Tuning of liver circadian transcriptome rhythms by thyroid hormone state in male mice.

Thyroid hormones (THs) are important regulators of systemic energy metabolism. In the liver, they stimulate lipid and cholesterol turnover and increase systemic energy bioavailability. It is still unknown how the TH state interacts with the circadian clock, another important regulator of energy metabolism. We addressed this question using a mouse model of hypothyroidism and performed circadian analyses. Low TH levels decreased locomotor activity, food intake, and body temperature mostly in the active phase. Concurrently, liver transcriptome profiling showed only subtle effects compared to elevated TH conditions. Comparative circadian transcriptome profiling revealed alterations in mesor, amplitude, and phase of transcript levels in the livers of low-TH mice. Genes associated with cholesterol uptake, biosynthesis, and bile acid secretion showed reduced mesor. Increased and decreased cholesterol levels in the serum and liver were identified, respectively. Combining data from low- and high-TH conditions allowed the identification of 516 genes with mesor changes as molecular markers of the liver TH state. We explored these genes and created an expression panel that assesses liver TH state in a time-of-day dependent manner. Our findings suggest that the liver has a low TH action under physiological conditions. Circadian profiling reveals genes as potential markers of liver TH state.

Evidence of questionable research practices in clinical prediction models.

BACKGROUND: Clinical prediction models are widely used in health and medical research. The area under the receiver operating characteristic curve (AUC) is a frequently used estimate to describe the discriminatory ability of a clinical prediction model. The AUC is often interpreted relative to thresholds, with "good" or "excellent" models defined at 0.7, 0.8 or 0.9. These thresholds may create targets that result in "hacking", where researchers are motivated to re-analyse their data until they achieve a "good" result. METHODS: We extracted AUC values from PubMed abstracts to look for evidence of hacking. We used histograms of the AUC values in bins of size 0.01 and compared the observed distribution to a smooth distribution from a spline. RESULTS: The distribution of 306,888 AUC values showed clear excesses above the thresholds of 0.7, 0.8 and 0.9 and shortfalls below the thresholds. CONCLUSIONS: The AUCs for some models are over-inflated, which risks exposing patients to sub-optimal clinical decision-making. Greater modelling transparency is needed, including published protocols, and data and code sharing.

Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals: a NASSS framework-informed scoping review.

BACKGROUND: Successful implementation and utilization of Computerized Clinical Decision Support Systems (CDSS) in hospitals is complex and challenging. Implementation science, and in particular the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework, may offer a systematic approach for identifying and addressing these challenges. This review aimed to identify, categorize, and describe barriers and facilitators to CDSS implementation in hospital settings and map them to the NASSS framework. Exploring the applicability of the NASSS framework to CDSS implementation was a secondary aim. METHODS: Electronic database searches were conducted (21 July 2020; updated 5 April 2022) in Ovid MEDLINE, Embase, Scopus, PyscInfo, and CINAHL. Original research studies reporting on measured or perceived barriers and/or facilitators to implementation and adoption of CDSS in hospital settings, or attitudes of healthcare professionals towards CDSS were included. Articles with a primary focus on CDSS development were excluded. No language or date restrictions were applied. We used qualitative content analysis to identify determinants and organize them into higher-order themes, which were then reflexively mapped to the NASSS framework. RESULTS: Forty-four publications were included. These comprised a range of study designs, geographic locations, participants, technology types, CDSS functions, and clinical contexts of implementation. A total of 227 individual barriers and 130 individual facilitators were identified across the included studies. The most commonly reported influences on implementation were fit of CDSS with workflows (19 studies), the usefulness of the CDSS output in practice (17 studies), CDSS technical dependencies and design (16 studies), trust of users in the CDSS input data and evidence base (15 studies), and the contextual fit of the CDSS with the user's role or clinical setting (14 studies). Most determinants could be appropriately categorized into domains of the NASSS framework with barriers and facilitators in the "Technology," "Organization," and "Adopters" domains most frequently reported. No determinants were assigned to the "Embedding and Adaptation Over Time" domain. CONCLUSIONS: This review identified the most common determinants which could be targeted for modification to either remove barriers or facilitate the adoption and use of CDSS within hospitals. Greater adoption of implementation theory should be encouraged to support CDSS implementation.

