Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution.

3' untranslated region (3'UTR) variants are strongly associated with human traits and diseases, yet few have been causally identified. We developed the massively parallel reporter assay for 3'UTRs (MPRAu) to sensitively assay 12,173 3'UTR variants. We applied MPRAu to six human cell lines, focusing on genetic variants associated with genome-wide association studies (GWAS) and human evolutionary adaptation. MPRAu expands our understanding of 3'UTR function, suggesting that simple sequences predominately explain 3'UTR regulatory activity. We adapt MPRAu to uncover diverse molecular mechanisms at base pair resolution, including an adenylate-uridylate (AU)-rich element of LEPR linked to potential metabolic evolutionary adaptations in East Asians. We nominate hundreds of 3'UTR causal variants with genetically fine-mapped phenotype associations. Using endogenous allelic replacements, we characterize one variant that disrupts a miRNA site regulating the viral defense gene TRIM14 and one that alters PILRB abundance, nominating a causal variant underlying transcriptional changes in age-related macular degeneration.

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome.

Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins-half the proteome-in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome whose structure encodes protein function, localization, and complex membership. Comparison across cell lines validates thousands of interactions and reveals extensive customization. Whereas shared interactions reside in core complexes and involve essential proteins, cell-specific interactions link these complexes, "rewiring" subnetworks within each cell's interactome. Interactions covary among proteins of shared function as the proteome remodels to produce each cell's phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.

Extensive global wetland loss over the past three centuries.

Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity1,2. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain3. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km2 (confidence interval 2.9-3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16-23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration4.

Global Landscape and Dynamics of Parkin and USP30-Dependent Ubiquitylomes in iNeurons during Mitophagic Signaling.

The ubiquitin ligase Parkin, protein kinase PINK1, USP30 deubiquitylase, and p97 segregase function together to regulate turnover of damaged mitochondria via mitophagy, but our mechanistic understanding in neurons is limited. Here, we combine induced neurons (iNeurons) derived from embryonic stem cells with quantitative proteomics to reveal the dynamics and specificity of Parkin-dependent ubiquitylation under endogenous expression conditions. Targets showing elevated ubiquitylation in USP30-/- iNeurons are concentrated in components of the mitochondrial translocon, and the ubiquitylation kinetics of the vast majority of Parkin targets are unaffected, correlating with a modest kinetic acceleration in accumulation of pS65-Ub and mitophagic flux upon mitochondrial depolarization without USP30. Basally, ubiquitylated translocon import substrates accumulate, suggesting a quality control function for USP30. p97 was dispensable for Parkin ligase activity in iNeurons. This work provides an unprecedented quantitative landscape of the Parkin-modified ubiquitylome in iNeurons and reveals the underlying specificity of central regulatory elements in the pathway.

Transcriptome analysis of polyploid giant cancer cells and their progeny reveals a functional role for p21 in polyploidization and depolyploidization.

Polyploid giant cancer cells (PGCC) are frequently detected in tumors and are increasingly recognized for their roles in chromosomal instability and associated genome evolution that leads to cancer recurrence. We previously reported that therapy stress promotes polyploidy, and that acid ceramidase plays a role in depolyploidization. In this study, we used an RNA-seq approach to gain a better understanding of the underlying transcriptomic changes that occur as cancer cells progress through polyploidization and depolyploidization. Our results revealed gene signatures that are associated with disease-free and/or overall survival in several cancers and identified the cell cycle inhibitor CDKN1A/p21 as the major hub in PGCC and early progeny. Increased expression of p21 in PGCC was limited to the cytoplasm. We previously demonstrated that the sphingolipid enzyme acid ceramidase is dispensable for polyploidization upon therapy stress but plays a crucial role in depolyploidization. The current study demonstrates that treatment of cells with ceramide is not sufficient for p53-independent induction of p21 and that knockdown of acid ceramidase, which hydrolyzes ceramide, does not interfere with upregulation of p21. In contrast, blocking the expression of p21 with UC2288 prevented the induction of acid ceramidase and inhibited both the formation of PGCC from parental cells as well as the generation of progeny from PGCC. Taken together, our data suggest that p21 functions upstream of acid ceramidase and plays an important role in polyploidization and depolyploidization.

ZMYM3 regulates BRCA1 localization at damaged chromatin to promote DNA repair.

