With great power, comes great responsibility: the importance of broadly measuring Fc-mediated effector function early in the antibody development process

The field of antibody therapeutics is rapidly growing, with over 210 antibodies currently approved or in regulatory review and ~ 1,250 antibodies in clinical development. Antibodies are highly versatile molecules that, with strategic design of their antigen-binding domain (Fab) and the domain responsible for mediating effector functions (Fc), can be used in a wide range of therapeutic indications. Building on many years of progress, the biopharmaceutical industry is now advancing innovative research and development by exploring new targets and new formats and using antibody engineering to fine-tune functions tailored to specific disease requirements. In addition to considering the target and the disease context, however, the unique features of each therapeutic antibody trigger a diverse set of Fc-mediated effector functions. To avoid unexpected results on safety and efficacy outcomes during the later stages of the development process, it is crucial to measure the impact of antibody design on Fc-mediated effector function early in the antibody development process. Given the breadth of effector functions antibodies can deploy and the close interplay between the antibody Fab and Fc functional domains, it is important to conduct a comprehensive evaluation of Fc-mediated functions using an array of antigen-specific biophysical and cell-mediated functional assays. Here, we review antibody and Fc receptor properties that influence Fc effector functions and discuss their implications on development of safe and efficacious antibody therapeutics.

Antimicrobial capping agents on silver nanoparticles made via green method using natural products from banana plant waste

Herein, we investigated the phytochemical composition and antibacterial activities of the organic layers from biosynthesized silver nanoparticles (AgNPs). AgNPs were synthesized using Musa paradisiaca and Musa sapientum extracts. UV-vis absorption in the 400-450 nm range indicated surface plasmonic resonance peak of AgNPs. Samples analyses using dynamic light scattering and transmission electron microscopy revealed the presence of particles within nanometric ranges, with sizes of 30-140 nm and 8-40 nm, respectively. Fourier transform infrared (FTIR) unveiled the presence of several organic functional groups on the surface of AgNPs, indicating the presence of phytochemicals from plant extracts. Thin layer chromatography (TLC) of the phytochemicals (capping agents) from AgNPs identified multiple groups of secondary metabolites. These phytochemical capping agents exhibited antibacterial activities against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, with minimum inhibitory concentrations ranging from 62.5 to 1000 µg/mL. Regardless of the bacterial species or plant parts (leaves or pseudo-stems), capping agents from M. sapientum nanoparticles displayed significantly enhanced antibacterial effectiveness compared to all other samples, including the raw plant extracts and biosynthesized capped and uncapped AgNPs. These results suggest the presence of antimicrobial phytochemicals on biosynthesized AgNPs, highlighting the promise of green nanoparticle synthesis as a valuable approach in bioprospecting antimicrobial agents.

Deep transfer learning hybrid techniques for precision in breast cancer tumor histopathology classification

The breast cancer is one of the most prevalent causes of cancer-related death globally. Preliminary diagnosis of breast cancer increases the patient's chances of survival. Breast cancer classification is a challenging problem due to dense tissue structures, subtle variations, cellular heterogeneity, artifacts, and variability. In this paper, we propose three hybrid deep-transfer learning models for breast cancer classification using histopathology images. These models use Xception model as a base model, and we add seven more layers to fine-tune the base model. We also performed an extensive comparative analysis of five prominent machine-learning classifiers, namely Random Forest Classifier (RFC), Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and Ada-boost. We incorporate the best performing two classifiers, namely RFC and SVC, in the fine-tuned Xception model, and accordingly, they are named as Xception Random Forest (XRF) and Xception Support Vector (XSV), respectively. The fine-tuned Xception model with softmax classifier is termed as Multi-layer Xception Classifier (MXC). These three models are evaluated on the two publically available datasets: BreakHis and Breast Histopathology Images Database (BHID). Our all three models perform better than the state-of-the-art methods. The XRF provides the best performance at the 40 × magnification level on the BreakHis dataset, with an accuracy (ACC) of 94.44%, F1 score (F1) of 94.44%, area under the receiver operating characteristic curve (AUC) of 95.12%, Matthew's correlation coefficient (MCC) of 88.98%, kappa (K) of 88.88%, and classification success index (CSI) of 89.23%. The MXC provides the best performance on the BHID dataset, with an ACC of 88.50%, F1 of 88.50%, AUC of 95.12%, MCC of 77.03%, K of 77.00%, and CSI of 79.13%. Further, to validate our models, we performed fivefold cross-validation on both datasets and obtained similar results.

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems

Significance

Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging).

Aim

This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs.

Approach

Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended.

Conclusion

Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Engineering multifunctional surface topography to regulate multiple biological responses

Surface topography or curvature plays a crucial role in regulating cell behavior, influencing processes such as adhesion, proliferation, and gene expression. Recent advancements in nano- and micro-fabrication techniques have enabled the development of biomimetic systems that mimic native extracellular matrix (ECM) structures, providing new insights into cell-adhesion mechanisms, mechanotransduction, and cell-environment interactions. This review examines the diverse applications of engineered topographies across multiple domains, including antibacterial surfaces, immunomodulatory devices, tissue engineering scaffolds, and cancer therapies. It highlights how nanoscale features like nanopillars and nanospikes exhibit bactericidal properties, while many microscale patterns can direct stem cell differentiation and modulate immune cell responses. Furthermore, we discuss the interdisciplinary use of topography for combined applications, such as the simultaneous regulation of immune and tissue cells in 2D and 3D environments. Despite significant advances, key knowledge gaps remain, particularly regarding the effects of topographical cues on multicellular interactions and dynamic 3D contexts. This review summarizes current fabrication methods, explores specific and interdisciplinary applications, and proposes future research directions to enhance the design and utility of topographically patterned biomaterials in clinical and experimental settings.

Numerical modeling of giant pore formation in vesicles under msPEF-induced electroporation: Role of charging time and waveform

Giant unilamellar vesicle is the closest prototypical model for investigating membrane electrodeformation and electroporation in biological cells. This work employs numerical simulations to investigate the effect of membrane charging time on vesicle electroporation under milli-second pulsed-electric-field (msPEF) of different waveforms. Our numerical approach, which implements the effect of electric stretching on membrane tension and precise calculation of pore energy, successfully predicts the formation of giant pores of O(1)μm size as observed in previous experiments. The poration zone is found to extend up to certain angles as measured from the poles, termed critical angles. An increase in charging time delays pore formation, decreases the pore density, and trims down the poration zone. Counterintuitively, this effect promotes significant pore growth. Moreover, there exists a cut-off charging time above which pore formation is completely inhibited. This phenomenon is particularly pronounced with square bipolar pulses. Comparisons with the previous experimental results reveal that electrodeformation-poration-induced membrane surface area variation and that induced only by electroporation evolves in a similar fashion. Therefore, although the agreements are qualitative, the present electroporation model can be used as the simplest tool to predict the evolution of vesicles under electric pulses in laboratory experiments.

Bioprinting spatially guided functional 3D neural circuits with agarose-xanthan gum copolymer hydrogels

Engineered three-dimensional (3D) tissue models are being used as predictive human in vitro assays for drug discovery and development. Tissue engineering technologies such as bioprinting are now available which use mixtures of polymeric hydrogels and cells for the construction of biomimetic engineered 3D tissue models. Many of the polymeric hydrogels used for bioprinting require post-printing processing steps which might hinder their application directly in multi-well plate platforms, thus limiting their utility in a drug screening setting. Here we describe an agarose and xanthan gum copolymer hydrogel (AG-XG) that has optimal rheological properties for high shape fidelity with extrusion-based printing, has long term stability in cell culture conditions, and is "ready-to-use" after printing, not requiring post-printing processing treatments, making it ideal for applications in multi-well plate format. This AG-XG hydrogel is non-degradable and has non-cell permissive features which makes it ideal to create customized spatially guided cellular patterns to enhance relevant tissue geometry and function. As a proof-of-concept, we show that a bioprinted AG-XG hydrogel casting mold significantly enhances functional connectivity of an engineered 3D neural circuit model made using human iPSC-derived GABAergic and dopaminergic neurons and astrocytes. The bioprinted AG-XG mold promotes the formation of strong functional synaptic connections between two spatially separated neuronal regions, as measured with calcium and optogenetic-based fluorescent biosensors with a customized fiber photometry device. The high shape fidelity of the AG-XG hydrogels described here enables the biofabrication of precisely positioned and spatially designed cellular models, in muti well-based platforms used for drug screening. The process of printing these AG-XG hydrogels uses commercially available extrusion-based bioprinters and can therefore be easily implemented in translational laboratories doing tissue modeling and drug screening without the need of additional specialized bioengineering equipment.

