The effect of EEG microstate on the characteristics of TMS-EEG

OBJECTIVE

Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach.

APPROACH

TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates.

MAIN RESULTS

The N100 component of microstate C group (-2.376 μV) was significantly higher (p = 0.003) than of microstate D group (-1.739 μV), and the P180 component of microstate D group (2.482 μV) was significantly higher (p = 0.024) than of microstate B group (1.766 μV) and slightly higher (p = 0.058) than of microstate C group (1.863 μV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates.

SIGNIFICANCE

This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.

Engineering hybrid lantibiotics yields the highly stable and bacteriocidal peptide cerocin V

Antimicrobial peptides (AMPs) show promise as alternatives to traditional antibiotics for treating drug-resistant infections. Their adaptability and diverse sequence possibilities allow for rational design by modulating physicochemical determinants to achieve desired biological properties, transforming them into peptides for potential new therapies. Nisin, one of the best-studied AMPs, is believed to have potential to be used as a therapeutic, particularly against antibiotic-resistant bacteria. However, its instability in physiological conditions limits its use in clinical applications and pharmaceutical development. Exploration of new natural variants of nisin has uncovered diverse properties using different domains. Shuffling peptide modules can fine-tune the chemical properties of these molecules, potentially enhancing stability while maintaining or improving antimicrobial activity. In this study, hybrid AMPs were created by combining domains from three unique nisin variants, i.e. nisin A, cesin and rombocin, leading to the identification of a promising variant, named cerocin A, which harbours only 25 amino acids compared to the typical 31-35 amino acid length of nisin. Cerocin A demonstrates potent antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA), approaching that of nisin itself. Cerocin A's mode of action involves a dual mechanism through the combination of two domains, consisting of a small ring/domain (6 amino acids) from the C-terminal end of rombocin attached to the preceding peptide of cesin, changing it from a bacteriostatic to a bactericidal peptide. Further mutation studies identified a new variant, cerocin V, with significantly improved resistance against trypsin degradation, while maintaining high potency. Importantly, cerocin V showed no undesired toxic effects on human red blood cells and remained stable in human plasma. In conclusion, we demonstrate that peptide construction using domain engineering is an effective strategy for manipulating both biological and physicochemical aspects, leading to the creation of novel bioactive molecules with desired properties. These constructs are appealing candidates for further optimization and development as novel antibiotics.

The anti-HIV drug abacavir stimulates β-catenin activity in osteoblast lineage cells

Bone mineral density (BMD) loss in people living with HIV occurs with the initiation of combined antiretroviral therapy (cART), particularly with tenofovir disoproxil fumarate (TDF) containing cART. Switching from TDF to abacavir (ABC) or dolutegravir (DTG) leads to increased BMD. Whether BMD gains are due to cessation of TDF or anabolic effects of ABC or DTG is unclear. We investigated the effects of ABC and DTG on osteoblast lineage cells in vitro and in vivo. Primary human osteoblasts and male C57BL/6 mice were treated with individual antiretrovirals (ARVs) or a combination of ABC/DTG/lamivudine (3TC). Nearly all ARVs and cART inhibited osteogenic activity in vitro. Due to the importance of Wnt/β-catenin in bone formation, we further investigated ARV effects on the Wnt/β-catenin pathway. ABC, alone and as part of ABC/DTG/3TC, increased osteoblastic β-catenin activity as indicated by increased TOPFlash activity, hypo-phosphorylated (active) β-catenin staining, and β-catenin targeted gene expression. Mice treated with TDF had decreased lumbar spine BMD and trabecular connectivity density in the vertebrae, while those treated with ABC/DTG/3TC reduced cortical area and thickness in the femur. Mice treated with ABC alone had no bone structural changes, increased circulating levels of the bone formation marker, P1NP, and elevated expression of the Wnt/β-catenin target gene, Lef1, in osteocyte enriched samples. Further, bones from ARV-treated mice were isolated to evaluate ARV distribution. All ARVs were detected in the bone tissue, which was inclusive of bone marrow, but when bone marrow was removed, only TDF, ABC, and DTG were detected at ~0.1% of the circulating levels. Overall, our findings demonstrate that ABC activates Wnt/β-catenin signaling, but whether this leads to increased bone formation requires further study. Assessing the impact of ARVs on bone is critical to informing ARV selection and/or discovery of regimens that do not negatively impact the skeleton.

The Application of Shear Wave Elastography to Determine the Elasticity of the Levator Ani Muscle and Vaginal Tissue in Patients With Pelvic Organ Prolapse

OBJECTIVES

The changes of the extracellular matrix of the connective tissue have significantly contributed to the incidence of pelvic organ prolapse (POP). It seems reasonable that sonoelastography could be a useful tool to evaluate the elasticity of pelvic floor tissue in patients with POP and compare it to those without POP. The main aim of this pilot study was to determine if there are differences in the elasticity of the levator ani muscle (LAM) and vaginal tissue between patients with and without POP.

METHODS

Prospective observation study, including 60 patients (30 with POP and 30 without POP). Sonoelastography was performed to evaluate the elasticity (in kilopascals, kPa) of the following regions of interest: vagina at the level of middle third of the urethra; vagina at the level of the bladder trigone; vagina in the anterior and posterior fornix; vagina at the level of middle third of the anorectal canal; posterior third of the LAM.

RESULTS

A total of 60 patients completed the study (30 with POP, 30 without POP). In the POP group, 18/30 (60%) had an anterior vaginal wall prolapse, 3/30 (10%) a uterine prolapse, 15/30 (50%) a rectocele, and 6/30 (20%) a enterocele. Patients with POP had higher elasticity in all anatomical study areas, with statistically significant differences in the anterior fornix (13.6 vs 11.2 kPa; P: .012). A multiple regression (controlling age, menopausal stage, and parity) allowed to detect statistically significant differences in the elasticity of the middle third of the urethra (P: .03) and the middle third of the anorectal canal (P: .019).

CONCLUSION

It is possible to evaluate the elasticity of the LAM and vaginal tissue using sonoelastography, detecting a higher elasticity in patients with POP than in those without POP.

Hemodynamics and Wall Mechanics of Vascular Graft Failure

Blood vessels are subjected to complex biomechanical loads, primarily from pressure-driven blood flow. Abnormal loading associated with vascular grafts, arising from altered hemodynamics or wall mechanics, can cause acute and progressive vascular failure and end-organ dysfunction. Perturbations to mechanobiological stimuli experienced by vascular cells contribute to remodeling of the vascular wall via activation of mechanosensitive signaling pathways and subsequent changes in gene expression and associated turnover of cells and extracellular matrix. In this review, we outline experimental and computational tools used to quantify metrics of biomechanical loading in vascular grafts and highlight those that show potential in predicting graft failure for diverse disease contexts. We include metrics derived from both fluid and solid mechanics that drive feedback loops between mechanobiological processes and changes in the biomechanical state that govern the natural history of vascular grafts. As illustrative examples, we consider application-specific coronary artery bypass grafts, peripheral vascular grafts, and tissue-engineered vascular grafts for congenital heart surgery as each of these involves unique circulatory environments, loading magnitudes, and graft materials.