Chromatin connects DNA damage response factors to sites of damaged DNA to promote the signaling and repair of DNA lesions. The histone H2A variants H2AX, H2AZ, and macroH2A represent key chromatin constituents that facilitate DNA repair. Through proteomic screening of these variants, we identified ZMYM3 (zinc finger, myeloproliferative, and mental retardation-type 3) as a chromatin-interacting protein that promotes DNA repair by homologous recombination (HR). ZMYM3 is recruited to DNA double-strand breaks through bivalent interactions with both histone and DNA components of the nucleosome. We show that ZMYM3 links the HR factor BRCA1 to damaged chromatin through specific interactions with components of the BRCA1-A subcomplex, including ABRA1 and RAP80. By regulating ABRA1 recruitment to damaged chromatin, ZMYM3 facilitates the fine-tuning of BRCA1 interactions with DNA damage sites and chromatin. Consistent with a role in regulating BRCA1 function, ZMYM3 deficiency results in impaired HR repair and genome instability. Thus, our work identifies a critical chromatin-binding DNA damage response factor, ZMYM3, which modulates BRCA1 functions within chromatin to ensure the maintenance of genome integrity.

A KMT2A-AFF1 gene regulatory network highlights the role of core transcription factors and reveals the regulatory logic of key downstream target genes.

Regulatory interactions mediated by transcription factors (TFs) make up complex networks that control cellular behavior. Fully understanding these gene regulatory networks (GRNs) offers greater insight into the consequences of disease-causing perturbations than can be achieved by studying single TF binding events in isolation. Chromosomal translocations of the lysine methyltransferase 2A (KMT2A) gene produce KMT2A fusion proteins such as KMT2A-AFF1 (previously MLL-AF4), causing poor prognosis acute lymphoblastic leukemias (ALLs) that sometimes relapse as acute myeloid leukemias (AMLs). KMT2A-AFF1 drives leukemogenesis through direct binding and inducing the aberrant overexpression of key genes, such as the anti-apoptotic factor BCL2 and the proto-oncogene MYC However, studying direct binding alone does not incorporate possible network-generated regulatory outputs, including the indirect induction of gene repression. To better understand the KMT2A-AFF1-driven regulatory landscape, we integrated ChIP-seq, patient RNA-seq, and CRISPR essentiality screens to generate a model GRN. This GRN identified several key transcription factors such as RUNX1 that regulate target genes downstream of KMT2A-AFF1 using feed-forward loop (FFL) and cascade motifs. A core set of nodes are present in both ALL and AML, and CRISPR screening revealed several factors that help mediate response to the drug venetoclax. Using our GRN, we then identified a KMT2A-AFF1:RUNX1 cascade that represses CASP9, as well as KMT2A-AFF1-driven FFLs that regulate BCL2 and MYC through combinatorial TF activity. This illustrates how our GRN can be used to better connect KMT2A-AFF1 behavior to downstream pathways that contribute to leukemogenesis, and potentially predict shifts in gene expression that mediate drug response.

SWAN pathway-network identification of common aneuploidy-based oncogenic drivers.

Haploinsufficiency drives Darwinian evolution. Siblings, while alike in many aspects, differ due to monoallelic differences inherited from each parent. In cancer, solid tumors exhibit aneuploid genetics resulting in hundreds to thousands of monoallelic gene-level copy-number alterations (CNAs) in each tumor. Aneuploidy patterns are heterogeneous, posing a challenge to identify drivers in this high-noise genetic environment. Here, we developed Shifted Weighted Annotation Network (SWAN) analysis to assess biology impacted by cumulative monoallelic changes. SWAN enables an integrated pathway-network analysis of CNAs, RNA expression, and mutations via a simple web platform. SWAN is optimized to best prioritize known and novel tumor suppressors and oncogenes, thereby identifying drivers and potential druggable vulnerabilities within cancer CNAs. Protein homeostasis, phospholipid dephosphorylation, and ion transport pathways are commonly suppressed. An atlas of CNA pathways altered in each cancer type is released. These CNA network shifts highlight new, attractive targets to exploit in solid tumors.

Barriers to and facilitators of implementing colorectal cancer screening evidence-based interventions in federally qualified health centers: a qualitative study.