Piezo-electronics: A paradigm for self-powered bioelectronics

Recent breakthroughs in electroactive piezo-biomaterials have driven significant progress towards the development of both, diagnostic and therapeutic purposes, enabling vital sign monitoring, such as heart rate, etc. while also supporting tissue regeneration. Bioelectronic medicine provides a promising method for controlling tissue and organ functions, with 'piezo-electronics' emphasizing the lasting role of electro-active piezo-biomaterials in self-powered devices. This article critically analyses a range of self-powered bioelectronic technologies, including wearable, implantable, regenerative, and cancer therapy applications. Piezoelectric nanogenerators (PENGs) are essential in wearable and implantable systems such as pressure and strain measurements, sensor for human-machine interface, self-powered pacemakers, deep brain stimulation, cochlear implant, tissue restoration and sustained drug delivery, controlled by electrical stimuli from PENGs etc. Regenerative bioelectronics play a key role in healing tissues, such as bone, neural, cardiac, tendon, ligament, skeletal muscle etc. using self-powered implants, which have ability to restore tissue functionality. Additionally, piezoelectric biomaterials are being utilized in cancer treatment, offering more targeted therapies with minimal side effects. Various cancerous tumors can be destroyed by reactive oxygen species (ROS), generated by piezo-biomaterials. Data science is also emerging as a crucial tool in optimizing self-powered bioelectronics, enhancing patient outcomes through data-driven strategies, and broadening the role of bioelectronic technologies in modern healthcare.

Cascade targeting selenium nanoparticles-loaded hydrogel microspheres for multifaceted antioxidant defense in osteoarthritis

Selenium (Se) deficiency is a critical factor contributing to the imbalance of redox homeostasis in chondrocytes and the progression of osteoarthritis (OA). However, traditional selenium supplements face challenges such as a narrow therapeutic window and lack of targeting. To address this, we designed hyaluronic acid (HA)-modified selenium nanoparticles (HA-SeNPs) and developed a cascade-targeted delivery system (HA-SeNPs@AHAMA-HMs) based on a nano-micron combined strategy. The system involves loading HA-SeNPs into aldehyde-functionalized hydrogel microspheres prepared via microfluidic technology. Through Schiff base reactions between the aldehyde groups of the microspheres and amino groups of the cartilage, the system selectively adheres to the surface of damaged cartilage, achieving micron-scale targeting while continuously releasing HA-SeNPs. Then, HA-SeNPs achieve nanoscale targeting by binding to CD44, which is highly expressed on OA chondrocyte membranes, via their HA surface. Once taken up by the cells, HA-SeNPs exert their effects by directly scavenging ROS and promoting selenoprotein synthesis through the generation of selenite, forming a multifaceted antioxidant defense system. This effectively alleviates oxidative stress and optimizes mitochondrial function. In vivo and in vitro results demonstrated that this system significantly improved the oxidative phosphorylation pathway associated with mitochondrial function, which markedly reduced joint space narrowing and cartilage matrix degradation, and delayed the progression of OA. In summary, this study suggests that the cascade-targeting hydrogel microspheres designed and constructed based on a nano-micron combined strategy represent a promising prospective approach for precise Se supplementation and OA treatment.

Biogenic nanoparticles as a promising drug delivery system

Nanotechnology has significantly influenced the worldwide medical services sector during the past few decades. Biological collection approaches for nanoparticles are economical, non-toxic, and ecologically benign. This review provides up-to-date information on nanoparticle production processes and biological sources, including algae, plants, bacteria, fungus, actinomycetes, and yeast. The biological technique of generating nanoparticles has advantages over chemical, physical, and biological methods, including low-toxicity and friendly to the environment, thereby providing a viable option for therapeutic applications as s promising drug delivery system. In addition to aiding researchers, the bio-mediated, obtained nanoparticles also modify particles to promote both health and safety. We also looked at the important medicinal uses of nanoparticles, including their antifungal, antimicrobial, antiviral, antidiabetic, anti-inflammatory, and antioxidant properties. The current study highlights the findings of recent research in this field and discusses various methods proposed to describe the bio-mediated acquisition of novel nanoparticles.. The production of nanoparticles via biogenic sources possess various benefits, such as low cost, bioavailability, and environmental friendliness. In addition to the determination of the bioactive chemicals mediated by nanoparticle as well as the examination of the biochemical pathways and enzyme reactions. The major focus of this review is highlighting on the essential role of biogenic nanoparticles as promising drug delivery system.

Recent advances of physiochemical cues on surfaces for directing cell fates

Surface modification plays an essential role in dictating cell behavior and fate, as it creates a microenvironment that profoundly influences cell attachment, migration, proliferation, and differentiation. This review aims to the intricate interplay of culture surface properties, including topography, stiffness, charge, and chemical modifications, demonstrating their profound impact on cell destiny. We explore the nuanced responses of cells to varying surface topographies, from nano- to microscale features, elucidating the influence of geometric patterns and roughness. We also investigate the impact of substrate stiffness, highlighting the way cells perceive and respond to mechanical cues mimicking their native environments. The role of surface charge is examined, revealing how electrostatic interactions influence cell adhesion, signaling, and cell fate decisions. Finally, we delve into the diverse effects of chemical modifications, including the presentation of bioactive molecules, growth factors, and extracellular matrix (ECM) components, demonstrating their ability to guide cell behavior and stimulate specific cellular responses. This review offers comprehensive insights into the important role of surface properties in shaping cell fate, offering promising avenues for developing sophisticated cell culture platforms for applications in drug discovery, regenerative medicine, and fundamental research.

Electroactive membranes enhance in-situ alveolar ridge preservation via spatiotemporal electrical modulation of cell motility

Post-extraction alveolar bone resorption invariably compromises implant placement and aesthetic restoration outcomes. Current non-resorbable membranes exhibit limited efficacy in alveolar ridge preservation (ARP) due to insufficient cell recruitment and osteoinductive capabilities. Herein, we introduce a multifunctional electroactive membrane (PPy-BTO/P(VDF-TrFE), PB/PT) designed to spatiotemporally regulate cell migration and osteogenesis, harmonizing with the socket healing process. Initially, the membrane's endogenous-level surface potential recruits stem cells from the socket. Subsequently, adherent cell-migration-triggered forces generate on-demand piezopotential, stimulating intracellular calcium ion fluctuations and activating the Ca2+/calcineurin/NFAT1 signaling pathway via Cav3.2 channels. This enhances cell motility and osteogenic differentiation predominantly in the coronal socket region, counteracting the natural healing trajectory. The membrane's self-powered energy supply, proportional to cell migration velocity and manifested as nanoparticle deformation, mitigates ridge shrinkage, both independently and in conjunction with bone grafts. This energy-autonomous membrane, based on the spatiotemporal modulation of cell motility, presents a novel approach for in-situ ARP treatment and the development of 4D bionic scaffolds.

The non-monotonic effect of sucrose on interactions between lipid-bearing surfaces

The extremely low sliding friction of articular cartilage in synovial joints has been attributed to phospholipid boundary layers, lubricating via the hydration lubrication mechanism at their exposed, highly hydrated polar-head-groups, in a medium - the synovial fluid - where osmolytes, which may modify the hydration layer, are ubiquitous. Here, using a surface force balance (SFB), we carried out a systematic study to elucidate the effect of sucrose, a known osmotic regulator solute, with concentrations csucrose, ranging from 5 to 20 wt%, on the normal and shear forces between interacting phosphatidylcholine (PC) bilayers, both in the gel (1,2-dipalmitoyl-sn-glycero-3-phosphocholine, DPPC) and liquid (1,2-dimyristoyl-sn-glycero-3-phosphocholine, DMPC) phases, supported on atomically-smooth mica substrates. Several additional approaches including cryo-transmission electron microscope, atomic force microscopy, small- and wide-angle X-ray scattering, differential scanning calorimetry, dynamic light scattering and zeta potential measurements are exploited to get additional insight into the nature of the sucrose-dependent interactions. As csucrose is varied, a remarkable variation in the friction is observed: a marked reduction in friction is seen at low csucrose, but at higher sucrose levels the friction increases, for both gel and liquid phase lipids. This challenges the expectation that hydration lubrication is degraded by osmotic solutes, due to their competing for water of hydration, and reveals for the first time a non-monotonic effect of a sugar on the interactions, particularly frictional forces, between lipid bilayers. This non-monotonic effect correlates with the bilayer potential, and is attributed to a concentration-dependent affinity of the sugar to the PC headgroups.