CGO-ensemble: Chaos game optimization algorithm-based fusion of deep neural networks for accurate Mpox detection

The rising global incidence of human Mpox cases necessitates prompt and accurate identification for effective disease control. Previous studies have predominantly delved into traditional ensemble methods for detection, we introduce a novel approach by leveraging a metaheuristic-based ensemble framework. In this research, we present an innovative CGO-Ensemble framework designed to elevate the accuracy of detecting Mpox infection in patients. Initially, we employ five transfer learning base models that integrate feature integration layers and residual blocks. These components play a crucial role in capturing significant features from the skin images, thereby enhancing the models' efficacy. In the next step, we employ a weighted averaging scheme to consolidate predictions generated by distinct models. To achieve the optimal allocation of weights for each base model in the ensemble process, we leverage the Chaos Game Optimization (CGO) algorithm. This strategic weight assignment enhances classification outcomes considerably, surpassing the performance of randomly assigned weights. Implementing this approach yields notably enhanced prediction accuracy compared to using individual models. We evaluate the effectiveness of our proposed approach through comprehensive experiments conducted on two widely recognized benchmark datasets: the Mpox Skin Lesion Dataset (MSLD) and the Mpox Skin Image Dataset (MSID). To gain insights into the decision-making process of the base models, we have performed Gradient Class Activation Mapping (Grad-CAM) analysis. The experimental results showcase the outstanding performance of the CGO-ensemble, achieving an impressive accuracy of 100% on MSLD and 94.16% on MSID. Our approach significantly outperforms other state-of-the-art optimization algorithms, traditional ensemble methods, and existing techniques in the context of Mpox detection on these datasets. These findings underscore the effectiveness and superiority of the CGO-Ensemble in accurately identifying Mpox cases, highlighting its potential in disease detection and classification.

Obstetric risk factors for levator ani muscle avulsion: A systematic review and meta-analysis

OBJECTIVES

Women have a 11% lifetime risk of undergoing surgery for vaginal prolapse. Levator ani muscle (LAM) avulsion is one etiological factor associated with primary and recurrent pelvic organ prolapse. Pelvic organ prolapse has been shown to greatly affect the quality of life and well-being of women. Conduct a meta-analysis identifying risk factors associated with LAM avulsion recognised on transperineal ultrasound (TPUS) or magnetic resonance imaging (MRI) in primiparous women after vaginal birth.

STUDY DESIGN

OVID Medline, Embase and the Cochrane Library from inception to January 2021 were searched. Review Manager 5.3 (The Cochrane Collaboration) was used to analyse data. Odds ratios (OR) with 95% confidence intervals (95% CIs) were calculated. The heterogeneity among studies was calculated using the I2statistic.

RESULTS

Twenty-five studies were eligible for inclusion (n = 9333 women). Major LAM avulsion was diagnosed in an average of 22 % (range 12.7-39.5 %) of cases. Twenty-two studies used TPUS and three used MRI to diagnose avulsion. Modifiable and non-modifiable risk factors were identified. Significant predictors identified were forceps (OR 6.25 [4.33 - 9.0]), obstetric anal sphincter injuries (OR 3.93 [2.85-5.42]), vacuum (OR 2.41 [1.40-4.16]), and maternal age (OR 1.06 [1.02-1.10]).

CONCLUSIONS

This is the first meta-analysis of both modifiable and non-modifiable risk factors associated with LAM avulsion. This information could be used to develop a clinically applicable risk prediction model to target postnatal women at risk of LAM avulsion with a view to prevent the onset of pelvic floor organ prolapse.

Trabecular bone volume fraction in Holocene and Late Pleistocene humans

Research suggests that recent modern humans have gracile skeletons in having low trabecular bone volume fraction (BV/TV) and that gracilization of the skeleton occurred in the last 10,000 years. This has been attributed to a reduction in physical activity in the Holocene. However, there has been no thorough sampling of BV/TV in Pleistocene humans due to limited access to high resolution images of fossil specimens. Therefore, our study investigates the gracilization of BV/TV in Late Pleistocene humans and recent (Holocene) modern humans to improve our understanding of the emergence of gracility. We used microcomputed tomography to measure BV/TV in the femora, humeri and metacarpals of a sample of Late Pleistocene humans from Dolní Věstonice (Czech Republic, ∼26 ka, n = 6) and Ohalo II (Israel, ∼19 ka, n = 1), and a sample of recent humans including farming groups (n = 39) and hunter-gatherers (n = 6). We predicted that 1) Late Pleistocene humans would exhibit greater femoral and humeral head BV/TV compared with recent humans and 2) among recent humans, metacarpal head BV/TV would be greater in hunter-gatherers compared with farmers. Late Pleistocene humans had higher BV/TV compared with recent humans in both the femur and humerus, supporting our first prediction, and consistent with previous findings that Late Pleistocene humans are robust as compared to recent humans. However, among recent humans, there was no significant difference in BV/TV in the metacarpals between the two subsistence groups. The results highlight the similarity in BV/TV in the hand of two human groups from different geographic locales and subsistence patterns and raise questions about assumptions of activity levels in archaeological populations and their relationships to trabecular BV/TV.

Apoptosis Induction by ZnFe2O4-Ag Biosynthesized by Chlorella vulgaris in MCF-7 Breast Cancer Cell Line

The incidence and mortality of breast cancer are growing which indicates the inefficiency of the current chemotherapy drugs. Due to the anticancer potential of Zn and Ag and the magnetic feature of iron oxide, in this work, we synthesized ZnFe2O4-Ag nanocomposite using Chlorella vulgaris and investigated its anticancer effect on breast cancer cell line. Physicochemical characterization was performed by FT-IR, XRD, SEM, TEM, VSM, EDS mapping, UV, and zeta potential assays. Cell cytotoxicity and apoptosis frequency were studied by the MTT and flow cytometry assays. Also, cell cycle analysis, Hoechst staining, and measuring ROS (reactive oxygen species) level were performed. The synthesized particles were almost spherical with a size range of 14-52 nm. The FT-IR and XRD assays confirmed the proper synthesis of the particles and VSM analysis showed that particles had magnetic property and the maximum saturation magnetization was 0.8 Emu/g. Also, the EDS mapping of the nanocomposite showed the Zn, Fe, O, and Ag elements. The MTT assay showed that the 50% inhibitory concentration (IC50) of ZnFe2O4-Ag for breast cancer and normal cells were 28 and 154 µg/mL, respectively, and the nanocomposite had stronger anticancer activity than cisplatin (IC50 = 84 µg/mL). Flow cytometry analysis showed that the exposure to the nanocomposite induced cell apoptosis by 77.5% and significantly induced ROS generation. Also, treating breast cancer cells with the nanocomposite induced cell cycle arrest and apoptotic features, including chromatin condensation and fragmentation. In conclusion, ZnFe2O4-Ag nanocomposite synthesized by C. vulgaris could suppress the proliferation of breast cancer cells by the generation of oxidative stress, apoptosis induction, and cell cycle arrest.

Next generation probiotics: Engineering live biotherapeutics

The population dynamics of the human microbiome have been associated with inflammatory bowel disease, cancer, obesity, autoimmune diseases, and many other human disease states. An emerging paradigm in treatment is the administration of live engineered organisms, also called next-generation probiotics. However, the efficacy of these microbial therapies can be limited by the organism's overall performance in the harsh and nutrient-limited environment of the gut. In this review, we summarize the current state of the art use of bacterial and yeast strains as probiotics, highlight the recent development of genetic tools for engineering new therapeutic functions in these organisms, and report on the latest therapeutic applications of engineered probiotics, including recent clinical trials. We also discuss the supplementation of prebiotics as a method of manipulating the microbiome and improving the overall performance of engineered live biotherapeutics.

Is RANKL a potential molecular target in osteoarthritis?

OBJECTIVE

Osteoarthritis (OA) is a disease of joints, in which the bone under the articular cartilage undergoes increased remodelling activity. The question is whether a better understanding of the causes and mechanisms of bone remodelling can predict disease-modifying treatments.

DESIGN

This review summarises the current understanding of the aetiology of OA, with an emphasis on events in the subchondral bone (SCB), and the cells and cytokines involved, to seek an answer to this question.

