Innovative technology-based interventions in Parkinson's disease: A systematic review and meta-analysis.

OBJECTIVE: Novel technology-based interventions have the potential to improve motor symptoms and gait in Parkinson's disease (PD). Promising treatments include virtual-reality (VR) training, robotic assistance, and biofeedback. Their effectiveness remains unclear, and thus, we conducted a Bayesian network meta-analysis. METHODS: We searched the Medline, Embase, Cochrane CENTRAL, and Clinicaltrials.gov databases until 2 April 2024 and only included randomized controlled trials. Outcomes included changes in UPDRS-III/MDS-UPDRS-III score, stride length, 10-meter walk test (10MWT), timed up-and-go (TUG) test, balance scale scores and quality-of-life (QoL) scores. Results were reported as mean differences (MD) or standardized mean differences (SMD), with 95% credible intervals (95% CrI). RESULTS: Fifty-one randomized controlled trials with 2095 patients were included. For UPDRS (motor outcome), all interventions had similar efficacies. VR intervention was the most effective in improving TUG compared with control (MD: -4.36, 95% CrI: -8.57, -0.35), outperforming robotic, exercise, and proprioceptive interventions. Proprioceptive intervention significantly improved stride length compared to control intervention (MD: 0.11 m, 95% CrI: 0.03, 0.19), outperforming VR, robotic and exercise interventions. Virtual reality improved balance scale scores significantly compared to exercise intervention (SMD: 0.75, 95% CrI: 0.12, 1.39) and control intervention (SMD: 1.42, 95% CrI: 0.06, 2.77). Virtual reality intervention significantly improved QoL scores compared to control intervention (SMD: -0.95, 95% CrI: -1.43, -0.52), outperforming Internet-based interventions. INTERPRETATION: VR-based and proprioceptive interventions were the most promising interventions, consistently ranking as the top treatment choices for most outcomes. Their use in clinical practice could be helpful in managing motor symptoms and QoL in PD.

Redesigned chondroitinase ABC degrades inhibitory chondroitin sulfate proteoglycans in vitro and in vivo in the stroke-injured rat brain.

Injuries to the central nervous system, such as stroke and traumatic spinal cord injury, result in an aggregate scar that both limits tissue degeneration and inhibits tissue regeneration. The aggregate scar includes chondroitin sulfate proteoglycans (CSPGs), which impede cell migration and axonal outgrowth. Chondroitinase ABC (ChASE) is a potent yet fragile enzyme that degrades CSPGs, and thus may enable tissue regeneration. ChASE37, with 37-point mutations to the native enzyme, has been shown to be more stable than ChASE, but its efficacy has never been tested. To answer this question, we investigated the efficacy of ChASE37 first in vitro using human cell-based assays and then in vivo in a rodent model of stroke. We demonstrated ChASE37 degradation of CSPGs in vitro and the consequent cell adhesion and axonal sprouting now possible using human induced pluripotent stem cell (hiPSC)-derived neurons. To enable prolonged release of ChASE37 to injured tissue, we expressed it as a fusion protein with a Src homology 3 (SH3) domain and modified an injectable, carboxymethylcellulose (CMC) hydrogel with SH3-binding peptides (CMC-bp) using inverse electron-demand Diels-Alder chemistry. We injected this affinity release CMC-bp/SH3-ChASE37 hydrogel epicortically to endothelin-1 stroke-injured rats and confirmed bioactivity via degradation of CSPGs and axonal sprouting in and around the lesion. With CSPG degradation shown both in vitro by greater cell interaction and in vivo with local delivery from a sustained release formulation, we lay the foundation to test the potential of ChASE37 and its delivery by local affinity release for tissue regeneration after stroke.

Are teachers meeting students' needs in untracked science classrooms? Evidence based on a causal inferential approach.