Prediction of lymph node metastasis in papillary thyroid carcinoma using non-contrast CT-based radiomics and deep learning with thyroid lobe segmentation: A dual-center study

Objectives

This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features.

Methods

This study included 271 thyroid lobes from 228 PTC patients who underwent preoperative neck non-contrast CT at Center 1 (May 2021-April 2024). LNM status was confirmed via postoperative pathology, with each thyroid lobe labeled accordingly. The cohort was divided into training (n = 189) and validation (n = 82) cohorts, with additional temporal (n = 59 lobes, Center 1, May-August 2024) and external (n = 66 lobes, Center 2) test cohorts. Thyroid lobes were manually segmented from the isthmus midline, ensuring interobserver consistency (ICC ≥ 0.8). Deep learning and radiomics features were selected using LASSO algorithms to compute DLRad scores. Logistic regression identified independent predictors, forming DLRad, clinical, and combined models. Model performance was evaluated using AUC, calibration, decision curves, and the DeLong test, compared against radiologists' assessments.

Results

Independent predictors of LNM included age, gender, multiple nodules, tumor size group, and DLRad. The combined model demonstrated superior diagnostic performance with AUCs of 0.830 (training), 0.799 (validation), 0.819 (temporal test), and 0.756 (external test), outperforming the DLRad model (AUCs: 0.786, 0.730, 0.753, 0.642), clinical model (AUCs: 0.723, 0.745, 0.671, 0.660), and radiologist evaluations (AUCs: 0.529, 0.606, 0.620, 0.503). It also achieved the lowest Brier scores (0.167, 0.184, 0.175, 0.201) and the highest net benefit in decision-curve analysis at threshold probabilities > 20 %.

Conclusions

The combined model integrating DLRad and clinical features exhibits good performance in predicting LNM in PTC patients.

Exploring the diagnostic landscape: Portable aptasensors in point-of-care testing

Aptamers, discovered in the 1990s, have marked a significant milestone in the fields of therapeutics and diagnostics. This review provides a comprehensive survey of aptamers, focusing on their diagnostic applications. It especially encapsulates a decade of aptamer, encompassing research, patents, and market trends. The unique properties and inherent stability of aptamers are discussed, highlighting their potential for various clinical applications. It goes on to introduce biosensor design, emphasizing the advantages of aptamers over antibodies as conventional molecular recognition interface. The operation and design of aptasensors are examined, with a focus on single- and dual-site binding configurations and their respective recognition modes. Paper-based sensors are highlighted as cost-effective, user-friendly alternatives that are gaining widespread adoption, particularly in point-of-care platforms.

Bioaugmented design and functional evaluation of low damage implantable array electrodes

Implantable neural electrodes are key components of brain-computer interfaces (BCI), but the mismatch in mechanical and biological properties between electrode materials and brain tissue can lead to foreign body reactions and glial scarring, and subsequently compromise the long-term stability of electrical signal transmission. In this study, we proposed a new concept for the design and bioaugmentation of implantable electrodes (bio-array electrodes) featuring a heterogeneous gradient structure. Different composite polyaniline-gelatin-alginate based conductive hydrogel formulations were developed for electrode surface coating. In addition, the design, materials, and performance of the developed electrode was optimized through a combination of numerical simulations and physio-chemical characterizations. The long-term biological performance of the bio-array electrodes were investigated in vivo using a C57 mouse model. It was found that compared to metal array electrodes, the surface charge of the bio-array electrodes increased by 1.74 times, and the impedance at 1 kHz decreased by 63.17 %, with a doubling of the average capacitance. Long-term animal experiments showed that the bio-array electrodes could consistently record 2.5 times more signals than those of the metal array electrodes, and the signal-to-noise ratio based on action potentials was 2.1 times higher. The study investigated the mechanisms of suppressing the scarring effect by the bioaugmented design, revealing reduces brain damage as a result of the interface biocompatibility between the bio-array electrodes and brain tissue, and confirmed the long-term in vivo stability of the bio-array electrodes.

Red blood cells-derived components as biomimetic functional materials: Matching versatile delivery strategies based on structure and function

Red blood cells (RBCs), often referred to as "intelligent delivery systems", can serve as biological or hybrid drug carriers due to their inherent advantages and characteristics. This innovative approach has the potential to enhance biocompatibility, pharmacokinetics, and provide targeting properties for drugs. By leveraging the unique structure and contents of RBCs, drug-loading pathways can be meticulously designed to align with these distinctive features. This review article primarily discusses the drug delivery strategies and their applications that are informed by the structural and functional properties of the main components of RBCs, including living RBCs, membranes, hollow RBCs, and hemoglobin. Overall, this review article would assist efforts to make better decisions on optimization and rational utilization of RBCs derivatives-based drug delivery strategies for the future direction in clinical translation.

Stiffness-tunable biomaterials provide a good extracellular matrix environment for axon growth and regeneration

Neuronal growth, extension, branching, and formation of neural networks are markedly influenced by the extracellular matrix-a complex network composed of proteins and carbohydrates secreted by cells. In addition to providing physical support for cells, the extracellular matrix also conveys critical mechanical stiffness cues. During the development of the nervous system, extracellular matrix stiffness plays a central role in guiding neuronal growth, particularly in the context of axonal extension, which is crucial for the formation of neural networks. In neural tissue engineering, manipulation of biomaterial stiffness is a promising strategy to provide a permissive environment for the repair and regeneration of injured nervous tissue. Recent research has fine-tuned synthetic biomaterials to fabricate scaffolds that closely replicate the stiffness profiles observed in the nervous system. In this review, we highlight the molecular mechanisms by which extracellular matrix stiffness regulates axonal growth and regeneration. We highlight the progress made in the development of stiffness-tunable biomaterials to emulate in vivo extracellular matrix environments, with an emphasis on their application in neural repair and regeneration, along with a discussion of the current limitations and future prospects. The exploration and optimization of the stiffness-tunable biomaterials has the potential to markedly advance the development of neural tissue engineering.

Soft neural interface with color adjusted PDMS encapsulation layer for spinal cord stimulation

BACKGROUND

Spinal cord stimulation (SCS) plays a crucial role in treating various neurological diseases. Utilizing soft spinal cord electrodes in SCS allows for a better fit with the physiological structure of the spinal cord and reduces tissue damage. Polydimethylsiloxane (PDMS) has emerged as an ideal material for soft bioelectronics. However, micromachining soft PDMS bioelectronics devices with low thermal effects and high uniformity remains challenging.

NEW METHOD

Here, we demonstrated a fully laser-micromachined soft neural interface for SCS. The native and color adjusted PDMS with variable absorbance characteristics were investigated in laser processing. In addition, we systematically evaluated the impact of electrode sizes on the electrochemical performance of neural interface. By fitting the equivalent circuit model, the electrochemical process of neural interface was revealed and the performance of the electrode was evaluated. The biocompatibility of color adjusted PDMS was confirmed by cytotoxicity assays. Finally, we validated the neural interface in mice.

RESULTS

Color adjusted PDMS has good biocompatibility and can significantly reduce the damage caused by thermal effects, enhancing the electrochemical performance of bioelectronic devices. The soft neural interface with color adjusted PDMS encapsulation layer can activate the motor function safely.

COMPARISON WITH EXISTING METHODS

The fully laser-micromachined soft neural interface was proposed for the first time. Compared with existing methods, this method showed low thermal effects, high uniformity, and could be easily scaled up.

CONCLUSIONS

The fully laser-micromachined soft neural interface device with color adjusted PDMS encapsulation layer shows great promise for applications in SCS.

