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Background: The staphylococcal protein A (spa) locus of Staphylococcus aureus contains a complex repeat structure and is commonly used for single-locus sequence-based genotyping. The real-time PCR platform supports genotyping methods that are single step and closed tube and potentially can be carried out simultaneously with diagnosis. We describe here a method for genotyping S. aureus using high-resolution melting (HRM) analysis of the spa polymorphic region X. Methods: The conventional PCR spa assay was modified and optimized for the Rotor-Gene 6000 instrument (Corbett Life Science). HRM analysis on the Corbett Rotor-Gene 6000 instrument was used to test 22 known spa sequences obtained from 44 diverse methicillin-resistant S. aureus (MRSA) isolates. Criteria for calling pairs of melting curves “same” or “different” were developed empirically by converting the data to difference graph format with one curve defined as the control. HRM curve comparison between runs was done to determine the portability of the method. The assay performance was assessed by genotyping uncharacterized isolates, carrying out blind trials, and comparing HRM profiles from different runs. Results: HRM analysis of 44 diverse MRSA isolates generated 20 profiles from 22 spa sequence types. The 2 unresolved HRM spa types differed by only 1 bp. Two blind trials demonstrated complete reproducibility with respect to calling the different spa types. Interrun comparisons of HRM curves were successfully developed, indicating the robustness of the method. Conclusion: Analysis of the spa locus by HRM resolves spa sequence variants. This single- and closed-tube single-step method for S. aureus genotyping can be easily combined with the interrogation of other genetic markers.

The exploration of new environments is a crucial challenge for mobile robots. This task becomes even more complex with the added requirement of ensuring safety. Here, safety refers to the robot staying in regions where the values of certain environmental conditions (such as terrain steepness or radiation levels) are within a predefined threshold. We consider two types of safe exploration problems. First, the robot has a map of its workspace, but the values of the environmental features relevant to safety are unknown beforehand and must be explored. Second, both the map and the environmental features are unknown, and the robot must build a map whilst remaining safe. Our proposed framework uses a Gaussian process to predict the value of the environmental features in unvisited regions. We then build a Markov decision process that integrates the Gaussian process predictions with the transition probabilities of the environmental model. The Markov decision process is then incorporated into an exploration algorithm that decides which new region of the environment to explore based on information value, predicted safety, and distance from the current position of the robot. We empirically evaluate the effectiveness of our framework through simulations and its application on a physical robot in an underground environment.

To the Editor: Hamdy and colleagues (April 27 issue) 1 report the 15-year outcomes of the Prostate Testing for Cancer and Treatment (ProtecT) trial, which involved a cohort of patients in the United Kingdom who were invited to undergo screening. 2 However, given the substantial differences in health care systems and patient case mixes between the United Kingdom and the United States, we feel compelled to comment on the generalizability of the results of this trial to a U.S. cohort. We interrogated our institutional database to find men 50 to 69 years of age in our health system who, between 1999 and . . .

Purpose This paper aims to describe a service evaluation study of “Balance” – a National Health Service Tier 2 pilot weight management course delivered in a primary care mental health service. The 12 weekly sessions included dietetic, psychological and behavioural elements underpinned by cognitive behavioural theory and “third-wave” approaches, including acceptance and commitment therapy, compassion-focused therapy and mindfulness. Design/methodology/approach A mixed-methods design was used in this service evaluation study that included analysis of outcome measures (weight, eating choices, weight-related self-efficacy and mental health) and focus group data (n = 6) analysed using thematic analysis. Eleven clients with a body mass index of 25–40 kg/m2 enrolled, and nine clients completed the course. Outcome data were collected weekly with follow-up at three and six months. Findings Quantitative data analysis using non-parametric Wilcoxon signed-rank tests showed that the group mean weight decreased significantly (p = 0.030) by the end of Balance, but the group mean weight loss was not statistically significant at the three-month (p = 0.345) or six-month (p = 0.086) follow-up. The qualitative results showed that participants valued the course ethos of choice and also welcomed learning new tools and techniques. Balance was very well-received by participants who reported benefitting from improved well-being, group support and developing new weight management skills. Research limitations/implications Only one client attended all sessions of the group, and it is possible that missed sessions impacted effectiveness. Some of the weight change data collected at the six-month follow-up was self-reported (n = 4), which could reduce data reliability. Focus group participants were aware that Balance was a pilot with a risk that the group would not be continued. As the group wanted the pilot to be extended, the feedback may have been positively skewed. A small sample size limits interpretation of the results. A group weight management intervention, including dietetic, psychological and behavioural elements, underpinned by cognitive behavioural theory was well-received by service users and effective for some. Commissioners and service users may have different definitions of successful outcomes in weight management interventions. Practical implications Longer-term support and follow-up after Tier 2 weight management interventions may benefit service users and improve outcomes. Originality/value The paper contributes to a small but growing evidence base concerned with the design and delivery of weight management interventions. Areas of particular interest include: a gap analysis between the course content and National Institute for Health and Care Excellence clinical guidelines, participants’ views on the most impactful course features and recommendations for course development. The results also show a disconnect between evidence-based guidelines (mandatory weight monitoring), participants’ preferences and clinicians' experience. The difference between client and commissioner priorities is also discussed.

