24 research outputs found
Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic
Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer
Development of a wearable sensor network for quantification of infant general movements for the diagnosis of Cerebral Palsy
Qualitative assessments of infant spontaneous movements can be performed to measure neurodevelopmental status and provide early insight into the presence of any abnormalities. Clinical assessments of infant movements at 12 weeks post term age are up to 98% predictive of the eventual development of Cerebral Palsy, but their reach is often limited to infants already identified as high-risk within the traditional healthcare system. We present the development of a network of wearable sensors designed to noninvasively measure spontaneous movements in infants from 12-20 weeks post-term- age both within the clinic and for future home use. Pilot data on a single healthy term infant is presented to demonstrate clinical functionality towards future validation studies in infants at high-risk of Cerebral Palsy. Using this system for tele- delivered assessments in the home could enhance screening of neurodevelopmental disorders for infants and families in rural and remote areas, a population with reduced health services. © 2019 IEEE
An Empirical Analysis of Software Reliability Prediction Through Reliability Growth Model Using Computational Intelligence
A prospective cohort study of prodromal Alzheimer's disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA)
This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics
Tissue-specific targeting of Hsp26 has no effect on heat resistance of neural function in larval Drosophila
Hsp26 belongs to the small heat-shock protein family and is normally expressed in all cells during heat stress. We aimed to determine if overexpression of this protein protects behavior and neural function in Drosophila melanogaster during heat stress, as has previously been shown for Hsp70. We used the UAS-GAL4 expression system to drive expression of Hsp26 in the whole animal (ubiquitously), in the motoneurons, and in the muscles of wandering third-instar larvae. There were slight increases in time to crawling failure and normalized excitatory junction potential (EJP) area for some of the transgenic lines, but these were not consistent. In addition, Hsp26 had no effect on the temperature at failure of EJPs, normalized EJP peak amplitude, and normalized EJP half-width. Overexpression larvae had a similar number of motoneuronal boutons and length of nerve terminals as controls, indicating that the occasional protective effects on locomotion were not due to changes at the synapse. We conclude that overexpression had a small thermoprotective effect on locomotion and no effect on neural function. As it has been shown that Hsp26 requires action of other Hsps to reactivate the denatured proteins to which it binds, we propose that at least in larvae, the function of Hsp26 was masked in the relative absence of other Hsps
Monthly evaporation forecasting using artificial neural networks and support vector machines
Beat-by-Beat Estimation of the Left Ventricular Pressure–Volume Loop Under Clinical Conditions
peer reviewedThis paper develops a method for the minimally invasive, beat-by-beat estimation of the left ventricular pressure–volume loop. This method estimates the left ventricular pressure and volume waveforms that make up the pressure–volume loop using clinically available inputs supported by a short, baseline echocardiography reading. Validation was performed across 142,169 heartbeats of data from 11 Piétrain pigs subject to two distinct protocols encompassing sepsis, dobutamine administration and clinical interventions. The method effectively located pressure–volume loops, with low overall median errors in end-diastolic volume of 8.6%, end-systolic volume of 17.3%, systolic pressure of 19.4% and diastolic pressure of 6.5%. The method further demonstrated a low overall mean error of 23.2% predicting resulting stroke work, and high correlation coefficients along with a high percentage of trend compass ‘in band’ performance tracking changes in stroke work as patient condition varied. This set of results forms a body of evidence for the potential clinical utility of the method. While further validation in humans is required, the method has the potential to aid in clinical decision making across a range of clinical interventions and disease state disturbances by providing real-time, beat-to-beat, patient specific information at the intensive care unit bedside without requiring additional invasive instrumentation. © 2017, Biomedical Engineering Society