Integration of rehabilitation and palliative care in cancer management: A futuristic model

It explores the integration of rehabilitation and palliative care in cancer management, advocating for a holistic approach that addresses the diverse needs of patients throughout their treatment journey. Traditional cancer care often prioritizes curative interventions at the expense of overall well-being, leading to a fragmented experience for patients. By combining rehabilitation-focused on restoring function and improving physical health-with palliative care-emphasizing symptom management and quality of life-healthcare providers can create a comprehensive support system. The essay highlights the importance of interdisciplinary collaboration among healthcare professionals, as well as the need for education and training to implement this integrated model effectively. Additionally, it addresses potential barriers such as funding limitations and institutional resistance. Ultimately, the integration of these two disciplines represents a critical evolution in cancer care, enhancing patient outcomes and ensuring that individuals receive compassionate, patient-centered support throughout their journey.

Latest advancements and trends in biomedical polymers for disease prevention, diagnosis, treatment, and clinical application

Biomedical polymers are at the forefront of medical advancements, offering innovative solutions in disease prevention, diagnosis, treatment, and clinical use due to their exceptional physicochemical properties. This review delves into the characteristics, classification, and preparation methods of these polymers, highlighting their diverse applications in drug delivery, medical imaging, tissue engineering, and regenerative medicine. We present a thorough analysis of the recent advancements in biomedical polymer research and their clinical applications, acknowledging the challenges that remain, such as immune response management, controlled degradation rates, and mechanical property optimization. Addressing these issues, we explore future directions, including personalization and the integration of nanotechnology, which hold significant potential for further advancing the field. This comprehensive review aims to provide a deep understanding of biomedical polymers and serve as a valuable resource for the development of innovative polymer materials in both fundamental research and clinical practice.

Injectable mineralized hydrogel microspheres for accelerated osteocyte network reconstruction and intelligent bone regeneration

The disruption and limited reconstruction capacity of the osteocyte network are pivotal factors underlying impaired bone regeneration. This study developed an injectable mineralized hydrogel microsphere that provides a mineral-rich environment and optimal matrix stiffness for osteocyte network restoration. Furthermore, it spatially activates Notch signaling through osteocyte-derived vesicles with high Jagged1 expression, promoting osteocyte differentiation and enhancing angiogenic regulatory function. Specifically, hydrogel microspheres combining gelatin methacrylate (GelMA), alginate methacrylate (AlgMA), and osteocyte membrane vesicles (OMVs) were fabricated via gas-shear microfluidics and photopolymerization, followed by in situ pre-mineralization to produce mineralized microspheres. Findings indicate that mineralized hydrogel microspheres exhibit significantly increased compressive modulus and in situ formation of amorphous calcium phosphate particles within the gel matrix. In vitro, the mineralized microspheres effectively facilitated osteogenic differentiation in bone marrow-derived mesenchymal stem cells (BMSCs), with adherent cells displaying accelerated osteocyte marker expression. Co-culture experiments further revealed enhanced vascular formation potential. Ectopic bone regeneration studies demonstrated that mineralized hydrogel microspheres promote rapid formation of mature osteocyte networks in vivo. Moreover, in a femoral critical bone defect model, these microspheres accelerated defect healing. Collectively, mineralized hydrogel microspheres expedite osteocyte network reconstruction, supporting intelligent bone regeneration, and present a promising approach for critical-sized bone defect repair.

Prediction of health anxiety using resting-state functional near-infrared spectroscopy and machine learning

BACKGROUND

The role of cortical networks in health anxiety remain poorly understood. This study aimed to develop a predictive model for health anxiety, using a machine-learning approach based on resting-state functional connectivity (rsFC) with functional near-infrared spectroscopy (fNIRS).

METHOD

One hundred and four university students experiencing school disclosure due to the Corona Virus Disease 2019 pandemic participated in the study, and the final sample consisted of 90 participants. All participants underwent a 6-min resting-state fNIRS recording session and filled out the Short Health Anxiety Inventory after the data collection. Stratified 10-fold cross-validation was used to train and evaluate the Lasso regression model. Additionally, a bootstrap method was used to determine which features significantly contributed to the prediction of health anxiety.

RESULTS

The contributing rsFC with negative weights was the functional connectivity between right medial superior frontal gyrus and right middle frontal gyrus, with a 99 % confidence interval (CI) of [-1.61, -0.35]. The contributing rsFC with positive weights was the functional connectivity between right supramarginal gyrus and left middle temporal gyrus (99 % CI = [0.02, 1.67]), as well as the functional connectivity between right medial superior frontal gyrus and right supramarginal gyrus (99 % CI = [0.03, 1.41]).

CONCLUSION

The findings reveal a predictive role of intrinsic cortical organization in health anxiety and suggest that health anxiety involves complex interactions between cognitive control, emotion regulation, and sensory processing. The work provides new insights into potential neural mechanisms underlying health anxiety, and implications for neuromodulation research and practice targeting severe health anxiety.

Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images

Brain tumors are incredibly harmful and can drastically reduce life expectancy. Most researchers use magnetic resonance (MR) scans to detect tumors because they can provide detailed images of the affected area. Recently, AI-based deep learning methods have emerged to enhance diagnostic accuracy through efficient data processing. This study investigates the effectiveness of deep transfer learning techniques for accurate brain tumor diagnosis. A preprocessing pipeline is used to enhance the image quality. This pipeline includes morphological operations such as erosion and dilation for shape refinement, Gaussian blurring for noise reduction, and thresholding for image cropping. Principal Component Analysis (PCA) is applied for dimensionality reduction, and data augmentation enriches the dataset. The dataset is partitioned into training (80 %) and testing (20 %). Pretrained ResNet152 and GoogleNet extract meaningful features from the images. These extracted features are then classified using conventional machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB). This study compares the performance of two pre-trained models for medical image analysis. Performance metrics such as accuracy, sensitivity, recall, and F1-Score evaluate the final classification results. ResNet152 outperforms GoogleNet, achieving a 98.53 % accuracy, an F1 score of 97.4 %, and a sensitivity of 96.52 %. This study highlights integrating deep learning and traditional machine-learning techniques in medical image analysis for effective brain tumor detection.

Exosomes and tissue engineering: A novel therapeutic strategy for nerve regenerative

Damage to nerves negatively impacts quality of life and causes considerable morbidity. Self-regeneration is a special characteristic of the nervous system, yet how successful regeneration is accomplished remains unclear. Research on nerve regeneration is advancing and accelerating successful nerve recovery with potential new approaches. Eukaryote cells release extracellular vesicles (EVs), which control intercellular communication in both health and disease. More and more, EVs such as microvesicles and exosomes (EXOs) are being recognized as viable options for cell-free therapies that address complex tissue regeneration. The present study highlights the functional relevance of EVs in regenerative medicine for nerve-related regeneration. A subclass of EVs, EXOs were first identified as a way for cells to expel undesirable cell products. These nanovesicles have a diameter of 30-150 nm and are secreted by a variety of cells in conditions of both health and illness. Their benefits include the ability to promote endothelial cell growth, inhibit inflammation, encourage cell proliferation, and regulate cell differentiation. They are also known to transport functional proteins, metabolites, and nucleic acids to recipient cells, thus playing a significant role in cellular communication. EXOs impact an extensive array of physiological functions, including immunological responses, tissue regeneration, stem cell conservation, communication within the central nervous system, and pathological processes involving cardiovascular disorders, neurodegeneration, cancer, and inflammation. Their biocompatibility and bi-layered lipid structure (which shields the genetic consignment from deterioration and reduces immunogenicity) make them appealing as therapeutic vectors. They can pass through the blood brain barrier and other major biological membranes because of their small size and membrane composition. The creation of modified EXOs is a dynamic area of research that supports the evaluation of diverse therapeutic freights, improvement of target selectivity, and manufacturing optimization.