RESULTS

SCB remodelling across OA changes the microstructure of the SCB, which alters the load-bearing properties of the joint and seems to have an important role in the initiation and progression of OA. Bone remodelling is tightly controlled by numerous cytokines, of which Receptor Activator of NFκB ligand (RANKL) and osteoprotegerin are central factors in almost all known bone conditions. In terms of finding therapeutic options for OA, an important question is whether controlling the rate of SCB remodelling would be beneficial. The role of RANKL in the pathogenesis and progression of OA and the effect of its neutralisation remain to be clarified.

CONCLUSIONS

This review further makes the case for SCB remodelling as important in OA and for additional study of RANKL in OA, both its pathophysiological role and its potential as an OA disease target.

Non-viral delivery of RNA for therapeutic T cell engineering

Adoptive T cell transfer has shown great success in treating blood cancers, resulting in a growing number of FDA-approved therapies using chimeric antigen receptor (CAR)-engineered T cells. However, the effectiveness of this treatment for solid tumors is still not satisfactory, emphasizing the need for improved T cell engineering strategies and combination approaches. Currently, CAR T cells are mainly manufactured using gammaretroviral and lentiviral vectors due to their high transduction efficiency. However, there are concerns about their safety, the high cost of producing them in compliance with current Good Manufacturing Practices (cGMP), regulatory obstacles, and limited cargo capacity, which limit the broader use of engineered T cell therapies. To overcome these limitations, researchers have explored non-viral approaches, such as membrane permeabilization and carrier-mediated methods, as more versatile and sustainable alternatives for next-generation T cell engineering. Non-viral delivery methods can be designed to transport a wide range of molecules, including RNA, which allows for more controlled and safe modulation of T cell phenotype and function. In this review, we provide an overview of non-viral RNA delivery in adoptive T cell therapy. We first define the different types of RNA therapeutics, highlighting recent advancements in manufacturing for their therapeutic use. We then discuss the challenges associated with achieving effective RNA delivery in T cells. Next, we provide an overview of current and emerging technologies for delivering RNA into T cells. Finally, we discuss ongoing preclinical and clinical studies involving RNA-modified T cells.

Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy

PURPOSE

Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study.

METHODS

We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions).

RESULTS

The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses.

CONCLUSION

A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.

Automated assessment of pelvic radiographs using deep learning: A reliable diagnostic tool for pelvic malalignment

Pelvic malalignment leads to general imbalance and adversely affects leg length. Timely and accurate diagnosis of pelvic alignment in patients is crucial to prevent additional complications arising from delayed treatment. Currently, doctors typically assess pelvic alignment either manually or through radiography. This study aimed to develop and assess the validity of a deep learning-based system for automatically measuring 10 radiographic parameters necessary for diagnosing pelvic displacement using standing anteroposterior pelvic X-rays. Between March 2016 and June 2021, pelvic radiographs from 1215 patients were collected. After applying specific selection criteria, 550 pelvic radiographs were chosen for analysis. These data were utilized to develop a deep learning-based system capable of automatically measuring radiographic parameters relevant to pelvic displacement diagnosis. The system's diagnostic accuracy was evaluated by comparing automatically measured values with those assessed by a clinician using 200 radiographs selected from the initial 550. The results indicated that the system exhibited high reliability, accuracy, and reproducibility, with a Pearson correlation coefficient of ≥0.9, an intra-class correlation coefficient of ≥0.9, a mean absolute error of ≤1 cm, mean square error of ≤1 cm, and root mean square error of ≤1 cm. Moreover, the system's measurement time for a single radiograph was found to be 18 to 20 times faster than that required by a clinician for manual inspection. In conclusion, our proposed deep learning-based system effectively utilizes standing anteroposterior pelvic radiographs to precisely and consistently measure radiographic parameters essential for diagnosing pelvic displacement.

A comprehensive review on the biomedical frontiers of nanowire applications

This comprehensive review examines the immense capacity of nanowires, nanostructures characterized by unbounded dimensions, to profoundly transform the field of biomedicine. Nanowires, which are created by combining several materials using techniques such as electrospinning and vapor deposition, possess distinct mechanical, optical, and electrical properties. As a result, they are well-suited for use in nanoscale electronic devices, drug delivery systems, chemical sensors, and other applications. The utilization of techniques such as the vapor-liquid-solid (VLS) approach and template-assisted approaches enables the achievement of precision in synthesis. This precision allows for the customization of characteristics, which in turn enables the capability of intracellular sensing and accurate drug administration. Nanowires exhibit potential in biomedical imaging, neural interfacing, and tissue engineering, despite obstacles related to biocompatibility and scalable manufacturing. They possess multifunctional capabilities that have the potential to greatly influence the intersection of nanotechnology and healthcare. Surmounting present obstacles has the potential to unleash the complete capabilities of nanowires, leading to significant improvements in diagnostics, biosensing, regenerative medicine, and next-generation point-of-care medicines.

Influence of the internal structure of straight microchannels on inertial transport behavior of particles

The rapid advancement of Micro-Electro-Mechanical Systems (MEMS) technology has established microfluidics as a pivotal field. This technology marks the onset of a new era in various applications, including drug testing, cell culture, and disease monitoring, underscoring its extensive practicality and potential for future exploration. This research delves into the intricate behavior of particle inertial migration within microchannels, particularly focusing on the impact of different channel structures and Reynolds numbers (Re). Our studies reveal that particles in microchannels with one-sided sharp-cornered microstructures migrate towards the sharp corner at a relative position of 0.4 under low flow rates, and towards the straight wall side at a relative position of 0.8 under high flow rates. The migration pattern of equilibrium positions varies with different arrangements of sharp-corner structures, achieving stability at the channel's center only when the sharp corners are symmetrically arranged on both sides. Our investigation into the shape of microstructures indicates that sharp-cornered structures generate a more stable secondary flow compared to rectangular and semi-circular structures, preventing particle aggregation at the outlet. To address the challenges associated with handling variable cross-section geometries and solid-wall boundaries in dissipative particle dynamics methods effectively, we have developed a dissipative particle dynamics model specifically for analyzing such microchannels. Building upon our previous research, this model introduces a conservative force coefficient for particles within the microstructured region and an interaction zone that only involves repulsive forces, aligning well with experimental outcomes. Through the study of microstructures' geometric shapes, this paper offers guidance for designing microchannels for particle enrichment. Furthermore, the dissipative particle dynamics model established for the particle flow and solid structure interaction within microstructured channels provides insights into the mesoscale dynamics of liquid-solid two-phase flow and particle motion. In conclusion, this paper aims to enhance particle motion sample preparation techniques, thereby broadening the scope of microfluidic applications in the biomedical field.

Nanobody-mediated targeting of zinc phthalocyanine with polymer micelles as nanocarriers

Photodynamic therapy (PDT) is a suitable alternative to currently employed cancer treatments. However, the hydrophobicity of most photosensitizers (e.g., zinc phthalocyanine (ZnPC)) leads to their aggregation in blood. Moreover, non-specific accumulation in skin and low clearance rate of ZnPC leads to long-lasting skin photosensitization, forcing patients with a short life expectancy to remain indoors. Consequently, the clinical implementation of these photosensitizers is limited. Here, benzyl-poly(ε-caprolactone)-b-poly(ethylene glycol) micelles encapsulating ZnPC (ZnPC-M) were investigated to increase the solubility of ZnPC and its specificity towards cancers cells. Asymmetric flow field-flow fractionation was used to characterize micelles with different ZnPC-to-polymer ratios and their stability in human plasma. The ZnPC-M with the lowest payload (0.2 and 0.4% ZnPC w/w) were the most stable in plasma, exhibiting minimal ZnPC transfer to lipoproteins, and induced the highest phototoxicity in three cancer cell lines. Nanobodies (Nbs) with binding specificity towards hepatocyte growth factor receptor (MET) or epidermal growth factor receptor (EGFR) were conjugated to ZnPC-M to facilitate cell targeting and internalization. MET- and EGFR-targeting micelles enhanced the association and the phototoxicity in cells expressing the target receptor. Altogether, these results indicate that ZnPC-M decorated with Nbs targeting overexpressed proteins on cancer cells may provide a better alternative to currently approved formulations.