Vital signs-based deterioration prediction model assumptions can lead to losses in prediction performance.

OBJECTIVE: Vital signs-based models are complicated by repeated measures per patient and frequently missing data. This paper investigated the impacts of common vital signs modeling assumptions during clinical deterioration prediction model development. STUDY DESIGN AND SETTING: Electronic medical record (EMR) data from five Australian hospitals (1 January 2019-31 December 2020) were used. Summary statistics for each observation's prior vital signs were created. Missing data patterns were investigated using boosted decision trees, then imputed with common methods. Two example models predicting in-hospital mortality were developed, as follows: logistic regression and eXtreme Gradient Boosting. Model discrimination and calibration were assessed using the C-statistic and nonparametric calibration plots. RESULTS: The data contained 5,620,641 observations from 342,149 admissions. Missing vitals were associated with observation frequency, vital sign variability, and patient consciousness. Summary statistics improved discrimination slightly for logistic regression and markedly for eXtreme Gradient Boosting. Imputation method led to notable differences in model discrimination and calibration. Model calibration was generally poor. CONCLUSION: Summary statistics and imputation methods can improve model discrimination and reduce bias during model development, but it is questionable whether these differences are clinically significant. Researchers should consider why data are missing during model development and how this may impact clinical utility.

Integrating economic considerations into cutpoint selection may help align clinical decision support toward value-based healthcare.

OBJECTIVE: Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) preventing intensive care unit readmission and (ii) preventing inpatient falls. MATERIALS AND METHODS: Parameter estimates for costs and effectiveness from prior studies were included in Monte Carlo simulations. For each use-case, we simulated the expected NMB resulting from the model-guided decision using a range of cutpoint selection approaches, including our new value-optimizing approach. Sensitivity analyses applied alternative event rates, model discrimination, and calibration performance. RESULTS: The proposed approach that considered expected downstream consequences was frequently NMB-maximizing compared with other methods. Sensitivity analysis demonstrated that it was or closely tracked the optimal strategy under a range of scenarios. Under scenarios of relatively low event rates and discrimination that may be considered realistic for intensive care (prevalence = 0.025, area under the receiver operating characteristic curve [AUC] = 0.70) and falls (prevalence = 0.036, AUC = 0.70), our proposed cutpoint method was either the best or similar to the best of the compared methods regarding NMB, and was robust to model miscalibration. DISCUSSION: Our results highlight the potential value of conditioning cutpoints on the implementation setting, particularly for rare and costly events, which are often the target of prediction model development research. CONCLUSIONS: This study proposes a cutpoint selection method that may optimize clinical decision support systems toward value-based care.

Corrigendum: Phenotypic and genetic parameters of circadian rhythms from core body temperature profiles and their relationships with beef steers' production efficiency profiles during successive winter feeding periods.

[This corrects the article DOI: 10.3389/fgene.2023.1026601.].

Phenotypic and genetic parameters of circadian rhythms from core body temperature profiles and their relationships with beef steers' production efficiency profiles during successive winter feeding periods.

