Spinal Cord Stimulation for Poststroke Hemiparesis: A Scoping Review.

IMPORTANCE: Spinal cord stimulation (SCS) is a neuromodulation technique that can improve paresis in individuals with spinal cord injury. SCS is emerging as a technique that can address upper and lower limb hemiparesis. Little is understood about its effectiveness with the poststroke population. OBJECTIVE: To summarize the evidence for SCS after stroke and any changes in upper extremity and lower extremity motor function. DATA SOURCES: PubMed, Web of Science, Embase, and CINAHL. The reviewers used hand searches and reference searches of retrieved articles. There were no limitations regarding publication year. STUDY SELECTION AND DATA COLLECTION: This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. The inclusion and exclusion criteria included a broad range of study characteristics. Studies were excluded if the intervention did not meet the definition of SCS intervention, used only animals or healthy participants, did not address upper or lower limb motor function, or examined neurological conditions other than stroke. FINDINGS: Fourteen articles met the criteria for this review. Seven studies found a significant improvement in motor function in groups receiving SCS. CONCLUSIONS AND RELEVANCE: Results indicate that SCS may provide an alternative means to improve motor function in the poststroke population. Plain-Language Summary: The results of this study show that spinal cord stimulation may provide an alternative way to improve motor function after stroke. Previous neuromodulation methods have targeted the impaired supraspinal circuitry after stroke. Although downregulated, spinal cord circuitry is largely intact and offers new possibilities for motor recovery.

Differential laboratory passaging of SARS-CoV-2 viral stocks impacts the in vitro assessment of neutralizing antibodies.

Viral populations in natural infections can have a high degree of sequence diversity, which can directly impact immune escape. However, antibody potency is often tested in vitro with a relatively clonal viral populations, such as laboratory virus or pseudotyped virus stocks, which may not accurately represent the genetic diversity of circulating viral genotypes. This can affect the validity of viral phenotype assays, such as antibody neutralization assays. To address this issue, we tested whether recombinant virus carrying SARS-CoV-2 spike (VSV-SARS-CoV-2-S) stocks could be made more genetically diverse by passage, and if a stock passaged under selective pressure was more capable of escaping monoclonal antibody (mAb) neutralization than unpassaged stock or than viral stock passaged without selective pressures. We passaged VSV-SARS-CoV-2-S four times concurrently in three cell lines and then six times with or without polyclonal antiserum selection pressure. All three of the monoclonal antibodies tested neutralized the viral population present in the unpassaged stock. The viral inoculum derived from serial passage without antiserum selection pressure was neutralized by two of the three mAbs. However, the viral inoculum derived from serial passage under antiserum selection pressure escaped neutralization by all three mAbs. Deep sequencing revealed the rapid acquisition of multiple mutations associated with antibody escape in the VSV-SARS-CoV-2-S that had been passaged in the presence of antiserum, including key mutations present in currently circulating Omicron subvariants. These data indicate that viral stock that was generated under polyclonal antiserum selection pressure better reflects the natural environment of the circulating virus and may yield more biologically relevant outcomes in phenotypic assays. Thus, mAb assessment assays that utilize a more genetically diverse, biologically relevant, virus stock may yield data that are relevant for prediction of mAb efficacy and for enhancing biosurveillance.

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.

Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.

Predators Induce Phenotypic Plasticity in Echinoderms across Life History Stages.

AbstractMarine invertebrates with biphasic life cycles feature life history transitions that coincide with habitat changes from benthic adults to planktonic embryos and larvae, then a return to the benthos as a juvenile at metamorphosis. The metamorphic transition exposes animals to a new suite of benthic predators, and high mortality often occurs in the hours and days following settlement. Juvenile invertebrates may produce phenotypically plastic morphological defenses when predator cues are detected. However, time lags inherent to phenotypic plasticity may delay the production of defenses until after the period of highest vulnerability. It should, therefore, be beneficial for planktonic larvae approaching settlement to detect waterborne cues from benthic predators and produce juvenile phenotypes appropriate for postmetamorphic survival. Echinoderms are useful models for testing transhabitat and trans-life history stage phenotypic plasticity because many species have larvae that construct their juvenile phenotype while still in the water column. In this study, we tested whether planktonic echinoderm larvae exposed to cues from benthic predators modified their juvenile phenotypes at settlement. Green urchin (Strongylocentrotus droebachiensis) and Pacific sand dollar (Dendraster excentricus) larvae were exposed to predatory green crab (Carcinus maenus) or red rock crab (Cancer productus) cues, respectively, from their early-stage juvenile rudiment formation through settlement. Green urchin larvae exposed to predator cues settled with significantly more juvenile spines compared to unexposed controls. Sand dollars exhibited earlier settlement, larger disk area, fewer spines, and shorter spines when exposed to benthic predator cues. Sand dollar larvae were also exposed to cues from planktonic crab larvae and in response settled sooner and larger, with even fewer and shorter spines than those exposed to benthic predator cues. These results suggest that echinoderm larvae alter their juvenile phenotype in response to predator cues, but the response varies between species, and responses to planktonic threats may be prioritized over benthic ones.

Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction.

Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information. Some methods require changing the NN architecture or training procedure, limiting the selection of NN models. Moreover, predictive uncertainty can come from different sources. It is important to have the ability to separately model different types of predictive uncertainty, as the model can take assorted actions depending on the source of uncertainty. In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at protein-ligand binding prediction. We use our prior knowledge on chemical compounds to design the experiments. By utilizing a visualization method we create non-overlapping and chemically diverse partitions from a collection of chemical compounds. These partitions are used as training and test set splits to explore NN model uncertainty. We demonstrate how the uncertainties estimated by the selected methods describe different sources of uncertainty under different partitions and featurization schemes and the relationship to prediction error.

Effect of Fiber Bundle Morphology on Fiber Dispersion for Long Fiber-Reinforced Thermoplastics.

Understanding the mechanics of fiber attrition during the extrusion process is highly important in predicting the strength of long fiber-reinforced thermoplastic composites. However, little work has been done to investigate the mechanics of fiber dispersion and its effects on fiber attrition. This study aims at investigating fiber dispersion in simple shear flows for long fiber-reinforced thermoplastic pellets. Depending on the fabrication process, fiber bundles display distinct levels of compaction within the pellets. Studies have shown that morphological differences can lead to differences in dispersion mechanics; therefore, using a Couette rheometer and a sliding plate rheometer, coated and pultruded pellets were subjected to simple shear deformation, and the amount of dispersion was quantified. Additionally, a new image-based analysis method is presented in this study to measure fiber dispersion for a multi-pellet-filled system. Results from the single-pellet dispersion study showed a small amount of correlation between the dimensionless morphological parameter and the dispersion measurement. Pultruded and coated pellets were both found to have similar dispersion rates in a multi-pellet system. However, pultruded pellets were found to have a higher dispersion value at all levels when compared with coated pellets in both dispersion studies.

A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2.

Minimizing the human and economic costs of the COVID-19 pandemic and future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. To this end, we introduce a new computational pipeline for the rapid identification and characterization of binding sites in viral proteins along with the key chemical features, which we call chemotypes, of the compounds predicted to interact with those same sites. The composition of source organisms for the structural models associated with an individual binding site is used to assess the site's degree of structural conservation across different species, including other viruses and humans. We propose a search strategy for novel therapeutics that involves the selection of molecules preferentially containing the most structurally rich chemotypes identified by our algorithm. While we demonstrate the pipeline on SARS-CoV-2, it is generalizable to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate predicted structures can be constructed.

Metagenomic Methods for Addressing NASA's Planetary Protection Policy Requirements on Future Missions: A Workshop Report.

