The Effects of Early Intervention on Parent-Premature Infant Interaction

Early intervention focusing on the interaction between premature infants and their parents as an attempt to increase their developmental outcomes has been evaluated in a number of studies. This paper will review the literature about the effects of early intervention on parent-infant interaction, specifically focusing on premature infants. This topic is being studied because the efficacy of how interaction affects the development of parent-infant interaction for premature infants and their parents needs to be determined. This will help researchers and parents develop successful methods to lessen the difficulty of interaction between parents and their premature infants. By providing such evidence, parents can implement these early intervention techniques into their interaction with their premature infants to create a closer bond with them. It is believed that early intervention, specifically with premature infants, is critical to create successful parent-infant interaction. Once this interaction is established, communication between the parent and child will increase significantly. The purpose of this paper is to examine multiple research studies to determine the validity of this claim

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

Objectives: Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. Setting: A regional cancer centre in Australia. Participants: Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. Primary and secondary outcome measures: Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC). Results: The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours. Conclusions: Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems

Tuberculosis in the era of infection with the human immunodeficiency virus: assessment and comparison of community knowledge of both infections in rural Uganda

<p>Abstract</p> <p>Background</p> <p>In Uganda, despite a significant public health burden of tuberculosis (TB) in the context of high human immunodeficiency virus (HIV) prevalence, little is known about community knowledge of TB. The purpose of this study was to assess and compare knowledge about TB and HIV in the general population of western Uganda and to examine common knowledge gaps and misconceptions.</p> <p>Methods</p> <p>We implemented a multi-stage survey design to randomly survey 360 participants from one district in western Uganda. Weighted summary knowledge scores for TB and HIV were calculated and multiple linear regression (with knowledge score as the dependant variable) was used to determine significant predictors. Six focus group discussions were conducted to supplement survey findings.</p> <p>Results</p> <p>Mean (SD) HIV knowledge score was 58 (12) and TB knowledge score was 33 (15), both scores out of 100. The TB knowledge score was statistically significantly (p < 0.001) lower. Multivariate regression models included age, sex, marital status, education, residence, and having a friend with HIV/TB as independent variables. TB knowledge was predicted by rural residence (coefficient = −6.27, 95% CI: -11.7 to −0.8), and age ≥45 years (coefficient = 7.45, 95% CI: 0.3-14.6). HIV knowledge was only predicted by higher education (coefficient = 0.94, 95%CI: 0.3-1.6). Focus group participants mentioned various beliefs in the aetiology of TB including sharing cups, alcohol consumption, smoking, air pollution, and HIV. Some respondents believed that TB was not curable.</p> <p>Conclusion</p> <p>TB knowledge is low and many misconceptions about TB exist: these should be targeted through health education programs. Both TB and HIV-infection knowledge gaps could be better addressed through an integrated health education program on both infections, whereby TB program managers include HIV information and vice versa.</p

Culture media composition influences patient-derived organoid ability to predict therapeutic responses in gastrointestinal cancers

BACKGROUND. A patient-derived organoid (PDO) platform may serve as a promising tool for translational cancer research. In this study, we evaluated PDO's ability to predict clinical response to gastrointestinal (GI) cancers.METHODS. We generated PDOs from primary and metastatic lesions of patients with GI cancers, including pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and cholangiocarcinoma. We compared PDO response with the observed clinical response for donor patients to the same treatments. RESULTS. We report an approximately 80% concordance rate between PDO and donor tumor response. Importantly, we found a profound influence of culture media on PDO phenotype, where we showed a significant difference in response to standard-of-care chemotherapies, distinct morphologies, and transcriptomes between media within the same PDO cultures.CONCLUSION. While we demonstrate a high concordance rate between donor tumor and PDO, these studies also showed the important role of culture media when using PDOs to inform treatment selection and predict response across a spectrum of GI cancers