Cost-effectiveness analysis of microwave ablation versus robot-assisted partial nephrectomy for patients with small renal masses in Australia.

OBJECTIVES: Microwave ablation (MWA) has gained attention as a minimally invasive and safe alternative to surgical intervention for patients with small renal masses; however, its cost-effectiveness in Australia remains unclear. This study conducted a cost-effectiveness analysis to evaluate the relative clinical and economic merits of MWA compared to robotic-assisted partial nephrectomy (RA-PN) in the treatment of small renal masses. METHODS: A Markov state-transition model was constructed to simulate the progression of Australian patients with small renal masses treated with MWA versus RA-PN over a 10-year horizon. Transition probabilities and utility data were sourced from comprehensive literature reviews, and cost data were estimated from the Australian health system perspective. Life-years, quality-adjusted life-years (QALYs), and lifetime costs were estimated. Modelled uncertainty was assessed using both deterministic and probabilistic sensitivity analyses. A willingness-to-pay (WTP) threshold of $50,000 per QALY was adopted. All costs are expressed in 2022 Australian dollars and discounted at 3% annually. To assess the broader applicability of our findings, a validated cost-adaptation method was employed to extend the analysis to 8 other high-income countries. RESULTS: Both the base case and cost-adaptation analyses revealed that MWA dominated RA-PN, producing both lower costs and greater effectiveness over 10 years. The cost-effectiveness outcome was robust across all model parameters. Probabilistic sensitivity analyses confirmed that MWA was dominant in 98.3% of simulations at the designated WTP threshold, underscoring the reliability of the model under varying assumptions. CONCLUSION: For patients with small renal masses in Australia and comparable healthcare settings, MWA is the preferred strategy to maximize health benefits per dollar, making it a highly cost-effective alternative to RA-PN.

Prediction of immunotherapy response using mutations to cancer protein assemblies.

While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non-small cell lung cancers are effectively predicted by factoring tumor mutation burden (TMB) into burdens on specific protein assemblies. This approach identifies 13 protein assemblies for which the assembly-level mutation burden (AMB) predicts treatment outcomes, which can be combined to powerfully separate responders from nonresponders in multiple cohorts (e.g., 76% versus 37% bladder cancer 1-year survival). These results are corroborated by (i) engineered disruptions in the predictive assemblies, which modulate immunotherapy response in mice, and (ii) histochemistry showing that predicted responders have elevated inflammation. The 13 assemblies have diverse roles in DNA damage checkpoints, oxidative stress, or Janus kinase/signal transducers and activators of transcription signaling and include unexpected genes (e.g., PIK3CG and FOXP1) for which mutation affects treatment response. This study provides a roadmap for using tumor cell biology to factor mutational effects on immune response.

Integrated germline and somatic features reveal divergent immune pathways driving response to immune checkpoint blockade.

Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however the mechanisms determining patient response remain poorly understood. Here, we used machine learning to predict ICB response from germline and somatic biomarkers and interpreted the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher infiltration of T follicular helper cells had responses even in the presence of defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered different ICB responses in tumors when responses were reliant on MHC-I versus MHC-II neoantigens. Despite similar response rates, MHC-II reliant responses were associated with significantly longer durable clinical benefit (Discovery: Median OS=63.6 vs. 34.5 months P=0.0074; Validation: Median OS=37.5 vs. 33.1 months, P=0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC-II but not MHC-I reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.

So is it better than something else? Using the results of a random-effects meta-analysis to characterize the magnitude of an effect size as a percentile.