Tracking has been criticized for relegating disadvantaged students to lower track courses in which students encounter a greater lack of instructional support. While an end to tracks through detracking is a possible solution, there are concerns that detracking will create more heterogeneous classrooms, making it harder for teachers to provide adequate support to their students. Using the 2015 PISA dataset, this study conducts a causal inferential analysis to understand the differences in student perceptions of teaching in tracked and untracked environments. The results provide evidence that students' needs, with respect to adaptation of instruction and provision of individualized feedback and support, are being met to the same extent on average in tracked and untracked science classes, suggesting that teachers may not necessarily have a harder time meeting the needs of students in untracked classes.

In-silico study of antisense oligonucleotide antibiotics.

Background: The rapid emergence of antibiotic-resistant bacteria directly contributes to a wave of untreatable infections. The lack of new drug development is an important driver of this crisis. Most antibiotics today are small molecules that block vital processes in bacteria. To optimize such effects, the three-dimensional structure of targeted bacterial proteins is imperative, although such a task is time-consuming and tedious, impeding the development of antibiotics. The development of RNA-based therapeutics has catalyzed a new platform of antibiotics-antisense oligonucleotides (ASOs). These molecules hybridize with their target mRNAs with high specificity, knocking down or interfering with protein translation. This study aims to develop a bioinformatics pipeline to identify potent ASO targets in essential bacterial genes. Methods: Three bacterial species (P. gingivalis, H. influenzae, and S. aureus) were used to demonstrate the utility of the pipeline. Open reading frames of bacterial essential genes were downloaded from the Database of Essential Genes (DEG). After filtering for specificity and accessibility, ASO candidates were ranked based on their self-hybridization score, predicted melting temperature, and the position on the gene in an operon. Enrichment analysis was conducted on genes associated with putative potent ASOs. Results: A total of 45,628 ASOs were generated from 348 unique essential genes in P. gingivalis. A total of 1,117 of them were considered putative. A total of 27,273 ASOs were generated from 191 unique essential genes in H. influenzae. A total of 847 of them were considered putative. A total of 175,606 ASOs were generated from 346 essential genes in S. aureus. A total of 7,061 of them were considered putative. Critical biological processes associated with these genes include translation, regulation of cell shape, cell division, and peptidoglycan biosynthetic process. Putative ASO targets generated for each bacterial species are publicly available here: https://github.com/EricSHo/AOA. The results demonstrate that our bioinformatics pipeline is useful in identifying unique and accessible ASO targets in bacterial species that post major public health issues.

Antibody drug conjugates, targeting cancer-expressed EGFR, exhibit potent and specific antitumor activity.

The monoclonal antibody '40H3' binds to EGFRvIII and to full-length EGFR when it is overexpressed on cancer cells. To generate candidate cytotoxic antibody-drug conjugates (ADCs), 40H3 was modified by the addition of small molecular weight payloads that included two tubulin-modifying agents, two topoisomerase inhibitors and a pyrrolobenzodiazepine (PBD) dimer. Conjugates retained antigen binding activity comparable to the unmodified 40H3 antibody. The cytotoxicity of five distinct ADCs was evaluated on a variety of EGFR-expressing cells including three triple negative breast cancer (TNBC) lines. Generally, the 40H3 conjugate with the PBD dimer (40H3-Tesirine) was the most active killing agent. The killing of EGFR-positive cells by 40H3-Tesirine correlated with the number of surface binding sites for 40H3. However, bystander killing was also evident in experiments with antigen-negative cells. In vivo tumor xenograft experiments were conducted on two TNBC tumor lines. Three treatments with the 40H3-Tesirine ADC at 1 mg/kg were sufficient to achieve complete remissions without evidence of mouse toxicity. Data support the development of ADCs derived from the 40H3 antibody for the treatment of cancers that express EGFRvIII or high levels of EGFR.

Tunable Surface Charge Enables the Electrostatic Adsorption-Controlled Release of Neuroprotective Peptides from a Hydrogel-Nanoparticle Drug Delivery System.