This 2-year study evaluated differences in circadian parameters obtained from measures of core body temperatures using telemetric reticulo-rumen and rectal devices during two winter feeding regimes in western Canada. The study also estimated phenotypic correlations and genetic parameters associated with circadian parameters and other production traits in each feeding regime. Each year, 80 weaned steer calves (initial age: 209 ± 11 days; BW: 264 ± 20 kg) from the same cohort were tested over two successive regimes, Fall-Winter (FW) and Winter-Spring (WS) at Lanigan, Saskatchewan, Canada. The steers received forage-based rations in both regimes where the individual feed intake was measured with automatic feeding units. During the trial, the reticulo-rumen (RTMP) and rectal (RCT) temperatures were simultaneously measured every 5 min using telemetric devices. These were used to calculate the circadian parameters (Midline Estimating Statistic Of Rhythms, amplitude, and acrophase/peak time) for both temperature measures. Growth and efficiency performance traits were also determined for all steers. Each steer was assigned into inefficient, neutral, and efficient classes based on the SD of the residual feed intake (RFI), residual gain (RG), and residual intake and gain (RIG) within each year and feeding regime. Higher (p < 0.0003) RTMP and rectal temperature MESORs were observed in the Fall-Winter compared to the Winter-Spring regime. While the two test regimes were different (p < 0.05) for the majority of the RTMP or RCT temperature parameters, they did not differ (p > 0.10) with the production efficiency profiles. The heritability estimates were higher in FW (0.78 ± 0.18 vs. 0.56 ± 0.26) than WS (0.50 ± 0.18 vs. 0.47 ± 0.22) for the rumen and rectal MESORs, respectively. There were positive genetic correlations between the two regimes for the RTMP (0.69 ± 0.21) and RCT (0.32 ± 0.59). There was a negative correlation (p < 0.001) between body temperature and ambient temperature. The high heritability estimates and genetic correlations for rumen and rectal temperature parameters demonstrate their potential as beef genetic improvement tools of economic traits associated with the parameters. However, there are limited practical implications of using only the core-body temperature as a proxy for production efficiency traits for beef steers during winter.

Inpatient Fall Prediction Models: A Scoping Review.

INTRODUCTION: The digitization of hospital systems, including integrated electronic medical records, has provided opportunities to improve the prediction performance of inpatient fall risk models and their application to computerized clinical decision support systems. This review describes the data sources and scope of methods reported in studies that developed inpatient fall prediction models, including machine learning and more traditional approaches to inpatient fall risk prediction. METHODS: This scoping review used methods recommended by the Arksey and O'Malley framework and its recent advances. PubMed, CINAHL, IEEE Xplore, and EMBASE databases were systematically searched. Studies reporting the development of inpatient fall risk prediction approaches were included. There was no restriction on language or recency. Reference lists and manual searches were also completed. Reporting quality was assessed using adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis statement (TRIPOD), where appropriate. RESULTS: Database searches identified 1,396 studies, 63 were included for scoping assessment and 45 for reporting quality assessment. There was considerable overlap in data sources and methods used for model development. Fall prediction models typically relied on features from patient assessments, including indicators of physical function or impairment, or cognitive function or impairment. All but two studies used patient information at or soon after admission and predicted fall risk over the entire admission, without consideration of post-admission interventions, acuity changes or length of stay. Overall, reporting quality was poor, but improved in the past decade. CONCLUSION: There was substantial homogeneity in data sources and prediction model development methods. Use of artificial intelligence, including machine learning with high-dimensional data, remains underexplored in the context of hospital falls. Future research should consider approaches with the potential to utilize high-dimensional data from digital hospital systems, which may contribute to greater performance and clinical usefulness.

Divergent remodeling of the skeletal muscle metabolome over 24 h between young, healthy men and older, metabolically compromised men.

24 h whole-body substrate metabolism and the circadian clock within skeletal muscle are both compromised upon metabolic disease in humans. Here, we assessed the 24 h muscle metabolome by serial muscle sampling performed under 24 h real-life conditions in young, healthy (YH) men versus older, metabolically compromised (OMC) men. We find that metabolites associated with the initial steps of glycolysis and hexosamine biosynthesis are higher in OMC men around the clock, whereas metabolites associated with glutamine-alpha-ketoglutarate, ketone, and redox metabolism are lower in OMC men. The night period shows the largest number of differently expressed metabolites. Both groups demonstrate 24 h rhythmicity in half of the metabolome, but rhythmic metabolites only partially overlap. Specific metabolites are only rhythmic in YH men (adenosine), phase shifted in OMC men (cis-aconitate, flavin adenine dinucleotide [FAD], and uridine diphosphate [UDP]), or have a reduced 24 h amplitude in OMC men (hydroxybutyrate and hippuric acid). Our data highlight the plasticity of the skeletal muscle metabolome over 24 h and large divergence across the metabolic health spectrum.

Rewiring of liver diurnal transcriptome rhythms by triiodothyronine (T3) supplementation.

