Spatioselective Occlusion of Copolymer Nanoparticles within Calcite Crystals Generates Organic-Inorganic Hybrid Materials with Controlled Internal Structures.

Efficient occlusion of particulate additives into a single crystal has garnered an ever-increasing attention in materials science because it offers a counter-intuitive yet powerful platform to make crystalline nanocomposite materials with emerging properties. However, precisely controlling the spatial distribution of the guest additives within a host crystal remains highly challenging. We herein demonstrate a unique, straightforward method to engineer the spatial distribution of copolymer nanoparticles within calcite (CaCO3) single crystals by judiciously adjusting initial [Ca2+] concentration used for the calcite precipitation. More specifically, polymerization-induced self-assembly is employed to synthesize well-defined and highly anionic poly(3-sulfopropyl methacrylate potassium)41-block-poly(benzyl methacrylate)500 [PSPMA41-PBzMA500] diblock copolymer nanoparticles, which are subsequently used as model additives during the growth of calcite crystals. Impressively, such guest nanoparticles are preferentially occluded into specific regions of calcite depending on the initial [Ca2+] concentration. These unprecedented phenomena are most probably caused by dynamic change in electrostatic interaction between Ca2+ ions and PSPMA41 chains based on systematic investigations. This study not only showcases a significant advancement in controlling the spatial distribution of guest nanoparticles within host crystals, enabling the internal structure of composite crystals to be rationally tailored via a spatioselective occlusion strategy, but also provides new insights into biomineralization.

Quantitative risk assessment for the introduction of bluetongue virus into mainland Europe by long-distance wind dispersal of Culicoides spp.: A case study from Sardinia.

Europe faces regular introductions and reintroductions of bluetongue virus (BTV) serotypes, most recently exemplified by the incursion of serotype 3 in the Netherlands. Although the long-distance wind dispersal of the disease vector, Culicoides spp., is recognized as a virus introduction pathway, it remains understudied in risk assessments. A Quantitative Risk Assessment framework was developed to estimate the risk of BTV-3 incursion into mainland Europe from Sardinia, where the virus has been present since 2018. We used an atmospheric transport model (HYbrid Single-Particle Lagrangian Integrated Trajectory) to infer the probability of airborne dispersion of the insect vector. Epidemiological disease parameters quantified the virus prevalence in vector population in Sardinia and its potential first transmission after introduction in a new area. When assuming a 24h maximal flight duration, the risk of BTV introduction from Sardinia is limited to the Mediterranean Basin, mainly affecting the southwestern area of the Italian Peninsula, Sicily, Malta, and Corsica. The risk extends to the northern and central parts of Italy, Balearic archipelago, and mainland France and Spain, mostly when maximal flight duration is longer than 24h. Additional knowledge on vector flight conditions and Obsoletus complex-specific parameters could improve the robustness of the model. Providing both spatial and temporal insights into BTV introduction risks, our framework is a key tool to guide global surveillance and preparedness against epizootics.

Molecular Engineering Regulation Achieving Out-of-Plane Polarization in Rare-Earth Hybrid Double Perovskites for Ferroelectrics and Circularly Polarized Luminescence.

Out-of-plane polarization is a highly desired property of two-dimensional (2D) ferroelectrics for application in vertical sandwich-type photoferroelectric devices, especially in ultrathin ferroelectronic devices. Nevertheless, despite great advances that have been made in recent years, out-of-plane polarization remains unrealized in the 2D hybrid double perovskite ferroelectric family. Here, from our previous work 2D hybrid double perovskite HQERN ((S3HQ)4EuRb(NO3)8, S3HQ = S-3-hydroxylquinuclidinium), we designed a molecular strategy of F-substitution on organic component to successfully obtain FQERN ((S3FQ)4EuRb(NO3)8, S3FQ = S-3-fluoroquinuclidinium) showing circularly polarized luminescence (CPL) response. Remarkably, compared to the monopolar axis ferroelectric HQERN, FQERN not only shows multiferroicity with the coexistence of multipolar axis ferroelectricity and ferroelasticity but also realizes out-of-plane ferroelectric polarization and a dramatic enhancement of Curie temperature of 94 K. This is mainly due to the introduction of F-substituted organic cations, which leads to a change in orientation and a reduction in crystal lattice void occupancy. Our study demonstrates that F-substitution is an efficient strategy to realize and optimize ferroelectric functional characteristics, giving more possibility of 2D ferroelectric materials for applications in micro-nano optoelectronic devices.

Engineering Versatile Bacteria-Derived Outer Membrane Vesicles: An Adaptable Platform for Advancing Cancer Immunotherapy.