We exploit the electrostatic interactions between the positively charged neuroprotective peptide, pituitary adenylate cyclase-activating polypeptide (PACAP), and negatively charged poly(lactic-co-glycolic acid) (PLGA) nanoparticles to control PACAP release from the surface of nanoparticles dispersed in a hyaluronan-methylcellulose (HAMC) hydrogel composite. PACAP is a promising therapeutic for the treatment of neurological disorders, yet it has been difficult to deliver in vivo. Herein, the PACAP release rate was tuned by manipulating peptide adsorption onto the surface of blank nanoparticles by modifying either nanoparticle loading in the hydrogel or nanoparticle surface charge. This peptide-nanoparticle interaction was controlled by the pH-responsive carboxylic acid end terminal groups of PLGA. We further validated this system with the controlled release of a novel stabilized PACAP analog: Ac-[Ala15, Ala20]PACAP-propylamide, which masks its recognition to peptidases in circulation. Both wild-type and stabilized PACAP released from the vehicle increased the production of neuroprotective Interleukin-6 from cultured primary astrocytes. Using computational fluid dynamics methods, PACAP release from the composite was predicted based on experimentally derived adsorption isotherms, which exhibited similar release profiles to experimental data. This versatile adsorption-based system was used to deliver PACAP locally to the brains of stroke-injured mice over a 10 day period in vivo, highlighting its effectiveness for the controlled release of PACAP to the central nervous system.

Relationships between changing communication networks and changing perceptions of psychological safety in a team science setting: Analysis with actor-oriented social network models.

A growing evidence base suggests that complex healthcare problems are optimally tackled through cross-disciplinary collaboration that draws upon the expertise of diverse researchers. Yet, the influences and processes underlying effective teamwork among independent researchers are not well-understood, making it difficult to fully optimize the collaborative process. To address this gap in knowledge, we used the annual NIH mHealth Training Institutes as a testbed to develop stochastic actor-oriented models that explore the communicative interactions and psychological changes of its disciplinarily and geographically diverse participants. The models help investigate social influence and social selection effects to understand whether and how social network interactions influence perceptions of team psychological safety during the institute and how they may sway communications between participants. We found a degree of social selection effects: in particular years, scholars were likely to choose to communicate with those who had more dissimilar levels of psychological safety. We found evidence of social influence, in particular, from scholars with lower psychological safety levels and from scholars with reciprocated communications, although the sizes and directions of the social influences somewhat varied across years. The current study demonstrated the utility of stochastic actor-oriented models in understanding the team science process which can inform team science initiatives. The study results can contribute to theory-building about team science which acknowledges the importance of social influence and selection.

Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping.

Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propose the use of a one-dimensional convolutional network (1D-CNN) architecture to build a binary classifier that distinguishes electrocardiograms (ECGs) between the post-stroke and the stroke-free. We have built two 1D-CNNs that were used to identify distinct patterns from an openly accessible ECG dataset collected from elderly post-stroke patients. In addition to prediction accuracy, which is the primary focus of existing ECG deep neural network methods, we have utilized Gradient-weighted Class Activation Mapping (GRAD-CAM) to facilitate model interpretation by uncovering subtle ECG patterns captured by our model. Our stroke model has achieved ~90 % accuracy and 0.95 area under the Receiver Operating Characteristic curve. Findings suggest that the core PQRST complex alone is important but not sufficient to differentiate the post-stroke and the stroke-free. In conclusion, we have developed an accurate stroke model using the latest DNN method. Importantly, our work has illustrated an approach to enhance model interpretation, overcoming the black-box issue confronting DNNs, fostering higher user confidence and adoption of DNNs in medicine.

Brain delivering RNA-based therapeutic strategies by targeting mTOR pathway for axon regeneration after central nervous system injury.