BACKGROUND: There is an urgent need to increase colorectal cancer screening (CRCS) uptake in Texas federally qualified health centers (FQHCs), which serve a predominantly vulnerable population with high demands. Empirical support exists for evidence-based interventions (EBIs) that are proven to increase CRCS; however, as with screening, their use remains low in FQHCs. This study aimed to identify barriers to and facilitators of implementing colorectal cancer screening (CRCS) evidence-based interventions (EBIs) in federally qualified health centers (FQHCs), guided by the Consolidated Framework for Implementation Research (CFIR). METHODS: We recruited employees involved in implementing CRCS EBIs (e.g., physicians) using data from a CDC-funded program to increase the CRCS in Texas FQHCs. Through 23 group interviews, we explored experiences with practice change, CRCS promotion and quality improvement initiatives, organizational readiness, the impact of COVID-19, and the use of CRCS EBIs (e.g., provider reminders). We used directed content analysis with CFIR constructs to identify the critical facilitators and barriers. RESULTS: The analysis revealed six primary CFIR constructs that influence implementation: information technology infrastructure, innovation design, work infrastructure, performance measurement pressure, assessing needs, and available resources. Based on experiences with four recommended EBIs, participants described barriers, including data limitations of electronic health records and the design of reminder alerts targeted at deliverers and recipients of patient or provider reminders. Implementation facilitators include incentivized processes to increase provider assessment and feedback, existing clinic processes (e.g., screening referrals), and available resources to address patient needs (e.g., transportation). Staff buy-in emerged as an implementation facilitator, fostering a conducive environment for change within clinics. CONCLUSIONS: Using CFIR, we identified barriers, such as the burden of technology infrastructure, and facilitators, such as staff buy-in. The results, which enhance our understanding of CRCS EBI implementation in FQHCs, provide insights into designing nuanced, practical implementation strategies to improve cancer control in a critical setting.

Image Reconstruction Requirements for Short-Range Inductive Sensors Used in Single-Coil MIT.

MIT (magnetic induction tomography) image reconstruction from data acquired with a single, small inductive sensor has unique requirements not found in other imaging modalities. During the course of scanning over a target, measured inductive loss decreases rapidly with distance from the target boundary. Since inductive loss exists even at infinite separation due to losses internal to the sensor, all other measurements made in the vicinity of the target require subtraction of the infinite-separation loss. This is accomplished naturally by treating infinite-separation loss as an unknown. Furthermore, since contributions to inductive loss decline with greater depth into a conductive target, regularization penalties must be decreased with depth. A pair of squared L2 penalty norms are combined to form a 2-term Sobolev norm, including a zero-order penalty that penalizes solution departures from a default solution and a first-order penalty that promotes smoothness. While constraining the solution to be non-negative and bounded from above, the algorithm is used to perform image reconstruction on scan data obtained over a 4.3 cm thick phantom consisting of bone-like features embedded in agarose gel, with the latter having a nominal conductivity of 1.4 S/m.

Reactivity to conditioned threat cues is distinct from exploratory drive in the elevated plus maze.

Fear and anxiety are adaptive states that allow humans and animals alike to respond appropriately to threatening cues in their environment. Commonly used tasks for studying behaviour akin to fear and anxiety in rodent models are Pavlovian threat conditioning and the elevated plus maze (EPM), respectively. In threat conditioning the rodents learn to associate an aversive event with a specific stimulus or context. The learnt association between the two stimuli (the 'memory') can then be recalled by re-exposing the subject to the conditioned stimulus. The elevated plus maze is argued to measure the agoraphobic avoidance of the brightly lit open maze arms in crepuscular rodents. These two tasks have been used extensively, yet research into whether they interact is scarce. We investigated whether recall of an aversive memory, across contextual, odour or auditory modalities, would potentiate anxiety-like behaviour in the elevated plus maze. The data did not support that memory recall, even over a series of time points, could influence EPM behaviour. Furthermore, there was no correlation between EPM behaviour and conditioned freezing in independent cohorts tested in the EPM before or after auditory threat conditioning. Further analysis found the production of 22 kHz ultrasonic vocalisations revealed the strongest responders to a conditioned threat cue. These results are of particular importance for consideration when using the EPM and threat conditioning to identify individual differences and the possibility to use the tasks in batteries of tests without cross-task interference.

Metabolism and Excretion of Therapeutic Peptides: Current Industry Practices, Perspectives, and Recommendations.

