The Effect of Massage on Maternal Role Adaptation in Mothers with Premature Infants.

Background: Accepting and adapting the maternal role by mothers with premature infants is complicated. Active participation of mothers in neonate massage may facilitate this process.

Primary Study Objective: Determining the effect of massage on maternal role adaptation in mothers of premature infants admitted to the neonatal intensive care unit (NICU).

Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.

Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research.

Retrospective Evaluation of Hairy Cell Leukemia Patients: Analysis of a Long-Term Single Center Data.

Hairy cell leukemia (HCL) is a distinct lymphoproliferative disorder with unique circulating lymphocyte morphology. It is now regarded as an indolent disease yet treatable with purine analogs. We are going to present a complete long-term clinical and prognostic report of our HCL patients as a large cohort in Iran.

An alternative test for determining autologous stem cell transplantation cell harvesting outcome: Comparison between predictive values of two tests.

Daily CD34+ cells enumeration as a success indicator of stem cell pheresis procedure using flow cytometry is costly, lengthy, and labor-intensive. Thus, finding a simpler method to achieve the optimum time for harvesting the minimum required stem cells for transplantation could be helpful. The aim of this study was to evaluate the predictive value of reticulocytes fractions and their sensesivity and specificity in guiding CD34+ cell harvesting by G-CSF mobilization strategy.

Coupled matrix-matrix and coupled tensor-matrix completion methods for predicting drug-target interactions.

Predicting the interactions between drugs and targets plays an important role in the process of new drug discovery, drug repurposing (also known as drug repositioning). There is a need to develop novel and efficient prediction approaches in order to avoid the costly and laborious process of determining drug-target interactions (DTIs) based on experiments alone. These computational prediction approaches should be capable of identifying the potential DTIs in a timely manner.

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug-target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner.