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Mechanical, optical, chemical, and biological evaluations of fish scale-derived scaffold for corneal replacements: A systematic review.
Corneal blindness is commonly treated through corneal replacement with allogeneic corneal donors, which may face shortage. Regarding this issue, xenogeneic alternatives are explored. Fish scale-derived scaffolds (FSSs) are among the alternatives due to the lower risk of infection and abundant sources of raw materials. Unfortunately, the information about mechanical, optical, chemical, and biological performances of FSSs for corneal replacements is still scattered, as well as about the fabrication techniques. This study aims to gather scattered pieces of information about the mentioned performances and fabrication techniques of FSSs for corneal replacements. Sorted from four scientific databases and using the PRISMA checklist, eleven relevant articles are collected. FSSs are commonly fabricated using decellularization and decalcification processes, generating FSSs with parallel multilayers or crossed fibers with topographic microchannels. In the collected studies, similar mechanical properties of FSSs to native tissues are discovered, as well as good transparency, light remittance, but poorer refractive indexes than native tissues. Biological evaluations mostly discuss histology, cell proliferations, and immune responses on FSSs, while only a few studies examine the vascularization. No studies completed comprehensive evaluations on the four properties. The current progress of FSS developments demonstrates the potential of FSS use for corneal replacements.
Trends and determinants of clinical staff retention in the English NHS: a double retrospective cohort study.
OBJECTIVES: To investigate how demographic, contractual and organisational factors are related to the retention of hospital workers in the English NHS. The study will specifically examine the trends in age-retention profiles. DESIGN: A double retrospective cross-cohort study using administrative data on senior and specialty doctors, nurses and midwives who were included in the 2009 and 2014 payrolls of all English NHS hospital Trusts. These individuals were tracked over time until 2019 to examine the associations between sociodemographic characteristics and the retention of hospital workers in each cohort. Logistic regressions were estimated at the individual worker level to analyse the data. Additionally, a multilevel panel regression was performed using linked payroll-survey data to investigate the association between hospital organisation characteristics and the retention of clinical staff. SETTING: Secondary acute and mental healthcare NHS hospital Trusts in England. PARTICIPANTS: 70 777 senior doctors (specialty and specialist doctors and hospital consultants) aged 30-70, and a total of 448 568 between nurses and midwives of any grade aged 20-70, employed by English NHS Trusts. PRIMARY OUTCOME MEASURES: Employee retention, measured through binary indicators for stayers and NHS leavers, at 1-year and 5-year horizons. RESULTS: Minority doctors had lower 1-year retention rates in acute care than white doctors, while minority nurses and midwives saw higher retention. Part-time roles decreased retention for doctors but improved it for nurses. Fixed-term contracts negatively impacted both groups' retention. Trends diverged for nurses and doctors from 2009 to 2014-nurses' retention declined while doctors' 5-year retention slightly rose. Engagement boosted retention among clinical staff under 51 years of age in acute care. For nurses over 50, addressing their feedback was positively associated with retention. CONCLUSIONS: Demographic and contractual factors appear to be stronger predictors of hospital staff retention than organisational characteristics.
Regulation of pulmonary plasma cell responses during secondary infection with influenza virus
During secondary infection with influenza virus, plasma cells (PCs) develop within the lung, providing a local source of antibodies. However, the site and mechanisms that regulate this process are poorly defined. Here we show that while circulating memory B cells entered the lung during rechallenge and were activated within inducible bronchusassociated lymphoid tissues (iBALTs), resident memory B (BRM) cells responded earlier, and their activation occurred in a different niche: directly near infected alveoli. This process required NK cells but was largely independent of CD4 and CD8 T cells. Innate stimuli induced by virus-like particles containing ssRNA triggered BRM cell differentiation in the absence of cognate antigen, suggesting a low threshold of activation. In contrast, expansion of PCs in iBALTs took longer to develop and was critically dependent on CD4 T cells. Our work demonstrates that spatially distinct mechanisms evolved to support pulmonary secondary PC responses, and it reveals a specialized function for BRM cells as guardians of the alveoli.
