Human Skeletal Muscle Myoblast Culture in Aligned Bacterial Nanocellulose and Commercial Matrices.

Bacterial nanocellulose (BNC) is a durable, flexible, and dynamic biomaterial capable of serving a wide variety of fields, sectors, and applications within biotechnology, healthcare, electronics, agriculture, fashion, and others. BNC is produced spontaneously in carbohydrate-rich bacterial culture media, forming a cellulosic pellicle via a nanonetwork of fibrils extruded from certain genera. Herein, we demonstrate engineering BNC-based scaffolds with tunable physical and mechanical properties through postprocessing. Human skeletal muscle myoblasts (HSMMs) were cultured on these scaffolds, and in vitro electrical stimulation was applied to promote cellular function for tissue engineering applications. We compared physiologic maturation markers of human skeletal muscle myoblast development using a 2.5-dimensional culture paradigm in fabricated BNC scaffolds, compared to two-dimensional (2D) controls. We demonstrate that the culture of human skeletal muscle myoblasts on BNC scaffolds developed under electrical stimulation produced highly aligned, physiologic morphology of human skeletal muscle myofibers compared to unstimulated BNC and standard 2D culture. Furthermore, we compared an array of metrics to assess the BNC scaffold in a rigorous head-to-head study with commercially available, clinically approved matrices, Kerecis Omega3 Wound Matrix (Marigen) and Phoenix as well as a gelatin methacryloyl (GelMA) hydrogel. The BNC scaffold outcompeted industry standard matrices as well as a 20% GelMA hydrogel in durability and sustained the support of human skeletal muscle myoblasts in vitro. This work offers a robust demonstration of BNC scaffold cytocompatibility with human skeletal muscle cells and sets the basis for future work in healthcare, bioengineering, and medical implant technological development.

Designing a Naturally Inspired Conductive Copolymer to Engineer Wearable Bioadhesives for Sensing Applications.

The design of adhesive and conductive soft hydrogels using biopolymers with tunable mechanical properties has received significant interest in the field of wearable sensors for detecting human motions. These hydrogels are primarily fabricated through the modification of biopolymers to introduce cross-linking sites, the conjugation of adhesive components, and the incorporation of conductive materials into the hydrogel network. The development of a multifunctional copolymer that integrates adhesive and conductive properties within a single polymer chain with suitable cross-linking sites eliminates the need for biopolymer modification and the addition of extra conductive and adhesive components. In this study, we synthesized a copolymer based on poly([2-(methacryloyloxy)ethyl] trimethylammonium chloride-co-dopamine methacrylamide) (p(METAC-DMA)) using a controlled radical polymerization, allowing for the efficient conjugation of both adhesive and conductive units within a single polymer chain. Subsequently, our multifunctional hydrogel named Gel-MD was fabricated by mixing the p(METAC-DMA) copolymer with non-modified gelatin in which cross-linking took place in an oxidative environment. We confirmed the biocompatibility of the Gel-MD hydrogel through in vitro studies using NIH 3T3 cells as well as in vivo subcutaneous implantation in rats. Furthermore, the Gel-MD hydrogel was effective and sensitive in detecting various human motions, making it a promising wearable sensor for health monitoring and diagnosis.

A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring.

Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), ß-cyclodextrin (ß-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27 kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensures high stretchability and flexibility. Combining pAAm with ß-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.

IgA Levels among Type 2 Diabetic and Non-Diabetic Patients with Periodontitis: A Prospective Clinical Study.

OBJECTIVES: To estimate gingival crevicular immunoglobulin A(IgA) using enzyme-linked immunosorbent assay (ELISA) among type II diabetic patients with periodontitis. MATERIALS AND METHODS: A non-randomized study was done of 40 periodontitis subjects with a mean age of 50 years and were recruited into two groups, Group A (Type II controlled diabetics with HbA1c < 7%) and Group B (non-diabetics with HbA1c between 4 and 6%). Both the groups underwent nonsurgical periodontal therapy (NSPT). The clinical parameters were recorded at baseline, 1, and 3 months. GCF sample was collected for the estimation of crevicular IgA at baseline and at 3 months. STATISTICAL ANALYSIS: Results were analyzed using parametric tests paired t-test and Student's t-test for every assessment point. The level of significance was set at p < 0.05. RESULTS: Difference in IgA levels and clinical parameters was seen between diabetic and non-diabetic groups, which was statistically significant. CONCLUSION: Changes in crevicular IgA levels in patients with diabetic periodontitis can be used as a novel biomarker in assessing the inflammatory status.

Vision sculpts a continuum of L2/3 cell types in the visual cortex during the critical period.

We previously reported that vision specifies Layer 2/3 (L2/3) glutamatergic cell-type identity in the primary visual cortex (V1). Using unsupervised clustering of single-nucleus RNA-sequencing data, we identified molecularly distinct L2/3 cell types in normal-reared (NR) and dark-reared (DR) mice, but the two sets exhibited poor correspondence. Here, we show that classification of cell types was confounded in DR by vision-dependent gene programs that are orthogonal to gene programs underlying cell-type identity. A focused clustering analysis successfully matches cell types between DR and NR, suggesting that cell identity-defining gene programs persist under vision deprivation but are overshadowed by vision-dependent transcriptomic variation. Using multi-tasking theory we show that L2/3 cell types form a continuum between three cell-archetypes. Visual deprivation markedly shifts this distribution along the continuum. Thus, dark-rearing markedly influences cell states thereby masking cell-type-identities and changes the distribution of L2/3 types along a transcriptomic continuum.

