Continuous Synthesis of Nanoscale Emulsions by Vapor Condensation (EVC)

Emulsions are widely used in many industrial applications, and the development of efficient techniques for synthesizing them is a subject of ongoing research. Vapor condensation is a promising method for energy-efficient, high-throughput production of monodisperse nanoscale emulsions. However, previous studies using this technique are limited to producing small volumes of water-in-oil dispersions. In this work, a new method for the continuous synthesis of nanoscale emulsions (water-in-oil and oil-in-water) is presented by condensing vapor on free-flowing surfactant solutions. The viability of oil vaporization and condensation is demonstrated under mild heating/cooling using diverse esters, terpenes, aromatic hydrocarbons, and alkanes. By systematically investigating water vapor and oil vapor condensation dynamics on bulk liquid-surfactant solutions, a rich diversity of outcomes, including floating films, nanoscale drops, and hexagonally packed microdrops is uncovered. It is demonstrated that surfactant concentration impacts oil spreading, self-emulsification, and such behavior can aid in the emulsification of condensed oil drops. This work represents a critical step toward advancing the vapor condensation method's applications for emulsions and colloidal systems, with broad implications for various fields and the development of new emulsion-based products and industrial processes.

Functional electrical stimulation to enhance reactive balance among people with hemiparetic stroke

BACKGROUND

Individuals with stroke demonstrate a twofold higher fall incidence compared to healthy counterparts, potentially associated with deficits in reactive balance control, which is crucial for regaining balance from unpredictable perturbations to the body. Moreover, people with higher stroke-related motor impairment exhibit greater falls and cannot recover balance during higher perturbation intensities. Thus, they might need supplemental agents for fall prevention or even to be included in a perturbation-based protocol. Functional electrical stimulation is a widely used clinical modality for improving gait performance; however, it remains unknown whether it can enhance or interfere with reactive balance control.

METHODS

We recruited twelve ambulatory participants with hemiparetic stroke (61.48 ± 6.77 years) and moderate-to-high motor impairment (Chedoke-McMaster Stroke Leg Assessment ≤ 4/7). Each participant experienced 4 unpredicted paretic gait-slips, with and without functional electrical stimulation (provided 50-500 ms after perturbation) in random order. The paretic quadriceps muscle group was chosen to receive electrical stimulation, considering the role of support limb knee extensors for preventing limb-collapse. Outcomes including primary (laboratory falls), secondary (reactive stability, vertical limb support) and tertiary (compensatory step length, step initiation, execution time) measures were compared between the two conditions.

RESULTS

Participants demonstrated fewer falls, higher reactive stability, and higher vertical limb support (p < 0.05) following gait-slips with functional electrical stimulation compared to those without. This was accompanied by reduced step initiation time and a longer compensatory step (p < 0.05).

CONCLUSION

The application of functional electrical stimulation to paretic quadriceps following gait-slips reduced laboratory fall incidence with enhanced reactive balance outcomes among people with higher stroke-related motor impairment. Our results lay the preliminary groundwork for understanding the instantaneous neuromodulatory effect of functional electrical stimulation in preventing gait-slip falls, future studies could test its therapeutic effect on reactive balance. Clinical registry number: NCT04957355.

Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks

Food allergy is usually difficult to diagnose in early life, and the inability to diagnose patients with atopic diseases at an early age may lead to severe complications. Numerous studies have suggested an association between the infant gut microbiome and development of allergy. In this work, we investigated the capacity of Long Short-Term Memory (LSTM) networks to predict food allergies in early life (0-3 years) from subjects' longitudinal gut microbiome profiles. Using the DIABIMMUNE dataset, we show an increase in predictive power using our model compared to Hidden Markov Model, Multi-Layer Perceptron Neural Network, Support Vector Machine, Random Forest, and LASSO regression. We further evaluated whether the training of LSTM networks benefits from reduced representations of microbial features. We considered sparse autoencoder for extraction of potential latent representations in addition to standard feature selection procedures based on Minimum Redundancy Maximum Relevance (mRMR) and variance prior to the training of LSTM networks. The comprehensive evaluation reveals that LSTM networks with the mRMR selected features achieve significantly better performance compared to the other tested machine learning models.

