Exploratory mapping of tumor associated macrophage nanoparticle article abstracts using an eLDA topic modeling machine learning approach.

The role of macrophages in regulating the tumor microenvironment has spurned the exponential generation of nanoparticle targeting technologies. With the large amount of literature and the speed at which it is generated it is difficult to remain current with the most up-to-date literature. In this study we performed a topic modeling analysis of 854 abstracts of peer-reviewed literature for the most common usages of nanoparticle targeting of tumor associated macrophages (TAMs) in solid tumors. The data spans 20 years of literature, providing a broad perspective of the nanoparticle strategies. Our topic model found 6 distinct topics: Immune and TAMs, Nanoparticles, Imaging, Gene Delivery and Exosomes, Vaccines, and Multi-modal Therapies. We also found distinct nanoparticle usage, tumor types, and therapeutic trends across these topics. Moreover, we established that the topic model could be used to assign new papers into the existing topics, thereby creating a Living Review. This type of "birds-eye-view" analysis provides a useful assessment tool for exploring new and emerging themes within a large field.

A smoking cessation smartphone app that delivers real-time 'context aware' behavioural support: the Quit Sense feasibility RCT.

Background: During a quit attempt, cues from a smoker's environment are a major cause of brief smoking lapses, which increase the risk of relapse. Quit Sense is a theory-guided Just-In-Time Adaptive Intervention smartphone app, providing smokers with the means to learn about their environmental smoking cues and provides 'in the moment' support to help them manage these during a quit attempt. Objective: To undertake a feasibility randomised controlled trial to estimate key parameters to inform a definitive randomised controlled trial of Quit Sense. Design: A parallel, two-arm randomised controlled trial with a qualitative process evaluation and a 'Study Within A Trial' evaluating incentives on attrition. The research team were blind to allocation except for the study statistician, database developers and lead researcher. Participants were not blind to allocation. Setting: Online with recruitment, enrolment, randomisation and data collection (excluding manual telephone follow-up) automated through the study website. Participants: Smokers (323 screened, 297 eligible, 209 enrolled) recruited via online adverts on Google search, Facebook and Instagram. Interventions: Participants were allocated to 'usual care' arm (n = 105; text message referral to the National Health Service SmokeFree website) or 'usual care' plus Quit Sense (n = 104), via a text message invitation to install the Quit Sense app. Main outcome measures: Follow-up at 6 weeks and 6 months post enrolment was undertaken by automated text messages with an online questionnaire link and, for non-responders, by telephone. Definitive trial progression criteria were met if a priori thresholds were included in or lower than the 95% confidence interval of the estimate. Measures included health economic and outcome data completion rates (progression criterion #1 threshold: ≥ 70%), including biochemical validation rates (progression criterion #2 threshold: ≥ 70%), recruitment costs, app installation (progression criterion #3 threshold: ≥ 70%) and engagement rates (progression criterion #4 threshold: ≥ 60%), biochemically verified 6-month abstinence and hypothesised mechanisms of action and participant views of the app (qualitative). Results: Self-reported smoking outcome completion rates were 77% (95% confidence interval 71% to 82%) and health economic data (resource use and quality of life) 70% (95% CI 64% to 77%) at 6 months. Return rate of viable saliva samples for abstinence verification was 39% (95% CI 24% to 54%). The per-participant recruitment cost was £19.20, which included advert (£5.82) and running costs (£13.38). In the Quit Sense arm, 75% (95% CI 67% to 83%; 78/104) installed the app and, of these, 100% set a quit date within the app and 51% engaged with it for more than 1 week. The rate of 6-month biochemically verified sustained abstinence, which we anticipated would be used as a primary outcome in a future study, was 11.5% (12/104) in the Quit Sense arm and 2.9% (3/105) in the usual care arm (estimated effect size: adjusted odds ratio = 4.57, 95% CIs 1.23 to 16.94). There was no evidence of between-arm differences in hypothesised mechanisms of action. Three out of four progression criteria were met. The Study Within A Trial analysis found a £20 versus £10 incentive did not significantly increase follow-up rates though reduced the need for manual follow-up and increased response speed. The process evaluation identified several potential pathways to abstinence for Quit Sense, factors which led to disengagement with the app, and app improvement suggestions. Limitations: Biochemical validation rates were lower than anticipated and imbalanced between arms. COVID-19-related restrictions likely limited opportunities for Quit Sense to provide location tailored support. Conclusions: The trial design and procedures demonstrated feasibility and evidence was generated supporting the efficacy potential of Quit Sense. Future work: Progression to a definitive trial is warranted providing improved biochemical validation rates. Trial registration: This trial is registered as ISRCTN12326962. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 17/92/31) and is published in full in Public Health Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