Therapeutic peptides (TPeps) have expanded from the initial endogenous peptides to complex modified peptides through medicinal chemistry efforts for almost a century. Different from small molecules and large proteins, the diverse submodalities of TPeps have distinct structures and carry different absorption, distribution, metabolism, and excretion (ADME) properties. There is no distinct regulatory guidance for the industry on conducting ADME studies (what, how, and when) for TPeps. Therefore, the Peptide ADME Working Group sponsored by the Translational and ADME Sciences Leadership Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) was formed with the goal to develop a white paper focusing on metabolism and excretion studies to support discovery and development of TPeps. In this paper, the key learnings from an IQ industry survey and U.S. Food and Drug Administration/European Medicines Agency submission documents of TPeps approved between 2011 and 2022 are outlined in detail. In addition, a comprehensive assessment of in vitro and in vivo metabolism and excretion studies, mitigation strategies for TPep metabolism, analytical tools to conduct studies, regulatory status, and Metabolites in Safety Testing considerations are provided. Finally, an industry recommendation on conducting metabolism and excretion studies is proposed for regulatory filing of TPeps. SIGNIFICANCE STATEMENT: This white paper presents current industry practices for metabolism and excretion studies of therapeutic peptides based on an industry survey, regulatory submission documents, and expert opinions from the participants in the Peptide Absorption, Distribution, Metabolism, and Excretion Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development. The group also provides recommendations on the Metabolites in Safety Testing considerations and metabolism and excretion studies for regulatory filing of therapeutic peptides.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

The impact of rare variation on gene expression across tissues.

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Architecture of the human interactome defines protein communities and disease networks.

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

Autophagy gene haploinsufficiency drives chromosome instability, increases migration, and promotes early ovarian tumors.

Autophagy, particularly with BECN1, has paradoxically been highlighted as tumor promoting in Ras-driven cancers, but potentially tumor suppressing in breast and ovarian cancers. However, studying the specific role of BECN1 at the genetic level is complicated due to its genomic proximity to BRCA1 on both human (chromosome 17) and murine (chromosome 11) genomes. In human breast and ovarian cancers, the monoallelic deletion of these genes is often co-occurring. To investigate the potential tumor suppressor roles of two of the most commonly deleted autophagy genes in ovarian cancer, BECN1 and MAP1LC3B were knocked-down in atypical (BECN1+/+ and MAP1LC3B+/+) ovarian cancer cells. Ultra-performance liquid chromatography mass-spectrometry metabolomics revealed reduced levels of acetyl-CoA which corresponded with elevated levels of glycerophospholipids and sphingolipids. Migration rates of ovarian cancer cells were increased upon autophagy gene knockdown. Genomic instability was increased, resulting in copy-number alteration patterns which mimicked high grade serous ovarian cancer. We further investigated the causal role of Becn1 haploinsufficiency for oncogenesis in a MISIIR SV40 large T antigen driven spontaneous ovarian cancer mouse model. Tumors were evident earlier among the Becn1+/- mice, and this correlated with an increase in copy-number alterations per chromosome in the Becn1+/- tumors. The results support monoallelic loss of BECN1 as permissive for tumor initiation and potentiating for genomic instability in ovarian cancer.

Regional trends and drivers of the global methane budget.

The ongoing development of the Global Carbon Project (GCP) global methane (CH4 ) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH4  yr-1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.

Autophagy modulating therapeutics inhibit ovarian cancer colony generation by polyploid giant cancer cells (PGCCs).

BACKGROUND: Genomic instability and chemoresistance can arise in cancer due to a unique form of plasticity: that of polyploid giant cancer cells (PGCCs). These cells form under the stress of chemotherapy and have higher than diploid chromosome content. PGCCs are able to then repopulate tumors through an asymmetric daughter cell budding process. PGCCs have been observed in ovarian cancer histology, including the deadly and common form high-grade serous ovarian carcinoma (HGSC). We previously discovered that drugs which disrupt the cellular recycling process of autophagy are uniquely efficacious in pre-clinical HGSC models. While autophagy induction has been associated with PGCCs, it has never been previously investigated if autophagy modulation interacts with the PGCC life cycle and this form of tumor cell plasticity. METHODS: CAOV3 and OVCAR3 ovarian cancer cell lines were treated with carboplatin or docetaxel to induce PGCC formation. Microscopy was used to characterize and quantify PGCCs formed by chemotherapy. Two clinically available drugs that inhibit autophagy, hydroxychloroquine and nelfinavir, and a clinically available activator of autophagy, rapamycin, were employed to test the effect of these autophagy modulators on PGCC induction and subsequent colony formation from PGCCs. Crystal violet-stained colony formation assays were used to quantify the tumor-repopulating stage of the PGCC life cycle. RESULTS: Autophagy inhibitors did not prevent PGCC formation in OVCAR3 or CAOV3 cells. Rapamycin did not induce PGCC formation on its own nor did it exacerbate PGCC formation by chemotherapy. However, hydroxychloroquine prevented efficient colony formation in CAOV3 PGCCs induced by carboplatin (27% inhibition) or docetaxel (41% inhibition), as well as in OVCAR3 cells (95% and 77%, respectively). Nelfinavir similarly prevented colony formation in CAOV3 PGCCs induced by carboplatin (64% inhibition) or docetaxel (94% inhibition) as well as in OVCAR3 cells (89% and 80%, respectively). Rapamycin surprisingly also prevented PGCC colony outgrowth (52-84% inhibition). CONCLUSIONS: While the autophagy previously observed to correlate with PGCC formation is unlikely necessary for PGCCs to form, autophagy modulating drugs severely impair the ability of HGSC PGCCs to form colonies. Clinical trials which utilize hydroxychloroquine, nelfinavir, and/or rapamycin after chemotherapy may be of future interest.