Studying the ssDNA loaded adeno-associated virus aggregation using coarse-grained molecular dynamics simulations

The aggregation of adeno-associated viral (AAV) capsids in an aqueous environment was investigated via coarse-grained molecular dynamics (CG-MD) simulations. The primary driving force and mechanism of the aggregation were investigated with or without single-strand DNA (ssDNA) loaded at various process temperatures. Capsid aggregation appeared to involve multiple residue interactions (i.e., hydrophobic, polar and charged residues) leading to complex protein aggregation. In addition, two aggregation mechanisms (i.e., the fivefold face-to-face contact and the edge-to-edge contact) were identified from this study. The ssDNA with its asymmetric structure could be the reason for destabilizing protein subunits and enhancing the interaction between the charged residues, and further result in the non-reversible face-to-face contact. At higher temperature, the capsid structure was found to be unstable with the significant size expansion of the loaded ssDNA which could be attributed to reduced number of intramolecular hydrogen bonds, the increased conformational deviations of protein subunits and the higher residue fluctuations. The CG-MD model was further validated with previous experimental and simulation data, including the full capsid size measurement and the capsid internal pressure. Thus, a good understanding of AAV capsid aggregation, instability and the role of ssDNA were revealed by applying the developed computational model.

Evaluation of the effects of zinc oxide (ZnO NPs) nanoparticles synthesized by green synthesis on Caenorhabditis elegans

In recent years, the rapid development of nanotechnology has caused the products obtained with this technology to be used more daily. Information on the effects of these products, which provide great advantages in every respect, on human health and the environment is insufficient. It has been suggested that these nanoparticles may have toxic effects on living things, mostly in animal experiments and cell cultures. In this paper, the organism Caenorhabditis elegans (C. elegans), which contains a genome and biochemical ways highly similar to humans, is used to understand and reveal the metabolism of Zinc oxide nanoparticles (ZnO NPs) toxicological effects. The toxicological effects of ZnO NPs on C. elegans organisms were investigated and the results were evaluated in terms of environment and human health. C. elegans was exposed to commercial ZnO NPs and green synthesized ZnO NPs from Olea europaea (olive tree, OLE). LC50 values were determined by probit analysis (green synthesized ZnO NP LC5024h = 84.97 mg/L, LC5072h = 33.27 mg/L, commercial ZnO NPs LC5024h = 5.75 mg/L, LC5072h = 1.91 mg/L). When the survival times of C. elegans were evaluated by the Kaplan-Meier method, it was seen that commercial ZnO NPs were more toxic than green synthesized ZnO NPs. In MTT tests, it was clearly seen that commercial ZnO NPs and green synthesized ZnO NPs entered the cell and caused different cytotoxicity. While there was a difference between control and 0.5, 2.5, 5, 10, 25, and 50 mg/L doses in commercial ZnO NP applications, there were significant differences between control and 25, 50 mg/L concentrations in green synthesized ZnO NP applications.

Analysis and prediction of interactions between transmembrane and non-transmembrane proteins

BACKGROUND

Most of the important biological mechanisms and functions of transmembrane proteins (TMPs) are realized through their interactions with non-transmembrane proteins(nonTMPs). The interactions between TMPs and nonTMPs in cells play vital roles in intracellular signaling, energy metabolism, investigating membrane-crossing mechanisms, correlations between disease and drugs.

RESULTS

Despite the importance of TMP-nonTMP interactions, the study of them remains in the wet experimental stage, lacking specific and comprehensive studies in the field of bioinformatics. To fill this gap, we performed a comprehensive statistical analysis of known TMP-nonTMP interactions and constructed a deep learning-based predictor to identify potential interactions. The statistical analysis describes known TMP-nonTMP interactions from various perspectives, such as distributions of species and protein families, enrichment of GO and KEGG pathways, as well as hub proteins and subnetwork modules in the PPI network. The predictor implemented by an end-to-end deep learning model can identify potential interactions from protein primary sequence information. The experimental results over the independent validation demonstrated considerable prediction performance with an MCC of 0.541.

CONCLUSIONS

To our knowledge, we were the first to focus on TMP-nonTMP interactions. We comprehensively analyzed them using bioinformatics methods and predicted them via deep learning-based solely on their sequence. This research completes a key link in the protein network, benefits the understanding of protein functions, and helps in pathogenesis studies of diseases and associated drug development.

A Hybrid Model for Fetal Growth Restriction Assessment by Automatic Placental Radiomics on T2-Weighted MRI and Multifeature Fusion

BACKGROUND

MRI-based placental analyses have been used to improve fetal growth restriction (FGR) assessment by complementing ultrasound-based measurements. However, these are still limited by time-consuming manual annotation in MRI data and the lack of mother-based information.

PURPOSE

To develop and validate a hybrid model for accurate FGR assessment by automatic placental radiomics on T2-weighted imaging (T2WI) and multifeature fusion.

STUDY TYPE

Retrospective.

POPULATION

274 pregnant women (29.5 ± $$ \pm $$  4.0 years) from two centers were included and randomly divided into training (N = 119), internal test (N = 40), time-independent validation (N = 43), and external validation (N = 72) sets.

FIELD STRENGTH/SEQUENCE

1.5-T, T2WI half-Fourier acquisition single-shot turbo spin-echo pulse sequence.

ASSESSMENT

First, the placentas on T2WI were manually annotated, and a deep learning model was developed to automatically segment the placentas. Then, the radiomic features were extracted from the placentas and selected by three-step feature selection. In addition, fetus-based measurement features and mother-based clinical features were obtained from ultrasound examinations and medical records, respectively. Finally, a hybrid model based on random forest was constructed by fusing these features, and further compared with models based on other machine learning methods and different feature combinations.

STATISTICAL TESTS

The performances of placenta segmentation and FGR assessment were evaluated by Dice similarity coefficient (DSC) and the area under the receiver operating characteristic curve (AUROC), respectively. A P-value <0.05 was considered statistically significant.

RESULTS

The placentas were automatically segmented with an average DSC of 90.0%. The hybrid model achieved an AUROC of 0.923, 0.931, and 0.880 on the internal test, time-independent validation, and external validation sets, respectively. The mother-based clinical features resulted in significant performance improvements for FGR assessment.

DATA CONCLUSION

The proposed hybrid model may be able to assess FGR with high accuracy. Furthermore, information complementation based on placental, fetal, and maternal features could also lead to better FGR assessment performance.

LEVEL OF EVIDENCE: 3

TECHNICAL EFFICACY

Stage 2.

