Overall Survival in Patients with Endometrial Cancer Treated with Dostarlimab plus Carboplatin-Paclitaxel in the Randomized ENGOT-EN6/GOG-3031/RUBY Trial.

BACKGROUND: Part 1 of the RUBY trial (NCT03981796) evaluated dostarlimab plus carboplatin-paclitaxel compared with placebo plus carboplatin-paclitaxel in patients with primary advanced or recurrent endometrial cancer. At the first interim analysis, the trial met one of its dual-primary endpoints with statistically significant progression-free survival benefits in the mismatch repair deficient/microsatellite instability-high (dMMR/MSI-H) and overall populations. Overall survival (OS) results are reported from the second interim analysis. PATIENTS AND METHODS: RUBY is a phase 3, global, double-blind, randomized, placebo-controlled trial. Part 1 of RUBY enrolled eligible patients with primary advanced stage III or IV or first recurrent endometrial cancer who were randomly assigned (1:1) to receive either dostarlimab (500 mg) or placebo, plus carboplatin-paclitaxel every 3 weeks for 6 cycles followed by dostarlimab (1000 mg) or placebo every 6 weeks for up to 3 years. OS was a dual-primary endpoint. RESULTS: A total of 494 patients were randomized (245 in dostarlimab arm; 249 in placebo arm). In the overall population, with 51% maturity, RUBY met the dual-primary endpoint for OS at this second interim analysis, with a statistically significant reduction in the risk of death (HR = 0.69; 95% CI, 0.54-0.89; P = 0.0020) in patients treated with dostarlimab plus carboplatin-paclitaxel versus carboplatin-paclitaxel alone. The risk of death was lower in the dMMR/MSI-H population (HR = 0.32; 95% CI, 0.17-0.63; nominal P = 0.0002) and a trend in favor of dostarlimab was seen in the mismatch repair proficient/microsatellite stable (MMRp/MSS) population (HR = 0.79; 95% CI, 0.60-1.04; nominal P = 0.0493). The safety profile for dostarlimab plus carboplatin-paclitaxel was consistent with the first interim analysis. CONCLUSIONS: Dostarlimab in combination with carboplatin-paclitaxel demonstrated a statistically significant and clinically meaningful overall survival benefit in the overall population of patients with primary advanced or recurrent endometrial cancer while demonstrating an acceptable safety profile.

Can the Reboot coaching programme support critical care nurses in coping with stressful clinical events? A mixed-methods evaluation assessing resilience, burnout, depression and turnover intentions.

BACKGROUND: Critical care nurses (CCNs) are routinely exposed to highly stressful situations, and at high-risk of suffering from work-related stress and developing burnout. Thus, supporting CCN wellbeing is crucial. One approach for delivering this support is by preparing CCNs for situations they may encounter, drawing on evidence-based techniques to strengthen psychological coping strategies. The current study tailored a Resilience-boosting psychological coaching programme [Reboot] to CCNs. Other healthcare staff receiving Reboot have reported improvements in confidence in coping with stressful clinical events and increased psychological resilience. The current study tailored Reboot for online, remote delivery to CCNs (as it had not previously been delivered to nurses, or in remote format), to (1) assess the feasibility of delivering Reboot remotely, and to (2) provide a preliminary assessment of whether Reboot could increase resilience, confidence in coping with adverse events and burnout. METHODS: A single-arm mixed-methods (questionnaires, interviews) before-after feasibility study design was used. Feasibility was measured via demand, recruitment, and retention (recruitment goal: 80 CCNs, retention goal: 70% of recruited CCNs). Potential efficacy was measured via questionnaires at five timepoints; measures included confidence in coping with adverse events (Confidence scale), Resilience (Brief Resilience Scale), depression (PHQ-9) and burnout (Oldenburg-Burnout-Inventory). Intention to leave (current role, nursing more generally) was measured post-intervention. Interviews were analysed using Reflexive Thematic Analysis. RESULTS: Results suggest that delivering Reboot remotely is feasible and acceptable. Seventy-seven nurses were recruited, 81% of whom completed the 8-week intervention. Thus, the retention rate was over 10% higher than the target. Regarding preliminary efficacy, follow-up measures showed significant increases in resilience, confidence in coping with adverse events and reductions in depression, burnout, and intention to leave. Qualitative analysis suggested that CCNs found the psychological techniques helpful and particularly valued practical exercises that could be translated into everyday practice. CONCLUSION: This study demonstrates the feasibility of remote delivery of Reboot and potential efficacy for CCNs. Results are limited due to the single-arm feasibility design; thus, a larger trial with a control group is needed.