Diurnal (i.e., 24 hr) physiological rhythms depend on transcriptional programs controlled by a set of circadian clock genes/proteins. Systemic factors like humoral and neuronal signals, oscillations in body temperature, and food intake align physiological circadian rhythms with external time. Thyroid hormones (THs) are major regulators of circadian clock target processes such as energy metabolism, but little is known about how fluctuations in TH levels affect the circadian coordination of tissue physiology. In this study, a high triiodothyronine (T3) state was induced in mice by supplementing T3 in the drinking water, which affected body temperature, and oxygen consumption in a time-of-day-dependent manner. A 24-hr transcriptome profiling of liver tissue identified 37 robustly and time independently T3-associated transcripts as potential TH state markers in the liver. Such genes participated in xenobiotic transport, lipid and xenobiotic metabolism. We also identified 10-15% of the liver transcriptome as rhythmic in control and T3 groups, but only 4% of the liver transcriptome (1033 genes) were rhythmic across both conditions - amongst these, several core clock genes. In-depth rhythm analyses showed that most changes in transcript rhythms were related to mesor (50%), followed by amplitude (10%), and phase (10%). Gene set enrichment analysis revealed TH state-dependent reorganization of metabolic processes such as lipid and glucose metabolism. At high T3 levels, we observed weakening or loss of rhythmicity for transcripts associated with glucose and fatty acid metabolism, suggesting increased hepatic energy turnover. In summary, we provide evidence that tonic changes in T3 levels restructure the diurnal liver metabolic transcriptome independent of local molecular circadian clocks.

Many environmental conditions, including light and temperature, vary with a daily rhythm that affects how animals interact with their surroundings. Indeed, most species have developed so-called circadian clocks: internal molecular timers that cycle approximately every 24 hours and regulate many bodily functions, including digestion, energy metabolism and sleep. The energy metabolism of the liver ­ the chemical reactions that occur in the organ to produce energy from nutrients ­ is controlled both by the circadian clock system, and by the hormones produced by a gland in the neck called the thyroid. However, the interaction between these two regulators is poorly understood. To address this question, de Assis, Harder et al. elevated the levels of thyroid hormones in mice by adding these hormones to their drinking water. Studying these mice showed that, although thyroid hormone levels were good indicators of how much energy mice burn in a day, they do not reflect daily fluctuations in metabolic rate faithfully. Additionally, de Assis, Harder et al. showed that elevating T₃, the active form of thyroid hormone, led to a rewiring of the daily rhythms at which genes were turned on and off in the liver, affecting the daily timing of processes including fat and cholesterol metabolism. This occurred without changing the circadian clock of the liver directly. De Assis, Harder et al.'s results indicate that time-of-day critically affects the action of thyroid hormones in the liver. This suggests that patients with hypothyroidism, who produce low levels of thyroid hormones, may benefit from considering time-of-day as a factor in disease diagnosis, therapy and, potentially, prevention. Further data on the rhythmic regulation of thyroid action in humans, including in patients with hypothyroidism, are needed to further develop this approach.

A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes.

BACKGROUND: Hospital patients experiencing clinical deterioration are at greater risk of adverse events. Monitoring patients through early warning systems is widespread, despite limited published evidence that they improve patient outcomes. Current limitations including infrequent or incorrect risk calculations may be mitigated by integration into electronic medical records. Our objective was to examine the impact on patient outcomes of systems for detecting and responding to real-time, automated alerts for clinical deterioration. METHODS: This review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. We searched Medline, CINAHL and Embase for articles implementing real-time, automated deterioration alerts in hospitalised adults evaluating one or more patient outcomes including intensive care unit admission, length of stay, in-hospital cardiopulmonary arrest and in-hospital death. RESULTS: Of 639 studies identified, 18 were included in this review. Most studies did not report statistically significant associations between alert implementation and better patient outcomes. Four studies reported statistically significant improvements in two or more patient outcomes, and were the only studies to directly involve the patient's clinician. However, only one of these four studies was robust to existing trends in patient outcomes. Of the six studies using robust study designs, one reported a statistically significant improvement in patient outcomes; the rest did not detect differences. CONCLUSIONS: Most studies in this review did not detect improvements in patient outcomes following the implementation of real-time deterioration alerts. Future implementation studies should consider: directly involving the patient's physician or a dedicated surveillance nurse in structured response protocols for deteriorating patients; the workflow of alert recipients; and incorporating model features into the decision process to improve clinical utility.