Bacteria-surface interactions: role of impacting bacteria-laden droplets

Understanding the interactions of pathogenic droplets with surfaces is crucial to biomedical applications. In this study, using E. coli as the model microbe, we investigate the impact of a bacteria-laden droplet on different substrates, both bare and antimicrobial. In doing so, we unveil the significance of kinetic energy and spreading parameters of the impacting droplet in determining the microbes' proliferation capabilities. Our results indicate an inverse relationship between the impact Weber number and the bacterial ability to proliferate. We reveal that the mechanical stress generated during impact impedes the capabilities of microbes present inside the droplet to create their progeny. Following an order analysis of the mechanical stress generated, we argue that the impact does not induce lysis-driven cell death of the bacteria; rather, it promotes a stress-driven transition of viable bacteria to a viable-but-non-culturable (VBNC) state. Furthermore, variations in the concentration of particles on the antimicrobial surfaces revealed the role of the post-impact spreading behaviour in dictating bacterial proliferation capabilities. Contrary to the conventional notion, we demonstrate that during the early stages of interaction, a bare substrate may outperform an antibacterial substrate in the inactivation of the bacterial load. Finally, we present an interaction map illustrating the complex relationship between bacterial colony-forming units, bactericide concentration on the surface, and the impact Weber number. We believe that the inferences of the study, highlighting the effect of mechanical stresses on the soft cell wall of microbes, could be a useful design consideration for the development of antimicrobial surfaces.

New semi-analytical method for fast transcranial ultrasonic field simulation

Objective.To optimize and ensure the safety of ultrasound brain therapy, personalized transcranial ultrasound simulations are very useful. They allow to predict the pressure field, depending on the patient skull and probe position. Most transcranial ultrasound simulations are based on numerical methods which have a long computation time and a high memory usage. The goal of this study is to develop a new semi-analytical field computation method that combines realism and computation speed.Approach.Instead of the classic ray tracing, the ultrasonic paths are computed by time of flight minimization. Then the pressure field is computed using the pencil method. This method requires a smooth and homogeneous skull model. The simulation algorithm, so-called SplineBeam, was numerically validated, by comparison with existing solvers, and experimentally validated by comparison with hydrophone measured pressure fields through anex vivohuman skull.Main results.SplineBeam simulated pressure fields were close to the experimentally measured ones, with a focus position difference of the order of the positioning error and a maximum pressure difference lower than 6.02%. In addition, for those configurations, SplineBeam computation time was lower than another simulation software, k-Wave's, by two orders of magnitude, thanks to its capacity to compute the field only at the focal spot.Significance.These results show the potential of this new method to compute fast and realistic transcranial pressure fields. The combination of this two assets makes it a promising tool for real time transcranial pressure field prediction during ultrasound brain therapy interventions.

On-Chip Neural Induction Boosts Neural Stem Cell Commitment: Toward a Pipeline for iPSC-Based Therapies

The clinical translation of induced pluripotent stem cells (iPSCs) holds great potential for personalized therapeutics. However, one of the main obstacles is that the current workflow to generate iPSCs is expensive, time-consuming, and requires standardization. A simplified and cost-effective microfluidic approach is presented for reprogramming fibroblasts into iPSCs and their subsequent differentiation into neural stem cells (NSCs). This method exploits microphysiological technology, providing a 100-fold reduction in reagents for reprogramming and a ninefold reduction in number of input cells. The iPSCs generated from microfluidic reprogramming of fibroblasts show upregulation of pluripotency markers and downregulation of fibroblast markers, on par with those reprogrammed in standard well-conditions. The NSCs differentiated in microfluidic chips show upregulation of neuroectodermal markers (ZIC1, PAX6, SOX1), highlighting their propensity for nervous system development. Cells obtained on conventional well plates and microfluidic chips are compared for reprogramming and neural induction by bulk RNA sequencing. Pathway enrichment analysis of NSCs from chip showed neural stem cell development enrichment and boosted commitment to neural stem cell lineage in initial phases of neural induction, attributed to a confined environment in a microfluidic chip. This method provides a cost-effective pipeline to reprogram and differentiate iPSCs for therapeutics compliant with current good manufacturing practices.

Gut microbial features may influence antiviral IgG levels after vaccination against viral respiratory infectious diseases: the evidence from two-sample bidirectional mendelian randomization

BACKGROUND

Vaccination is effective in preventing viral respiratory infectious diseases through protective antibodies and the gut microbiome has been proven to regulate human immunity. This study explores the causal correlations between gut microbial features and serum-specific antiviral immunoglobulin G (IgG) levels.

METHODS

We conduct a two-sample bidirectional Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary data to explore the causal relationships between 412 gut microbial features and four antiviral IgG (for influenza A, measles, rubella, and mumps) levels. To make the results more reliable, we used four robust methods and performed comprehensive sensitivity analyses.

RESULTS

The MR analyses revealed 26, 13, 20, and 18 causal associations of the gut microbial features influencing four IgG levels separately. ​Interestingly, ten microbial features, like genus Collinsella, species Bifidobacterium longum, and the biosynthesis of L-alanine have shown the capacity to regulate multiple IgG levels with consistent direction (rise or fall). The ​reverse MR analysis suggested several potential causal associations of IgG levels affecting microbial features.

CONCLUSIONS

The human immune response against viral respiratory infectious diseases could be modulated by changing the abundance of gut microbes, which provided new approaches for the intervention of viral respiratory infections.

ANTXR1 deficiency promotes fibroblast senescence: implications for GAPO syndrome as a progeroid disorder

ANTXR1 is one of two cell surface receptors mediating the uptake of the anthrax toxin into cells. Despite substantial research on its role in anthrax poisoning and a proposed function as a collagen receptor, ANTXR1's physiological functions remain largely undefined. Pathogenic variants in ANTXR1 lead to the rare GAPO syndrome, named for its four primary features: Growth retardation, Alopecia, Pseudoanodontia, and Optic atrophy. The disease is also associated with a complex range of other phenotypes impacting the cardiovascular, skeletal, pulmonary and nervous systems. Aberrant accumulation of extracellular matrix components and fibrosis are considered to be crucial components in the pathogenesis of GAPO syndrome, contributing to the shortened life expectancy of affected individuals. Nonetheless, the specific mechanisms connecting ANTXR1 deficiency to the clinical manifestations of GAPO syndrome are largely unexplored. In this study, we present evidence that ANTXR1 deficiency initiates a senescent phenotype in human fibroblasts, correlating with defects in nuclear architecture and actin dynamics. We provide novel insights into ANTXR1's physiological functions and propose GAPO syndrome to be reconsidered as a progeroid disorder highlighting an unexpected role for an integrin-like extracellular matrix receptor in human aging.

Exploring Present and Future Directions in Nano-Enhanced Optoelectronic Neuromodulation

ConspectusElectrical neuromodulation has achieved significant translational advancements, including the development of deep brain stimulators for managing neural disorders and vagus nerve stimulators for seizure treatment. Optoelectronics, in contrast to wired electrical systems, offers the leadless feature that guides multisite and high spatiotemporal neural system targeting, ensuring high specificity and precision in translational therapies known as "photoelectroceuticals". This Account provides a concise overview of developments in novel optoelectronic nanomaterials that are engineered through innovative molecular, chemical, and nanostructure designs to facilitate neural interfacing with high efficiency and minimally invasive implantation.This Account outlines the progress made both within our laboratory and across the broader scientific community, with particular attention to implications in materials innovation strategies, studying bioelectrical activation with spatiotemporal methods, and applications in regenerative medicine. In materials innovation, we highlight a nongenetic, biocompatible, and minimally invasive approach for neuromodulation that spans various length scales, from single neurons to nerve tissues using nanosized particles and monolithic membranes. Furthermore, our discussion exposes the critical unresolved questions in the field, including mechanisms of interaction at the nanobio interface, the precision of cellular or tissue targeting, and integration into existing neural networks with high spatiotemporal modulation. In addition, we present the challenges and pressing needs for long-term stability and biocompatibility, scalability for clinical applications, and the development of noninvasive monitoring and control systems.In addressing the existing challenges in the field of nanobio interfaces, particularly for neural applications, we envisage promising strategic directions that could significantly advance this burgeoning domain. This involves a deeper theoretical understanding of nanobiointerfaces, where simulations and experimental validations on how nanomaterials interact spatiotemporally with biological systems are crucial. The development of more durable materials is vital for prolonged applications in dynamic neural interfaces, and the ability to manipulate neural activity with high specificity and spatial resolution, paves the way for targeting individual neurons or specific neural circuits. Additionally, integrating these interfaces with advanced control systems, possibly leveraging artificial intelligence and machine learning algorithms and programming dynamically responsive materials designs, could significantly ease the implementation of stimulation and recording. These innovations hold the potential to introduce novel treatment modalities for a wide range of neurological and systemic disorders.