Pilot Randomized Controlled Trial of Feasibility, Acceptability, and Preliminary Efficacy of a Web-Based Physical Activity and Sedentary Time Intervention for Survivors of Physical Inactivity-Related Cancers.

BACKGROUND: This pilot study explored the feasibility, acceptability, and usability of a web-based intervention for survivors of physical inactivity-related cancers through a two-arm, 12-week randomized controlled trial. Secondarily, this study tested the change in physical activity (PA) and sedentary time with intervention exposure. METHODS: Prior to randomization to the intervention (n = 45) or behavior "as usual" wait-listed control (n = 40) groups, participants completed baseline surveys and an accelerometer protocol. The intervention focused on increasing PA and decreasing sedentary time through social cognitive theory techniques. Follow-up acceptability/usability surveys (intervention group only) and accelerometers were sent after the intervention period. Information on intervention completion, adverse events, and user statistics were collected to determine feasibility. Median login time and mean acceptability/usability scores were calculated. RESULTS: Participants (mean age = 60 ± 7 years) included female (n = 80, 94%) and male survivors of breast (82%), colon (6%), endometrial (6%), bladder (4%), and kidney (2%) cancer. Seventy-eight (91.7%) participants returned partially or fully complete post-intervention data. There were no reported injuries or safety concerns. Intervention participants logged into the website for a total of 95 min (Q1, Q3 = 11, 204). System usability scores (72 ± 3) indicated above average usability of the website. Changes in time spent active and sedentary were not statistically significantly different between groups (p = 0.45), but within-group changes suggested intervention group participants spent more time active and less time sedentary after the intervention. CONCLUSION: Results of this pilot study suggest its feasibility and acceptability for survivors of several inactivity-related cancers. Additional research to determine long-term efficacy is warranted. This low-cost online-only intervention has the potential to have a very broad reach. TRIAL REGISTRATION: Clinical Trials Number: NCT03983083. Date registered: June 12th, 2019.

Attention control in a trial of an online psychoeducational intervention for caregivers.

In reports of randomized controlled trials, thorough description of the attention control condition has been recommended, yet is frequently lacking. The Tele-Savvy Caregiver program for informal caregivers of persons living with dementia was tested in a randomized controlled trial with an attention control condition. The purpose of this trial was to test Tele-Savvy's efficacy in reducing the negative effects of caregiving on caregivers, promoting quality of life for persons living with dementia, and improving caregiver mastery. We describe the design and implementation of and examine the outcomes associated with the attention control condition. Caregivers were randomized to the immediate Tele-Savvy (active condition), Healthy Living (attention control), or waitlist. The attention control content was focused on healthy lifestyle and was not intended to affect the outcomes that Tele-Savvy targeted. The attention control group was similar to Tele-Savvy in the intervention structure and duration: it consisted of seven weekly group videoconferences and 36 video lessons. Data on outcomes of caregivers and persons living with dementia were collected at baseline and 3 and 6 months postbaseline. Multilevel mixed effects models were used to determine changes in the outcomes. One hundred and eleven caregivers were randomized to the attention control condition (attrition 21.6%). Eighteen formative assessment interviews focusing on caregivers' experience in the attention control condition were conducted. The attention control condition completers had no statistically significant changes in the variables that Tele-Savvy targeted. These results may be used in the design and implementation of attention control conditions in behavioral intervention research.