Assessment of 28-Day In-Hospital Mortality in Mechanically Ventilated Patients With Coronavirus Disease 2019: An International Cohort Study.

Factors associated with mortality in coronavirus disease 2019 patients on invasive mechanical ventilation are still not fully elucidated. OBJECTIVES: To identify patient-level parameters, readily available at the bedside, associated with the risk of in-hospital mortality within 28 days from commencement of invasive mechanical ventilation or coronavirus disease 2019. DESIGN SETTING AND PARTICIPANTS: Prospective observational cohort study by the global Coronavirus Disease 2019 Critical Care Consortium. Patients with laboratory-confirmed coronavirus disease 2019 requiring invasive mechanical ventilation from February 2, 2020, to May 15, 2021. MAIN OUTCOMES AND MEASURES: Patient characteristics and clinical data were assessed upon ICU admission, the commencement of invasive mechanical ventilation and for 28 days thereafter. We primarily aimed to identify time-independent and time-dependent risk factors for 28-day invasive mechanical ventilation mortality. RESULTS: One-thousand five-hundred eighty-seven patients were included in the survival analysis; 588 patients died in hospital within 28 days of commencing invasive mechanical ventilation (37%). Cox-regression analysis identified associations between the hazard of 28-day invasive mechanical ventilation mortality with age (hazard ratio, 1.26 per 10-yr increase in age; 95% CI, 1.16-1.37; p < 0.001), positive end-expiratory pressure upon commencement of invasive mechanical ventilation (hazard ratio, 0.81 per 5 cm H2O increase; 95% CI, 0.67-0.97; p = 0.02). Time-dependent parameters associated with 28-day invasive mechanical ventilation mortality were serum creatinine (hazard ratio, 1.28 per doubling; 95% CI, 1.15-1.41; p < 0.001), lactate (hazard ratio, 1.22 per doubling; 95% CI, 1.11-1.34; p < 0.001), Paco2 (hazard ratio, 1.63 per doubling; 95% CI, 1.19-2.25; p < 0.001), pH (hazard ratio, 0.89 per 0.1 increase; 95% CI, 0.8-14; p = 0.041), Pao2/Fio2 (hazard ratio, 0.58 per doubling; 95% CI, 0.52-0.66; p < 0.001), and mean arterial pressure (hazard ratio, 0.92 per 10 mm Hg increase; 95% CI, 0.88-0.97; p = 0.003). CONCLUSIONS AND RELEVANCE: This international study suggests that in patients with coronavirus disease 2019 on invasive mechanical ventilation, older age and clinically relevant variables monitored at baseline or sequentially during the course of invasive mechanical ventilation are associated with 28-day invasive mechanical ventilation mortality hazard. Further investigation is warranted to validate any causative roles these parameters might play in influencing clinical outcomes.

Diet-altered body temperature rhythms are associated with altered rhythms of clock gene expression in peripheral tissues in vivo.

Temperature rhythms can act as potent signals for the modulation of the amplitude and phase of clock gene expression in peripheral organs in vitro, but the relevance of the circadian rhythm of core body temperature (Tc) as a modulating signal in vivo has not yet been investigated. Using calorie restriction and cafeteria feeding, we induced a larger and a dampened Tc amplitude, respectively, in male Wistar rats, and investigated the circadian expression profile of the core clock genes Bmal1, Per2, Cry1, and Rev-erbα, the heat-responsive genes heat shock protein 90 (Hsp90) and cold-inducible RNA binding protein (Cirbp), and Pgc1α, Pparα/γ/δ, Glut1/4, and Chop10 in the liver, skeletal muscle, white adipose tissue (WAT), and adrenal glands. Diet-altered Tc rhythms differentially affected the profiles of clock genes, Hsp90, and Cirbp expression in peripheral tissues. Greater Tc amplitudes elicited by calorie restriction were associated with large amplitudes of Hsp90 and Cirbp expression in the liver and WAT, in which larger amplitudes of clock gene expression were also observed. The amplitudes of metabolic gene expression were greater in the WAT, but not in the liver, in calorie-restricted rats. Conversely, diet-altered Tc rhythms were not translated to distinct changes in the amplitude of Hsp90, Cirbp, or clock or metabolic genes in the skeletal muscle or adrenal glands. While it was not possible to disentangle the effects of diet and temperature in this model, taken together with previous in vitro studies, our study presents novel data consistent with the notion that the circadian Tc rhythm can modulate the amplitude of circadian gene expression in vivo. The different responses of Hsp90 and Cirbp in peripheral tissues may be linked to the tissue-specific responses of peripheral clocks to diet and/or body temperature rhythms, but the association with the amplitude of metabolic gene expression is limited to the WAT.