Incidence and risk factors for potential drug-drug interactions in outpatients receiving opioid analgesics

BACKGROUND

Opioids are the most frequently used drugs to treat pain in cancer patients. However opioid analgesics can cause adverse effects and potential drug-drug interaction.

RESEARCH DESIGN AND METHODS

This cross-sectional retrospective study analyzed pDDI in 1839 patients with opioid analgesics in a large comprehensive hospital in China from January 1 to 31 December 2022. Three drug interaction databases were used to screen for pDDI including Drugs (U.S.A.), Medscape (U.S.A.), and Drug Assistant of Dingxiangyuan (China).

RESULTS

The prevalence of pDDIs among 1839 patients was around 41.27% of 759 patients, and 564 patients (74.31%) with pDDIs were diagnosed with tumor. Further, the total of 275 various pDDIs combinations were identified. The combination of oxycodone with morphine had the most frequent occurrence of 229 times, and its adverse effects mainly related to exacerbate central respiratory depression. While, gender, tumor, number of diagnoses, and the variety of opioid analgesics used were independent risk factors for pDDIs.

CONCLUSIONS

Outpatients taking opioid analgesics had a higher incidence of pDDIs. As consequently, optimized monitoring and management of patients taking opioid analgesics is recommended in order to ensure patient medication safety.

Designer peptide-DNA cytoskeletons regulate the function of synthetic cells

The bottom-up engineering of artificial cells requires a reconfigurable cytoskeleton that can organize at distinct locations and dynamically modulate its structural and mechanical properties. Here, inspired by the vast array of actin-binding proteins and their ability to reversibly crosslink or bundle filaments, we have designed a library of peptide-DNA crosslinkers varying in length, valency and geometry. Peptide filaments conjoint through DNA hybridization give rise to tactoid-shaped bundles with tunable aspect ratios and mechanics. When confined in cell-sized water-in-oil droplets, the DNA crosslinker design guides the localization of cytoskeletal structures at the cortex or within the lumen of the synthetic cells. The tunable spatial arrangement regulates the passive diffusion of payloads within the droplets and complementary DNA handles allow for the reversible recruitment and release of payloads on and off the cytoskeleton. Heat-induced reconfiguration of peptide-DNA architectures triggers shape deformations of droplets, regulated by DNA melting temperatures. Altogether, the modular design of peptide-DNA architectures is a powerful strategy towards the bottom-up assembly of synthetic cells.

Mechanisms of chondrocyte regulated cell death in osteoarthritis: Focus on ROS-triggered ferroptosis, parthanatos, and oxeiptosis

Osteoarthritis (OA) is a common chronic inflammatory degenerative disease. Since chondrocytes are the only type of cells in cartilage, their survival is critical for maintaining cartilage morphology. This review offers a comprehensive analysis of how reactive oxygen species (ROS), including superoxide anions, hydrogen peroxide, hydroxyl radicals, nitric oxide, and their derivatives, affect cartilage homeostasis and trigger several novel modes of regulated cell death, including ferroptosis, parthanatos, and oxeiptosis, which may play roles in chondrocyte death and OA development. Moreover, we discuss potential therapeutic strategies to alleviate OA by scavenging ROS and provide new insight into the research and treatment of the role of regulated cell death in OA.

Microphysiologically Engineered Vessel-Tumor Model to Investigate Vascular Transport Dynamics of Immune Cells

Cancer immunotherapy has emerged as a promising therapeutic strategy to combat cancer effectively. However, it is hard to observe and quantify how this in vivo process happens. Three-dimensional (3D) microfluidic vessel-tumor models offer valuable capability to study how immune cells transport during cancer progression. We presented an advanced 3D vessel-supported tumor model consisting of the endothelial lumen and vessel network for the study of T cells' transportation. The process of T cell transport through the vessel network and interaction with tumor spheroids was represented and monitored in vitro. Specifically, we demonstrate that the endothelial glycocalyx serving in the T cells' transport can influence the endothelium-immune interaction. Furthermore, after vascular transport, how programmed cell death protein 1 (PD-1) immune checkpoint inhibition influences the delivered activated-T cells on tumor killing was evaluated. Our in vitro vessel-tumor model provides a microphysiologically engineered platform to represent T cell vascular transportation during tumor immunotherapy. The reported innovative vessel-tumor platform is believed to have the potential to explore the tumor-induced immune response mechanism and preclinically evaluate immunotherapy's effectiveness.

Current Progress, Challenges and Prospects in the Development of COVID-19 Vaccines

The COVID-19 pandemic has resulted in over 772 million confirmed cases, including nearly 7 million deaths, according to the World Health Organization (WHO). Leveraging rapid development, accelerated vaccine approval processes, and large-scale production of various COVID-19 vaccines using different technical platforms, the WHO declared an end to the global health emergency of COVID-19 on May 5, 2023. Current COVID-19 vaccines encompass inactivated, live attenuated, viral vector, protein subunit, nucleic acid (DNA and RNA), and virus-like particle (VLP) vaccines. However, the efficacy of these vaccines is diminishing due to the constant mutation of SARS-CoV-2 and the heightened immune evasion abilities of emerging variants. This review examines the impact of the COVID-19 pandemic, the biological characteristics of the virus, and its diverse variants. Moreover, the review underscores the effectiveness, advantages, and disadvantages of authorized COVID-19 vaccines. Additionally, it analyzes the challenges, strategies, and future prospects of developing a safe, broad-spectrum vaccine that confers sufficient and sustainable immune protection against new variants of SARS-CoV-2. These discussions not only offer insight for the development of next-generation COVID-19 vaccines but also summarize experiences for combating future emerging viruses.

Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review

BACKGROUND

Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction.

MAIN BODY

In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations.

SHORT CONCLUSION

As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.

The Effects of Hyperbaric Oxygen Therapy on Neuroprotection and Recovery after Brain Resuscitation

Objective: Analyze the impact of hyperbaric oxygen therapy on neuroprotection and recovery post severe traumatic brain injury (sTBI) resuscitation. Methods: Retrospective analysis of clinical data from 83 sTBI patients admitted between January 2022 to January 2024. Patients were divided into control (n = 41) and observation (n = 42) groups based on treatment received. Control received standard therapy, while the observation group received hyperbaric oxygen therapy. Effects on clinical outcomes, neuroinjury markers (S100β, GFAP, UCH-L1, NSE), neurotrophic factors (NGF, BDNF), neurological function indicators (NIHSS, CSS), and adverse reactions were compared. Results: The observation group showed a higher total effective rate (80.95%) compared to control (60.98%) (P < 0.05). Neuroinjury markers decreased post-treatment in both groups, with the observation group lower (P < 0.05). NGF and BDNF levels increased post-treatment in both groups, with the observation group higher (P < 0.05). NIHSS and CSS scores decreased post-treatment in both groups, with the observation group lower (P < 0.05). No significant difference in adverse reactions between groups (P > 0.05). Conclusion: Hyperbaric oxygen therapy effectively treats sTBI by improving brain resuscitation success, reducing neuroinjury factors, enhancing neurotrophic factors, and promoting neurological function recovery, without increasing adverse reaction risk.

