison to the free drug. Overall, pH/temperature dual-sensitive HMNP demonstrates promising potential as a nanoplatform for cancer nanomedicine, with prospects for magnetic hyperthermia therapy.

Preclinical Evaluation of HER2-Targeting DARPin G3: Impact of Albumin-Binding Domain (ABD) Fusion.

Designed ankyrin repeat protein (DARPin) G3 is an engineered scaffold protein. This small (14.5 kDa) targeting protein binds with high affinity to human epidermal growth factor receptor 2 (HER2). HER2 is overexpressed in several cancers. The use of the DARPin G3 for radionuclide therapy is complicated by its high renal reabsorption after clearance via the glomeruli. We tested the hypothesis that a fusion of the DARPin G3 with an albumin-binding domain (ABD) would prevent rapid renal excretion and high renal reabsorption resulting in better tumour targeting. Two fusion proteins were produced, one with the ABD at the C-terminus (G3-ABD) and another at the N-terminus (ABD-G3). Both variants were labelled with 177Lu. The binding properties of the novel constructs were evaluated in vitro and their biodistribution was compared in mice with implanted human HER2-expressing tumours. Fusion with the ABD increased the retention time of both constructs in blood compared with the non-ABD-fused control. The effect of fusion with the ABD depended strongly on the order of the domains in the constructs, resulting in appreciably better targeting properties of [177Lu]Lu-G3-ABD. Our data suggest that the order of domains is critical for the design of targeting constructs based on scaffold proteins.

Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland

Abstract. Conventional laboratory analysis of soil properties is often expensive and requires much time if various soil properties are to be measured. Visual and near-infrared (vis–NIR) spectroscopy offers a complementary and cost-efficient way to gain a wide variety of soil information at high spatial and temporal resolutions. Yet, applying vis–NIR spectroscopy requires confidence in the prediction accuracy of the infrared models. In this study, we used soil data from six agricultural fields in eastern Switzerland and calibrated (i) field-specific (local) models and (ii) general models (combining all fields) for soil organic carbon (SOC), permanganate oxidizable carbon (POXC), total nitrogen (N), total carbon (C) and pH using partial least-squares regression. The 30 local models showed a ratio of performance to deviation (RPD) between 1.14 and 5.27, and the root mean square errors (RMSE) were between 1.07 and 2.43 g kg−1 for SOC, between 0.03 and 0.07 g kg−1 for POXC, between 0.09 and 0.14 g kg−1 for total N, between 1.29 and 2.63 g kg−1 for total C, and between 0.04 and 0.19 for pH. Two fields with high carbonate content and poor correlation between the target properties were responsible for six local models with a low performance (RPD &lt; 2). Analysis of variable importance in projection, as well as of correlations between spectral variables and target soil properties, confirmed that high carbonate content masked absorption features for SOC. Field sites with low carbonate content can be combined with general models with only a limited loss in prediction accuracy compared to the field-specific models. On the other hand, for fields with high carbonate contents, the prediction accuracy substantially decreased in general models. Whether the combination of soils with high carbonate contents in one prediction model leads to satisfying prediction accuracies needs further investigation.

Comparison of aggregation effect of axial and polar Magnetotactic bacteria for tumor therapy

Magnetotactic bacteria (MTB), possessing chains of iron oxide nanoparticles, are potential tools for controlled therapy due to their responsiveness to weak magnetic fields. This study aims to evaluate the targeting and aggregation capabilities of axial AMB-1 and polar MO-1 MTB for therapeutic applications. A three-dimensional focusing magnetic field (fMF)-producing device was designed and established with a gradient magnetic field of 0.08 mT/mm at the central region for the guidance of MTB in vitro and in vivo. Under a unidirectional magnetic field, polar Magneto-ovoid MO-1 formed a single line in the culture dish, whereas axial Magnetospirillum magneticum AMB-1 displayed a small portion aggregated on the centerline. With simultaneous X and Y magnetic field application, MO-1 cells aggregated at a point, while AMB-1 bacteria gathered incompletely. Further, magnetic resonance imaging of breast cancer-bearing nude mice injected subcutaneously with MTB peritumorally and navigated by a fMF revealed more substantial aggregation of MO-1 cells within the tumor than AMB-1 bacteria. Prussian blue staining corroborated the penetration of MO-1 cells into the tumor tissue. These findings suggest that polar MO-1 bacteria exhibit superior targeting and aggregation properties compared to axial AMB-1, indicating their potential suitability for targeted therapeutic applications.

Zwitterionic polymer-dexamethasone conjugates penetrate and protect cartilage from inflammation

Improving the pharmacokinetics of intra-articularly injected therapeutics is a major challenge in treating joint disease. Small molecules and biologics are often cleared from the joint within hours, which greatly reduces their therapeutic efficacy. Furthermore, they are often injected at high doses, which can lead to local cytotoxicity and systemic side effects. In this study, we present modular polymer-drug conjugates of zwitterionic poly(carboxybetaine acrylamide) (pCBAA) and the anti-inflammatory glucocorticoid dexamethasone (DEX) to create cartilage-targeted carriers with slow-release kinetics. pCBAA polymers showed excellent cartilage penetration (full thickness in 1 h) and retention (>50 % after 2 weeks of washing). DEX was loaded onto the pCBAA polymer by employing two different DEX-bearing comonomers to produce pCBAA-co-DEX conjugates with different release kinetics. The slow-releasing conjugate showed zero-order release kinetics in PBS over 70 days. The conjugates elicited no oxidative stress on chondrocytes compared to dose-matched free DEX and protected bovine cartilage explants from the inflammatory response after treatment with IL-1β. By combining cartilage targeting and sustained drug release properties, the pCBAA-co-DEX conjugates solve many issues of today's intra-articular therapeutics, which could ultimately enable better long-term clinical outcomes with fewer side effects.