Molecular biology methods and technologies have advanced substantially over the past decade. These new molecular methods should be incorporated among the standard tools of planetary protection (PP) and could be validated for incorporation by 2026. To address the feasibility of applying modern molecular techniques to such an application, NASA conducted a technology workshop with private industry partners, academics, and government agency stakeholders, along with NASA staff and contractors. The technical discussions and presentations of the Multi-Mission Metagenomics Technology Development Workshop focused on modernizing and supplementing the current PP assays. The goals of the workshop were to assess the state of metagenomics and other advanced molecular techniques in the context of providing a validated framework to supplement the bacterial endospore-based NASA Standard Assay and to identify knowledge and technology gaps. In particular, workshop participants were tasked with discussing metagenomics as a stand-alone technology to provide rapid and comprehensive analysis of total nucleic acids and viable microorganisms on spacecraft surfaces, thereby allowing for the development of tailored and cost-effective microbial reduction plans for each hardware item on a spacecraft. Workshop participants recommended metagenomics approaches as the only data source that can adequately feed into quantitative microbial risk assessment models for evaluating the risk of forward (exploring extraterrestrial planet) and back (Earth harmful biological) contamination. Participants were unanimous that a metagenomics workflow, in tandem with rapid targeted quantitative (digital) PCR, represents a revolutionary advance over existing methods for the assessment of microbial bioburden on spacecraft surfaces. The workshop highlighted low biomass sampling, reagent contamination, and inconsistent bioinformatics data analysis as key areas for technology development. Finally, it was concluded that implementing metagenomics as an additional workflow for addressing concerns of NASA's robotic mission will represent a dramatic improvement in technology advancement for PP and will benefit future missions where mission success is affected by backward and forward contamination.

Defining and targeting tumor-associated macrophages in malignant mesothelioma.

Defining the ontogeny of tumor-associated macrophages (TAM) is important to develop therapeutic targets for mesothelioma. We identified two distinct macrophage populations in mouse peritoneal and pleural cavities, the monocyte-derived, small peritoneal/pleural macrophages (SPM), and the tissue-resident large peritoneal/pleural macrophages (LPM). SPM rapidly increased in tumor microenvironment after tumor challenge and contributed to the vast majority of M2-like TAM. The selective depletion of M2-like TAM by conditional deletion of the Dicer1 gene in myeloid cells (D-/-) promoted tumor rejection. Sorted SPM M2-like TAM initiated tumorigenesis in vivo and in vitro, confirming their capacity to support tumor development. The transcriptomic and single-cell RNA sequencing analysis demonstrated that both SPM and LPM contributed to the tumor microenvironment by promoting the IL-2-STAT5 signaling pathway, inflammation, and epithelial-mesenchymal transition. However, while SPM preferentially activated the KRAS and TNF-α/NFkB signaling pathways, LPM activated the IFN-γ response. The importance of LPM in the immune response was confirmed by depleting LPM with intrapleural clodronate liposomes, which abrogated the antitumoral memory immunity. SPM gene signature could be identified in pleural effusion and tumor from patients with untreated mesothelioma. Five genes, TREM2, STAB1, LAIR1, GPNMB, and MARCO, could potentially be specific therapeutic targets. Accordingly, Trem2 gene deletion led to reduced SPM M2-like TAM with compensatory increase in LPM and slower tumor growth. Overall, these experiments demonstrate that SPM M2-like TAM play a key role in mesothelioma development, while LPM more specifically contribute to the immune response. Therefore, selective targeting of monocyte-derived TAM may enhance antitumor immunity through compensatory expansion of tissue-resident TAM.

Processing Human Milk to Increase Nutrient Density for Preterm Infants.

BACKGROUND: Human milk is the optimal food for newborns. Choices to feed preterm infants in neonatal intensive care units are mother's milk, donor milk, or formula. Preterm infants have better tolerance for human milk, but the lower caloric density of donor milk might not meet preterm infant growth needs. Preterm infants have higher protein and energy requirements with a limited stomach capacity. Therefore, there is a need for human milk with increased nutrient density. RESEARCH AIM: To concentrate donor milk to have a higher caloric and protein density while avoiding side effects of high osmolality by precipitating lactose at low temperatures. METHODS: We investigated the results of volume reduction and lactose removal processes on the lactose, protein, osmolality, and viscosity of human milk. Donor milk was obtained from WakeMed Mothers' Milk Bank. Homogenization and evaporative condensation were applied to samples (N = 36) before they were stored frozen overnight, followed by refrigerated centrifugation for lactose removal at 0 °C. Supernatants were separated and compared to the composition of controls. RESULTS: A significant reduction of lactose (SW = -262, p < .0001) and osmolality (SW = -211.5 p < .01) was achieved in the concentrated milk without a significant protein loss from centrifugation (SW = -44.5, p = .49). A 30%-40% volume reduction is within the American Academy of Pediatrics recommended osmolality for infant feeding. CONCLUSION: Concentrating human milk in a milk bank setting for feeding preterm infants might be a simple and low-cost process to achieve a product with higher nutrient density and no non-human components.