In recent years, cancer immunotherapy has undergone a transformative shift toward personalized and targeted therapeutic strategies. Bacteria-derived outer membrane vesicles (OMVs) have emerged as a promising and adaptable platform for cancer immunotherapy due to their unique properties, including natural immunogenicity and the ability to be engineered for specific therapeutic purposes. In this review, a comprehensive overview is provided of state-of-the-art techniques and methodologies employed in the engineering of versatile OMVs for cancer immunotherapy. Beginning by exploring the biogenesis and composition of OMVs, unveiling their intrinsic immunogenic properties for therapeutic appeal. Subsequently, innovative approaches employed to engineer OMVs are delved into, ranging from the genetic engineering of parent bacteria to the incorporation of functional molecules. The importance of rational design strategies is highlighted to enhance the immunogenicity and specificity of OMVs, allowing tailoring for diverse cancer types. Furthermore, insights into clinical studies and potential challenges utilizing OMVs as cancer vaccines or adjuvants are also provided, offering a comprehensive assessment of the current landscape and future prospects. Overall, this review provides valuable insights for researchers involved in the rapidly evolving field of cancer immunotherapy, offering a roadmap for harnessing the full potential of OMVs as a versatile and adaptable platform for cancer treatment.

Physicochemical Studies on Amino Acid Based Metallosurfactants in Combination with Phospholipid.

Dicarboxylate metallosurfactants (AASM), synthesized by mixing N-dodecyl aminomalonate, -aspartate and -glutamate with CaCl2, MnCl2 and CdCl2, were characterized by XRD, FTIR, and NMR spectroscopy. Layered structures, formed by metallosurfactants, were evidenced from differential scanning calorimetry and thermogravimetric analyses. Solvent-spread monolayer of AASM in combination with soyphosphatidylcholine (SPC) and cholesterol (CHOL) were studied using Langmuir surface balance. With increasing mole fraction of AASM mean molecular area increased and passed through maxima at ~60 mol% of AASMs, indicating molecular packing reorganization. Systems with 20 and 60 mol% AASM exhibited positive deviations from ideal behavior signifying repulsive interaction between the AASM and SPC, while synergistic interactions were established from the negative deviation at other combinations. Dynamic surface elasticity increased with increasing surface pressure signifying formation of rigid monolayer. Transition of monolayer from gaseous to liquid expanded to liquid condensed state was established by Brewster angle microscopic studies. Stability of the hybrid vesicles, formed by AASM+SPC+CHOL, was established by monitoring their size, zeta potential and polydispersity index values over 100 days. Size and spherical morphology of hybrid vesicles were confirmed by transmission electron microscopic studies. Biocompatibility of the hybrid vesicles were established by cytotoxicity studies revealing their possible applications in drug delivery and imaging.

Combining Nuclear Medicine With Other Modalities: Future Prospect for Multimodality Imaging.

This meeting report summarizes a consultants meeting that was held at International Atomic Energy Agency Headquarters, Vienna, in July 2022 to provide an update on the development of multimodality imaging by combining nuclear medicine imaging agents with other nonradioactive molecular probes and/or biomedical imaging techniques.

Recovery kinetics of dual AAV-mediated human otoferlin expression.

Deafness-causing deficiencies in otoferlin (OTOF) have been addressed preclinically using dual adeno-associated virus (AAV)-based approaches. However, timing of transduction, recombination of mRNA, and protein expression with dual hybrid AAV methods methods have not previously been characterized. Here, we have established an ex vivo assay to determine the kinetics of dual-AAV mediated expression of OTOF in hair cells of the mouse utricle. We utilized two different recombinant vectors that comprise DB-OTO, one containing the 5' portion of OTOF under the control of the hair cell-specific Myo15 promoter, and the other the 3' portion of OTOF. We explored specificity of the Myo15 promoter in hair cells of the mouse utricle, established dose response characteristics of DB-OTO ex vivo in an OTOF-deficient mouse model, and demonstrated tolerability of AAV1 in utricular hair cells. Furthermore, we established deviations from a one-to-one ratio of 5' to 3' vectors with little impact on recombined OTOF. Finally, we established a plateau in quantity of recombined OTOF mRNA and protein expression by 14 to 21 days ex vivo with comparable recovery timing to that in vivo model. These findings demonstrate the utility of an ex vivo model system for exploring expression kinetics and establish in vivo and ex vivo recovery timing of dual AAV-mediated OTOF expression.

Development of nanogap-rich hybrid gold nanostructures by use of two non-lithographic deposition techniques for a sensitive and reliable SERS biosensor.