Injuries to the central nervous system (CNS) such as stroke, brain, and spinal cord trauma often result in permanent disabilities because adult CNS neurons only exhibit limited axon regeneration. The brain has a surprising intrinsic capability of recovering itself after injury. However, the hostile extrinsic microenvironment significantly hinders axon regeneration. Recent advances have indicated that the inactivation of intrinsic regenerative pathways plays a pivotal role in the failure of most adult CNS neuronal regeneration. Particularly, substantial evidence has convincingly demonstrated that the mechanistic target of rapamycin (mTOR) signaling is one of the most crucial intrinsic regenerative pathways that drive axonal regeneration and sprouting in various CNS injuries. In this review, we will discuss the recent findings and highlight the critical roles of mTOR pathway in axon regeneration in different types of CNS injury. Importantly, we will demonstrate that the reactivation of this regenerative pathway can be achieved by blocking the key mTOR signaling components such as phosphatase and tensin homolog (PTEN). Given that multiple mTOR signaling components are endogenous inhibitory factors of this pathway, we will discuss the promising potential of RNA-based therapeutics which are particularly suitable for this purpose, and the fact that they have attracted substantial attention recently after the success of coronavirus disease 2019 vaccination. To specifically tackle the blood-brain barrier issue, we will review the current technology to deliver these RNA therapeutics into the brain with a focus on nanoparticle technology. We will propose the clinical application of these RNA-mediated therapies in combination with the brain-targeted drug delivery approach against mTOR signaling components as an effective and feasible therapeutic strategy aiming to enhance axonal regeneration for functional recovery after CNS injury.

A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility.

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

Fostering interdisciplinary collaboration: A longitudinal social network analysis of the NIH mHealth Training Institutes.

BACKGROUND/OBJECTIVE: Growing recognition that collaboration among scientists from diverse disciplines fosters the emergence of solutions to complex scientific problems has spurred initiatives to train researchers to collaborate in interdisciplinary teams. Evaluations of collaboration patterns in these initiatives have tended to be cross-sectional, rather than clarifying temporal changes in collaborative dynamics. Mobile health (mHealth), the science of using mobile, wireless devices to improve health outcomes, is a field whose advancement needs interdisciplinary collaboration. The NIH-supported annual mHealth Training Institute (mHTI) was developed to meet that need and provides a unique testbed. METHODS: In this study, we applied a longitudinal social network analysis technique to evaluate how well the program fostered communication among the disciplinarily diverse scholars participating in the 2017-2019 mHTIs. By applying separable temporal exponential random graph models, we investigated the formation and persistence of project-based and fun conversations during the mHTIs. RESULTS: We found that conversations between scholars of different disciplines were just as likely as conversations within disciplines to form or persist in the 2018 and 2019 mHTI, suggesting that the mHTI achieved its goal of fostering interdisciplinary conversations and could be a model for other team science initiatives; this finding is also true for scholars from different career stages. The presence of team and gender homophily effects in certain years suggested that scholars tended to communicate within the same team or gender. CONCLUSION: Our results demonstrate the usefulness of longitudinal network models in evaluating team science initiatives while clarifying the processes driving interdisciplinary communications during the mHTIs.

Alkyl selenol reactivity with common solvents and ligands: influences on phase control in nanocrystal synthesis.

This study develops mechanistic understanding of the factors which control the phase in syntheses of copper selenide nanocrystals by investigating how the chemistry of the dodecylselenol reactant is altered by the ligand and solvent environment. 1H NMR and 77Se NMR were used to study how commonly used solvents (octadecene and dioctylether) and ligands (oleylamine, oleic acid, stearylamine, stearic acid and trioctyl phosphine) change the nature of the dodecylselenol reactant at 25 °C, 155 °C and 220 °C. Unsaturations were prone to selenol additons, carboxylates underwent selenoesterification, amines caused the release of H2Se gas, and the phosphine formed phosphine selenide. Adventitious water caused oxidation to didodecyldiselenide. NMR studies were correlated with the phases that resulted in syntheses of nanocrystalline copper selenides, in which berzalianite, umangite or a metastable hexagonal phase were produced as identified by X-ray diffraction, depending on the ligand and solvent environemnts. Formation of the rare hexagonal Cu2-xSe phase could be assigned to cases that included DD2Se2 as a reactive intermediate, or strong L-type ligation of amines which was dependant on alkyl chain length.

Conversational Task Increases Heart Rate Variability of Individuals Susceptible to Perceived Social Isolation.

