Outcomes reported in trials of treatments for severe malaria: The need for a core outcome set

OBJECTIVES: Malaria is one of the most important parasitic infectious diseases worldwide. Despite the scale-up of effective antimalarials, mortality rates from severe malaria (SM) remain significantly high; thus, numerous trials are investigating both antimalarials and adjunctive therapy. This review aimed to summarise all the outcome measures used in trials in the last 10 years to see the need for a core outcome set. METHODS: A systematic review was undertaken to summarise outcomes of individually randomised trials assessing treatments for SM in adults and children. We searched key databases and trial registries between 1 January 2010 and 30 July 2020. Non-randomised trials were excluded to allow comparison of similar trials. Trial characteristics including phase, region, population, interventions, were summarised. All primary and secondary outcomes were extracted and categorised using a taxonomy table. RESULTS: Twenty-seven of 282 screened trials met our inclusion criteria, including 10,342 patients from 19 countries: 19 (70%) trials from Africa and 8 (30%) from Asia. A large amount of heterogeneity was observed in the selection of outcomes and instruments, with 101 different outcomes measures recorded, 78/101 reported only in a single trial. Parasitological outcomes (17 studies), neurological status (14 studies), death (14 studies) and temperature (10 studies), were the most reported outcomes. Where an outcome was reported in >1 study it was often measured differently: temperature (4 different measures), renal function (7 measures), nervous system (13 measures) and parasitology (10 measures). CONCLUSION: Outcomes used in SM trials are inconsistent and heterogeneous. Absence of consensus for outcome measures used impedes research synthesis and comparability of different interventions. This systematic review demonstrates the need to develop a standardised collection of core outcomes for clinical trials of treatments for SM and next steps to include the development of a panel of experts in the field, a Delphi process, and a consensus meeting

HISTOLOGICAL CHANGES FOLLOWING OVARIECTOMY IN MICE : I. dba HIGH TUMOR STRAIN

1. In dba mice ovariectomized at birth the vagina, uterus, and mammary glands showed a gradual recovery from the castrate state, and finally reached the stage which they presumably can attain only under the influence of estrogenic hormones. Tumors of the mammary glands developed in 37 animals, of 75 examined, between the ages of 14 and 28 months (3 adenomas and 34 carcinomas). 2. As ovarian regeneration had not taken place the probability that estrogen originated in some other organ in the absence of the ovaries is suggested. 3. The consistent nodular hyperplasia of the suprarenal cortex and close morphological similarity of cells of these nodules to lutein-like cells of the ovaries points to the abnormal suprarenals as possible sources of the estrogenic hormones

Growth hormone (GH)–releasing hormone and GH secretagogues in normal aging: Fountain of Youth or Pool of Tantalus?

Although growth hormone (GH) is primarily associated with linear growth in childhood, it continues to have important metabolic functions in adult life. Adult GH deficiency (AGHD) is a distinct clinical entity, and GH replacement in AGHD can improve body composition, strength, aerobic capacity, and mood, and may reduce vascular disease risk. While there are some hormone-related side effects, the balance of benefits and risks is generally favorable, and several countries have approved GH for clinical use in AGHD. GH secretion declines progressively and markedly with aging, and many age-related changes resemble those of partial AGHD. This suggests that replacing GH, or stimulating GH with GH-releasing hormone or a GH secretagogue could confer benefits in normal aging similar to those observed in AGHD – in particular, could reduce the loss of muscle mass, strength, and exercise capacity leading to frailty, thereby prolonging the ability to live independently. However, while most GH studies have shown body composition effects similar to those in AGHD, functional changes have been much less inconsistent, and older adults are more sensitive to GH side effects. Preliminary reports of improved cognition are encouraging, but the overall balance of benefits and risks of GH supplementation in normal aging remains uncertain

Sodium content as a predictor of the advanced evolution of globular cluster stars

The asymptotic giant branch (AGB) phase is the final stage of nuclear burning for low-mass stars. Although Milky Way globular clusters are now known to harbour (at least) two generations of stars they still provide relatively homogeneous samples of stars that are used to constrain stellar evolution theory. It is predicted by stellar models that the majority of cluster stars with masses around the current turn-off mass (that is, the mass of the stars that are currently leaving the main sequence phase) will evolve through the AGB phase. Here we report that all of the second-generation stars in the globular cluster NGC 6752 -- 70 per cent of the cluster population -- fail to reach the AGB phase. Through spectroscopic abundance measurements, we found that every AGB star in our sample has a low sodium abundance, indicating that they are exclusively first-generation stars. This implies that many clusters cannot reliably be used for star counts to test stellar evolution timescales if the AGB population is included. We have no clear explanation for this observation.Comment: Published in Nature (online 29 May 2013, hard copy 13 June), 12 pages, 3 figures + supplementary information sectio