Multiple Mutations Associated with Emergent Variants Can Be Detected as Low-Frequency Mutations in Early SARS-CoV-2 Pandemic Clinical Samples.

Genetic analysis of intra-host viral populations provides unique insight into pre-emergent mutations that may contribute to the genotype of future variants. Clinical samples positive for SARS-CoV-2 collected in California during the first months of the pandemic were sequenced to define the dynamics of mutation emergence as the virus became established in the state. Deep sequencing of 90 nasopharyngeal samples showed that many mutations associated with the establishment of SARS-CoV-2 globally were present at varying frequencies in a majority of the samples, even those collected as the virus was first detected in the US. A subset of mutations that emerged months later in consensus sequences were detected as subconsensus members of intra-host populations. Spike mutations P681H, H655Y, and V1104L were detected prior to emergence in variant genotypes, mutations were detected at multiple positions within the furin cleavage site, and pre-emergent mutations were identified in the nucleocapsid and the envelope genes. Because many of the samples had a very high depth of coverage, a bioinformatics pipeline, "Mappgene", was established that uses both iVar and LoFreq variant calling to enable identification of very low-frequency variants. This enabled detection of a spike protein deletion present in many samples at low frequency and associated with a variant of concern.

PDBspheres: a method for finding 3D similarities in local regions in proteins.

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions ('spheres') adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. PDBspheres uses the LGA (Local-Global Alignment) structure alignment algorithm as the main engine for detecting structural similarities between the protein of interest and template spheres from the library, which currently contains >2 million spheres. To assess confidence in structural matches, an all-atom-based similarity metric takes side chain placement into account. Here, we describe the PDBspheres method, demonstrate its ability to detect and characterize binding sites in protein structures, show how PDBspheres-a strictly structure-based method-performs on a curated dataset of 2528 ligand-bound and ligand-free crystal structures, and use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of 4876 structures in the 'refined set' of the PDBbind 2019 dataset.

MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design.

The growing capabilities of synthetic biology and organic chemistry demand tools to guide syntheses toward useful molecules. Here, we present Molecular AutoenCoding Auto-Workaround (MACAW), a tool that uses a novel approach to generate molecules predicted to meet a desired property specification (e.g., a binding affinity of 50 nM or an octane number of 90). MACAW describes molecules by embedding them into a smooth multidimensional numerical space, avoiding uninformative dimensions that previous methods often introduce. The coordinates in this embedding provide a natural choice of features for accurately predicting molecular properties, which we demonstrate with examples for cetane and octane numbers, flash points, and histamine H1 receptor binding affinity. The approach is computationally efficient and well-suited to the small- and medium-size datasets commonly used in biosciences. We showcase the utility of MACAW for virtual screening by identifying molecules with high predicted binding affinity to the histamine H1 receptor and limited affinity to the muscarinic M2 receptor, which are targets of medicinal relevance. Combining these predictive capabilities with a novel generative algorithm for molecules allows us to recommend molecules with a desired property value (i.e., inverse molecular design). We demonstrate this capability by recommending molecules with predicted octane numbers of 40, 80, and 120, which is an important characteristic of biofuels. Thus, MACAW augments classical retrosynthesis tools by providing recommendations for molecules on specification.

Accelerators for Classical Molecular Dynamics Simulations of Biomolecules.

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.

Natural Rubber Blend Optimization via Data-Driven Modeling: The Implementation for Reverse Engineering.

Natural rubber formulation methodologies implemented within industry primarily implicate a high dependence on the formulator's experience as it involves an educated guess-and-check process. The formulator must leverage their experience to ensure that the number of iterations to the final blend composition is minimized. The study presented in this paper includes the implementation of blend formulation methodology that targets material properties relevant to the application in which the product will be used by incorporating predictive models, including linear regression, response surface method (RSM), artificial neural networks (ANNs), and Gaussian process regression (GPR). Training of such models requires data, which is equal to financial resources in industry. To ensure minimum experimental effort, the dataset is kept small, and the model complexity is kept simple, and as a proof of concept, the predictive models are used to reverse engineer a current material used in the footwear industry based on target viscoelastic properties (relaxation behavior, tanδ, and hardness), which all depend on the amount of crosslinker, plasticizer, and the quantity of voids used to create the lightweight high-performance material. RSM, ANN, and GPR result in prediction accuracy of 90%, 97%, and 100%, respectively. It is evident that the testing accuracy increases with algorithm complexity; therefore, these methodologies provide a wide range of tools capable of predicting compound formulation based on specified target properties, and with a wide range of complexity.