Mechanisms of aortic dissection: From pathological changes to experimental and in silico models

Aortic dissection continues to be responsible for significant morbidity and mortality, although recent advances in medical data assimilation and in experimental and in silico models have improved our understanding of the initiation and progression of the accumulation of blood within the aortic wall. Hence, there remains a pressing necessity for innovative and enhanced models to more accurately characterize the associated pathological changes. Early on, experimental models were employed to uncover mechanisms in aortic dissection, such as hemodynamic changes and alterations in wall microstructure, and to assess the efficacy of medical implants. While experimental models were once the only option available, more recently they are also being used to validate in silico models. Based on an improved understanding of the deteriorated microstructure of the aortic wall, numerous multiscale material models have been proposed in recent decades to study the state of stress in dissected aortas, including the changes associated with damage and failure. Furthermore, when integrated with accessible patient-derived medical data, in silico models prove to be an invaluable tool for identifying correlations between hemodynamics, wall stresses, or thrombus formation in the deteriorated aortic wall. They are also advantageous for model-guided design of medical implants with the aim of evaluating the deployment and migration of implants in patients. Nonetheless, the utility of in silico models depends largely on patient-derived medical data, such as chosen boundary conditions or tissue properties. In this review article, our objective is to provide a thorough summary of medical data elucidating the pathological alterations associated with this disease. Concurrently, we aim to assess experimental models, as well as multiscale material and patient data-informed in silico models, that investigate various aspects of aortic dissection. In conclusion, we present a discourse on future perspectives, encompassing aspects of disease modeling, numerical challenges, and clinical applications, with a particular focus on aortic dissection. The aspiration is to inspire future studies, deepen our comprehension of the disease, and ultimately shape clinical care and treatment decisions.

Bioengineered Probiotics for Clostridioides difficile Infection: An Overview of the Challenges and Potential for This New Treatment Approach

The rapid increase in microbial antibiotic resistance in Clostridioides difficile (C. difficile) strains and the formation of hypervirulent strains have been associated with a global increase in the incidence of C. difficile infection (CDI) and subsequently, an increase in the rate of recurrence. These consequences have led to an urgent need to develop new and promising alternative strategies to control this pathogen. Engineered probiotics are exciting new bacterial strains produced by editing the genome of the original probiotics. Recently, engineered probiotics have been used to develop delivery vehicles for vaccines, diagnostics, and therapeutics. Recent studies have demonstrated engineered probiotics may potentially be an effective approach to control or treat CDI. This review provides a brief overview of the considerations for engineered probiotics for medicinal use, with a focus on recent preclinical research using engineered probiotics to prevent or treat CDI. We also address the challenges faced in the production of engineered strains and how they may be overcome in the application of these agents to meet patient needs in the future.

An injectable oleogel-based bupivacaine formulation for prolonged non-opioid post-operative analgesia

Opioid-based medications remain the mainstay of post-operative pain management, even though they are associated with a plethora of adverse effects including addiction, nausea, constipation, cognitive impairment, respiratory depression, and accidental death due to overdose. Local anesthetics are effective at controlling the intense pain after surgery but their short duration of effect limits their clinical utility in post-operative pain management. In this manuscript, an optimized injectable oleogel-based formulation of bupivacaine for multi-day post-operative pain management was characterized on the benchtop and assessed in two clinically-relevant porcine post-operative pain models. Benchtop characterization verified the optimized oleogel-based bupivacaine formulation design, demonstrating a homogenous stable oleogel with sufficient injectability due to shear-thinning properties, high drug loading capacity and first-order drug release kinetics over 5 days. In vivo assessment in two pig post-operative pain models demonstrated that the oleogel-based bupivacaine formulation can provide statistically significant multi-day analgesia in two routes of administration: local instillation directly into a surgical site and ultrasound-guided peripheral nerve block injection. Pharmacokinetic assessment of ALX005 found that Cmax values were not statistically different from the bupivacaine HCl control, with no clinical signs of local anesthetic systemic toxicity observed, when administering up to 2.7 and 8.1 times the control dose of bupivacaine HCl. This study demonstrates the pre-clinical safety and efficacy of an injectable oleogel-based bupivacaine formulation and explores its utility as a single-administration long-acting local anesthetic product for post-operative pain management that can be used in both local and regional anesthetic applications.

Measuring full-field strain of the muscle-tendon junction using confocal microscopy combined with digital volume correlation

The muscle-tendon junction (MTJ) is a specialized interface that facilitates the transmission of force from the muscle to the tendon which has been implicated in muscle strains and tears. Understanding the transmission of forces and the strain generated in the MTJ is therefore important. For the first time, we report the 3D full-field strain distribution across the muscle-tendon junction (MTJ) using in-situ tensile testing and confocal microscopy coupled with digital volume correlation (DVC). This approach allowed us to measure the mechanical behaviour of the MTJ at the fibre/fascicle level. Acridine orange (AO) in 70% ethanol was used to enhance the contrast of the mouse Achilles-gastrocnemius MTJ, and the specimens were rehydrated prior to the tensile testing, which was performed using custom made tensile rig that fitted under the confocal microscopy. The 3D full-field strain distribution was obtained using DVC, where the strain changes were measured from confocal images taken with the MTJ under preload (0.4 N) and loaded (0.8 N and 1.2 N) representing 2.7- and 4-times body weight. High strain concentration was observed at the junction for both 0.8 N and 1.2 N loads. At the junction, the first principal stain (εp1), shear strain (γ) and von Mises strain (εVM) reached 15.2, 34.2 and 19.2% respectively. This study allowed us to measure fascicle level strain distribution at the MTJ. Using histology, microtears at the MTJ were seen in specimens loaded with 1.2 N which were associated with von Mises strain concentration in the adjacent region. The microtears occurred in regions where the strain level was between 8 and 15%. This study developed a methodology to determine high-resolution strain distribution at the MTJ and has the potential to be used to analyse the strain at the cellular level using higher magnification objectives.

An "EVs-in-ECM" mimicking system orchestrates transcription and translation of RUNX1 for in-situ cartilage regeneration

The self-repair ability of articular cartilage is limited, which is one of the most difficult diseases to treat clinically. Kartogenin (KGN) induces chondrogenesis by regulating RUNX1 mRNA translation and the small molecule compound TD-198946 (TD) promotes chondrogenic differentiation of mesenchymal stem cells (MSCs) through increasing the transcription of RUNX1 mRNA. GelMA hydrogel and liposomes are respectively similar to the extracellular matrix (ECM) and extracellular vesicles (EVs). So, we developed an "EVs-in-ECM" mimicking system by incorporating GelMA and KGN/TD-loaded liposomes to investigate the repair effects of cartilage defect. First, western-blot, RNA fluorescence in situ hybridization (FISH), cellular immuno-fluorescence, co-immuno-precipitation (CO-IP), and qRT-PCR techniques showed that KGN regulated RUNX1 mRNA expression, and then promote chondrogenic differentiation of MSCs. Second, the role of RUNX1 was amplified by orchestrating RUNX1 transcription and translation through TD-198946 (TD) and KGN respectively, and the synergistic effects of TD and KGN on chondrogenesis of MSCs in vitro were discovered. Finally, an "EVs-in-ECM" mimicking system was designed for in situ cartilage repair. When GelMA loaded with KGN and TD liposomes, the hydrogel (KGN + TD@ GelMA) showed biological functions by the continuously controlled release of KGN and TD while maintaining its porous structure and mechanical strength, which enhanced the chondrogenesis of MSCs in one system. The repair performance of "EVs-in-ECM" in vivo was assessed using the articular osteochondral defect model of rat. The implantation of KGN + TD@ GelMA hydrogels effectively exerted favorable osteochondral repair effects showing structures similar to the native tissue, and prevented chondrocyte hypertrophy. The study indicate that the "EVs-in-ECM" mimicking system can act as a highly efficient and potent scaffold for osteochondral defect regeneration.

3D bioprinting technology innovation in female reproductive system

Several diseases affect the female reproductive system, and both disease factors and treatments impact its integrity and function. Consequently, understanding the mechanisms of disease occurrence and exploring treatment methods are key research focuses in obstetrics and gynecology. However, constructing accurate disease models requires a microenvironment closely resembling the human body, and current animal models and 2D in vitro cell models fall short in this regard. Thus, innovative in vitro female reproductive system models are urgently needed. Additionally, female reproductive system diseases often cause tissue loss, yet effective tissue repair and regeneration have long been a bottleneck in the medical field. 3D bioprinting offers a solution by enabling the construction of implants with tissue repair and regeneration capabilities, promoting cell adhesion, extension, and proliferation. This helps maintain the long-term efficacy of bioactive implants and achieves both structural and functional repair of the reproductive system. By combining live cells with biomaterials, 3D bioprinting can create in vitro 3D biomimetic cellular models, facilitating in-depth studies of cell-cell and cell-extracellular microenvironment interactions, which enhances our understanding of reproductive system diseases and supports disease-specific drug screening. This article reviews 3D bioprinting methods and materials applicable to the female reproductive system, discussing their advantages and limitations to aid in selecting optimal 3D bioprinting strategies. We also summarize and critically evaluate recent advancements in 3D bioprinting applications for tissue regeneration and in vitro disease models and address the prospects and challenges for translating 3D bioprinting technology into clinical applications within the female reproductive system.