A global timing mechanism regulates cell-type-specific wiring programmes.

The assembly of neural circuits is dependent on precise spatiotemporal expression of cell recognition molecules1-5. Factors controlling cell type specificity have been identified6-8, but how timing is determined remains unknown. Here we describe induction of a cascade of transcription factors by a steroid hormone (ecdysone) in all fly visual system neurons spanning target recognition and synaptogenesis. We demonstrate through single-cell sequencing that the ecdysone pathway regulates the expression of a common set of targets required for synaptic maturation and cell-type-specific targets enriched for cell-surface proteins regulating wiring specificity. Transcription factors in the cascade regulate the expression of the same wiring genes in complex ways, including activation in one cell type and repression in another. We show that disruption of the ecdysone pathway generates specific defects in dendritic and axonal processes and synaptic connectivity, with the order of transcription factor expression correlating with sequential steps in wiring. We also identify shared targets of a cell-type-specific transcription factor and the ecdysone pathway that regulate specificity. We propose that neurons integrate a global temporal transcriptional module with cell-type-specific transcription factors to generate different cell-type-specific patterns of cell recognition molecules regulating wiring.

Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition.

In this paper, we propose a deep learning-based algorithm to improve the performance of automatic speech recognition (ASR) systems for aphasia, apraxia, and dysarthria speech by utilizing electroencephalography (EEG) features recorded synchronously with aphasia, apraxia, and dysarthria speech. We demonstrate a significant decoding performance improvement by more than 50% during test time for isolated speech recognition task and we also provide preliminary results indicating performance improvement for the more challenging continuous speech recognition task by utilizing EEG features. The results presented in this paper show the first step towards demonstrating the possibility of utilizing non-invasive neural signals to design a real-time robust speech prosthetic for stroke survivors recovering from aphasia, apraxia, and dysarthria. Our aphasia, apraxia, and dysarthria speech-EEG data set will be released to the public to help further advance this interesting and crucial research.

Ascertaining Framingham heart failure phenotype from inpatient electronic health record data using natural language processing: a multicentre Atherosclerosis Risk in Communities (ARIC) validation study.

OBJECTIVES: Using free-text clinical notes and reports from hospitalised patients, determine the performance of natural language processing (NLP) ascertainment of Framingham heart failure (HF) criteria and phenotype. STUDY DESIGN: A retrospective observational study design of patients hospitalised in 2015 from four hospitals participating in the Atherosclerosis Risk in Communities (ARIC) study was used to determine NLP performance in the ascertainment of Framingham HF criteria and phenotype. SETTING: Four ARIC study hospitals, each representing an ARIC study region in the USA. PARTICIPANTS: A stratified random sample of hospitalisations identified using a broad range of International Classification of Disease, ninth revision, diagnostic codes indicative of an HF event and occurring during 2015 was drawn for this study. A randomly selected set of 394 hospitalisations was used as the derivation dataset and 406 hospitalisations was used as the validation dataset. INTERVENTION: Use of NLP on free-text clinical notes and reports to ascertain Framingham HF criteria and phenotype. PRIMARY AND SECONDARY OUTCOME MEASURES: NLP performance as measured by sensitivity, specificity, positive-predictive value (PPV) and agreement in ascertainment of Framingham HF criteria and phenotype. Manual medical record review by trained ARIC abstractors was used as the reference standard. RESULTS: Overall, performance of NLP ascertainment of Framingham HF phenotype in the validation dataset was good, with 78.8%, 81.7%, 84.4% and 80.0% for sensitivity, specificity, PPV and agreement, respectively. CONCLUSIONS: By decreasing the need for manual chart review, our results on the use of NLP to ascertain Framingham HF phenotype from free-text electronic health record data suggest that validated NLP technology holds the potential for significantly improving the feasibility and efficiency of conducting large-scale epidemiologic surveillance of HF prevalence and incidence.

Augmented reality in patient education and health literacy: a scoping review protocol.

BACKGROUND: Health literacy enables the patients in understanding the basic healthcare information and taking informed health decisions; thus, it is a desirable goal of any healthcare system. It increases patients' adherence to treatment, improves the quality of care and eases the overall burden on the healthcare system. In recent years, technological solutions are being increasingly used in educating patients and achieving better health literacy. Augmented reality (AR) provides powerful, contextual and situated learning experiences and supplements the real world with virtual objects. AR could potentially be an effective learning methodology for the patients, thus, warranting a comprehensive overview of the current state of AR in patient education and health literacy. METHODS: The proposed scoping review will be based on the framework developed by Arksey and O'Malley, including the refinements suggested by Levac et al. A systematic search for references in the published literature will be conducted in nine research databases-Institute of Electrical and Electronics Engineers (IEEE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, PsycInfo, Embase, Web of Science, Scopus, Association for Computing Machinery (ACM) and Association for Information Systems eLibrary (AISeL). The unpublished studies from ProQuest Dissertations and Theses, Conference Proceedings Citation Index and grey literature references obtained from a web search will also be included. Databases will be searched from inception to 14 January 2020. Two independent reviewers will screen the studies from the search results in two successive stages of title/abstract screening followed by full-text screening. Data variables will be extracted from the selected studies to characterise study design, type of AR technology employed and the relational factors affecting patient education. Lastly, key stakeholders will be consulted to gather their insights about the study findings. ETHICS AND DISSEMINATION: The results will be disseminated through stakeholder meetings and conference presentations. The data used are from publicly available secondary sources, so this study does not require ethical review.