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking.

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

Intestinal Stem Cell Marker ASCL2 is a Novel Prognostic Predictor in Esophageal Adenocarcinoma.

Purpose Intestinal stem cell markers play a significant role in esophageal adenocarcinoma carcinogenesis via Barrett's esophagus; however, its utility as a prognostic biomarker has not been established. Methods We analyzed the immunohistochemical expression of intestinal stem cell markers, ASCL2 and LGR5, using whole slides (35 cases) and tissue microarray (TMA; 64 cases). On TMA slides, adjacent normal squamous epithelium, metaplastic glandular epithelium (Barrett's esophagus), and dysplastic glandular epithelium were inserted when applicable. Two pathologists semi-quantitatively scored stained slides independently, and the results were correlated with clinicopathologic factors and outcomes. Results In whole slides, 51% and 57% expressed high ASCL2 and high LGR5; in TMA, 69% and 88% expressed high ASCL2 and high LGR5, respectively. In TMA, high ASCL2 and low LGR5 expression significantly correlated to a higher number of involved lymph nodes (p=0.027 and p=0.0039), and LGR5 expression significantly correlated to the pathological stage (p=0.0032). Kaplan-Meier analysis showed a negative impact of high ASCL2 expression on overall survival (OS; WS p=0.0168, TMA p=0.0276) as well as progression-free survival (PFS; WS p=0.000638, TMA p=0.0466) but not LGR5. Multivariate Cox regression analysis revealed that ASCL2 expression is an independent prognostic factor for esophageal adenocarcinoma (OS; WS p=0.25, TMA p=0.011. PFS; WS p=0.012, TMA p=0.038). Analysis of the TCGA dataset showed that ASCL2 mRNA levels were correlated to nodal status but not overall survival. Conclusion High expression of the intestinal stem cell marker ASCL2 may predict unfavorable outcomes in surgically resected esophageal adenocarcinoma.

A cross-study analysis of drug response prediction in cancer cell lines.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

Prehabilitation with wearables versus standard of care before major abdominal cancer surgery: a randomised controlled pilot study (trial registration: NCT04047524).

BACKGROUND: Prehabilitation aims to improve post-operative outcomes by enhancing pre-operative fitness but is labour-intensive. This pilot study aimed to assess the efficacy of a tri-modal prehabilitation programme delivered by smartwatches for improving functional fitness prior to major abdominal cancer surgery. METHODS: A single-centre, randomised controlled pilot study, in which 22 patients were randomised to: (a) a prehabilitation group (n = 11), comprising of home-based exercise, nutritional, and dietary advice delivered using a wrist-worn smartwatch connected to a smartphone application; or (b) a control group (n = 11) receiving usual care, with patients given a smartwatch as a placebo. Eligible participants had over two weeks until planned surgery. The primary outcome was pre-operative physical activity including 6-min walk test (6MWT) distance, with secondary outcomes including change in body weight and hospital anxiety and depression score (HADS). RESULTS: Recruitment was 67% of eligible patients, with groups matched for baseline characteristics. The prehabilitation group engaged in more daily minutes of moderate [25.1 min (95% CI 9.79-40.44) vs 13.1 min (95% CI 5.97-20.31), p = 0.063] and vigorous physical activity [36.1 min (95% CI 21.24-50.90) vs 17.5 min (95% CI 5.18-29.73), p = 0.022] compared to controls. They also had significantly greater improvements in 6MWT distance compared to controls [+ 85.6 m (95% CI, + 18.06 to + 153.21) vs + 13.23 m (95% CI - 6.78 to 33.23), p = 0.014]. HADS scores remained unchanged from baseline in both groups. CONCLUSION: Prehabilitation in the colorectal cancer care setting can be delivered using smartwatches and mobile applications. Furthermore, this study provides early indicative evidence that such technologies can improve functional capacity prior to surgery TRIAL REGISTRATION: NCT04047524.