Creation of Dystrophin Expressing Chimeric Cells of Myoblast Origin as a Novel Stem Cell Based Therapy for Duchenne Muscular Dystrophy

Over the past decade different stem cell (SC) based approaches were tested to treat Duchenne Muscular Dystrophy (DMD), a lethal X-linked disorder caused by mutations in dystrophin gene. Despite research efforts, there is no curative therapy for DMD. Allogeneic SC therapies aim to restore dystrophin in the affected muscles; however, they are challenged by rejection and limited engraftment. Thus, there is a need to develop new more efficacious SC therapies. Chimeric Cells (CC), created via ex vivo fusion of donor and recipient cells, represent a promising therapeutic option for tissue regeneration and Vascularized Composite Allotransplantation (VCA) due to tolerogenic properties that eliminate the need for lifelong immunosuppression. This proof of concept study tested feasibility of myoblast fusion for Dystrophin Expressing. Chimeric Cell (DEC) therapy through in vitro characterization and in vivo assessment of engraftment, survival, and efficacy in the mdx mouse model of DMD. Murine DEC were created via ex vivo fusion of normal (snj) and dystrophin–deficient (mdx) myoblasts using polyethylene glycol. Efficacy of myoblast fusion was confirmed by flow cytometry and dystrophin immunostaining, while proliferative and myogenic differentiation capacity of DEC were assessed in vitro. Therapeutic effect after DEC transplant (0.5 × 10⁶) into the gastrocnemius muscle (GM) of mdx mice was assessed by muscle functional tests. At 30 days post-transplant dystrophin expression in GM of injected mdx mice increased to 37.27 ± 12.1% and correlated with improvement of muscle strength and function. Our study confirmed feasibility and efficacy of DEC therapy and represents a novel SC based approach for treatment of muscular dystrophies.

MetaLonDA: a flexible R package for identifying time intervals of differentially abundant features in metagenomic longitudinal studies

BACKGROUND

Microbial longitudinal studies are powerful experimental designs utilized to classify diseases, determine prognosis, and analyze microbial systems dynamics. In longitudinal studies, only identifying differential features between two phenotypes does not provide sufficient information to determine whether a change in the relative abundance is short-term or continuous. Furthermore, sample collection in longitudinal studies suffers from all forms of variability such as a different number of subjects per phenotypic group, a different number of samples per subject, and samples not collected at consistent time points. These inconsistencies are common in studies that collect samples from human subjects.

RESULTS

We present MetaLonDA, an R package that is capable of identifying significant time intervals of differentially abundant microbial features. MetaLonDA is flexible such that it can perform differential abundance tests despite inconsistencies associated with sample collection. Extensive experiments on simulated datasets quantitatively demonstrate the effectiveness of MetaLonDA with significant improvement over alternative methods. We applied MetaLonDA to the DIABIMMUNE cohort ( https://pubs.broadinstitute.org/diabimmune ) substantiating significant early lifetime intervals of exposure to Bacteroides and Bifidobacterium in Finnish and Russian infants. Additionally, we established significant time intervals during which novel differentially relative abundant microbial genera may contribute to aberrant immunogenicity and development of autoimmune disease.

CONCLUSION

MetaLonDA is computationally efficient and can be run on desktop machines. The identified differentially abundant features and their time intervals have the potential to distinguish microbial biomarkers that may be used for microbial reconstitution through bacteriotherapy, probiotics, or antibiotics. Moreover, MetaLonDA can be applied to any longitudinal count data such as metagenomic sequencing, 16S rRNA gene sequencing, or RNAseq. MetaLonDA is publicly available on CRAN ( https://CRAN.R-project.org/package=MetaLonDA ).