Smokers often fail to quit because of urges to smoke triggered by their surroundings (e.g. being around smokers). We developed a smartphone app ('Quit Sense') which learns about an individual's surroundings and locations where they smoke. During a quit attempt, Quit Sense uses in-built sensors to identify when smokers are in those locations and sends 'in the moment' advice to help prevent them from smoking. We ran a feasibility study to help plan for a future large study to see if Quit Sense helps smokers to quit. This feasibility study was designed to tell us how many participants complete study measures; recruitment costs; how many participants install and use Quit Sense; and estimate whether Quit Sense may help smokers to stop and how it might do this. We recruited 209 smokers using online adverts on Google search, Facebook and Instagram, costing £19 per participant. Participants then had an equal chance of receiving a web link to the National Health Service SmokeFree website ('usual care group') or receive that same web link plus a link to the Quit Sense app ('Quit Sense group'). Three-quarters of the Quit Sense group installed the app on their phone and half of these used the app for more than 1 week. We followed up 77% of participants at 6 months to collect study data, though only 39% of quitters returned a saliva sample for abstinence verification. At 6 months, more people in the Quit Sense group had stopped smoking (12%) than the usual care group (3%). It was not clear how the app helped smokers to quit based on study measures, though interviews found that the process of training the app helped people quit through learning about what triggered their smoking behaviour. The findings support undertaking a large study to tell us whether Quit Sense really does help smokers to quit.

Meta-analysis of macrophage nanoparticle targeting across blood and solid tumors using an eLDA Topic modeling Machine Learning approach.

The role of macrophages in regulating the tumor microenvironment has spurned the exponential generation of nanoparticle targeting technologies. With the large amount of literature and the speed at which it is generated it is difficult to remain current with the most up-to-date literature. In this study we performed a topic modeling analysis of the most common usages of nanoparticle targeting of macrophages in solid tumors. The data spans 20 years of literature, providing an extensive meta-analysis of the nanoparticle strategies. Our topic model found 6 distinct topics: Immune and TAMs, Nanoparticles, Imaging, Gene Delivery and Exosomes, Vaccines, and Multi-modal Therapies. We also found distinct nanoparticle usage, tumor types, and therapeutic trends across these topics. Moreover, we established that the topic model could be used to assign new papers into the existing topics, thereby creating a Living Review. This type of meta-analysis provides a useful assessment tool for aggregating data about a large field.

An Automated, Online Feasibility Randomized Controlled Trial of a Just-In-Time Adaptive Intervention for Smoking Cessation (Quit Sense).