Purpose Muscle-invasive bladder cancer (MIBC) is an aggressive malignancy with limited survival improvements despite advancements in treatment. Socioeconomic disparities significantly affect patient outcomes, yet the Area Deprivation Index (ADI), a robust measure of socioeconomic status, has been underexplored in MIBC. This study evaluates the association between ADI and cancer-specific mortality (CSM) in MIBC. Materials and methods We retrospectively reviewed patients with MIBC (≥ T2; Any N; Any M) from the Michigan Department of Health and Human Services database (2004–2019). ADI scores were assigned based on residential census block groups and stratified into quartiles, with the 4th quartile (ADI 75–100) being the most deprived. Cumulative incidence functions compared CSM between quartiles, and competing-risk regression analysis assessed the association between ADI and CSM after adjusting for covariates. Results Among 6120 patients (90% Non-Hispanic Whites; median age 73 [IQR 64–81]), most resided in metropolitan areas (80%) and were insured through Medicare (35%). Patients were distributed across ADI quartiles: 437 (1st), 1442 (2nd), 2171 (3rd), and 2070 (4th). At 10 years, CSM rates were 50%, 52%, 54%, and 55% for the 1st, 2nd, 3rd, and 4th quartiles, respectively ( p  = 0.01). Patients in the 3rd and 4th quartiles had 1.25 (HR 1.25, 95% CI 1.07–1.47, p  = 0.016) and 1.30 (HR 1.30, 95% CI 1.11–1.54, p  = 0.005) higher risks of CSM than those in the 1st quartile. Conclusions Higher ADI was associated with increased CSM in our cohort. Further studies are needed to explore potential causal mechanisms.

Purpose To assess active surveillance (AS) adherence for prostate cancer (PCa) in a “real-world” clinical practice. Materials and methods We utilized our institutional database which was built by interrogating electronic medical records for all men who got diagnosed with PCa from 1995 to 2022. Our cohort included all patients aged < 76 years, with PCa Gleason Grade (GG) 1 or 2, ≤ cT2c, PSA ≤ 20 ng/ml at diagnosis, enrolled on AS, and with at least one biopsy after diagnosis. Patients were separated into two groups based on the monitoring intensity. Patients with at least 1 PSA/year and at least 1 biopsy every 4 years were categorized as adherent to guidelines. Univariable and Multivariable logistic regression analyses were used to examine the impact of covariates on non-adherence to guidelines. Competing risks cumulative incidence was used to depict prostate cancer-specific mortality (PCSM). Results A total of 546 men met the inclusion criteria. Overall, 63 (11%) patients were adherent to guidelines (Group 1), while 483 (89%) were not (Group 2). Median PSAs/year and median biopsies/year were 2.3 (2.0-2.7) and 0.4 (0.3–0.6) for Group 1, and 1.2 (0.7–1.8) and 0.2 (0.1–0.2) for Group 2, respectively (both p  < 0.0001). At multivariable analysis, Black men had a 2.20-fold higher risk of being in Group 2 than White men ( p  < 0.05). Patients with cT2 (OR:0.24, CI:0.11–0.52) and those with CCI ≥2 (OR:0.40, CCI:0.19–0.82) were less likely to be in Group 2, when compared to cT1 stage and CCI = 0, respectively (both p  < 0.05). At 10 years, the cumulative incidence estimate of PCSM for the entire cohort was 2.1%. Conclusion We found substantial deviations from AS monitoring guidelines, particularly in biopsy frequency, which did not seem to compromise PCSM in patients with stable PSA. Notably, our findings suggest that strict adherence to guidelines, especially in patients with cT2 at diagnosis, remains crucial.

