The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces.

Stroke is one of the most common and debilitating neurological conditions worldwide. Those who survive experience motor, sensory, speech, vision, and/or cognitive deficits that severely limit remaining quality of life. While rehabilitation programs can help improve patients' symptoms, recovery is often limited, and patients frequently continue to experience impairments in functional status. In this review, invasive neuromodulation techniques to augment the effects of conventional rehabilitation methods are described, including vagus nerve stimulation (VNS), deep brain stimulation (DBS) and brain-computer interfaces (BCIs). In addition, the evidence base for each of these techniques, pivotal trials, and future directions are explored. Finally, emerging technologies such as functional near-infrared spectroscopy (fNIRS) and the shift to artificial intelligence-enabled implants and wearables are examined. While the field of implantable devices for chronic stroke recovery is still in a nascent stage, the data reviewed are suggestive of immense potential for reducing the impact and impairment from this globally prevalent disorder.

Opening the Black Box of Family-Based Treatments: An Artificial Intelligence Framework to Examine Therapeutic Alliance and Therapist Empathy.

The evidence-based treatment (EBT) movement has primarily focused on core intervention content or treatment fidelity and has largely ignored practitioner skills to manage interpersonal process issues that emerge during treatment, especially with difficult-to-treat adolescents (delinquent, substance-using, medical non-adherence) and those of color. A chief complaint of "real world" practitioners about manualized treatments is the lack of correspondence between following a manual and managing microsocial interpersonal processes (e.g. negative affect) that arise in treating "real world clients." Although family-based EBTs share core similarities (e.g. focus on family interactions, emphasis on practitioner engagement, family involvement), most of these treatments do not have an evidence base regarding common implementation and treatment process problems that practitioners experience in delivering particular models, especially in mid-treatment when demands on families to change their behavior is greatest in treatment - a lack that characterizes the field as a whole. Failure to effectively address common interpersonal processes with difficult-to-treat families likely undermines treatment fidelity and sustained use of EBTs, treatment outcome, and contributes to treatment dropout and treatment nonadherence. Recent advancements in wearables, sensing technologies, multivariate time-series analyses, and machine learning allow scientists to make significant advancements in the study of psychotherapy processes by looking "under the skin" of the provider-client interpersonal interactions that define therapeutic alliance, empathy, and empathic accuracy, along with the predictive validity of these therapy processes (therapeutic alliance, therapist empathy) to treatment outcome. Moreover, assessment of these processes can be extended to develop procedures for training providers to manage difficult interpersonal processes while maintaining a physiological profile that is consistent with astute skills in psychotherapeutic processes. This paper argues for opening the "black box" of therapy to advance the science of evidence-based psychotherapy by examining the clinical interior of evidence-based treatments to develop the next generation of audit- and feedback- (i.e., systemic review of professional performance) supervision systems.

Detection of Quorum-Sensing Molecules for Pathogenic Molecules Using Cell-Based and Cell-Free Biosensors.

Since the discovery and subsequent use of penicillin, antibiotics have been used to treat most bacterial infections in the U.S. Over time, the repeated prescription of many antibiotics has given rise to many antibiotic-resistant microbes. A bacterial strain becomes resistant by horizontal gene transfer, where surviving microbes acquire genetic material or DNA fragments from adjacent bacteria that encode for resistance. In order to avoid significant bacterial resistance, novel and target therapeutics are needed. Further advancement of diagnostic technologies could be used to develop novel treatment strategies. The use of biosensors to detect quorum-sensing signaling molecules has the potential to provide timely diagnostic information toward mitigating the multidrug-resistant bacteria epidemic. Resistance and pathogenesis are controlled by quorum-sensing (QS) circuits. QS systems secrete or passively release signaling molecules when the bacterial concentration reaches a certain threshold. Signaling molecules give an early indication of virulence. Detection of these compounds in vitro or in vivo can be used to identify the onset of infection. Whole-cell and cell-free biosensors have been developed to detect quorum-sensing signaling molecules. This review will give an overview of quorum networks in the most common pathogens found in chronic and acute infections. Additionally, the current state of research surrounding the detection of quorum-sensing molecules will be reviewed. Followed by a discussion of future works toward the advancement of technologies to quantify quorum signaling molecules in chronic and acute infections.