A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation.
Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory). We illustrate its improved performance in recovering clinical proof-of-concept therapeutic targets. Importantly, it identifies specific diseases where pleiotropy informs clinical therapeutics. Furthermore, we illustrate its versatility in accomplishing advanced tasks, including pathway crosstalk identification and downstream crosstalk-based analyses. To conclude, our integrated solution helps bridge the gap between pleiotropy studies and therapeutics discovery.
Assessing the value of incorporating a polygenic risk score with non-genetic factors for predicting breast cancer diagnosis in the UK Biobank.
BACKGROUND: Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorisation needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI). METHODS: We analysed data from 126,490 post-menopausal women of "White British" ancestry, aged 40-69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer-Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected. RESULTS: The Harrell's C statistic of the 10-year risk from the Tyrer-Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models (0.080 (95% confidence interval: 0.053, 0.104) and 0.051 (95% CI: 0.030, 0.073), respectively), with negligible impact on controls. CONCLUSIONS: The addition of a PRS for breast cancer to the well-established Tyrer-Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification. IMPACT: These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.
Common comorbidities and COVID-19 mortality: An empirical comparison of primary care alone vs additional hospital data linkage for association analyses
Introduction Routinely collected primary care data in the UK has been predominantly utilised as a stand-alone resource for studying interactions between various non-communicable diseases (NCD) and COVID-19. The findings from these early studies played a crucial role in informing early policy responses to the pandemic, such as formulating the Shielded Patient List and prioritising vaccination schemes. However, few studies have been conducted to explore the potential value of adding linked hospital data to improve the completeness of recording for association studies. Methods A prospective cohort of 408,395 participants from UK Biobank alive on 1st March 2020 (index date) was included, with linkage to individual-level primary care (GP) and secondary care datasets (HES, Hospital Episode Statistics). Thirteen common NCDs were studied, including hypertension, diabetes, cancer (excluding lung and haematological), haematological malignancy, asthma, chronic heart disease, chronic neurological diseases (excluding stroke and dementia), chronic respiratory disease (excluding asthma), chronic kidney disease, chronic liver disease, psychiatric disorders, common autoimmune diseases (rheumatoid arthritis, systemic lupus erythematosus and psoriasis), and dementia. We calculated the proportion of GP and HES-identified NCD cases and examined the risk of COVID-19 death associated with GP-ascertained NCD vs GP plus HES-ascertained one using multivariable Cox models. Results Nine out of thirteen NCDs were well captured in the GP data source, with proportions of only HES identifiable cases ranging from 8.0% for diabetes to 24.3% for chronic heart disease. However, 50.5% of dementia, 78.4% of chronic liver disease, 87.7% of chronic neurological disease and 94.2% of chronic kidney disease were not recorded in GP diagnosis records. In the subsequent association analyses, most of the studied NCDs were associated with an increased risk of COVID-19 death. The magnitude and direction of association estimates were highly comparable, either using GP or GP plus HES to ascertain NCD cases. One noticeable exception was hypertension, where analysis of GP data showed a negative association with COVID-19 death (adjusted hazard ratio 0.77, 95%CI 0.67 to 0.88), but a positive one after secondary care was added (1.34, 95%CI 1.13 to 1.59). In addition, estimation appeared to be more pronounced if based on GP-identified chronic liver disease, chronic neurological disease, and chronic kidney disease than a combination of two data sources. Conclusion The widely adopted assumption that primary care data can capture most chronic NCDs is generally met but shows important exceptions. Also, although the under-recording of most NCDs had a negligible impact on quantifying COVID-19 death relating to NCD, caution should be taken when a stand-alone primary care-based study produced counterintuitive results, such as a lower COVID-19 death risk associated with hypertension. In future real-world studies, evidence triangulation should be pursued by incorporating multiple data sources with varying data generation mechanisms whenever possible.
Unsupervised Learning to Understand Patterns of Comorbidity in 633,330 Patients Diagnosed with Osteoarthritis
With the advent of big data in healthcare, machine learning has rapidly gained popularity due to its potential to analyse large volumes of complex data from a variety of sources. Unsupervised learning can be used to mine data and discover patterns such as sub-groups within large patient populations. However challenges with implementation in large-scale datasets and interpretability of solutions in a real-world context remain. This work presents an application of unsupervised clustering techniques for discovering patterns of comorbidities in a large dataset of osteoarthritis patients with a view to discover interpretable and clinically-meaningful patterns.
Costes y beneficios del programa de prevención de fractura FLS en España
5. Costes y beneficios del programa de prevención de fractura FLS en España Pinedo Villanueva R1, Burn E1, Cancio JM2, Naranjo A3, Nogués Solán X4, Díez Pérez A4 , Khalid S1 , Pineda Moncusí M1 , Prieto Alhambra D1 , Cooper C5 , Javaid K1 1 Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences. University of Oxford (Reino Unido); 2 Badalona Servicios Asistenciales (BSA). Centro Sociosanitario El Carme. Badalona (España); 3 Servicio de Reumatología. Hospital Universitario de Gran Canaria Dr. Negrín Las Palmas de Gran Canaria (España); 4 Hospital del Mar. Universitat Autònoma de Barcelona (España); 5 MRC Lifecourse Epidemiology Unit. University of Southampton (Reino Unido) Introducción: Las fracturas por fragilidad representan importantes implicaciones a nivel sanitario, social, y económico. Presentar una fractura incrementa el riesgo de padecer nuevas fracturas. Los mode‐ los de asistencia coordinada postfractura de referencia son los de tipo FLS (Fracture Liaison Services). Se presentan los resultados de la ver‐ sión de la “IOF Benefit Calculator” adaptada al sistema sanitario es‐ pañol. Metodología: Se diseñó un modelo de microsimulación con 5 años de seguimiento del modelo FLS en comparación con la atención es‐ tándar de la fractura en personas mayores de 50 años. Se han tenido en cuenta el impacto en la salud, así como los recursos sanitarios y sociales que supondría la implementación FLS. Se incluyeron datos preliminares de riesgo de fractura, identificación, investigación e inicio de prescripción farmacológica, efectividad, mo‐ nitorización, adherencia, mortalidad, costes hospitalarios y sociales. La fuente de datos fueron estudios previos publicados, nacionales si estaban disponibles, o consenso de expertos en su ausencia. Se cal‐ culan los costes e impacto farmacológico y su adherencia, atención primaria y hospitalaria, atención social y calidad de vida después de cada fractura. Resultados: El modelo base de implementación es una FLS que iden‐ tifica al 55‐80% de fracturas con inicio de tratamiento en el 77‐90% de los pacientes y prescripción mayoritaria de alendronato y deno‐ sumab. Resultados preliminares indican reducción de 4.995 fracturas durante los primeros 5 años y ganancia de 6.223 años de vida ajusta‐ dos por calidad (AVAC). Además, se ahorrarían 18.197 días de estan‐ cia hospitalaria, se evitarían 1.509 intervenciones, 56.724 consultas médicas y 274 pacientes/año de ingreso en residencias. El ahorro en costes de servicios de salud y sociales sería de €23 y €9 millones, res‐ pectivamente, que combinado con la inversión de €156 millones en prevención de fracturas (personal, evaluación, medicación, monitori‐ zación), implicaría una inversión neta de €124 millones durante los 5 años, equivalente a un incremento del 1,5% del coste en relación a la atención actual. El coste por AVAC (descontado al 3%) sería de €20.023. Conclusiones: Generalizar la prestación de servicios de prevención de fracturas osteoporóticas en España a partir de Unidades FLS re‐ duciría significativamente el número de fracturas y aumento en la ca‐ lidad de vida de personas mayores de 50 años, reduciendo recursos sanitarios y sociales, con una inversión coste‐efectiva.