The characterization of an effect size is best made in reference to effect sizes found in the literature. A random-effects meta-analysis is the systematic synthesis of related effects from across a literature, producing an estimate of the distribution of effects in the population. We propose using the estimated mean and variance from a random-effects meta-analysis to inform the characterization of an observed effect size. The percentile of an observed effect size within the estimated distribution of population effects can describe the magnitude of the observed effect. Because there is uncertainty in the population estimates, we propose using the prediction distribution (used frequently to estimate the prediction interval in a meta-analysis) to serve as the reference distribution when characterizing an effect size. Doing so, the percentile of an observed effect and the limits of the effect size's 95% confidence interval within the prediction distribution are calculated. With numerous meta-analyses available including various outcomes and contexts, the presented method can be useful to many researchers and practitioners. We demonstrate the application of an easy-to-use Excel worksheet to automate these percentile calculations. We follow this with a simulation study evaluating the method's performance over a range of conditions. Recommendations (and cautions) for meta-analysts and researchers conducting a single study are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluating the tailored implementation of a multisite care navigation service for mental health in rural and remote Australia (The Bridging Study): protocol for a community-engaged hybrid effectiveness-implementation study.

BACKGROUND: A dramatic decline in mental health of people worldwide in the early COVID-19 pandemic years has not recovered. In rural and remote Australia, access to appropriate and timely mental health services has been identified as a major barrier to people seeking help for mental ill-health. From 2020 to 2021 a care navigation model, Navicare, was co-designed with rural and remote communities in the Greater Whitsunday Region of Central Queensland in Australia. The Exploration, Preparation, Implementation and Sustainment (EPIS) framework was used to design and guide multiple aspects of a multisite study, The Bridging Study, to evaluate the implementation of Navicare in Australia. METHODS: A community-engaged hybrid effectiveness-implementation study design will focus on the tailored implementation of Navicare at three new sites as well as monitoring implementation at an existing site established since 2021. Study outcomes assessed will include sustained access as the co-primary outcome (measured using access to Navicare mental health referral services) and Proctor's Implementation Outcomes of feasibility, acceptability, appropriateness, adoption, fidelity, implementation cost, and sustainability. Data collection for the implementation evaluation will include service usage data, community consultations, interviews, and workshops; analysed using mixed methods and guided by EPIS and other implementation frameworks. Pre-post effectiveness and cost-consequence study components are embedded in the implementation and sustainment phases, with comparison to pre-implementation data and value assessed for each EPIS phase using hospital, service, and resource allocation data. A scaling up strategy will be co-developed using a national roundtable forum in the final year of the study. Qualitative exploration of other aspects of the study (e.g., mechanisms of action and stakeholder engagement) will be conducted. DISCUSSION: Our study will use tailoring to local sites and a community-engaged approach to drive implementation of a mental health care navigation service in rural and remote Australia, with expected benefits to mental healthcare access. This approach is consistent with policy recommendations nationally and internationally as building blocks for rural health including the World Health Organization Framework for Action on Strengthening Health Systems to Improve Health Outcomes. TRIAL REGISTRATION: Prospectively registered on April 2, 2024, on the Australian New Zealand Clinical Trials Registry, no. ACTRN12624000382572. https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=386665&isReview=true .

A systematic review of health state utility values for older people with acute myeloid leukaemia.

PURPOSE: Older people with acute myeloid leukaemia (AML) have a poor prognosis, reduced health-related quality of life (HRQoL) and require substantial healthcare resources. The objectives of this systematic review were to determine what health state utility values (HSUVs) are reported in the literature that can be used in economic evaluations of interventions for older people with AML, identify research gaps, and discuss directions for future research. METHODS: The following databases were searched for studies published from inception until Feb 2023: PubMed, EMBASE, CINAHL, PsycINFO, Cochrane, and EconLit. Studies were included if they reported on HSUVs of people with AML >60 years, or HRQoL data that could be mapped to HSUVs using currently published algorithms. RESULTS: Of 532 studies identified, 7 met inclusion (4 full studies and 3 conference abstracts). Twenty-eight potentially eligible studies were excluded as they did not report HRQoL measures in sufficient detail to be mapped to utility values. Included studies reported on health states of newly diagnosed disease (n=4 studies), intensive therapy (n=1 study), controlled remission (n=3 studies), and relapsed or refractory disease (n=2 studies). No studies reported on low intensity therapy or supportive care health states. Utility values were largely reported via the EuroQol and ranged from 0.535 (intensive therapy) to 0.834 (controlled remission). CONCLUSION: There are gaps in knowledge on HSUVs for older people with AML, particularly for certain treatment-related health states. Future articles should publish comprehensive HRQoL outcomes to enable use in economic evaluation.

Genetic association and machine learning improves discovery and prediction of type 1 diabetes.

Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can lead to novel biological and therapeutic discovery and improved risk prediction. In this study, we performed genetic association and fine-mapping analyses in 817,718 European ancestry samples genome-wide and 29,746 samples at the MHC locus, which identified 165 independent risk signals for T1D of which 19 were novel. We used risk variants to train a machine learning model (named T1GRS) to predict T1D, which highly differentiated T1D from non-disease and type 2 diabetes (T2D) in Europeans as well as African Americans at or beyond the level of current standards. We identified extensive non-linear interactions between risk loci in T1GRS, for example between HLA-DQB1*57 and INS, coding and non-coding HLA alleles, and DEXI, INS and other beta cell loci, that provided mechanistic insight and improved risk prediction. T1D individuals formed distinct clusters based on genetic features from T1GRS which had significant differences in age of onset, HbA1c, and renal disease severity. Finally, we provided T1GRS in formats to enhance accessibility of risk prediction to any user and computing environment. Overall, the improved genetic discovery and prediction of T1D will have wide clinical, therapeutic, and research applications.

The conveyor belt for older people nearing the end of life.

The current fallback position for the elderly frail nearing the end of life (less than 12 months to live) is hospitalisation. There is a reluctance to use the term 'terminally ill' for this population, resulting in overtreatment, overdiagnosis and management that is not consistent with the wishes of people. This is the major contributor to the so-called hospital crisis, including decreased capacity of hospitals, reduced ability to conduct elective surgery, increased attendances at emergency departments and ambulance ramping. The authors recently conducted the largest randomised study, to their knowledge, attempting to inform specialist hospital medical teams about the terminally ill status of their admitted patients. This information did not influence their clinical decisions in any way. The authors discuss the reasons why this may have occurred, such as the current avoidance of discussing death and dying by society and the concentration of healthcare workers on actively managing the acute presenting problem and ignoring the underlying prognosis in the elderly frail. The authors discuss ways of improving the management of the elderly nearing the end of life, such as more detailed goals of care discussions using the concept of shared decision-making rather than simply completing Advanced Care Decision documents. Empowering people in this way could become the most important driver of people's health care.

Immune mechanisms in fibrotic interstitial lung disease.

Fibrotic interstitial lung diseases (fILDs) have poor survival rates and lack effective therapies. Despite evidence for immune mechanisms in lung fibrosis, immunotherapies have been unsuccessful for major types of fILD. Here, we review immunological mechanisms in lung fibrosis that have the potential to impact clinical practice. We first examine innate immunity, which is broadly involved across fILD subtypes. We illustrate how innate immunity in fILD involves a complex interplay of multiple cell subpopulations and molecular pathways. We then review the growing evidence for adaptive immunity in lung fibrosis to provoke a re-examination of its role in clinical fILD. We close with future directions to address key knowledge gaps in fILD pathobiology: (1) longitudinal studies emphasizing early-stage clinical disease, (2) immune mechanisms of acute exacerbations, and (3) next-generation immunophenotyping integrating spatial, genetic, and single-cell approaches. Advances in these areas are essential for the future of precision medicine and immunotherapy in fILD.

GRIEVOUS: your command-line general for resolving cross-dataset genotype inconsistencies.

SUMMARY: Harmonizing variant indexing and allele assignments across datasets is crucial for data integrity in cross-dataset studies such as multi-cohort genome-wide association studies, meta-analyses, and the development, validation, and application of polygenic risk scores. Ensuring this indexing and allele consistency is a laborious, time-consuming, and error-prone process requiring a certain degree of computational proficiency. Here, we introduce GRIEVOUS, a command-line tool for cross-dataset variant homogenization. By means of an internal database and a custom indexing methodology, GRIEVOUS identifies, formats, and aligns all biallelic single nucleotide polymorphisms (SNPs) across all summary statistic and genotype files of interest. Upon completion of dataset harmonization, GRIEVOUS can also be used to extract the maximal set of biallelic SNPs common to all datasets. AVAILABILITY AND IMPLEMENTATION: GRIEVOUS and all supporting documentation and tutorials can be found at https://github.com/jvtalwar/GRIEVOUS. It is freely and publicly available under the MIT license and can be installed via pip.