Role of shear stress-induced red blood cell released ATP in atherosclerosis

Altered hemodynamics is a key factor for atherosclerosis. For decades, endothelial cell (EC) responses to fluid-generated wall shear stress have been the central focus for atherogenesis. However, circulating blood is not a cell-free fluid, it contains mechanosensitive red blood cells (RBCs) that are also subjected to altered hemodynamics and release a large amount of ATP, but their impact on atherosclerosis has been overlooked. The focus of this study is the role of shear stress (SS)-induced RBC-released ATP in atherosclerosis. Hypercholesterolemic mouse models with and without RBC-Pannexin 1 deletion were used for the study. Results showed that SS-induced release of ATP from RBCs was at µM concentrations, three-orders of magnitude higher than that from other cell types. Suppression of RBC-released ATP via deletion of Pannexin 1, a mechanosensitive ATP-permeable channel, reduced high-fat diet-induced aortic plaque burden by 40%-60%. Importantly, the location and the extent of aortic atherosclerotic lesions spatially matched with the ATP deposition profile at aortic wall predicted by a computational fluid dynamic (CFD) model. Furthermore, hypercholesterolemia increases EC susceptibility to ATP with potentiated increase in [Ca2+]i, an initial signaling for aortic EC barrier dysfunction, and an essential cause for lipid accumulation and inflammatory cell infiltration. The computational prediction also provides a physics-based explanation for RBC-released ATP-induced sex disparities in atherosclerosis. Our study reveals an important role of RBC-released ATP in the initiation and progression of atherosclerosis. These novel findings provide a more comprehensive view of how altered hemodynamics and systemic risk factors synergistically contribute to atherosclerosis.NEW & NOTEWORTHY This study reveals that, in addition to fluid-derived wall shear stress, the disturbed blood flow-induced release of ATP from mechanosensitive red blood cells (RBCs), the major cellular components of blood, along with hypercholesterolemia-induced increases in endothelial cell susceptibility to ATP contribute significantly to the initiation and progression of atherosclerosis. These novel findings advance our current understanding of how altered hemodynamics and hypercholesterolemia synergistically contribute to atherosclerosis for the first time with the inclusion of RBCs.

Resonance-Induced Therapeutic Technique for Skin Cancer Cells

OBJECTIVE

This study aims to evaluate the viability of a hypothesis for selective targeting of skin cancer cells by exploiting the spectral gap with healthy cells using analytical and numerical simulation.

METHODS

The spectral gap was first identified using a viscoelastic dynamic model, with the physical and mechanical properties of healthy and cancerous skin cells deduced from previous experimental studies conducted on cell lines. The outcome of the analytical simulation was verified numerically using modal and harmonic analysis. Finally, transient analyses were conducted analytically and numerically to evaluate the difference in vibrational response of healthy and cancerous cells when their resonant frequencies were closely matched. For analysis, we used healthy nucleus diameters of 3 µm, 5 µm and 7 µm, whereas 34 kPa was taken as the stiffness of healthy skin epithelial cells. Based on established trends, the nucleus-to-cytoplasm ratio was utilised to predict physical and mechanical properties as cells undergo neoplastic transformation.

RESULTS

Analytical and numerical simulation revealed an approximate frequency difference of 50-100 KHz for the different nucleus diameters. The transient simulation revealed a significant difference in the growth rate of cancer cells' vibration amplitude, which was 10 times greater than that of healthy cells.

CONCLUSIONS

This study highlights that cancer cells are more prone to resonance with tuned ultrasound frequencies, emphasising the need for detailed dynamic models incorporating the basement membrane's influence and experimental validation.

Decellularization of human iliac artery: A vascular scaffold for peripheral repairs with human mesenchymal cells

This work presents strong evidence supporting the use of decellularized human iliac arteries combined with adipose tissue-derived stem cells (hASCs) as a promising alternative for vascular tissue engineering, opening the path to future treatments for peripheral artery disease (PAD). PAD is a progressive condition with high rates of amputation and mortality due to ischemic damage and limited graft options. Traditional synthetic grafts often fail due to poor integration, while autologous grafts may be unsuitable for patients with compromised vascular health. This study explores the potential of decellularized human iliac arteries as scaffolds for vascular grafts, focusing on preserving extracellular matrix (ECM) ultrastructure while minimizing immunogenic response. A perfusion-based protocol with enzymatic and detergent agents effectively removed cellular material, resulting in scaffolds with preserved ECM architecture, including organized collagen and elastin fibers. To assess scaffold bioactivity, hASCs were seeded onto the decellularized ECM, demonstrating high viability. Structural assessments, including histological staining and mechanical testing, confirmed that decellularized arteries retained their hierarchical structure and exhibited increased stiffness, suggesting an adaptive realignment of ECM fibers. Thermal and ultrastructural analyses further showed that decellularized scaffolds maintained stability and integrity comparable to native tissue, underscoring their durability for clinical applications. The human iliac artery shows potential as a vascular scaffold due to its accessibility and the ability to support the viability of hASC. Future research will emphasize in vivo validation and strategies for functional recellularization to evaluate the clinical viability of these engineered vascular grafts.

Reactive EEG Biomarkers for Diagnosis and Prognosis of Alzheimer's Disease and Mild Cognitive Impairment

Alzheimer's disease (AD) is a devastating neurodegenerative condition characterized by progressive cognitive decline with currently no effective treatment available. One of the most critical areas in AD research is the identification of reliable biomarkers, which are essential for accurate diagnosis, prognostic assessment, and the development of targeted therapies. In this study, we introduce two novel reactive EEG (rEEG) biomarkers aimed at enhancing the diagnosis of AD and mild cognitive impairment (MCI). These biomarkers, previously unexplored in the literature, offer new insights into differentiating between various cognitive states. The first biomarker demonstrates a significant ability to distinguish between AD patients and normal controls (NC), while also effectively distinguishing MCI patients from NC. The second biomarker is designed to identify a subset of AD patients exhibiting hyperconductivity or hyperactivity, characterized by distinctive neural electrical patterns. A cohort of 90 elderly participants (mean age 76.63 ± 6.08 years) was recruited, including 30 AD patients, 30 individuals with MCI, and 30 NC subjects. Psychiatric diagnoses of participants were made by qualified professionals at Daejeon St. Mary's Hospital, The Catholic University of Korea, utilizing comprehensive neuropsychological assessments. Notably, the rEEG biomarkers achieved accuracies of 95%, 95%, and 98% in distinguishing between AD and NC, AD and MCI, and MCI and NC groups, respectively. These results underscore the potential of rEEG as a highly accurate and reliable diagnostic tool for cognitive impairments, including AD and MCI.

Caenorhabditis elegans as in vivo model for the screening of natural plants-derived novel anti-aging compounds: a short introduction

The global aging population highlights the need for effective anti-aging treatments. Natural products show promise, but thorough evaluation requires in vivo models due to the complexity of aging. Ethical concerns are driving a shift from traditional models like rabbits and mice to alternatives such as Caenorhabditis elegans. This microscopic nematode, with its short life cycle, genetic similarities to humans, and cost-effectiveness, is ideal for testing anti-aging compounds. We review studies using C. elegans to assess natural products, suggesting it could serve as a primary model for -evaluating the safety and efficacy of plant-derived anti-aging compounds.

Molecular dynamics study on the mitigation of radiation damage caused by electron pulses

The reduction of radiation damage represents a long-term objective for electron microscopists, particularly those engaged in the study of biological and organic matter. Recently, electron pulses in ultrafast transmission electron microscopy have been demonstrated to serve as a damage mitigation technique for radiation-sensitive materials. Nevertheless, the underlying mechanism of the mitigation effects remains unclear. In this study, we investigate the radiation damage of graphene induced by pulsed electrons using molecular dynamics simulations within the framework of binary elastic collisions. For electron irradiation at 200 keV, it was found that the pulsed electron beam corresponds to a larger threshold angle (1.4 rad) than that for a random beam (1.0 rad). This is because two electrons can be prevented from briefly interacting with the same or a neighboring atom by the use of well-controlled electron pulses. While such a mitigation of radiation damage is only apparent near the threshold angle, and there are likely other reduction mechanisms, our results provide insight into the mitigated radiation damage of electron pulses.