Evaluation metrics for data-driven propensity score estimation: A simulation study
Background: Machine learning (ML) methods are promising alternatives for data-driven propensity score (PS) estimation. Different metrics are available for model evaluation and hyper-parameter tuning, but there is no clear guidance on which (if any) should be used for PS estimation using data-driven ML methods. Objectives: We aimed to assess the usefulness of different metrics for model and hyper-parameter selection of ML-based PS. To do this, we investigated the association between these metrics and observed bias using 4 different ML models. Methods: Data (n = 100 000) were generated using parametric Monte Carlo simulations with 100 iterations and 100 confounders. Binary exposure with prevalence of 0.5 and binary outcome with prevalence of 0.02 were generated via logistic distributions. We tested two treatment effect scenarios with true odds ratios (OR) 1.5 and 2. First, we used four ML methods to estimate PS: 1. LASSO (Least Absolute Shrinkage and Selection Operator), 2. RF (Random Forest), 3. MLP (Multilayer Perceptron), and 4. XgBoost (eXtreme Gradient Boosting). Second, PS 1:1 matching was applied using each of the estimated PS to obtain relative bias and ASAM (average absolute standardized mean difference) of treatment effect estimation. Third, we explored the relationship between relative bias and ASAM with the following PS metrics: 1. AUC (Area Under the ROC Curve), 2. Calibration slope, and 3. Brier score. Lastly, we tested different hyper-parameters and reported the treatment effect estimation bias and the best metrics for PS estimation from the previous step. Results: We found that with fixed ML hyper-parameters, Brier score and calibration slope performed best at predicting bias after PS matching for both OR. For example, with OR 1.5, the lowest relative bias was obtained by MLP at 19.78% (95% CI: 15.22%, 24.32%), with calibration slope 0.88 (95% CI: 0.86, 0.90) and Brier score 0.27 (95% CI: 0.25, 0.29) being the best among all tested ML methods. Conversely, AUC was not consistently associated with bias. For example, RF had the best AUC at 0.99 (95% CI: 0.98, 1) but led to the largest bias at 34.32% (95% CI: 30.20%, 38.44%). Experiments after hyper-parameter tests for different ML models also showed that PS models with better Brier scores and calibration slopes can generate the lowest bias. Conclusions: We found metrics including calibration (Brier score and calibration slope) were useful evaluation metrics for model selection and hyper-parameter tuning of PS estimation using ML models. Conversely, discrimination estimates (AUC) could be misleading, with some scenarios showing almost perfect discrimination but very large bias. More research is needed to confirm these findings and to provide guidance for data-driven ML-based PS estimation.
The performance of different strategies to manage confounding in cardinality matching for medical device epidemiology: A simulation study
Background: Residual confounding, either related to patient or surgeon variables, leads to bias in the estimation of treatment effects in observational studies of medical devices. Objectives: To estimate the performance (bias and precision) of confounder balance criteria settings for cardinality matching in medical device epidemiology studies. Methods: Cardinality matching (CM) is a matching method that finds the largest matched sample according to user’s prespecified confounder balance criteria. Multi-level Monte Carlo simulations (1,000 iterations) with patients nested under surgeon (ratio 500:1) and sample size of n=10,000 were conducted. Five patient confounders, one instrumental variable and one risk factor were generated, all binary and based on a bernoulli distribution. Fixed true treatment effect at Odds ratio (OR) 1.5. A surgeon-level confounder was generated with Pois(2), with an OR = 2 association with treatment choice, and an OR ranging from 1.01 to 5 for effect on outcome. CM was used to balance the confounders. A range of different confounder balance criteria were tested as part of CM, namely standardised mean difference (SMD) of 0, 0.001, 0.01, and 0.1. Treatment effects were then estimated using logistic regression as the outcome model on the matched sample obtained from CM. The resulting treatment effects were compared to the true effect, and % bias and root mean square errors (RMSE) estimated. Results: Confounder balance of SMD = 0.1 results in lower bias and RMSE for weaker surgeon effects on outcome (OR <= 1.5), whilst SMD < 0.1 performed best for strong surgeon confounding scenarios (OR > 1.5). For example, for an OR of 1.25 for surgeon effect on outcome, %bias and RMSE for SMD = 0.1 was 5.4% and 0.056 compared to 11.6% and 0.069 for SMD = 0.01. Conversely, for OR of 2.5 for surgeon effects, %bias and RMSE for SMD = 0.1 were 24.1% and 0.124 respectively, compared to 1.1% and 0.056 for SMD = 0.01. Conclusions: Confounder balance choice for CM impacts the bias, and should be informed by observable effects of surgeon on outcome. More research is needed to guide the use of CM in medical device epidemiology.
Real-world and genetic evidence for the management of opioid epidemic and COVID-19 pandemic: population-based studies
Background: The widespread use of opioids and the emergence of SARS-CoV-2 represent two pressing public health crises that require evidence-based decision-making. This thesis comprises six interconnected studies aimed at translating large, routinely-collected and Biobank data into reliable evidence to inform the management of the ongoing opioid epidemic and COVID-19 pandemic. Methods: Four data sources were studied: primary care records from SIDIAP (Spain), US claims, and the UK Biobank. Exposures included opioids, ibuprofen, COVID-19 vaccines, human leucocyte antigen (HLA) genes, and SARS-CoV-2 infections. Cohort studies were used as the primary design, complemented by statistical techniques including regression, propensity scores, survival analyses, and negative and positive control outcomes, as well as Mendelian randomization. Study outcomes were adverse drug events, COVID-19 susceptibility and severity, and related complications. Results: Incident opioid use increased in Catalonia from 2007 to 2019, with Tramadol being the most frequently used opioid in 2019. Compared to codeine, tramadol was associated with a higher risk of all-cause mortality, cardiovascular events, and fractures. No differential risk of COVID-19 was observed among users of ibuprofen versus other analgesics. When compared to two doses of ChAdOx1, vaccination with BNT162b2 was associated with lower risks of COVID-19 infection and hospitalization, respectively, during the study period when the Delta variant was dominant. Six independent HLA alleles significantly affected antibody response to COVID-19 vaccines, and the aggregated genetic score had a strong, collective, and causal influence on breakthrough COVID-19. COVID-19 infection was associated with an increased risk of venous thromboembolism (VTE) within 30 days, with highest risk in unvaccinated individuals. People with older age, male sex, obesity, and inherited thrombophilia were also at a higher VTE risk post-COVID-19. Conclusion: The integration of real-world and linked biobank data can be effectively leveraged using advanced analytical tools to generate timely and actionable evidence to tackle global health crises like the ongoing opioids epidemic and COVID-19 pandemic.
A Phase I Study of the Oral Dual-Acting Pan-PI3K/mTOR Inhibitor Bimiralisib in Patients with Advanced Solid Tumors
Background: Bimiralisib is a pan-PI3K/mTOR inhibitor demonstrating antitumor efficacy in preclinical models. The objectives of this study were to identify a maximum tolerated dose (MTD), pharmacokinetics (PK), a dosing schedule, and adverse events (AEs) in patients with advanced solid tumors. Patients and Methods: Patients received oral bimiralisib to determine the MTD of one continuous (once daily) and two intermittent schedules (A: Days 1, 2 weekly; B: Days 1, 4 weekly) until progression or unacceptable AEs occurred. Results: The MTD for the continuous schedule was 80 mg, with grade three fatigue as the dose-limiting toxicity (DLT). No MTD was reached with intermittent schedules, with only one DLT in schedule B. PK analysis suggested that 140 mg (schedule A) was within the biologically active dose range and was selected for further exploration. The most frequent treatment-emergent AEs were hyperglycemia (76.2%) in the continuous schedule, and nausea (56–62.5%) in schedules A and B. The most frequent treatment-emergent > grade three AE for all schedules combined was hyperglycemia (28.6%, continuous schedule; 12.0%, schedule A; 12.5%, schedule B). There was one partial response in a head and neck squamous cancer patient with a NOTCH1T1997M mutation. Conclusions: Bimiralisib demonstrated a manageable AE profile consistent with this compound class. Intermittent schedules had fewer > grade three AEs, while also maintaining favorable PK profiles. Intermittent schedule A is proposed for further development in biomarker-selected patient populations.
Mitochondrial control of lymphocyte homeostasis.
Mitochondria play a multitude of essential roles within mammalian cells, and understanding how they control immunity is an emerging area of study. Lymphocytes, as integral cellular components of the adaptive immune system, rely on mitochondria for their function, and mitochondria can dynamically instruct their differentiation and activation by undergoing rapid and profound remodelling. Energy homeostasis and ATP production are often considered the primary functions of mitochondria in immune cells; however, their importance extends across a spectrum of other molecular processes, including regulation of redox balance, signalling pathways, and biosynthesis. In this review, we explore the dynamic landscape of mitochondrial homeostasis in T and B cells, and discuss how mitochondrial disorders compromise adaptive immunity.
A single cell atlas of frozen shoulder capsule identifies features associated with inflammatory fibrosis resolution
Frozen shoulder is a spontaneously self-resolving chronic inflammatory fibrotic human disease, which distinguishes the condition from most fibrotic diseases that are progressive and irreversible. Using single-cell analysis, we identify pro-inflammatory MERTKlowCD48+ macrophages and MERTK + LYVE1 + MRC1+ macrophages enriched for negative regulators of inflammation which co-exist in frozen shoulder capsule tissues. Micro-cultures of patient-derived cells identify integrin-mediated cell-matrix interactions between MERTK+ macrophages and pro-resolving DKK3+ and POSTN+ fibroblasts, suggesting that matrix remodelling plays a role in frozen shoulder resolution. Cross-tissue analysis reveals a shared gene expression cassette between shoulder capsule MERTK+ macrophages and a respective population enriched in synovial tissues of rheumatoid arthritis patients in disease remission, supporting the concept that MERTK+ macrophages mediate resolution of inflammation and fibrosis. Single-cell transcriptomic profiling and spatial analysis of human foetal shoulder tissues identify MERTK + LYVE1 + MRC1+ macrophages and DKK3+ and POSTN+ fibroblast populations analogous to those in frozen shoulder, suggesting that the template to resolve fibrosis is established during shoulder development. Crosstalk between MerTK+ macrophages and pro-resolving DKK3+ and POSTN+ fibroblasts could facilitate resolution of frozen shoulder, providing a basis for potential therapeutic resolution of persistent fibrotic diseases.
d-Mannose for Prevention of Recurrent Urinary Tract Infection Among Women: A Randomized Clinical Trial.
IMPORTANCE: Recurrent urinary tract infection (UTI) is a common debilitating condition in women, with limited prophylactic options. d-Mannose has shown promise in trials based in secondary care, but effectiveness in placebo-controlled studies and community settings has not been established. OBJECTIVE: To determine whether d-mannose taken for 6 months reduces the proportion of women with recurrent UTI experiencing a medically attended UTI. DESIGN, SETTING, AND PARTICIPANTS: This 2-group, double-blind randomized placebo-controlled trial took place across 99 primary care centers in the UK. Participants were recruited between March 28, 2019, and January 31, 2020, with 6 months of follow-up. Participants were female, 18 years or older, living in the community, and had evidence in their primary care record of consultations for at least 2 UTIs in the preceding 6 months or 3 UTIs in 12 months. Invitation to participate was made by their primary care center. A total of 7591 participants were approached, 830 responded, and 232 were ineligible or did not proceed to randomization. Statistical analysis was reported in December 2022. INTERVENTION: Two grams daily of d-mannose powder or matched volume of placebo powder. MAIN OUTCOMES AND MEASURES: The primary outcome measure was the proportion of women experiencing at least 1 further episode of clinically suspected UTI for which they contacted ambulatory care within 6 months of study entry. Secondary outcomes included symptom duration, antibiotic use, time to next medically attended UTI, number of suspected UTIs, and UTI-related hospital admissions. RESULTS: Of 598 women eligible (mean [range] age, 58 [18-93] years), 303 were randomized to d-mannose (50.7%) and 295 to placebo (49.3%). Primary outcome data were available for 583 participants (97.5%). The proportion contacting ambulatory care with a clinically suspected UTI was 150 of 294 (51.0%) in the d-mannose group and 161 of 289 (55.7%) in the placebo group (risk difference, -5%; 95% CI, -13% to 3%; P = .26). Estimates were similar in per protocol analyses, imputation analyses, and preplanned subgroups. There were no statistically significant differences in any secondary outcome measures. CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, daily d-mannose did not reduce the proportion of women with recurrent UTI in primary care who experienced a subsequent clinically suspected UTI. d-Mannose should not be recommended for prophylaxis in this patient group. TRIAL REGISTRATION: isrctn.org Identifier: ISRCTN13283516.
Physiotherapy Rehabilitation Post Patellar Dislocation (PRePPeD)-protocol for an external pilot randomised controlled trial and qualitative study comparing supervised versus self-managed rehabilitation for people after acute patellar dislocation.
BACKGROUND: Patellar dislocations mainly affect adolescents and young adults. After this injury, patients are usually referred to physiotherapy for exercise-based rehabilitation. Currently, limited high-quality evidence exists to guide rehabilitation practice and treatment outcomes vary. A full-scale trial comparing different rehabilitation approaches would provide high-quality evidence to inform rehabilitation practice. Whether this full-scale trial is feasible is uncertain: the only previous trial that compared exercise-based programmes in this patient population had high loss to follow-up. This study aims to assess the feasibility of conducting a future full-scale trial comparing the clinical and cost-effectiveness of two different rehabilitation approaches for people with an acute patellar dislocation. METHODS: Two-arm parallel external pilot randomised controlled trial and qualitative study. We aim to recruit at least 50 participants aged ≥ 14 years with an acute first-time or recurrent patellar dislocation from at least three English National Health Service hospitals. Participants will be randomised 1:1 to supervised rehabilitation (four to six, one-to-one, physiotherapy sessions of advice and prescription of tailored progressive home exercise over a maximum of 6 months) or self-managed rehabilitation (one physiotherapy session of self-management advice, exercise, and provision of self-management materials). Pilot objectives are (1) willingness to be randomised, (2) recruitment rate, (3) retention, (4) intervention adherence, and (5) intervention and follow-up method acceptability to participants assessed through one-to-one semi-structured interviews (maximum 20 participants). Follow-up data will be collected 3, 6, and 9 months after randomisation. Quantitative pilot and clinical outcomes will be numerically summarised, with 95% confidence intervals generated for the pilot outcomes using Wilson's and exact Poisson methods as appropriate. DISCUSSION: This study will assess the feasibility of conducting a full-scale trial comparing supervised versus self-managed rehabilitation for people after acute first-time or recurrent patellar dislocation. This full-scale trial's results would provide high-quality evidence to guide rehabilitation provision for patients with this injury. TRIAL REGISTRATION: ISRCTN registry ISRCTN14235231 . Registered on 09 August 2022.