B835: Landfills and Municpal Solid Waste in Maine

Municipal leaders need current information about alternative disposal methods to make rational decisions on handling their town\u27s waste. To provide an overview of landfilling and other waste-handling methods used in the upper New England states, a group of university researchers from New Hampshire, Maine, and Vermont initiated a study of landfills and solid waste management practices. The study involved a comprehensive mail survey of municipalities in Maine, New Hampshire, and Vermont. This report focuses upon and discusses the results of the landfill and solid waste management survey for Maine.https://digitalcommons.library.umaine.edu/aes_bulletin/1019/thumbnail.jp

CD8+ T-Cells in Juvenile-Onset SLE: From Pathogenesis to Comorbidities

Diagnosis of systemic lupus erythematosus (SLE) in childhood [juvenile-onset (J) SLE], results in a more severe disease phenotype including major organ involvement, increased organ damage, cardiovascular disease risk and mortality compared to adult-onset SLE. Investigating early disease course in these younger JSLE patients could allow for timely intervention to improve long-term prognosis. However, precise mechanisms of pathogenesis are yet to be elucidated. Recently, CD8+ T-cells have emerged as a key pathogenic immune subset in JSLE, which are increased in patients compared to healthy individuals and associated with more active disease and organ involvement over time. CD8+ T-cell subsets have also been used to predict disease prognosis in adult-onset SLE, supporting the importance of studying this cell population in SLE across age. Recently, single-cell approaches have allowed for more detailed analysis of immune subsets in JSLE, where type-I IFN-signatures have been identified in CD8+ T-cells expressing high levels of granzyme K. In addition, JSLE patients with an increased cardiometabolic risk have increased CD8+ T-cells with elevated type-I IFN-signaling, activation and apoptotic pathways associated with atherosclerosis. Here we review the current evidence surrounding CD8+ T-cell dysregulation in JSLE and therapeutic strategies that could be used to reduce CD8+ T-cell inflammation to improve disease prognosis

Development of artificial neural network models for paediatric critical illness in South Africa

OBJECTIVES: Failures in identification, resuscitation and appropriate referral have been identified as significant contributors to avoidable severity of illness and mortality in South African children. In this study, artificial neural network models were developed to predict a composite outcome of death before discharge from hospital or admission to the PICU. These models were compared to logistic regression and XGBoost models developed on the same data in cross-validation. DESIGN: Prospective, analytical cohort study. SETTING: A single centre tertiary hospital in South Africa providing acute paediatric services. PATIENTS: Children, under the age of 13 years presenting to the Paediatric Referral Area for acute consultations. OUTCOMES: Predictive models for a composite outcome of death before discharge from hospital or admission to the PICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 765 patients were included in the data set with 116 instances (15.2%) of the study outcome. Models were developed on three sets of features. Two derived from sequential floating feature selection (one inclusive, one parsimonious) and one from the Akaike information criterion to yield 9 models. All developed models demonstrated good discrimination on cross-validation with mean ROC AUCs greater than 0.8 and mean PRC AUCs greater than 0.53. ANN1, developed on the inclusive feature-et demonstrated the best discrimination with a ROC AUC of 0.84 and a PRC AUC of 0.64 Model calibration was variable, with most models demonstrating weak calibration. Decision curve analysis demonstrated that all models were superior to baseline strategies, with ANN1 demonstrating the highest net benefit. CONCLUSIONS: All models demonstrated satisfactory performance, with the best performing model in cross-validation being an ANN model. Given the good performance of less complex models, however, these models should also be considered, given their advantage in ease of implementation in practice. An internal validation study is now being conducted to further assess performance with a view to external validation

Elicitation of domain knowledge for a machine learning model for paediatric critical illness in South Africa

OBJECTIVES: Delays in identification, resuscitation and referral have been identified as a preventable cause of avoidable severity of illness and mortality in South African children. To address this problem, a machine learning model to predict a compound outcome of death prior to discharge from hospital and/or admission to the PICU was developed. A key aspect of developing machine learning models is the integration of human knowledge in their development. The objective of this study is to describe how this domain knowledge was elicited, including the use of a documented literature search and Delphi procedure. DESIGN: A prospective mixed methodology development study was conducted that included qualitative aspects in the elicitation of domain knowledge, together with descriptive and analytical quantitative and machine learning methodologies. SETTING: A single centre tertiary hospital providing acute paediatric services. PARTICIPANTS: Three paediatric intensivists, six specialist paediatricians and three specialist anaesthesiologists. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The literature search identified 154 full-text articles reporting risk factors for mortality in hospitalised children. These factors were most commonly features of specific organ dysfunction. 89 of these publications studied children in lower- and middle-income countries. The Delphi procedure included 12 expert participants and was conducted over 3 rounds. Respondents identified a need to achieve a compromise between model performance, comprehensiveness and veracity and practicality of use. Participants achieved consensus on a range of clinical features associated with severe illness in children. No special investigations were considered for inclusion in the model except point-of-care capillary blood glucose testing. The results were integrated by the researcher and a final list of features was compiled. CONCLUSION: The elicitation of domain knowledge is important in effective machine learning applications. The documentation of this process enhances rigour in such models and should be reported in publications. A documented literature search, Delphi procedure and the integration of the domain knowledge of the researchers contributed to problem specification and selection of features prior to feature engineering, pre-processing and model development