Toward cancer detection by label-free microscopic imaging in oncological surgery: Techniques, instrumentation and applications

This review examines the clinical application of label-free microscopy and spectroscopy, which are based on optical signals emitted by tissue components. Over the past three decades, a variety of techniques have been investigated with the aim of developing an in situ histopathology method that can rapidly and accurately identify tumor margins during surgical procedures. These techniques can be divided into two groups. One group encompasses techniques exploiting linear optical signals, and includes infrared and Raman microspectroscopy, and autofluorescence microscopy. The second group includes techniques based on nonlinear optical signals, including harmonic generation, coherent Raman scattering, and multiphoton autofluorescence microscopy. Some of these methods provide comparable information, while others are complementary. However, all of them have distinct advantages and disadvantages due to their inherent nature. The first part of the review provides an explanation of the underlying physics of the excitation mechanisms and a description of the instrumentation. It also covers endomicroscopy and data analysis, which are important for understanding the current limitations in implementing label-free techniques in clinical settings. The second part of the review describes the application of label-free microscopy imaging to improve oncological surgery with focus on brain tumors and selected gastrointestinal cancers, and provides a critical assessment of the current state of translation of these methods into clinical practice. Finally, the potential of confocal laser endomicroscopy for the acquisition of autofluorescence is discussed in the context of immediate clinical applications.

Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging

PURPOSE

The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task. Recent advances in CT-less PET imaging further highlight the necessity for an effective PET organ segmentation pipeline that does not rely on CT images. Therefore, the goal of this study was to develop a CT-less multitracer PET segmentation framework.

PATIENTS AND METHODS

We collected 2062 PET/CT images from multiple scanners. The patients were injected with either 18 F-FDG (1487) or 68 Ga-PSMA (575). PET/CT images with any kind of mismatch between PET and CT images were detected through visual assessment and excluded from our study. Multiple organs were delineated on CT components using previously trained in-house developed nnU-Net models. The segmentation masks were resampled to coregistered PET images and used to train 4 different deep learning models using different images as input, including noncorrected PET (PET-NC) and attenuation and scatter-corrected PET (PET-ASC) for 18 F-FDG (tasks 1 and 2, respectively using 22 organs) and PET-NC and PET-ASC for 68 Ga tracers (tasks 3 and 4, respectively, using 15 organs). The models' performance was evaluated in terms of Dice coefficient, Jaccard index, and segment volume difference.

RESULTS

The average Dice coefficient over all organs was 0.81 ± 0.15, 0.82 ± 0.14, 0.77 ± 0.17, and 0.79 ± 0.16 for tasks 1, 2, 3, and 4, respectively. PET-ASC models outperformed PET-NC models ( P < 0.05) for most of organs. The highest Dice values were achieved for the brain (0.93 to 0.96 in all 4 tasks), whereas the lowest values were achieved for small organs, such as the adrenal glands. The trained models showed robust performance on dynamic noisy images as well.

CONCLUSIONS

Deep learning models allow high-performance multiorgan segmentation for 2 popular PET tracers without the use of CT information. These models may tackle the limitations of using CT segmentation in PET/CT image quantification, kinetic modeling, radiomics analysis, dosimetry, or any other tasks that require organ segmentation masks.

Toward low-friction and high-adhesion solutions: Emerging strategies for nanofibrous scaffolds in articular cartilage engineering

Aging, trauma, pathology, and poor natural tissue regeneration are the leading causes of osteoarthritis (OA), an articular cartilage disease. Electrospun scaffolds have gained attention as potential matrices for the treatment of OA because of their high degree of ECM mimicry, which suits chondrocyte migration, adhesion, and proliferation. However, none of the products recently introduced in the market are nanofiber-based. This study aimed to review the scope and tribology of nanofibrous articular cartilage scaffolds. Herein, we briefly discuss cartilage lubrication and strategies for promoting cell adhesion in electrospun materials. Next, we discuss the emerging need to study the biotribological properties of scaffolds. Finally, we review new perspectives on surface functionalization, surface segregation, Janus membranes, layer-by-layer fabrication, and nanofibrous composites. We conclude that cell adhesion and low-friction conciliation remain poorly explored in the recent literature. The topic intersection might create novelties in the field.

Unlocking exosome therapeutics: The critical role of pharmacokinetics in clinical applications

Exosomes are microscopic vesicles released by cells that transport various biological materials and play a vital role in intercellular communication. When they are engineered, they serve as efficient delivery systems for therapeutic agents, making it possible to precisely deliver active pharmaceutical ingredients to organs, tissues, and cells. Exosomes' pharmacokinetics, or how they are transported and metabolized inside the body, is affected by several factors, including their source of origination and the proteins in their cell membranes. The pharmacokinetics and mobility of both native and modified exosomes are being observed in living organisms using advanced imaging modalities such as in vitro-in vivo simulation, magnetic resonance imaging, and positron emission tomography. Establishing comprehensive criteria for the investigation of exosomal pharmacokinetic is essential, given its increasing significance in both therapy and diagnostics. To obtain a thorough understanding of exosome intake, distribution, metabolism, and excretion, molecular imaging methods are crucial. The development of industrial processes and therapeutic applications depends on the precise measurement of exosome concentration in biological samples. To ensure a seamless incorporation of exosomes into clinical practice, as their role in therapeutics grows, it is imperative to conduct a complete assessment of their pharmacokinetics. This review provides a brief on how exosome-based research is evolving and the need for pharmacokinetic consideration to realize the full potential of these promising new therapeutic approaches.

Exopolysaccharide-selenium composite nanoparticle: Characterization, antioxidant properties and selenium release kinetics in simulated gastrointestinal conditions

An exopolysaccharide-selenium nanoparticles (EPS-SeNPs) was successfully synthesized by conjugating with Weissella confusa EPS through the reduction of SeO32-. The EPS-SeNPs composite was comprehensively characterized. These analyses confirmed that the EPS-SeNPs composite had an amorphous nature and a uniform size distribution of around 100 nm. The OH groups in EPS interacted with SeNPs, replacing intermolecular interactions in native EPS, which resulted in the stable dispersion of SeNPs within the EPS network. Furthermore, compared to native EPS, EPS-SeNPs with varying Se/EPS ratios demonstrated enhanced radical scavenging capabilities against ABTS, DPPH, superoxide anion radical (O2-), H2O2, and hydroxyl group radicals (OH·). This suggests that the conjugation of SeNP improved the antioxidant properties of EPS. Furthermore, the investigation delved into the dynamics and mechanism of selenium liberation from EPS-SeNPs under simulated gastric (SGF) and intestinal fluids (SIF). The EPS-SeNPs experienced a decrease in particle size from 223.03 ± 1.67 nm to 98.40 ± 5.57 nm. The release kinetics of selenium in SIF followed a conventional Fickian diffusion pattern. Notably, EPS-SeNPs demonstrated significant Se release following SIF digestion while exhibiting minimal release after SGF digestion, indicating their potential use as a controlled-release selenium-enriched supplement for addressing selenium deficiency.

Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques

OBJECTIVE

Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.

METHODS

First, fast and high-quality unshielded imaging is achieved using active electromagnetic shielding and basic super-resolution. Then, the speed of basic super-resolution imaging is further improved by reducing the number of excitations. Next, the feasibility of using cross-field super-resolution to map low-field low-resolution images to high-field ultra-high-resolution images is analyzed. Finally, by cascading basic and cross-field super-resolution, the quality of the low-field low-resolution image is improved to the level of the high-field ultra-high-resolution image.

RESULTS

Under unshielded conditions, our 0.2 T scanner can achieve image quality comparable to that of a 1.5 T scanner (acquisition resolution of 512 × 512, spatial resolution of 0.45 mm2), and a single-orientation imaging time of less than 3.3 min.