Practical application of surface-enhanced Raman spectroscopy (SERS) has suffered from several limitations by heterogeneous distribution of hot-spots, such as high signal fluctuation and the resulting low reliability in detection. Herein, we develop a strategy of more sensitive and reliable SERS platform through designing spatially homogeneous gold nanoparticles (GNPs) on a uniform gold nanoisland (GNI) pattern. The proposed SERS substrate is successfully fabricated by combining two non-lithographic techniques of electron beam evaporation and convective self-assembly. These bottom-up methods allow a simple, cost-effective, and large-area fabrication. Compared to the SERS substrates obtained from two separate nanofabrication methods, Raman spectra measured by the samples with both GNPs and GNIs present a significant increase in the signal intensity as well as a notable improvement in signal fluctuation. The simulated near-field analyses demonstrate the formation of highly amplified plasmon modes within and at the gaps of the GNP-GNI interfaces. Moreover, the suggested SERS sensor is evaluated to detect the glucose concentration, exhibiting that the detection sensitivity is improved by more than 10 times compared to the sample with only GNI patterns and a fairly good spatial reproducibility of 7% is accomplished. It is believed that our suggestion could provide a potential for highly sensitive, low-cost, and reliable SERS biosensing platforms that include many advantages for healthcare devices. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-024-00381-4.

Antimalarial and antioxidant activities of novel artesunate-ellagic acid hybrid compound in vitro and in vivo.

Introduction: Emergence of drug resistant strains of Plasmodium species has necessitated the search for novel antimalarials with unique mechanisms of action. Synthesis of hybrid compounds has been one approach to tackling this challenge. In this study, the synthesis of artesunate-ellagic acid hybrid compound (EA31) from ellagic acid and artesunate and its evaluation for antimalarial and antioxidant activities using in vitro and in vivo models were carried out. Method: EA31 was synthesized from artesunate and ellagic acid. The activities of the hybrid compound against Plasmodium falciparum W2 and P. berghei NK65 were evaluated, and its antioxidant activities were also determined. Results: The results revealed that EA31 was more active against P. falciparum W2 (chloroquine resistant) clone and less cytotoxic to buffalo green monkey kidney cell line compared to artesunate. EA31 was also active against Plasmodium berghei NK65 in vivo. The results revealed inhibition of ß-hematin formation as one of the mechanisms of action of EA31. EA31 also exhibited antioxidant activities. Conclusion: The results revealed that EA31 may exert dual action of killing malaria parasite and mopping the reactive oxygen species that mediate the secondary complications of malaria.

Signal, detection and estimation using a hybrid quantum circuit.

We investigate a hybrid device allowing a photon-phonon coupling of a transmission line radiation (TLR) and a nanoeletromechanical system (NEMS), mediated by a superconducting qubit population imbalance. We demonstrate the derivation of an effective Hamiltonian for the strongly dispersive regime for this system. The qubit works as a quantum switch, allowing a conditioned transfer of excitations between the TLR and NEMS. We show that this regime allows the system to be employed for signal processing and force estimation. Additionally, we explore the ability of the quantum switch to generate non-classical states.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs.

Organ segmentation is a fundamental requirement in medical image analysis. Many methods have been proposed over the past 6 decades for segmentation. A unique feature of medical images is the anatomical information hidden within the image itself. To bring natural intelligence (NI) in the form of anatomical information accumulated over centuries into deep learning (DL) AI methods effectively, we have recently introduced the idea of hybrid intelligence (HI) that combines NI and AI and a system based on HI to perform medical image segmentation. This HI system has shown remarkable robustness to image artifacts, pathology, deformations, etc. in segmenting organs in the Thorax body region in a multicenter clinical study. The HI system utilizes an anatomy modeling strategy to encode NI and to identify a rough container region in the shape of each object via a non-DL-based approach so that DL training and execution are applied only to the fuzzy container region. In this paper, we introduce several advances related to modeling of the NI component so that it becomes substantially more efficient computationally, and at the same time, is well integrated with the DL portion (AI component) of the system. We demonstrate a 9-40 fold computational improvement in the auto-segmentation task for radiation therapy (RT) planning via clinical studies obtained from 4 different RT centers, while retaining state-of-the-art accuracy of the previous system in segmenting 11 objects in the Thorax body region.

Anatomic attention regions via optimal anatomy modeling and recognition for DL-based image segmentation.