Studies showed that introversion is the strongest personality trait related to perceived social isolation (loneliness), which can predict various complications beyond objective isolation such as living alone. Lonely individuals are more likely to resort to social media for instantaneous comfort, but it is not a perpetual solution. Largely negative implications including poorer interpersonal relationship and depression were reported due to excessive social media usage. Conversational task is an established intervention to improve verbal communication, cognitive and behavioral adaptation among lonely individuals. Despite that behavioral benefits have been reported, it is unclear if they are accompanied by objective benefits underlying physiological changes. Here, we investigate the physiological signals from 28 healthy individuals during a conversational task. Participants were ranked by trait extraversion, where greater introversion is associated with increased susceptibility to perceived social isolation as compared to participants with greater extraversion as controls. We found that introverts had a greater tendency to be neurotic, and these participants also exhibited significant differences in task-related electrodermal activity (EDA), heart rate (HR) and HR variability (HRV) as compared to controls. Notably, resting state HRV among individuals susceptible to perceived loneliness was below the healthy thresholds established in literature. Conversational task with a stranger significantly increased HRV among individuals susceptible to isolation up to levels as seen in controls. Since HRV is also elevated by physical exercise and administration of oxytocin hormone (one form of therapy for behavioral isolation), conversational therapy among introverts could potentially confer physiological benefits to ameliorate social isolation and loneliness. Our findings also suggest that although the recent pandemic has changed how people are interacting typically, we should maintain a healthy dose of social interaction innovatively.

Working Memory Performance under a Negative Affect Is More Susceptible to Higher Cognitive Workloads with Different Neural Haemodynamic Correlates.

The effect of stress on task performance is complex, too much or too little stress negatively affects performance and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload-related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor- and workload-related stress were more dominant in the medial and lateral PFC, respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of a negative effect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.

Valuation system connectivity is correlated with poly-drug use in young adults.

Poly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population sample (n = 992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.

Value, drug addiction and the brain.

Over the years, various models have been proposed to explain the psychology and biology of drug addiction, built primarily around the habit and compulsion models. Recent research indicates drug addiction may be goal-directed, motivated by excessive valuation of drugs. Drug consumption may initially occur for the sake of pleasure but may transition to a means of escaping withdrawal, stress and negative emotions. In this hypothetical paper, we propose a value-based neurobiological model for drug addiction. We posit that during dependency, the value-based decision-making system in the brain is not inactive but has instead prioritized drugs as the reward of choice. In support of this model, we consider the role of valuation in choice, its influence on pleasure and punishment, and how valuation is contrasted in impulsive and compulsive behaviours. We then discuss the neurobiology of value, beginning with the dopaminergic system and its relationship with incentive salience before moving to brain-wide networks involved in valuation, control and prospection. These value-based neurobiological components are then integrated into the cycle of addiction as we consider the development of drug dependency from a valuation perspective. We conclude with a discussion of cognitive interventions utilizing value-based decision-making, highlighting not just advances in recalibrating the valuation system to focus on non-drug rewards, but also areas for improvement in refining this approach.

Associations Between Delay Discounting and Connectivity of the Valuation-control System in Healthy Young Adults.

The process of valuation assists in determining if an object or course of action is rewarding. Delay discounting is the observed decay of a rewards' subjective value over time. Encoding the subjective value of rewards across a spectrum has been attributed to brain regions belonging to the valuation and executive control systems. The valuation system (VS) encodes reward value over short and long delays, influencing reinforcement learning and reward representation. The executive control system (ECS) becomes more active as choice difficulty increases, integrating contextual and mnemonic information with salience signals in the modulation of decision-making. Here, we aimed to identify resting-state functional connectivity-based patterns of the VS and ECS correlated with value-setting and delay discounting (outside-scanner paradigm) in a large (n = 992) cohort of healthy young adults from the Human Connectome Project (HCP). Results suggest the VS may be involved in value-setting of small, immediate rewards while the ECS may be involved in value-setting and delay discounting for large and small rewards over a range of delays. We observed magnitude sensitive connections involving the posterior cingulate cortex, time-sensitive connections with the ventromedial and lateral prefrontal cortex while connections involving the posterior parietal cortex appeared both magnitude- and time-sensitive. The ventromedial prefrontal cortex and posterior parietal cortex could act as "comparator" regions, weighing the value of small rewards against large rewards across various delay duration to aid in decision-making.