Interface-guided phenotyping of coding variants in the transcription factor RUNX1.

Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.

Enterococcus faecalis-derived adenine enhances enterohaemorrhagic Escherichia coli Type 3 Secretion System-dependent virulence.

Interactions between microbiota and enteric pathogens can promote colonization resistance or enhance pathogenesis. The pathobiont Enterococcus faecalis increases enterohaemorrhagic E. coli (EHEC) virulence by upregulating Type 3 Secretion System (T3SS) expression, effector translocation, and attaching and effacing (AE) lesion formation on enterocytes, but the mechanisms underlying this remain unknown. Using co-infection of organoids, metabolomics, supplementation experiments and bacterial genetics, here we show that co-culture of EHEC with E. faecalis increases the xanthine-hypoxanthine pathway activity and adenine biosynthesis. Adenine or E. faecalis promoted T3SS gene expression, while transcriptomics showed upregulation of adeP expression, which encodes an adenine importer. Mechanistically, adenine relieved High hemolysin activity (Hha)-dependent repression of T3SS gene expression in EHEC and promoted AE lesion formation in an AdeP-dependent manner. Microbiota-derived purines, such as adenine, support multiple beneficial host responses; however, our data show that this metabolite also increases EHEC virulence, highlighting the complexity of pathogen-microbiota-host interactions in the gut.

The receiver operating characteristic curve accurately assesses imbalanced datasets.

Many problems in biology require looking for a "needle in a haystack," corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) have been reported as ill-suited to evaluate prediction performance on imbalanced problems where there is more interest in performance on the positive minority class, while the precision-recall (PR) curve is preferable. We show via simulation and a real case study that this is a misinterpretation of the difference between the ROC and PR spaces, showing that the ROC curve is robust to class imbalance, while the PR curve is highly sensitive to class imbalance. Furthermore, we show that class imbalance cannot be easily disentangled from classifier performance measured via PR-AUC.

Healthy lifestyle and prostate cancer risk in the Million Veteran Program.

BACKGROUND: This study aims to assess the impact of healthy lifestyle on prostate cancer (PCa) risk in a diverse population. METHODS: Data for 281,923 men from the Million Veteran Program (MVP), a nationwide, health system-based cohort study, were analyzed. Self-reported information at enrollment included smoking status, exercise, diet, family history of PCa, and race/ethnicity. Body mass index (BMI) was obtained from clinical records. Genetic risk was assessed via a validated polygenic score. Cox proportional hazards models were used to assess associations with PCa outcomes. RESULTS: After accounting for ancestry, family history, and genetic risk, smoking was associated with an increased risk of metastatic PCa (hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.64-2.02; p < 10-16) and fatal PCa (HR, 2.73; 95% CI, 2.36-3.25; p < 10-16). Exercise was associated with a reduced risk of fatal PCa (HR, 0.86; 95% CI, 0.76-0.98; p = .03). Higher BMI was associated with a slightly reduced risk of fatal PCa, and diet score was not independently associated with any end point. Association with exercise was strongest among those who had nonmetastatic PCa at MVP enrollment. Absolute reductions in the risk of fatal PCa via lifestyle factors were greatest among men of African ancestry (1.7% for nonsmokers vs. 6.1% for smokers) or high genetic risk (1.4% for nonsmokers vs. 4.3% for smokers). CONCLUSIONS: Healthy lifestyle is minimally related to the overall risk of developing PCa but is associated with a substantially reduced risk of dying from PCa. In multivariable analyses, both exercise and not smoking remain independently associated with reduced metastatic and fatal PCa.

In vitro evolution and whole genome analysis to study chemotherapy drug resistance in haploid human cells.