INTRODUCTION: Learned smoking cues from a smoker's environment are a major cause of lapse and relapse. Quit Sense, a theory-guided Just-In-Time Adaptive Intervention smartphone app, aims to help smokers learn about their situational smoking cues and provide in-the-moment support to help manage these when quitting. METHODS: A two-arm feasibility randomized controlled trial (N = 209) to estimate parameters to inform a definitive evaluation. Smoker's willing to make a quit attempt were recruited using online paid-for adverts and randomized to "usual care" (text message referral to NHS SmokeFree website) or "usual care" plus a text message invitation to install Quit Sense. Procedures, excluding manual follow-up for nonresponders, were automated. Follow-up at 6 weeks and 6 months included feasibility, intervention engagement, smoking-related, and economic outcomes. Abstinence was verified using cotinine assessment from posted saliva samples. RESULTS: Self-reported smoking outcome completion rates at 6 months were 77% (95% CI 71%, 82%), viable saliva sample return rate was 39% (95% CI 24%, 54%), and health economic data 70% (95% CI 64%, 77%). Among Quit Sense participants, 75% (95% CI 67%, 83%) installed the app and set a quit date and, of those, 51% engaged for more than one week. The 6-month biochemically verified sustained abstinence rate (anticipated primary outcome for definitive trial), was 11.5% (12/104) among Quit Sense participants and 2.9% (3/105) for usual care (adjusted odds ratio = 4.57, 95% CIs 1.23, 16.94). No evidence of between-group differences in hypothesized mechanisms of action was found. CONCLUSIONS: Evaluation feasibility was demonstrated alongside evidence supporting the effectiveness potential of Quit Sense. IMPLICATIONS: Running a primarily automated trial to initially evaluate Quit Sense was feasible, resulting in modest recruitment costs and researcher time, and high trial engagement. When invited, as part of trial participation, to install a smoking cessation app, most participants are likely to do so, and, for those using Quit Sense, an estimated one-half will engage with it for more than 1 week. Evidence that Quit Sense may increase verified abstinence at 6-month follow-up, relative to usual care, was generated, although low saliva return rates to verify smoking status contributed to considerable imprecision in the effect size estimate.

Organ Transplants From Deceased Donors With Primary Brain Tumors and Risk of Cancer Transmission.

Importance: Cancer transmission is a known risk for recipients of organ transplants. Many people wait a long time for a suitable transplant; some never receive one. Although patients with brain tumors may donate their organs, opinions vary on the risks involved. Objective: To determine the risk of cancer transmission associated with organ transplants from deceased donors with primary brain tumors. Key secondary objectives were to investigate the association that donor brain tumors have with organ usage and posttransplant survival. Design, Setting, and Participants: This was a cohort study in England and Scotland, conducted from January 1, 2000, to December 31, 2016, with follow-up to December 31, 2020. This study used linked data on deceased donors and solid organ transplant recipients with valid national patient identifier numbers from the UK Transplant Registry, the National Cancer Registration and Analysis Service (England), and the Scottish Cancer Registry. For secondary analyses, comparators were matched on factors that may influence the likelihood of organ usage or transplant failure. Statistical analysis of study data took place from October 1, 2021, to May 31, 2022. Exposures: A history of primary brain tumor in the organ donor, identified from all 3 data sources using disease codes. Main Outcomes and Measures: Transmission of brain tumor from the organ donor into the transplant recipient. Secondary outcomes were organ utilization (ie, transplant of an offered organ) and survival of kidney, liver, heart, and lung transplants and their recipients. Key covariates in donors with brain tumors were tumor grade and treatment history. Results: This study included a total of 282 donors (median [IQR] age, 42 [33-54] years; 154 females [55%]) with primary brain tumors and 887 transplants from them, 778 (88%) of which were analyzed for the primary outcome. There were 262 transplants from donors with high-grade tumors and 494 from donors with prior neurosurgical intervention or radiotherapy. Median (IQR) recipient age was 48 (35-58) years, and 476 (61%) were male. Among 83 posttransplant malignancies (excluding NMSC) that occurred over a median (IQR) of 6 (3-9) years in 79 recipients of transplants from donors with brain tumors, none were of a histological type matching the donor brain tumor. Transplant survival was equivalent to that of matched controls. Kidney, liver, and lung utilization were lower in donors with high-grade brain tumors compared with matched controls. Conclusions and Relevance: Results of this cohort study suggest that the risk of cancer transmission in transplants from deceased donors with primary brain tumors was lower than previously thought, even in the context of donors that are considered as higher risk. Long-term transplant outcomes are favorable. These results suggest that it may be possible to safely expand organ usage from this donor group.

Randomised controlled trial of a just-in-time adaptive intervention (JITAI) smoking cessation smartphone app: the Quit Sense feasibility trial protocol.