DISCUSSION

The proposed strategy overcomes the physical limitations of the hardware and rapidly acquires images close to the high-field level on a low-field unshielded MRI scanner. These findings have significant practical implications for the advances in MRI technology, supporting the shift from conventional scanners to point-of-care imaging systems.

Comprehensive review of 3D printing techniques emphasizing thermal characterization in biomedical prototyping

The rapid advancement of 3D printing technology has revolutionized biomedical engineering, enabling the creation of complex and personalized prototypes. Thermal properties play a crucial role in the performance and safety of these biomedical devices. Understanding their thermal behavior is essential for ensuring their effectiveness, reliability, and compatibility with the human body. This review article aims to provide a comprehensive overview of the thermal properties of 3D printed biomedical prototypes. It categorizes these prototypes based on thermal characteristics, examines the thermal attributes of various 3D printing materials, explores the thermal considerations for different biomedical devices, and identifies the challenges and future prospects in this dynamic field.

Multimodal convolutional neural networks for the prediction of acute kidney injury in the intensive care

Increased monitoring of health-related data for ICU patients holds great potential for the early prediction of medical outcomes. Research on whether the use of clinical notes and concepts from knowledge bases can improve the performance of prediction models is limited. We investigated the effects of combining clinical variables, clinical notes, and clinical concepts. We focus on the early prediction of Acute Kidney Injury (AKI) in the intensive care unit (ICU). AKI is a sudden reduction in kidney function measured by increased serum creatinine (SCr) or decreased urine output. AKI may occur in up to 30% of ICU stays. We developed three models based on convolutional neural networks using data from the Medical Information Mart for Intensive Care (MIMIC) database. The models used clinical variables, free-text notes, and concepts from the Elsevier H-Graph. Our models achieved good predictive performance (AUROC 0.73-0.90). These models were assessed both when using Scr and urine output as predictors and when omitting them. When Scr and urine output were used as predictors, models that included clinical notes and concepts together with clinical variables performed on par with models that only used clinical variables. When excluding SCr and urine output, predictive performance improved by combining multiple modalities. The models that used only clinical variables were externally validated on the eICU dataset and transported fairly to the new population (AUROC 0.68-0.77). Our in-depth comparison of modalities and text representations may further guide researchers and practitioners in applying multimodal models for predicting AKI and inspire them to investigate multimodality and contextualized embeddings for other tasks. Our models can support clinicians to promptly recognize and treat deteriorating AKI patients and may improve patient outcomes in the ICU.

Solid-state NMR protocols for unveiling dynamics and (drug) interactions of membrane-bound proteins

Magic angle spinning solid-state NMR (MAS ssNMR) is a versatile tool for studying the structure and dynamics of membrane proteins, as well as their interactions with ligands and drugs. Its power lies in the ability to provide atomic-level information on samples under physiological-like conditions. Moreover, it can illuminate dynamics across a wide range of timescales with great relevance to membrane protein function and dysfunction. In this protocol paper, we highlight key aspects of sample preparation, data acquisition, and interpretation, based on our own experience and the broader literature. We discuss key protocol steps along with important considerations for sample preparation and parameters for ssNMR measurements, with reference to the special requirements of membrane-based samples. Such samples display physiologically relevant dynamics across different motional regimes that can be probed by NMR but also can interfere with certain NMR measurements. We guide the reader through the whole process from sample preparation to complex NMR characterization techniques. Throughout the report, we refer back to examples from our own prior work on the interactions between cytochrome c and cardiolipin-containing membranes, with a discussion of the lipid dependence and interactions with a peroxidase-activity inhibitor. We conclude with a short discussion of alternative and new methods that are further boosting the power and versatility of ssNMR as a tool to study membrane-bound proteins and their ligands or drug interactions.

Biomaterial scaffold stiffness influences the foreign body reaction, tissue stiffness, angiogenesis and neuroregeneration in spinal cord injury

Biomaterial scaffold engineering presents great potential in promoting axonal regrowth after spinal cord injury (SCI), yet persistent challenges remain, including the surrounding host foreign body reaction and improper host-implant integration. Recent advances in mechanobiology spark interest in optimizing the mechanical properties of biomaterial scaffolds to alleviate the foreign body reaction and facilitate seamless integration. The impact of scaffold stiffness on injured spinal cords has not been thoroughly investigated. Herein, we introduce stiffness-varied alginate anisotropic capillary hydrogel scaffolds implanted into adult rat C5 spinal cords post-lateral hemisection. Four weeks post-implantation, scaffolds with a stiffness approaching that of the spinal cord effectively minimize the host foreign body reaction via yes-associated protein (YAP) nuclear translocation. Concurrently, the softest scaffolds maximize cell infiltration and angiogenesis, fostering significant axonal regrowth but limiting the rostral-caudal linear growth. Furthermore, as measured by atomic force microscopy (AFM), the surrounding spinal cord softens when in contact with the stiffest scaffold while maintaining a physiological level in contact with the softest one. In conclusion, our findings underscore the pivotal role of stiffness in scaffold engineering for SCI in vivo, paving the way for the optimal development of efficacious biomaterial scaffolds for tissue engineering in the central nervous system.

Recent progress in polysaccharide microsphere-based hemostatic material for intravascular and extravascular hemostasis: A review

Hemorrhage, a common consequence of diseases, surgical procedures, and traffic accidents, poses a significant threat to public health. Effective hemostasis is crucial for patient survival and prognosis, particular in case of internal bleeding. While polysaccharide microsphere-based hemostatic materials have gained clinical acceptance due to their effectiveness, good biocompatibility, and versatility in both intravascular and extravascular hemostasis, they are limited by their single function and insufficient hemostatic properties. Recently, booming developments have been witnessed in microsphere-based biomaterials to achieve a combination therapy for hemostasis. This review first examines the fundamentals of coagulation process, hemostatic mechanisms, and microsphere fabrication techniques. We then discuss the latest investigations in functionalized microsphere-based hemostatic materials for controlling intravascular and extravascular hemorrhage, focusing on design strategies, hemostatic properties, and clinical implementation. Finally, we also propose some limitations and challenges of these hemostatic materials, aiming to provide valuable insights for future research in novel polysaccharide microsphere-based biomaterial.

Identification and validation of lncRNA mutation hotspot SNPs associated with myasthenia gravis susceptibility

Background

Myasthenia gravis (MG) is an autoimmune disorder caused by antibodies that target the postsynaptic muscle membrane. Recent evidence suggests that genetic variants and long noncoding RNAs (lncRNAs) play crucial roles in the pathogenesis of MG. The purpose of this study was to investigate the associations between lncRNA-related single-nucleotide polymorphisms (SNPs) and MG susceptibility in Chinese populations.

Methods

First, we identified lncRNA mutation hotspot regions based on the improved Kolmogorov‒Smirnov test and the cumulative hypergeometric distribution principle. Next, we further identified lncRNA mutation hotspot SNPs by calculating conservative scores. Finally, experiments were conducted to verify the associations between lncRNA mutation hotspot SNPs and MG susceptibility. A total of 82 patients with MG and 82 healthy controls were recruited for genotyping of lncRNA mutation hotspot SNPs using the SNaPshot technique. Quantitative real-time PCR was used to investigate lncRNA expression in 34 patients with MG and 37 healthy controls.

Results

In the multistep calculation, 14 candidate SNPs of 3 lncRNAs (AL031686.1, NONHSAT028539.2 and AC245014.3) in MG were identified as mutation hotspot SNPs. The genotyping results of the 14 SNPs in our study revealed no statistically significant differences in the frequencies of genotypes and alleles between patients with MG and controls. However, in the lncRNA AL031686.1, rs1000383 and rs6094353 were in perfect linkage disequilibrium (LD) and were associated with an increased risk of ocular MG. Additionally, rs6094347 was associated with an increased risk of ocular MG. Nevertheless, no SNP was found to be associated with factors such as sex, age, the presence or absence of thymoma, or the genetic model of MG. Further experiments revealed that NONHSAT028539.2 expression was upregulated in peripheral blood mononuclear cells (PBMCs) from patients with MG compared with those from healthy controls.

Conclusion

In our study, we did not find an association between the 14 mutation hotspot SNPs of lncRNAs and susceptibility to MG. However, we observed that the rs6094347 and rs1000383/rs6094353 polymorphisms in the lncRNA AL031686.1 were associated with the risk of ocular MG.