Organ segmentation is a crucial task in various medical imaging applications. Many deep learning models have been developed to do this, but they are slow and require a lot of computational resources. To solve this problem, attention mechanisms are used which can locate important objects of interest within medical images, allowing the model to segment them accurately even when there is noise or artifact. By paying attention to specific anatomical regions, the model becomes better at segmentation. Medical images have unique features in the form of anatomical information, which makes them different from natural images. Unfortunately, most deep learning methods either ignore this information or do not use it effectively and explicitly. Combined natural intelligence with artificial intelligence, known as hybrid intelligence, has shown promising results in medical image segmentation, making models more robust and able to perform well in challenging situations. In this paper, we propose several methods and models to find attention regions in medical images for deep learning-based segmentation via non-deep-learning methods. We developed these models and trained them using hybrid intelligence concepts. To evaluate their performance, we tested the models on unique test data and analyzed metrics including false negatives quotient and false positives quotient. Our findings demonstrate that object shape and layout variations can be explicitly learned to create computational models that are suitable for each anatomic object. This work opens new possibilities for advancements in medical image segmentation and analysis.

Introgression affects Salmo trutta juvenile life-history traits generations after stocking with non-native strains.

Introgression of non-native conspecifics changes the genetic composition of wild populations, potentially leading to loss of local adaptations and fitness declines. However, long-term data from wild populations are still relatively few. Here, we studied the effects of introgression in a Danish brown trout (Salmo trutta, L.) population, subjected to intensive stocking with domesticated hatchery fish of non-native origin. We used wild-caught genetically wild and admixed trout as well as fish from the partly domesticated hatchery strain used for stocking the river up until ~15 years prior to this study, to produce 22 families varying in hatchery/wild admixture. Following a replicated common-garden experiment conducted in fish tanks from first feeding through 23 weeks at 7, 12, and 16°C, we observed a significant positive relationship between family admixture and fish size upon termination, an effect observed through all levels of admixture. Furthermore, the admixture effect was most distinct at the higher rearing temperatures. Although the hatchery strain used for stocking had been in culture for ~7 generations, it had not been deliberately selected for increased growth. These data thus demonstrate: (i) that growth had increased in the hatchery strain even in the absence of deliberate directional selection for this trait, (ii) that the increasing effect of admixture by temperature could represent inadvertent selection for performance in the hatchery strain at higher temperatures, and most significantly, (iii) that despite undergoing up to five generations of natural selection in the admixed wild population, the genetically increased growth potential was still detectable and thus persistent. Our findings suggest that altered growth patterns and potentially their cascading effects are of importance to the severity of hatchery/wild introgression, especially under changing-climate scenarios and are of general significance to conservation practitioners seeking to evaluate long-term effects of intra-specific hybridization including under recovery.

Medical image registration via neural fields.

Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain shift. Here we present a new neural network based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural networks to model deformations between image pairs. NIR represents the transformation between two images with a continuous function implemented via neural fields, receiving a 3D coordinate as input and outputting the corresponding deformation vector. NIR provides two ways of generating deformation field: directly output a displacement vector field for general deformable registration, or output a velocity vector field and integrate the velocity field to derive the deformation field for diffeomorphic image registration. The optimal registration is discovered by updating the parameters of the neural field via stochastic mini-batch gradient descent. We describe several design choices that facilitate model optimization, including coordinate encoding, sinusoidal activation, coordinate sampling, and intensity sampling. NIR is evaluated on two 3D MR brain scan datasets, demonstrating highly competitive performance in terms of both registration accuracy and regularity. Compared to traditional optimization-based methods, our approach achieves better results in shorter computation times. In addition, our methods exhibit performance on a cross-dataset registration task, compared to the pre-trained learning-based methods.

Catalytic reduction of nitroarenes by palladium nanoparticles decorated silica@poly(chitosan-N-isopropylacrylamide-methacrylic acid) hybrid microgels.

Conversion of toxic nitroarenes into less toxic aryl amines, which are the most suitable precursors for different types of compounds, is done with various materials which are costly or take more time for this conversion. In this regards, a silica@poly(chitosan-N-isopropylacrylamide-methacrylic acid) Si@P(CS-NIPAM-MAA) Si@P(CNM) core-shell microgel system was synthesized through free radical precipitation polymerization (FRPP) and then fabricated with palladium nanoparticles (Pd NPs) by in situ-reduction method to form Si@Pd-P(CNM) and characterized with XRD, TEM, FTIR, SEM, and EDX. The catalytic efficiency of Si@Pd-P(CNM) hybrid microgels was studied for reduction of 4-nitroaniline (4NiA) under diverse conditions. Different nitroarenes were successfully transformed into their corresponding aryl amines with high yields using the Si@Pd-P(CNM) system as catalyst and NaBH4 as reductant. The Si@Pd-P(CNM) catalyst exhibited remarkable catalytic efficiency and recyclability as well as maintaining its catalytic effectiveness over multiple cycles.