INTRODUCTION: A lapse (any smoking) early in a smoking cessation attempt is strongly associated with reduced success. A substantial proportion of lapses are due to urges to smoke triggered by situational cues. Currently, no available interventions proactively respond to such cues in real time. Quit Sense is a theory-guided just-in-time adaptive intervention smartphone app that uses a learning tool and smartphone sensing to provide in-the-moment tailored support to help smokers manage cue-induced urges to smoke. The primary aim of this randomised controlled trial (RCT) is to assess the feasibility of delivering a definitive online efficacy trial of Quit Sense. METHODS AND ANALYSES: A two-arm parallel-group RCT allocating smokers willing to make a quit attempt, recruited via online adverts, to usual care (referral to the NHS SmokeFree website) or usual care plus Quit Sense. Randomisation will be stratified by smoking rate (<16 vs ≥16 cigarettes/day) and socioeconomic status (low vs high). Recruitment, enrolment, baseline data collection, allocation and intervention delivery will be automated through the study website. Outcomes will be collected at 6 weeks and 6 months follow-up via the study website or telephone, and during app usage. The study aims to recruit 200 smokers to estimate key feasibility outcomes, the preliminary impact of Quit Sense and potential cost-effectiveness, in addition to gaining insights on user views of the app through qualitative interviews. ETHICS AND DISSEMINATION: Ethics approval has been granted by the Wales NHS Research Ethics Committee 7 (19/WA/0361). The findings will be disseminated to the public, the funders, relevant practice and policy representatives and other researchers. TRIAL REGISTRATION NUMBER: ISRCTN12326962.

Sleep disorders in rare genetic syndromes: a meta-analysis of prevalence and profile.

BACKGROUND: Sleep disorders are common in people with intellectual disability (ID) and autism, with growing evidence of diverse sleep profiles across ID associated genetic syndromes. Documenting the prevalence and profile of specific sleep disorders in syndromes will quantify syndrome-driven 'risk', inform prognosis and enhance understanding of aetiology of sleep disorders. METHOD: Following PRISMA guidelines for meta-analysis, we searched Ovid PsycINFO, Ovid MEDLINE, Ovid Embase, Web of Science and PubMed Central with use of syndrome-specific keywords and 60 sleep-related search terms. We screened and extracted papers that reported sleep disorder prevalence data for five or more individuals within a genetic syndrome, and applied quality criteria to produce a quality-effects prevalence model of six types of sleep disorder across nineteen syndromes. Relative risk estimates were calculated for the prevalence of each sleep disorder in each syndrome. RESULTS: Two hundred and seventy three papers were identified, generating 463 prevalence estimates for Angelman, CHARGE, Cornelia de Lange, Down, fragile X, Prader-Willi, Rett, Smith-Magenis and Williams syndromes, mucopolysaccharidoses (MPS disorders), neurofibromatosis and tuberous sclerosis complex. Prevalence estimates were higher in genetic syndromes than published equivalents for typically developing individuals, with few exceptions. Between-syndrome differences for some disorders were evident; sleep-disordered breathing was most prevalent in MPS disorders (72-77%), while excessive daytime sleepiness was highest in Smith-Magenis syndrome (60%). Conversely, insomnia, which was reported at a higher rate than TD estimates in all syndromes except fragile X, was not associated with specific genetic risk. This suggests insomnia could emerge because of the individual's environment or associated developmental delay, rather than any specific genetic syndromes. LIMITATIONS: Due to the broad scope of the meta-analysis, only syndromes previously identified as reporting preliminary sleep research were included. Other syndromes may also experience elevated prevalence rates of specific types of sleep disorder. Only English language papers were included. CONCLUSIONS: Differing prevalence rates between types of sleep disorder suggest differing causal mechanisms, such as cranio-facial morphology in Down and Prader-Willi syndromes and the build-up of mucopolysaccharides in MPS disorders. Priorities for clinical assessment and intervention for sleep disorders are discussed.

Comparison of automated and manual methods for washing red blood cells.