First Evidence of Axial Shape Asymmetry and Configuration Coexistence in

The excited states of N=44 ^{74}Zn were investigated via γ-ray spectroscopy following ^{74}Cu ß decay. By exploiting γ-γ angular correlation analysis, the 2_{2}^{+}, 3_{1}^{+}, 0_{2}^{+}, and 2_{3}^{+} states in ^{74}Zn were firmly established. The γ-ray branching and E2/M1 mixing ratios for transitions deexciting the 2_{2}^{+}, 3_{1}^{+}, and 2_{3}^{+} states were measured, allowing for the extraction of relative B(E2) values. In particular, the 2_{3}^{+}→0_{2}^{+} and 2_{3}^{+}→4_{1}^{+} transitions were observed for the first time. The results show excellent agreement with new microscopic large-scale shell-model calculations, and are discussed in terms of underlying shapes, as well as the role of neutron excitations across the N=40 gap. Enhanced axial shape asymmetry (triaxiality) is suggested to characterize ^{74}Zn in its ground state. Furthermore, an excited K=0 band with a significantly larger softness in its shape is identified. A shore of the N=40 "island of inversion" appears to manifest above Z=26, previously thought as its northern limit in the chart of the nuclides.

Genomic characterisation of hormone receptor-positive breast cancer arising in very young women.

BACKGROUND: Very young premenopausal women diagnosed with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+HER2-) early breast cancer (EBC) have higher rates of recurrence and death for reasons that remain largely unexplained. PATIENTS AND METHODS: Genomic sequencing was applied to HR+HER2- tumours from patients enrolled in the Suppression of Ovarian Function Trial (SOFT) to determine genomic drivers that are enriched in young premenopausal women. Genomic alterations were characterised using next-generation sequencing from a subset of 1276 patients (deep targeted sequencing, n = 1258; whole-exome sequencing in a young-age, case-control subsample, n = 82). We defined copy number (CN) subgroups and assessed for features suggestive of homologous recombination deficiency (HRD). Genomic alteration frequencies were compared between young premenopausal women (<40 years) and older premenopausal women (≥40 years), and assessed for associations with distant recurrence-free interval (DRFI) and overall survival (OS). RESULTS: Younger women (<40 years, n = 359) compared with older women (≥40 years, n = 917) had significantly higher frequencies of mutations in GATA3 (19% versus 16%) and CN amplifications (CNAs) (47% versus 26%), but significantly lower frequencies of mutations in PIK3CA (32% versus 47%), CDH1 (3% versus 9%), and MAP3K1 (7% versus 12%). Additionally, they had significantly higher frequencies of features suggestive of HRD (27% versus 21%) and a higher proportion of PIK3CA mutations with concurrent CNAs (23% versus 11%). Genomic features suggestive of HRD, PIK3CA mutations with CNAs, and CNAs were associated with significantly worse DRFI and OS compared with those without these features. These poor prognostic features were enriched in younger patients: present in 72% of patients aged <35 years, 54% aged 35-39 years, and 40% aged ≥40 years. Poor prognostic features [n = 584 (46%)] versus none [n = 692 (54%)] had an 8-year DRFI of 84% versus 94% and OS of 88% versus 96%. Younger women (<40 years) had the poorest outcomes: 8-year DRFI 74% versus 85% and OS 80% versus 93%, respectively. CONCLUSION: These results provide insights into genomic alterations that are enriched in young women with HR+HER2- EBC, provide rationale for genomic subgrouping, and highlight priority molecular targets for future clinical trials.

Influence of Metal Guide Sleeves on the Accuracy and Precision of Dental Implant Placement Using Guided Implant Surgery: An In Vitro Study.