In vitro evolution and whole genome analysis has proven to be a powerful method for studying the mechanism of action of small molecules in many haploid microbes but has generally not been applied to human cell lines in part because their diploid state complicates the identification of variants that confer drug resistance. To determine if haploid human cells could be used in MOA studies, we evolved resistance to five different anticancer drugs (doxorubicin, gemcitabine, etoposide, topotecan, and paclitaxel) using a near-haploid cell line (HAP1) and then analyzed the genomes of the drug resistant clones, developing a bioinformatic pipeline that involved filtering for high frequency alleles predicted to change protein sequence, or alleles which appeared in the same gene for multiple independent selections with the same compound. Applying the filter to sequences from 28 drug resistant clones identified a set of 21 genes which was strongly enriched for known resistance genes or known drug targets (TOP1, TOP2A, DCK, WDR33, SLCO3A1). In addition, some lines carried structural variants that encompassed additional known resistance genes (ABCB1, WWOX and RRM1). Gene expression knockdown and knockout experiments of 10 validation targets showed a high degree of specificity and accuracy in our calls and demonstrates that the same drug resistance mechanisms found in diverse clinical samples can be evolved, discovered and studied in an isogenic background.

Impact of a prospective feedback loop aimed at reducing non-beneficial treatments in older people admitted to hospital and potentially nearing the end of life. A cluster stepped-wedge randomised controlled trial.

OBJECTIVES: To investigate if a prospective feedback loop that flags older patients at risk of death can reduce non-beneficial treatment at end of life. DESIGN: Prospective stepped-wedge cluster randomised trial with usual care and intervention phases. SETTING: Three large tertiary public hospitals in south-east Queensland, Australia. PARTICIPANTS: 14 clinical teams were recruited across the three hospitals. Teams were recruited based on a consistent history of admitting patients aged 75+ years, and needed a nominated lead specialist consultant. Under the care of these teams, there were 4,268 patients (median age 84 years) who were potentially near the end of life and flagged at risk of non-beneficial treatment. INTERVENTION: The intervention notified clinicians of patients under their care determined as at-risk of non-beneficial treatment. There were two notification flags: a real-time notification and an email sent to clinicians about the at-risk patients at the end of each screening day. The nudge intervention ran for 16-35 weeks across the three hospitals. MAIN OUTCOME MEASURES: The primary outcome was the proportion of patients with one or more intensive care unit (ICU) admissions. The secondary outcomes examined times from patients being flagged at-risk. RESULTS: There was no improvement in the primary outcome of reduced ICU admissions (mean probability difference [intervention minus usual care] = -0.01, 95% confidence interval -0.08 to 0.01). There were no differences for the times to death, discharge, or medical emergency call. There was a reduction in the probability of re-admission to hospital during the intervention phase (mean probability difference -0.08, 95% confidence interval -0.13 to -0.03). CONCLUSIONS: This nudge intervention was not sufficient to reduce the trial's non-beneficial treatment outcomes in older hospital patients. TRIAL REGISTRATION: Australia New Zealand Clinical Trial Registry, ACTRN12619000675123 (registered 6 May 2019).

Agent orange exposure and prostate cancer risk in the million veteran program.

BACKGROUND: The US government considers veterans to have been exposed to Agent Orange if they served in Vietnam while the carcinogen was in use, and these veterans are often deemed at high risk of prostate cancer (PCa). Here, we assess whether presumed Agent Orange exposure is independently associated with increased risk of any metastatic or fatal PCa in a diverse Veteran cohort still alive in the modern era (at least 2011), when accounting for race/ethnicity, family history, and genetic risk. PATIENTS AND METHODS: Participants in the Million Veteran Program (MVP; enrollment began in 2011) who were on active duty during the Vietnam War era (August 1964-April 1975) were included (n = 301,470). Agent Orange exposure was determined using the US government definition. Genetic risk was assessed via a validated polygenic hazard score. Associations with age at diagnosis of any PCa, metastatic PCa, and death from PCa were assessed via Cox proportional hazards models. RESULTS AND INTERPRETATION: On univariable analysis, exposure to Agent Orange was not associated with increased PCa (hazard ratio [HR]: 1.02, 95% confidence interval [CI]: 1.00-1.04, p = 0.06), metastatic PCa (HR: 0.98, 95% CI: 0.91-1.05, p = 0.55), or fatal PCa (HR: 0.94, 95% CI: 0.79-1.09, p = 0.41). When accounting for race/ethnicity and family history, Agent Orange exposure was independently associated with slightly increased risk of PCa (HR: 1.06, 95% CI: 1.04-1.09, <10-6) but not with metastatic PCa (HR: 1.07, 95% CI: 0.98-1.15, p = 0.10) or PCa death (HR: 1.02, 95% CI: 0.83-1.23, p = 0.09). Similar results were found when accounting for genetic risk. Agent Orange exposure history may not improve modern PCa risk stratification.