BACKGROUND: We sought to compare the quality of washed red blood cells (RBCs) produced using the ACP215 device or manual methods with different combinations of wash and storage solutions. Our aim was to establish manual methods of washing that would permit a shelf life of more than 24 hours. STUDY DESIGN AND METHODS: Fourteen-day-old RBCs were pooled, split, and washed in one of five ways: 1) using the ACP215 and stored in SAGM, 2) manually washed and stored in saline, 3) manually washed in saline and stored in SAGM, 4) manually washed in saline-glucose and stored in SAGM, and 5) manually washed and stored in SAGM. Additional units were pooled and split, washed manually or using the ACP215, and irradiated on Day 14. Units were sampled to 14 days after washing and storage at 4 ± 2°C. RESULTS: All washed RBCs met specification for volume (200-320 mL) and hemoglobin (Hb) content (>40 g/unit). Removal of plasma proteins was better using manual methods: residual immunoglobulin A in saline-glucose-washed cells 0.033 (0.007-0.058) mg/dL manual versus 0.064 (0.026-0.104) mg/dL ACP215 (median, range). Hb loss was lower in manually washed units (mean, ≤ 2.0g/unit) than in ACP215-washed units (mean, 6.1 g/unit). Disregarding saline-washed and stored cells, hemolysis in all nonirradiated units was less than 0.8% 14 days after washing. As expected, the use of SAGM to store manually washed units improved adenosine triphosphate, glucose, lactate, and pH versus storage in saline. CONCLUSION: The data suggest that the shelf life of manually washed RBCs could be extended to 14 days if stored in SAGM instead of saline.

Cost-effectiveness Analysis of Golimumab in the Treatment of Non-Radiographic Axial Spondyloarthritis in Scotland.

INTRODUCTION: The aim of this study is to assess the cost-effectiveness of golimumab for the treatment of non-radiographic axial spondyloarthritis (nr-axSpA) vs. conventional therapy and other tumor necrosis factor inhibitors from the Scottish payer perspective. METHODS: A model comprising a short-term decision tree and a long-term Markov model was developed to compare cost-effectiveness (incremental costs per quality-adjusted life-year [QALY]) for patients in Scotland with nr-axSpA treated by conventional therapy, adalimumab, certolizumab pegol, etanercept, or golimumab for a lifetime period. A network meta-analysis (NMA) was conducted to identify clinical and safety data for treatments and synthesize the available evidence into relative treatment effects between comparators. The probability of patients achieving an Assessment of SpondyloArthritis International Society 20/40% response criteria (ASAS20/ASAS40) or a 50% improvement in Bath Ankylosing Spondylitis Disease Activity Index score (BASDAI50) at week 12 was obtained from the NMA for each of the comparators. Baseline health state utilities were based on the EQ-5D questionnaire collected in the golimumab GO-AHEAD study. The cost of treatment was calculated based on drug acquisition, drug administration, and initiation/monitoring costs. RESULTS: Golimumab resulted in an increase of 2.06 QALYs and additional cost of £39,770 compared with conventional therapy. Incremental cost per QALY gained was £19,280 for golimumab, which was lower than adalimumab (£19,737), etanercept (£20,089), and higher than certolizumab pegol (£18,710). Golimumab remained cost-effective throughout a range of sensitivity analyses where key assumptions were tested. CONCLUSIONS: From a Scottish perspective, golimumab was a cost-effective treatment for nr-axSpA compared with conventional therapy at a willingness-to-pay threshold of £30,000 per QALY. FUNDING: Merck & Co., Inc.

Cost-Effectiveness of Golimumab in Ankylosing Spondylitis from the UK Payer Perspective.