PURPOSE: Metal sleeves are commonly used in implant guides for guided surgery. Cost and sleeve specification limit the applications. This in vitro study examined the differences in the implant position deviations produced by a digitally designed surgical guide with no metal sleeve in comparison to a conventional one with a metal sleeve. MATERIALS AND METHODS: The experiment was conducted in two steps for each step: n = 20 casts total, 10 casts each group; Step 1 to examine one guide from each group with ten implant placements in a dental cast, and Step 2 to examine one guide to one cast. Implant placement was performed using a guided surgical protocol. Postoperative cone-beam computed tomography images were made and were superimposed onto the treatment-planning images. The implant horizontal and angulation deviations from the planned position were measured and analyzed using t-test and F-test (p = 0.05). RESULTS: For Step 1 and 2, respectively, implant deviations for the surgical guide with sleeve were -0.3 ±0.17 mm and 0.15 ±0.23 mm mesially, 0.60 ±1.69 mm, and -1.50 ±0.99 mm buccolingual at the apex, 0.20 ±0.47 mm and -0.60 ±0.27 mm buccolingual at the cervical, and 2.73° ±4.80° and -1.49° ±2.91° in the buccolingual angulation. For Step 1 and 2, respectively, the implant deviations for the surgical guide without sleeve were -0.17 ±0.14 mm and -0.06 ±0.07 mm mesially, 0.35 ±1.04 mm and -1.619 ±1.03 mm buccolingual at the apex, 0.10 ±0.27 mm and -0.62 ±0.27 mm buccolingual at the cervical, and 1.73° ±3.66° and -1.64° ±2.26° in the buccolingual angulation. No statistically significant differences were found in any group except for mesial deviation of the Step 2 group (F-test, p < 0.001). CONCLUSIONS: A digitally designed surgical guide with no metal sleeve demonstrates similar accuracy but higher precision compared to a surgical guide with a metal sleeve. Metal sleeves may not be required for guided surgery.

Unilateral inguinofemoral lymphadenectomy in patients with early-stage vulvar squamous cell carcinoma and a unilateral metastatic sentinel lymph node is safe.

OBJECTIVE: Optimal management of the contralateral groin in patients with early-stage vulvar squamous cell carcinoma (VSCC) and a metastatic unilateral inguinal sentinel lymph node (SN) is unclear. We analyzed patients who participated in GROINSS-V I or II to determine whether treatment of the contralateral groin can safely be omitted in patients with a unilateral metastatic SN. METHODS: We selected the patients with a unilateral metastatic SN from the GROINSS-V I and II databases. We determined the incidence of contralateral additional non-SN metastases in patients with unilateral SN-metastasis who underwent bilateral inguinofemoral lymphadenectomy (IFL). In those who underwent only ipsilateral groin treatment or no further treatment, we determined the incidence of contralateral groin recurrences during follow-up. RESULTS: Of 1912 patients with early-stage VSCC, 366 had a unilateral metastatic SN. Subsequently, 244 had an IFL or no treatment of the contralateral groin. In seven patients (7/244; 2.9% [95% CI: 1.4%-5.8%]) disease was diagnosed in the contralateral groin: five had contralateral non-SN metastasis at IFL and two developed an isolated contralateral groin recurrence after no further treatment. Five of them had a primary tumor ≥30 mm. Bilateral radiotherapy was administered in 122 patients, of whom one (1/122; 0.8% [95% CI: 0.1%-4.5%]) had a contralateral groin recurrence. CONCLUSION: The risk of contralateral lymph node metastases in patients with early-stage VSCC and a unilateral metastatic SN is low. It appears safe to limit groin treatment to unilateral IFL or inguinofemoral radiotherapy in these cases.

Calibration and sensitivity analysis of a novel water flow and pollution model for future city planning: Future Urban Stormwater Simulation (FUSS).

Planning for future urban development and water infrastructure is uncertain due to changing human activities and climate. To quantify these changes, we need adaptable and fast models that can reliably explore scenarios without requiring extensive data and inputs. While such models have been recently considered for urban development, they are lacking for stormwater pollution assessment. This work proposes a novel Future Urban Stormwater Simulation (FUSS) model, utilizing a previously developed urban planning algorithm (UrbanBEATS) to dynamically assess pollution changes in urban catchments. By using minimal input data and adding stochastic point-source pollution to the build-up/wash-off approach, this study highlights calibration and sensitivity analysis of flow and pollution modules, across the range of common stormwater pollutants. The results highlight excellent fit to measured values in a continuous rainfall simulation for the flow model, with one significant calibration parameter. The pollution model was more variable, with TSS, TP and Pb showing high model efficiency, while TN was predicted well only across event-based assessment. The work further explores the framework for the model application in future pollution assessment, and points to the future work aiming to developing land-use dependent model parameter sets, to achieve flexibility for model application across varied urban catchments.

Study protocol for the online adaptation and evaluation of the 'Reboot' (Recovery-boosting) coaching programme, to prepare critical care nurses for, and aid recovery after, stressful clinical events.