A chemodosimetric chemosensor for the ratiometric detection of nerve agent-mimic DCP in solution and vapor phases

Nerve agents are among the most deadly and lethal chemical warfare agents (CWAs). Rapid identification is crucial for specialized individuals to take action against dangerous drugs. This paper describes the synthesis and characterisation of a probe (MNFZ) based on the methoxy naphthalene-furoic hydrazide group. The probe rapidly (100 s) detects and quantifies the nerve-agent simulant diethyl chlorophosphate (DCP) in both solution and vapor phases. This sensor uses a new recognition center, furoic hydrazide, where the nitrogen atom of the imine group (CN) attacks the electrophilic core phosphorus atom of DCP, followed by the hydrolysis of the imine group in the acetonitrile (ACN) solution to produce the corresponding aldehyde MNPA. The development of ICT character resulted in a distinct red-shifted ratiometric fluorescence response to DCP, with a very low limit of detection (12.2 nM). The probe is an efficient chemosensor due to its high selectivity over other organophosphorus compounds as well as its chemical stability across a wide pH range. DFT calculations, 1H NMR and HRMS were performed to finalize the sensing mechanism. Lastly, the as-designed sensor was successfully used to build a highly sensitive portable kit in test strips and a cotton biopolymer for simple and safe real-time monitoring of DCP.

3D bioprinting approaches for spinal cord injury repair

Regenerative healing of spinal cord injury (SCI) poses an ongoing medical challenge by causing persistent neurological impairment and a significant socioeconomic burden. The complexity of spinal cord tissue presents hurdles to successful regeneration following injury, due to the difficulty of forming a biomimetic structure that faithfully replicates native tissue using conventional tissue engineering scaffolds. 3D bioprinting is a rapidly evolving technology with unmatched potential to create 3D biological tissues with complicated and hierarchical structure and composition. With the addition of biological additives such as cells and biomolecules, 3D bioprinting can fabricate preclinical implants, tissue or organ-like constructs, andin vitromodels through precise control over the deposition of biomaterials and other building blocks. This review highlights the characteristics and advantages of 3D bioprinting for scaffold fabrication to enable SCI repair, including bottom-up manufacturing, mechanical customization, and spatial heterogeneity. This review also critically discusses the impact of various fabrication parameters on the efficacy of spinal cord repair using 3D bioprinted scaffolds, including the choice of printing method, scaffold shape, biomaterials, and biological supplements such as cells and growth factors. High-quality preclinical studies are required to accelerate the translation of 3D bioprinting into clinical practice for spinal cord repair. Meanwhile, other technological advances will continue to improve the regenerative capability of bioprinted scaffolds, such as the incorporation of nanoscale biological particles and the development of 4D printing.

RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge

BACKGROUND AND OBJECTIVE

The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology.

METHODS

We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The registration time is low, allowing one to perform efficient registration even for large datasets. The method was proposed for the ACROBAT 2023 challenge organized during the MICCAI 2023 conference and scored 1st place. The method is released as open-source software.

RESULTS

The proposed method is evaluated using three open datasets: (i) Automatic Nonrigid Histological Image Registration Dataset (ANHIR), (ii) Automatic Registration of Breast Cancer Tissue Dataset (ACROBAT), and (iii) Hybrid Restained and Consecutive Histological Serial Sections Dataset (HyReCo). The target registration error (TRE) is used as the evaluation metric. We compare the proposed algorithm to other state-of-the-art solutions, showing considerable improvement. Additionally, we perform several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset.

CONCLUSIONS

The article presents an automatic and robust registration method that outperforms other state-of-the-art solutions. The method does not require any fine-tuning to a particular dataset and can be used out-of-the-box for numerous types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level (resolution up to 220k x 220k). We provide free access to the software. The results are fully and easily reproducible. The proposed method is a significant contribution to improving the WSI registration quality, thus advancing the field of digital pathology.

DNA nanostructures for exploring cell-cell communication

The process of coordinating between the same or multiple types of cells to jointly execute various instructions in a controlled and carefully regulated environment is a very appealing field. In order to provide clearer insight into the role of cell-cell interactions and the cellular communication of this process in their local communities, several interdisciplinary approaches have been employed to enhance the core understanding of this phenomenon. DNA nanostructures have emerged in recent years as one of the most promising tools in exploring cell-cell communication and interactions due to their programmability and addressability. Herein, this review is dedicated to offering a new perspective on using DNA nanostructures to explore the progress of cell-cell communication. After briefly outlining the anchoring strategy of DNA nanostructures on cell membranes and the subsequent dynamic regulation of DNA nanostructures, this paper highlights the significant contribution of DNA nanostructures in monitoring cell-cell communication and regulating its interactions. Finally, we provide a quick overview of the current challenges and potential directions for the application of DNA nanostructures in cellular communication and interactions.

Portable platform for leukocyte extraction from blood using sheath-free microfluidic DLD

Leukocyte count is routinely performed for diagnostic purposes and is rapidly emerging as a significant biomarker for a wide array of diseases. Additionally, leukocytes have demonstrated considerable promise in novel cell-based immunotherapies. However, the direct retrieval of leukocytes from whole blood is a significant challenge due to their low abundance compared to erythrocytes. Here, we introduce a microfluidic-based platform that isolates and recovers leukocytes from diluted whole blood in a single step. Our platform utilizes a novel, sheathless method to initially sediment and focus blood cells into a dense stream while flowing through a tubing before entering the microfluidic device. A hexagonal-shaped structure, patterned at the device's inlet, directs all the blood cells against the channel's outer walls. The focused cells are then separated based on their size using the deterministic lateral displacement (DLD) microfluidic technique. We evaluated various parameters that could influence leukocyte separation, including different focusing structures (assessed both computationally and experimentally), the orientation of the tubing-chip interface, the effects of blood sample hematocrit (dilution), and flow rate. Our device demonstrated the ability to isolate leukocytes from diluted blood with a separation efficiency of 100%, a recovery rate of 76%, and a purity of 80%, while maintaining a cell viability of 98%. The device operates for over 30 min at a flow rate of 2 μL min-1. Furthermore, we developed a handheld pressure controller to drive fluid flow, enhancing the operability of our platform outside of central laboratories and enabling near-patient testing. Our platform can be integrated with downstream cell-based assays and analytical methods that require high leukocyte purity (80%), ranging from cell counting to diagnostics and cell culture applications.

Dual-Wavelength Volumetric Microlithography for Rapid Production of 4D Microstructures

4D microstructured actuators are micro-objects made of stimuli-responsive materials capable of induced shape deformations, with applications ranging from microrobotics to smart micropatterned haptic surfaces. The novel technology dual-wavelength volumetric microlithography (DWVML) realizes rapid printing of high-resolution 3D microstructures and so has the potential to pave the way to feasible manufacturing of 4D microdevices. In this work, DWVML is applied for the first time to printing stimuli-responsive materials, namely, liquid crystal networks (LCNs). An LCN photoresist is developed and characterized, and large arrays of up to 5625 LCN micropillars with programmable shape changes are produced by means of DWVML in the time span of seconds, over areas as large as ∼5.4 mm2. The production rate of 0.24 mm3 h-1 is achieved, exceeding speeds previously reported for additive manufacturing of LCNs by 2 orders of magnitude. Finally, a membrane with tunable, micrometer-sized pores is fabricated to illustrate the potential DWVML holds for real-world applications.