INTRODUCTION: Golimumab is a tumor necrosis factor-α (TNF-α) inhibitor for treatment of patients with severe, active ankylosing spondylitis. This study evaluated the cost-effectiveness of golimumab compared with conventional care and other TNF-α inhibitors in treatment of AS from the UK National Health Service perspective. METHODS: A long-term Markov model (with initial decision tree) was developed to simulate the progression of a hypothetical cohort of patients with active AS over a lifetime. The effectiveness outcome was quality-adjusted life-years (QALYs). Utilities were estimated by mapping Bath Ankylosing Spondylitis Functional Index scores, and the primary response measure was ≥50% improvement on the Bath Ankylosing Spondylitis Disease Activity Index at 12 weeks. Direct, medication, and AS management costs were included. Costs and outcomes were discounted at 3.5%. RESULTS: All TNF-α inhibitors were comparable to each other and superior to conventional care. The incremental cost-effectiveness ratios (ICERs) for TNF-α inhibitors were £19,070-42,532 per QALY gained compared with conventional care. Analyses of the ICERs for each TNF-α inhibitor compared with conventional care demonstrated that golimumab was the most cost-effective treatment, and that adalimumab and etanercept were dominated by golimumab. Sensitivity analyses confirmed the robustness of these analyses. CONCLUSIONS: Golimumab may be considered a cost-effective treatment alternative for patients with active AS. With comparable costs and efficacy among TNF-α inhibitors, the choice of TNF-α inhibitor to treat AS is likely to be driven by patient and physician choice. FUNDING: Merck & Co., Inc.

A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study.

BACKGROUND: A major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. OBJECTIVE: We sought to (1) assess smokers' compliance with reporting their smoking in real time and identify reasons for noncompliance, (2) assess the app's accuracy in identifying user-specific high-risk locations for smoking, (3) explore the feasibility and user perspective of geofence-triggered support, and (4) identify any technological issues or privacy concerns. METHODS: An explanatory sequential mixed-methods design was used, where data collected by the app informed semistructured interviews. Participants were smokers who owned an Android mobile phone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, end-of-day (EoD) surveys of smoking beliefs and behavior, support message ratings, and app interaction data. Interviews were undertaken and analyzed thematically (N=13). Quantitative and qualitative data were analyzed separately and findings presented sequentially. RESULTS: Out of 15 participants, 3 (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 37.8 (SD 21.2) per participant, or 2.0 (SD 2.2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56.2% of days. Geolocation was collected in 97.0% of smoking reports with a mean accuracy of 31.6 (SD 16.8) meters. A total of 5 out of 9 (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50.0% (137/274) of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 78.2% (158/202) of delivered messages were rated by participants. Qualitative findings identified multiple reasons for noncompliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app's identification of their smoking locations, were largely positive about the value of geofence-triggered support, and had no privacy concerns about the data collected by the app. CONCLUSIONS: User-initiated self-report is feasible for training a cessation app about an individual's smoking behavior, although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants.

The vascular disrupting agent BNC105 potentiates the efficacy of VEGF and mTOR inhibitors in renal and breast cancer.

BNC105 is a tubulin targeting compound that selectively disrupts vasculature within solid tumors. The severe tumor hypoxia and necrosis that ensues translates to short term tumor growth inhibition. We sought to identify the molecular and cellular events activated following BNC105 treatment that drives tumor recovery. We investigated tumor adaptation to BNC105-induced hypoxia in animal models of breast and renal cancer. HIF-1α and GLUT-1 were found to be strongly upregulated by BNC105 as was the VEGF signaling axis. Phosphorylation of mTOR, 4E-BP-1 and elF2α were upregulated, consistent with increased protein synthesis and increased expression of VEGF-A. We sought to investigate the potential therapeutic utility of combining BNC105 with agents targeting VEGF and mTOR signaling. Bevacizumab and pazopanib target the VEGF axis and have been approved for first line use in renal cancer. Everolimus targets mTOR and is currently approved in second line therapy of renal and particular breast cancers. We combined these agents with BNC105 to explore the effects on tumor vasculature, tumor growth inhibition and animal survival. Bevacizumab hindered tumor vascular recovery following BNC105 treatment leading to greater tumor growth inhibition in a breast cancer model. Consistent with this, addition of BNC105 to pazopanib treatment resulted in a significant increase in survival in an orthotopic renal cancer model. Combination treatment of BNC105 with everolimus also increased tumor growth inhibition. BNC105 is currently being evaluated in a randomized phase II clinical trial in combination with everolimus in renal cancer.