Classifying changes in LN-18 glial cell morphology: a supervised machine learning approach to analyzing cell microscopy data via FIJI and WEKA.

In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. With the traditional, end-point assay-based approach being error-prone, and existing computational approaches being complex, we tested existing machine learning frameworks to find methods that are relatively simple, yet powerful enough to accomplish the goal of analyzing cell microscopy data. This paper details the machine learning pipeline for pixel-based classification and object-based classification. Furthermore, it compares the performances of three classifiers. The classifiers evaluated were the fast-random forest (RF), the sequential minimal optimization (SMO), and the Bayesian network (BN). Images were first preprocessed using smoothing and contrast methods found in FIJI. For pixel-based classification, the preprocessed images were fed into the Trainable Waikato Segmentation (TWS). For object-based classification, training and classification were conducted within the Waikato Environment for Knowledge Analysis (WEKA) interface. All classifiers' performance was evaluated using the WEKA experimental explorer. In terms of performance, the BN had the lowest classification accuracy for both the pixel-based and object-based model. The object-based SMO classifier had the best performance with the lowest mean absolute error of 0.05. The TWS and WEKA interface allows users to easily create and train classifiers for image analysis. However, for analyzing large image datasets, they are not ideal. Grapical abstract.

Characterization of the Dielectrophoretic Response of Different Candida Strains Using 3D Carbon Microelectrodes.

Bloodstream infection with Candida fungal cells remains one of the most life-threatening complications among hospitalized patients around the world. Although most of the cases are still due to Candida albicans, the rising incidence of infections caused by other Candida strains that may not respond to traditional anti-fungal treatments merits the development of a method for species-specific isolation of Candida. To this end, here we present the characterization of the dielectrophoresis (DEP) response of Candida albicans, Candida tropicalis and Candida parapsilosis. We complement such characterization with a study of the Candida cells morphology. The Candida strains exhibited subtle differences in their morphology and dimensions. All the Candida strains exhibited positive DEP in the range 10-500 kHz, although the strength of the DEP response was different for each Candida strain at different frequencies. Only Candida tropicalis showed positive DEP at 750 kHz. The current results show potential for manipulation and enrichment of a specific Candida strain at specific DEP conditions towards aiding in the rapid identification of Candida strains to enable the effective and timely treatment of Candida infections.

Impedance-Based Biosensing of Pseudomonas putida via Solution Blow Spun PLA: MWCNT Composite Nanofibers.

Quantifiable sensing of common microbes in chronic wounds has the potential to enable an objective assessment of wound healing for diagnostic applications. Sensing platforms should be robust, simple, and flexible to provide clinicians with a point-of-care tool. In this work, solution blow spun poly (lactic acid)/multiwalled carbon nanotube nanofiber composites are used to detect the presence and concentration of Pseudomonas putida in vitro using changes in impedance. Impedance microbiology (IM) is a well-documented diagnostic technique used in many applications, including cancer detection, tuberculosis screening and pregnancy tests. Twenty-four hour real-time measurements of the equivalent circuit of three culture media were taken with an inductance, capacitance, and resistance (LCR) meter. Variations in impedance were calculated to correspond to the growth of P. putida. Additionally, instantaneous measurements of bacterial cultures were taken over a one-minute time point to display the fast sensing of bacterial load via IM. This proof-of-concept shows that conductive solution blow spun fiber mats is a valid fabrication technique to develop in situ wound dressing impedance sensors. Study results indicate successful measurement and quantification of bacterial growth in this proof-of-concept study.

Conductive Nonwoven Carbon Nanotube-PLA Composite Nanofibers Towards Wound Sensors via Solution Blow Spinning.

This paper characterizes the use of the emerging solution blow spinning technique to create conductive carbon nanotube-PLA composite nanofibers. Poly(lactic acid) (PLA) and multi-walled carbon nanotubes (MWCNT) were combined in various solvents to produce conductive fiber mats. In addition, the authors were able to tune the conductivity of the fiber mats by modifying the MWCNT concentration. Conductive fiber mats were able to reach conductivity values as high as 474 S/cm which is more than 2 × higher than the ubiquitous germanium. Conductive fiber mats will be later used as sensors in wound healing applications. A simple light emitting diode circuit featuring the fiber mat was developed as a proof of concept toward this biomedical sensing application.

Evaluation of permeability and fluid wicking in woven fiber bone scaffolds.