Investigating the synergistic effects of immunotherapy and normalization treatment in modulating tumor microenvironment and enhancing treatment efficacy

We developed a comprehensive mathematical model of cancer immunotherapy that takes into account: i) Immune checkpoint blockers (ICBs) and the interactions between cancer cells and the immune system, ii) characteristics of the tumor microenvironment, such as the tumor hydraulic conductivity, interstitial fluid pressure, and vascular permeability, iii) spatial and temporal variations of the modeled components within the tumor and the surrounding host tissue, iv) the transport of modeled components through the vasculature and between the tumor-host tissue with convection and diffusion, and v) modeling of the tumor draining lymph nodes were the antigen presentation and the development of cytotoxic immune cells take place. Our model successfully reproduced experimental data from various murine tumor types and predicted immune system profiling, which is challenging to achieve experimentally. It showed that combination of ICB therapy and normalization treatments, that aim to improve tumor perfusion, decreases interstitial fluid pressure and increases the concentration of both innate and adaptive immune cells at the tumor center rather than the periphery. Furthermore, using the model, we investigated the impact of modeled components on treatment outcomes. The analysis found that the number of functional vessels inside the tumor region and the ICB dose administered have the largest impact on treatment outcomes.

Injectable bioactive poly(propylene fumarate) and polycaprolactone based click chemistry bone cement for spinal fusion in rabbits

Degenerative spinal pathology is a widespread medical issue, and spine fusion surgeries are frequently performed. In this study, we fabricated an injectable bioactive click chemistry polymer cement for use in spinal fusion and bone regrowth. Taking advantages of the bioorthogonal click reaction, this cement can be crosslinked by itself eliminating the addition of a toxic initiator or catalyst, nor any external energy sources like UV light or heat. Furthermore, nano-hydroxyapatite (nHA) and microspheres carrying recombinant human bone morphogenetic protein-2 (rhBMP-2) and recombinant human vascular endothelial growth factor (rhVEGF) were used to make the cement bioactive for vascular induction and osteointegration. After implantation into a rabbit posterolateral spinal fusion (PLF) model, the cement showed excellent induction of new bone formation and bridging bone, achieving results comparable to autograft control. This is largely due to the osteogenic properties of nano-hydroxyapatite (nHA) and the released rhBMP-2 and rhVEGF growth factors. Since the availability of autograft sources is limited in clinical settings, this injectable bioactive click chemistry cement may be a promising alternative for spine fusion applications in addressing various spinal conditions.

Recent advances of focused ultrasound induced blood-brain barrier opening for clinical applications of neurodegenerative diseases

With the aging population on the rise, neurodegenerative disorders have taken center stage as a significant health concern. The blood-brain barrier (BBB) plays an important role to maintain the stability of central nervous system, yet it poses a formidable obstacle to delivering drugs for neurodegenerative disease therapy. Various methods have been devised to confront this challenge, each carrying its own set of limitations. One particularly promising noninvasive approach involves the utilization of focused ultrasound (FUS) combined with contrast agents-microbubbles (MBs) to achieve transient and reversible BBB opening. This review provides a comprehensive exploration of the fundamental mechanisms behind FUS/MBs-mediated BBB opening and spotlights recent breakthroughs in its application for neurodegenerative diseases. Furthermore, it addresses the current challenges and presents future perspectives in this field.

From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications

Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.

Navigating the future: Microfluidics charting new routes in drug delivery

Microfluidics has emerged as a transformative force in the field of drug delivery, offering innovative avenues to produce a diverse range of nano drug delivery systems. Thanks to its precise manipulation of small fluid volumes and its exceptional command over the physicochemical characteristics of nanoparticles, this technology is notably able to enhance the pharmacokinetics of drugs. It has initiated a revolutionary phase in the domain of drug delivery, presenting a multitude of compelling advantages when it comes to developing nanocarriers tailored for the delivery of poorly soluble medications. These advantages represent a substantial departure from conventional drug delivery methodologies, marking a paradigm shift in pharmaceutical research and development. Furthermore, microfluidic platformsmay be strategically devised to facilitate targeted drug delivery with the objective of enhancing the localized bioavailability of pharmaceutical substances. In this paper, we have comprehensively investigated a range of significant microfluidic techniques used in the production of nanoscale drug delivery systems. This comprehensive review can serve as a valuable reference and offer insightful guidance for the development and optimization of numerous microfluidics-fabricated nanocarriers.

Impact of different forms of selenium supplementation on growth and physiological performance of New Zealand white rabbits

Forty-eight weaned male New Zealand White rabbits aged 6 weeks with an initial body weight of (709.67 ± 13 g) were randomly divided into six experimental groups (8 rabbits each) for 6-14 weeks of age experimental periods. The present study was planned to evaluate the effect of using different forms of Selenium element (inorganic, nanoparticles and organic) as dietary supplementation on productive performance of rabbits. Six experimental groups in completely randomized design were used. The first group (G1, control) was fed the basal diet to cover maintenance and production allowances. Rabbits in the other groups G2, G3, G4 and G5 were fed the basal diet supplemented with Nano- Se at 0.02, 0.03, 0.04 and 0.05 mg/kg diet, respectively. The 6th group (G6) was fed the basal diet supplemented with 0.1 mg/kg diet of salinized yeast (Se-yeast) as organic form. The results indicated that the highest values of nitrogen free extract (NFE) and crude fiber (CF) digestibility, live body weight, daily weight gain, hot carcass weight and dressing percentage were observed with those supplemented with Nano-Se at all levels compared with other treatments. However, feed conversion, net revenue and economic efficiency values were improved with Nano-Se groups followed by organic Se group in comparisons with the control group. Conclusively, the Nano-Se in rabbit's diet has a positive effect in improving rabbit's performance and economic efficiency compared to the inorganic Selenium.

Building Synthetic Cells─From the Technology Infrastructure to Cellular Entities

The de novo construction of a living organism is a compelling vision. Despite the astonishing technologies developed to modify living cells, building a functioning cell "from scratch" has yet to be accomplished. The pursuit of this goal alone has─and will─yield scientific insights affecting fields as diverse as cell biology, biotechnology, medicine, and astrobiology. Multiple approaches have aimed to create biochemical systems manifesting common characteristics of life, such as compartmentalization, metabolism, and replication and the derived features, evolution, responsiveness to stimuli, and directed movement. Significant achievements in synthesizing each of these criteria have been made, individually and in limited combinations. Here, we review these efforts, distinguish different approaches, and highlight bottlenecks in the current research. We look ahead at what work remains to be accomplished and propose a "roadmap" with key milestones to achieve the vision of building cells from molecular parts.

Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis

Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models' predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications. The study dataset consisted of 969 ultrasound images from unique patients; 646 control images and 323 cases of kidney anomalies, including 259 cases of unilateral urinary tract dilation and 64 cases of unilateral multicystic dysplastic kidney. The best performing model achieved a cross-validated area under the ROC curve of 91.28% ± 0.52%, with an overall accuracy of 84.03% ± 0.76%, sensitivity of 77.39% ± 1.99%, and specificity of 87.35% ± 1.28%. Our findings emphasize the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.

Sulfate: a neglected (but potentially highly relevant) anion

Sulfate is an important anion as sulfonation is essential in modulation of several compounds, such as exogens, polysaccharide chains of proteoglycans, cholesterol or cholesterol derivatives and tyrosine residues of several proteins. Sulfonation requires the presence of both the sulfate donor 3'-phosphoadenosine-5'-phosphosulfate (PAPS) and a sulfotransferase. Genetic disorders affecting sulfonation, associated with skeletal abnormalities, impaired neurological development and endocrinopathies, demonstrate the importance of sulfate. Yet sulfate is not measured in clinical practice. This review addresses sulfate metabolism and consequences of sulfonation defects, how to measure sulfate and why we should measure sulfate more often.