Multicomponent processes to identify and prioritise low-value care in hospital settings: a scoping review.

OBJECTIVES: This scoping review mapped and synthesised original research that identified low-value care in hospital settings as part of multicomponent processes. DESIGN: Scoping review. DATA SOURCES: Electronic databases (EMBASE, PubMed, CINAHL, PsycINFO and Cochrane CENTRAL) and grey literature were last searched 11 July and 3 June 2022, respectively, with no language or date restrictions. ELIGIBILITY CRITERIA: We included original research targeting the identification and prioritisation of low-value care as part of a multicomponent process in hospital settings. DATA EXTRACTION AND SYNTHESIS: Screening was conducted in duplicate. Data were extracted by one of six authors and checked by another author. A framework synthesis was conducted using seven areas of focus for the review and an overuse framework. RESULTS: Twenty-seven records were included (21 original studies, 4 abstracts and 2 reviews), originating from high-income countries. Benefit or value (11 records), risk or harm (10 records) were common concepts referred to in records that explicitly defined low-value care (25 records). Evidence of contextualisation including barriers and enablers of low-value care identification processes were identified (25 records). Common components of these processes included initial consensus, consultation, ranking exercise or list development (16 records), and reviews of evidence (16 records). Two records involved engagement of patients and three evaluated the outcomes of multicomponent processes. Five records referenced a theory, model or framework. CONCLUSIONS: Gaps identified included applying systematic efforts to contextualise the identification of low-value care, involving people with lived experience of hospital care and initiatives in resource poor contexts. Insights were obtained regarding the theories, models and frameworks used to guide initiatives and ways in which the concept 'low-value care' had been used and reported. A priority for further research is evaluating the effect of initiatives that identify low-value care using contextualisation as part of multicomponent processes.

Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment.

BACKGROUND: It is essential to consider the evidence of consumer preferences and their specific needs when determining which strategies to use to improve patient attendance at scheduled healthcare appointments. OBJECTIVES: This study aimed to identify key attributes and elicit healthcare consumer preferences for a healthcare appointment reminder system. METHODS: A discrete choice experiment was conducted in a general Australian population sample. The respondents were asked to choose between three options: their preferred reminder (A or B) or a 'neither' option. Attributes were developed through a literature review and an expert panel discussion. Reminder options were defined by four attributes: modality, timing, content and interactivity. Multinomial logit and mixed multinomial logit models were estimated to approximate individual preferences for these attributes. A scenario analysis was performed to estimate the likelihood of choosing different reminder systems. RESULTS: Respondents (n = 361) indicated a significant preference for an appointment reminder to be delivered via a text message (ß = 2.42, p < 0.001) less than 3 days before the appointment (ß = 0.99, p < 0.001), with basic details including the appointment cost (ß = 0.13, p < 0.10), and where there is the ability to cancel or modify the appointment (ß = 1.36, p < 0.001). A scenario analysis showed that the likelihood of choosing an appointment reminder system with these characteristics would be 97%. CONCLUSIONS: Our findings provide evidence on how healthcare consumers trade-off between different characteristics of reminder systems, which may be valuable to inform current or future systems. Future studies may focus on exploring the effectiveness of using patient-preferred reminders alongside other mitigation strategies used by providers.

Bento: a toolkit for subcellular analysis of spatial transcriptomics data.

The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell-cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.