Research characterizing transport of nutrients and waste in tissue engineering scaffolds has led to the study of scaffold properties that contribute to permeability and porosity of the scaffold. Both permeability and porosity contribute to the transport properties of the scaffold; however, permeability relates to the degree to which pores are interconnected within the scaffold. This work evaluated permeability for woven polymer fiber scaffolds by modulating the following scaffold parameters: material combination, weave configuration, and fiber geometry. Materials tested were poly-l-lactide and poly-l-lactide-co-ɛ-caprolactone in various combinations. Plain and crowfoot weave configurations were compared, and grooved wicking fibers were compared with round cross-section fibers to study fiber geometry. A modification of the constant head hydraulic conductivity test was used in combination with a vertical wicking test to determine levels of permeability of the woven scaffolds. Results showed a significant effect on permeability for combinations of weave configuration, fiber geometry, and material combination. However, modulating fiber geometry demonstrated the most significant contribution to permeability. This result suggests the grooved wicking geometry may be used in scaffold development to regulate transport by selectively moving fluid away or toward the area of interest by capillary action. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 107B: 306-313, 2019.

Smart Wound Dressings for Diabetic Chronic Wounds.

Given their severity and non-healing nature, diabetic chronic wounds are a significant concern to the 30.3 million Americans diagnosed with diabetes mellitus (2015). Peripheral arterial diseases, neuropathy, and infection contribute to the development of these wounds, which lead to an increased incidence of lower extremity amputations. Early recognition, debridement, offloading, and controlling infection are imperative for timely treatment. However, wound characterization and treatment are highly subjective and based largely on the experience of the treating clinician. Many wound dressings have been designed to address particular clinical presentations, but a prescriptive method is lacking for identifying the particular state of chronic, non-healing wounds. The authors suggest that recent developments in wound dressings and biosensing may allow for the quantitative, real-time representation of the wound environment, including exudate levels, pathogen concentrations, and tissue regeneration. Development of such sensing capability could enable more strategic, personalized care at the onset of ulceration and limit the infection leading to amputation. This review presents an overview of the pathophysiology of diabetic chronic wounds, a brief summary of biomaterial wound dressing treatment options, and biosensor development for biomarker sensing in the wound environment.

Design and optimization of a novel bio-loom to weave melt-spun absorbable polymers for bone tissue engineering.

Bone graft procedures are currently among the most common surgical procedures performed worldwide, but due to high risk of complication and lack of viable donor tissue, there exists a need to develop alternatives for bone defect healing. Tissue engineering, for example, combining biocompatible scaffolds with mesenchymal stem cells to achieve new bone growth, is a possible solution. Recent work has highlighted the potential for woven polymer meshes to serve as bone tissue engineering scaffolds; since, scaffolds can be iteratively designed by adjusting weave settings, material types, and mesh parameters. However, there are a number of material and system challenges preventing the implementation of such a tissue engineering strategy. Fiber compliance, tensile strength, brittleness, cross-sectional geometry, and size present specific challenges for using traditional textile weaving methods. In the current work, two potential scaffold materials, melt-spun poly-l-lactide, and poly-l-lactide-co-ε-caprolactone, were investigated. An automated bio-loom was engineered and built to weave these materials. The bio-loom was used to successfully demonstrate the weaving of these difficult-to-handle fiber types into various mesh configurations and material combinations. The dobby-loom design, adapted with an air jet weft placement system, warp tension control system, and automated collection spool, provides minimal damage to the polymer fibers while overcoming the physical constraints presented by the inherent material structure. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 1342-1351, 2017.

Challenges in the Use of Compact Disc-Based Centrifugal Microfluidics for Healthcare Diagnostics at the Extreme Point of Care.

Since its inception, Compact Disc (CD)-based centrifugal microfluidic technology has drawn a great deal of interest within research communities due to its potential use in biomedical applications. The technology has been referred to by different names, including compact-disc microfluidics, lab-on-a-disk, lab-on-a-CD and bio-disk. This paper critically reviews the state-of-the-art in CD-based centrifugal microfluidics devices and attempts to identify the challenges that, if solved, would enable their use in the extreme point of care. Sample actuation, manufacturing, reagent storage and implementation, target multiplexing, bio-particle detection, required hardware and system disposal, and sustainability are the topics of focus.