Purpose This study examined the impact of cannabis use disorder (CUD) on inpatient morbidity, length of stay (LOS), and inpatient cost (IC) of patients undergoing urologic oncologic surgery. Methods The National Inpatient Sample (NIS) from 2003 to 2014 was analyzed for patients undergoing prostatectomy, nephrectomy, or cystectomy ( n  = 1,612,743). CUD was identified using ICD-9 codes. Complex-survey procedures were used to compare patients with and without CUD. Inpatient major complications, high LOS (4th quartile), and high IC (4th quartile) were examined as endpoints. Univariable and multivariable analysis (MVA) were performed to compare groups. Results The incidence of CUD increased from 51 per 100,000 admissions in 2003 to 383 per 100,000 in 2014 ( p  < 0.001). Overall, 3,503 admissions had CUD. Patients with CUD were more frequently younger (50 vs. 61), male (86% vs. 78.4%), Black (21.7% vs. 9.2%), and had 1st quartile income (36.1% vs. 20.6%); all p  < 0.001. CUD had no impact on any complication rates (all p  > 0.05). However, CUD patients had higher LOS (3 vs. 2 days; p  < 0.001) and IC ($15,609 vs. $12,415; p < 0.001). On MVA, CUD was not an independent predictor of major complications ( p  = 0.6). Conversely, CUD was associated with high LOS (odds ratio (OR) 1.31; 95% CI 1.08–1.59) and high IC (OR 1.33; 95% CI 1.12–1.59), both p  < 0.01. Conclusion The incidence of CUD at the time of urologic oncologic surgery is increasing. Future research should look into the cause of our observed phenomena and how to decrease LOS and IC in CUD patients.

Purpose We aimed to assess the appropriateness of ChatGPT in providing answers related to prostate cancer (PCa) screening, comparing GPT-3.5 and GPT-4. Methods A committee of five reviewers designed 30 questions related to PCa screening, categorized into three difficulty levels. The questions were formulated identically for both GPTs three times, varying the prompts. Each reviewer assigned a score for accuracy, clarity, and conciseness. The readability was assessed by the Flesch Kincaid Grade (FKG) and Flesch Reading Ease (FRE). The mean scores were extracted and compared using the Wilcoxon test. We compared the readability across the three different prompts by ANOVA. Results In GPT-3.5 the mean score (SD) for accuracy, clarity, and conciseness was 1.5 (0.59), 1.7 (0.45), 1.7 (0.49), respectively for easy questions; 1.3 (0.67), 1.6 (0.69), 1.3 (0.65) for medium; 1.3 (0.62), 1.6 (0.56), 1.4 (0.56) for hard. In GPT-4 was 2.0 (0), 2.0 (0), 2.0 (0.14), respectively for easy questions; 1.7 (0.66), 1.8 (0.61), 1.7 (0.64) for medium; 2.0 (0.24), 1.8 (0.37), 1.9 (0.27) for hard. GPT-4 performed better for all three qualities and difficulty levels than GPT-3.5. The FKG mean for GPT-3.5 and GPT-4 answers were 12.8 (1.75) and 10.8 (1.72), respectively; the FRE for GPT-3.5 and GPT-4 was 37.3 (9.65) and 47.6 (9.88), respectively. The 2nd prompt has achieved better results in terms of clarity (all p  < 0.05). Conclusions GPT-4 displayed superior accuracy, clarity, conciseness, and readability than GPT-3.5. Though prompts influenced the quality response in both GPTs, their impact was significant only for clarity.

Purpose This study examined cancer control metrics between surgery and radiation for clinically positive nodal prostate cancer in an other-cause mortality weighted cohort, to circumvent limitations in previous studies. Methods The Surveillance, Epidemiology, and End Results Research Plus database was queried to identify men with clinically positive nodal prostate cancer at diagnosis between 2004 and 2017 who were treated with surgery or radiation. A competing-risks regression model was used to calculate the 10-year other-cause mortality risk using available covariates, including treatment type. Inverse probability of treatment weighting was then used to balance covariates, including other-cause mortality risk. Then, competing-risks cumulative incidence curves and multivariable models, which were weighted on the calculated other-cause mortality risk, were used to examine the impact of treatment type on cancer-specific mortality, after accounting for covariates. Results 4739 patients underwent surgery whereas 1039 underwent radiation. The median follow-up was 4.7 years (2.6–8.2). Other-cause mortality was statistically different between treatment arms in the unweighted cohort (Gray’s p = 0.005), but that difference disappeared in the weighted cohort (Gray’s p = 0.2). At 10 years, the cancer-specific mortality rate was 27.6% (22.2–33.9) for radiation versus 18.1% (16.2–20.3) for surgery (p < 0.001). On competing-risks multivariable analysis, radiation had 1.86-fold (95% CI 1.69–2.12) higher hazard likelihood from one year to the next compared to surgery (p < 0.001). Conclusion Clinically positive nodal patients treated with radiation fare worst cancer-specific mortality than those that underwent surgery, using calculated other-cause mortality risk.