Clinical prediction models for hospital falls: a scoping review protocol.

INTRODUCTION: Falls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients' fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment. METHODS AND ANALYSIS: This scoping review will follow the Arksey and O'Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis. ETHICS AND DISSEMINATION: Ethical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences.

CircaCompare: a method to estimate and statistically support differences in mesor, amplitude and phase, between circadian rhythms.

MOTIVATION: A fundamental interest in chronobiology is to compare patterns between groups of rhythmic data. However, many existing methods are ill-equipped to derive statements concerning the statistical significance of differences between rhythms that may be visually apparent. This is attributed to both the form of data used (longitudinal versus cross-sectional) and the limitations of the statistical tests used to draw conclusions. RESULTS: To address this problem, we propose that a cosinusoidal curve with a particular parametrization be used to model and compare data of two sets of observations collected over a 24-h period. The novelty of our test is in the parametrization, which allows the explicit estimation of rhythmic parameters [mesor (the rhythm-adjusted mean level of a response variable around which a wave function oscillates), amplitude and phase], and simultaneously testing for statistical significance in all three parameters between two or more groups of datasets. A statistically significant difference between two groups, regarding each of these rhythmic parameters, is indicated by a P-value. The method is evaluated by applying the model to publicly available datasets, and is further exemplified by comparison to the currently recommended method, DODR. The results suggest that the method proposed may be highly sensitive to detect rhythmic differences between groups in phase, amplitude and mesor. AVAILABILITY AND IMPLEMENTATION: https://github.com/RWParsons/circacompare/.

Lu177-PSMA therapy for men with advanced prostate cancer: Initial 18 months experience at a single Australian tertiary institution.

INTRODUCTION: Lutetium-177-PSMA (LuPSMA) is a targeted systemic radioligand treatment for metastatic castration-resistant prostate cancer (mCRPC). LuPSMA is considered as an experimental treatment not yet used in routine practice. Here, we report our experience following the introduction of LuPSMA therapy at our institution. METHODS: Referred mCRPC patients were assessed for treatment suitability including Gallium-68-PSMA PET/CT and blood tests. Suitable patients underwent up to four cycles of LuPSMA treatment. Response to treatment was assessed by changes in serum prostate-specific antigen (PSA) levels. Toxicity was assessed by recording of adverse events. RESULTS: In an 18 month period, 50 patients underwent 132 cycles of LuPSMA therapy. Patients underwent a median of three cycles each (range: 1-4) and the mean administered amount of activity per cycle was 5.9 GBq (range: 3.5-8.2 GBq). PSA decline could be calculated for 49 patients, with a best PSA decline of ≥ 50% observed in 22 patients (44.9%). Adverse events were reported across 45 of 132 LuPSMA cycles. Most adverse events were grade I (42/45) and the remaining three events were grade II. CONCLUSION: Initial experience at our site supports the use of LuPSMA as an emerging safe and effective treatment for mCRPC. Since introducing LuPSMA therapy at our institution, 44.9% of patients have experienced a decline in PSA level ≥ 50% with low or minimal toxicity. This is an important finding as these patients had previously exhausted all available treatment options. Overall, we have found patients and their primary care doctors have eagerly accepted LuPSMA as another line of defence against mCRPC.

Five-year trends of bone scan and prostate-specific membrane antigen positron emission tomography utilization in prostate cancer: A retrospective review in a private centre.