BACKGROUND: Critical care nurses (CCNs) are routinely exposed to highly stressful events, exacerbated during the COVID-19 pandemic. Supporting resilience and wellbeing of CCNs is therefore crucial to prevent burnout. One approach for delivering this support is by preparing critical care nurses for situations they may encounter, drawing on evidence-based techniques to strengthen relevant psychological coping strategies. As such, the current study seeks to tailor a Resilience-boosting psychological coaching programme [Reboot] for CCNs, based on cognitive behavioural therapy (CBT) principles and the Bi-Dimensional Resilience Framework (BDF), and (1) to assess the feasibility of delivering Reboot via online, remote delivery to CCNs, and (2) to provide a preliminary assessment of whether Reboot could increase resilience and confidence in coping with adverse events. METHODS: Eighty CCNs (n=80) will be recruited to the 8-week Reboot programme, comprised of two group workshops and two individual coaching calls. The study uses a single-arm before-after feasibility study design and will be evaluated with a mixed-methods approach, using online questionnaires (all participants) and telephone interviews (25% of participants). Primary outcomes will be confidence in coping with adverse events (the Confidence scale) and resilience (the Brief Resilience Scale) measured at four time points. DISCUSSION: Results will determine whether it is feasible to deliver and evaluate a remote version of the Reboot coaching programme to CCNs, and will indicate whether participating in the programme is associated with increases in confidence in coping with adverse events, resilience and wellbeing (as indicated by levels of depression).

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images.

MOTIVATION: Multiplexed imaging is a nascent single-cell assay with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available. RESULTS: We implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Furthermore, we find that dividing multiplexed imaging data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in multiplexed imaging. AVAILABILITY AND IMPLEMENTATION: Source code is provided at: https://github.com/statimagcoll/MultiplexedNormalization and an R package to implement these methods is available here: https://github.com/ColemanRHarris/mxnorm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee.

INTRODUCTION: The SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the USA, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities. METHODS: We combined county-level COVID-19 testing data, COVID-19 vaccination rates and SDOH information in Tennessee. Between February and May 2021, we trained machine learning models on a semimonthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance. RESULTS: Our results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased. CONCLUSION: Incorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policy-makers with additional data resources to improve health equity and resilience to future public health emergencies.

Predictive modeling of COVID-19 case growth highlights evolving racial and ethnic risk factors in Tennessee and Georgia.

INTRODUCTION: The SARS-CoV-2 (COVID-19) pandemic has exposed the need to understand the risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health (SDOH) that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections. METHODS: Our work combined publicly available COVID-19 statistics with county-level SDOH information. Machine learning models were trained to predict COVID-19 case growth and understand the social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. RESULTS: The predictive models achieved a mean R2 of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the importance of SDOH data features over time to uncover the specific racial demographic characteristics strongly associated with COVID-19 incidence in Tennessee and Georgia counties. Our results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. For example, we find that African American and Asian racial demographics present comparable, and contrasting, patterns of risk depending on locality. CONCLUSION: The dichotomy of demographic trends presented here emphasizes the importance of understanding the unique factors that influence COVID-19 incidence. Identifying these specific risk factors tied to COVID-19 case growth can help stakeholders target regional interventions to mitigate the burden of future outbreaks.

Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

The SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the United States, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities. We combined county-level COVID-19 testing data, COVID-19 vaccination rates, and SDOH information in Tennessee. Between February-May 2021, we trained machine learning models on a semi-monthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance. Our results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race, and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased. Incorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policymakers with additional data resources to improve health equity and resilience to future public health emergencies.

Predictive Modeling of COVID-19 Case Growth Highlights Evolving Demographic Risk Factors in Tennessee and Georgia.

The COVID-19 pandemic has exposed the need to understand the unique risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections in the future. Our work combined publicly available COVID-19 statistics with county-level social determinants of health information. Machine learning models were trained to predict COVID-19 case growth and understand the unique social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. The predictive models achieved a mean r-squared (R2) of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the social determinants of health, with a specific focus on demographics, that were strongly associated with COVID-19 case growth in Tennessee and Georgia counties. The demographic results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. Identifying the specific risk factors tied to COVID-19 case growth can assist public health officials and policymakers target regional interventions to mitigate the burden of future outbreaks and minimize long-term consequences including emergence or exacerbation of chronic diseases that are a direct consequence of infection.

Predictive Modeling of COVID-19 Case Growth Highlights Evolving Demographic Risk Factors in Tennessee and Georgia

The COVID-19 pandemic has exposed the need to understand the unique risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections in the future. Our work combined publicly available COVID-19 statistics with county-level social determinants of health information. Machine learning models were trained to predict COVID-19 case growth and understand the unique social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. The predictive models achieved a mean r-squared (R2) of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the social determinants of health, with a specific focus on demographics, that were strongly associated with COVID-19 case growth in Tennessee and Georgia counties. The demographic results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. Identifying the specific risk factors tied to COVID-19 case growth can assist public health officials and policymakers target regional interventions to mitigate the burden of future outbreaks and minimize long-term consequences including emergence or exacerbation of chronic diseases that are a direct consequence of infection.