A phase I open-label, dose-escalation study of NUC-3373, a targeted thymidylate synthase inhibitor, in patients with advanced cancer (NuTide:301).
PURPOSE: 5-fluorouracil (5-FU) is inefficiently converted to the active anti-cancer metabolite, fluorodeoxyuridine-monophosphate (FUDR-MP), is associated with dose-limiting toxicities and challenging administration schedules. NUC-3373 is a phosphoramidate nucleotide analog of fluorodeoxyuridine (FUDR) designed to overcome these limitations and replace fluoropyrimidines such as 5-FU. PATIENTS AND METHODS: NUC-3373 was administered as monotherapy to patients with advanced solid tumors refractory to standard therapy via intravenous infusion either on Days 1, 8, 15 and 22 (Part 1) or on Days 1 and 15 (Part 2) of 28-day cycles until disease progression or unacceptable toxicity. Primary objectives were maximum tolerated dose (MTD) and recommended Phase II dose (RP2D) and schedule of NUC-3373. Secondary objectives included pharmacokinetics (PK), and anti-tumor activity. RESULTS: Fifty-nine patients received weekly NUC-3373 in 9 cohorts in Part 1 (n = 43) and 3 alternate-weekly dosing cohorts in Part 2 (n = 16). They had received a median of 3 prior lines of treatment (range: 0-11) and 74% were exposed to prior fluoropyrimidines. Four experienced dose-limiting toxicities: two Grade (G) 3 transaminitis; one G2 headache; and one G3 transient hypotension. Commonest treatment-related G3 adverse event of raised transaminases occurred in
Effects of high-dose versus standard-dose quadrivalent influenza vaccine among patients with diabetes: A post-hoc analysis of the DANFLU-1 trial.
AIM: High-dose quadrivalent influenza vaccine (QIV-HD) has been shown to be more effective than standard-dose (QIV-SD) in reducing influenza infection, but whether diabetes status affects relative vaccine effectiveness (rVE) is unknown. We aimed to assess rVE on change in glycated haemoglobin [HbA1c (∆HbA1c)], incident diabetes, total all-cause hospitalizations (first + recurrent), and a composite of all-cause mortality and hospitalization for pneumonia or influenza. METHODS: DANFLU-1 was a pragmatic, open-label trial randomizing adults (65-79 years) 1:1 to QIV-HD or QIV-SD during the 2021/22 influenza season. Cox proportional hazards regression was used to estimate rVE against incident diabetes and the composite endpoint, negative binomial regression to estimate rVE against all-cause hospitalizations, and ANCOVA when assessing rVE against ∆HbA1c. RESULTS: Of the 12 477 participants, 1162 (9.3%) had diabetes at baseline. QIV-HD, compared with QIV-SD, was associated with a reduction in the rate of all-cause hospitalizations irrespective of diabetes [overall: 647 vs. 742 events, incidence rate ratio (IRR): 0.87, 95% CI (0.76-0.99); diabetes: 93 vs. 118 events, IRR: 0.80, 95% CI (0.55-1.15); without diabetes: 554 vs. 624 events, IRR: 0.88, 95% CI (0.76-1.01), pinteraction = 0.62]. Among those with diabetes, QIV-HD was associated with a lower risk of the composite outcome [2 vs. 11 events, HR: 0.18, 95% CI (0.04-0.83)] but had no effect on ∆HbA1c; QIV-HD adjusted mean difference: ∆ + 0.2 mmol/mol, 95% CI (-0.9 to 1.2). QIV-HD did not affect the risk of incident diabetes [HR 1.18, 95% CI (0.94-1.47)]. CONCLUSIONS: In this post-hoc analysis, QIV-HD versus QIV-SD was associated with an increased rVE against the composite of all-cause death and hospitalization for pneumonia/influenza, and the all-cause hospitalization rate irrespective of diabetes status.