INTRODUCTION: In the last 5 years, there has been a significant focus on the use of positron emission tomography (PET) for primary and secondary staging of prostate cancer. In this study, we aim to describe the trends of use between Gallium-68 prostate-specific membrane antigen ligand (Ga-68 PSMA) PET/computed tomography (PSMA PET/CT) and nuclear medicine bone scan (NMBS) for prostate cancer staging in the first institution in Australia to offer both modalities. METHODS: We evaluated trends in prostate cancer staging/restaging imaging modalities at our facility between the time period January 2013-April 2018. Imaging logs were filtered to identify NMBS and PSMA PET/CT scans done within the time period for prostate cancer. Sub-analysis was undertaken (i) to investigate the number of patients who were imaged using both modalities, (ii) to compare the age of the patients in the NMBS group and the PSMA PET/CT group and (iii) to compare the use of PSMA PET/CT for pre-treatment staging compared to the detection of recurrence or metastatic disease (secondary staging). RESULTS: A total of 3144 examinations were performed in the time period reviewed, with 546 NMBS and 2598 PSMA PET/CT scans performed. In the 6 months after PSMA PET/CT was introduced, there was a 45.7% decrease in the number of NMBS performed and 95.3% decrease across the duration of the study. In the PSMA PET/CT cohort, 1569 examinations were performed for pre-treatment staging and 1029 performed for secondary staging. There was a significant difference in the proportion of PSMA PET/CT conducted for pre-treatment staging compared with secondary staging when comparing the first and final 500 examinations (P < 0.05). CONCLUSION: After the introduction of PSMA PET/CT there was a marked decline in the use of NMBS for prostate cancer staging. This finding is of note as it occurred before there was clinical data or guidelines supporting the use of PSMA PET/CT for prostate cancer imaging.

Clocking In Time to Gate Memory Processes: The Circadian Clock Is Part of the Ins and Outs of Memory.

Learning, memory consolidation, and retrieval are processes known to be modulated by the circadian (circa: about; dies: day) system. The circadian regulation of memory performance is evolutionarily conserved, independent of the type and complexity of the learning paradigm tested, and not specific to crepuscular, nocturnal, or diurnal organisms. In mammals, long-term memory (LTM) formation is tightly coupled to de novo gene expression of plasticity-related proteins and posttranslational modifications and relies on intact cAMP/protein kinase A (PKA)/protein kinase C (PKC)/mitogen-activated protein kinase (MAPK)/cyclic adenosine monophosphate response element-binding protein (CREB) signaling. These memory-essential signaling components cycle rhythmically in the hippocampus across the day and night and are clearly molded by an intricate interplay between the circadian system and memory. Important components of the circadian timing mechanism and its plasticity are members of the Period clock gene family (Per1, Per2). Interestingly, Per1 is rhythmically expressed in mouse hippocampus. Observations suggest important and largely unexplored roles of the clock gene protein PER1 in synaptic plasticity and in the daytime-dependent modulation of learning and memory. Here, we review the latest findings on the role of the clock gene Period 1 (Per1) as a candidate molecular and mechanistic blueprint for gating the daytime dependency of memory processing.

Musculoskeletal workload versus musculoskeletal clinical confidence among primary care physicians in rural practice.

We studied the prevalence of musculoskeletal conditions in a rural Oregon primary care practice and the self-assessed musculoskeletal knowledge and skills of primary physicians practicing in the area. We recorded how many musculoskeletal complaints were evaluated and treated in a primary care physician's office between April 1 and April 30, 2002. In addition, to all primary care physicians in the surrounding county, we distributed a self-administered questionnaire assessing physician confidence regarding common musculoskeletal conditions and procedures. Prevalence of musculoskeletal conditions for the month was 17.5% (48/274 office visits). Questionnaire results indicated that local physicians felt relatively uncomfortable with common musculoskeletal conditions (mean confidence scores on a 10-point Likert scale: 4.2, musculoskeletal conditions; 9.0, nonmusculoskeletal medical conditions). These findings suggest a disparity between rural primary care physicians' self-assessed musculoskeletal knowledge and skill and the levels they require for their practice.