Efficacy of advanced hybrid closed loop systems in cystic fibrosis related diabetes: a pilot study.

Background and aims: Cystic fibrosis related diabetes (CFRD) is correlated with worsening of nutritional status and greater deterioration of lung function. The role of new technologies for the treatment of CFRD is little explored. The aim of the study was to evaluate the efficacy of Advanced Hybrid Closed Loop (AHCL) systems on glycemic control in CF patients. Methods: A single-center retrospective study on CFRD patients using AHCL systems was performed. Glycated hemoglobin (HbA1c) values and Continuous Glucose Monitoring (CGM) metrics were collected at T0 (AHCL placement), T1 (1-month), T2 (6-months) and T3 (1-year) to evaluate glycemic control. Results: 10 patients were included in the study. Data showed a reduction of HbA1c value (7.31 ± 0.34 to 6.35 ± 1.00; p=0.03), glycemic variability (p=0.05) and insulin requirement (p=0.03). The study population reached American Diabetes Association (ADA) recommended glycemic targets at 1-year. An increase in the Time in Range (TIR) and a reduction in time in hyperglycemia were also observed, although not statistically significant. Conclusions: In patients with CFRD, the use of AHCL leads to an improvement in glycemic control in terms of HbA1c and glycemic variability. The increase in TIR and the reduction of time in hyperglycemia, although not statistically significant, are extremely encouraging from a clinical point of view. Further studies with a larger population and a longer follow-up are needed. The results of this study demonstrate the importance of proposing the use of AHCL even in CF patients, who could benefit from glycemic improvement also in terms of nutritional status and respiratory function.

Hybrid convergent procedure with proactive oesophageal cooling for the treatment of long-standing persistent atrial fibrillation: a case series.

Background: The hybrid convergent procedure is approved to treat symptomatic patients with long-standing persistent atrial fibrillation (AF). Despite direct visualization during surgical ablation as well as the use of luminal oesophageal temperature (LET) monitoring, oesophageal injury is still possible. A dedicated device for proactive oesophageal cooling has recently been cleared by the Food and Drug Administration to reduce the likelihood of ablation-related oesophageal injury resulting from radiofrequency cardiac ablation procedures. This report describes the first uses of proactive oesophageal cooling for oesophageal protection during the epicardial ablation portion of hybrid convergent procedures. Case summary: Five patients with long-standing persistent AF underwent hybrid convergent ablations with the use of proactive oesophageal cooling as means of oesophageal protection. All cases were completed successfully with no adverse effects. Most notably, cases were shorter when compared to cases using LET monitoring, likely due to lack of pauses for overheating of the oesophagus that would otherwise be required to prevent damage to the oesophagus. Discussion: This report describes the first uses of proactive oesophageal cooling for oesophageal protection during the epicardial ablation portion of five hybrid convergent procedures. Use of cooling enabled uninhibited deployment of lesions without the need to pause energy delivery due to elevated temperatures in the oesophagus, providing a feasible alternative to LET monitoring.

Improvement of optical and structural properties of ZIF-8 by producing multifunctional Zn/Co bimetallic ZIFs for wastewater treatment from copper ions and dye.

In the paper, high specific surface area (SSA) mono and bimetallic zeolitic imidazolate frameworks (ZIFs) based on zinc and cobalt metals are successfully synthesized at room temperature using different ratios of Zn to Co salts as precursors and ammonium as a solvent to tailor the properties of the produced ZIF and optimize the efficiency of the particles in water treatment from dye and copper ions, simultaneously. The results declare that monometallic and bimetallic ZIF microparticles are formed using ammonium and the tuning of pore sizes and also increasing the SSA by inserting the Co ions in Zn-ZIF particles is accessible. It leads to a significant increase in the thermal stability of bimetallic Zn/Co-ZIF and the appearance of an absorption band in the visible region due to the existence of Co in the bimetallic structures. The bandgap energies of bimetallic ZIFs are close to that of the monometallic Co-ZIF-8, indicating controlling the bandgap by Co ZIF. Furthermore, the ZIFs samples are applied for water treatment from copper ions (10 and 184 ppm) and methylene blue (10 ppm) under visible irradiation and the optimized multifunctional bimetallic Zn/Co ZIF is introduced as an admirable candidate for water treatment even in acidic conditions.