Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures.

Background: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays. Methods: We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15). Results: Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time. Conclusions: Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.

Predicting Recurrent Care Seeking of Physical Therapy for Musculoskeletal Pain Conditions.

OBJECTIVE: Musculoskeletal pain conditions are a leading cause of pain and disability internationally and a common reason to seek health care. Accurate prediction of recurrence of health care seeking due to musculoskeletal conditions could allow for better tailoring of treatment. The aim of this project was to characterize patterns of recurrent physical therapy seeking for musculoskeletal pain conditions and to develop a preliminary prediction model to identify those at increased risk of recurrent care seeking. DESIGN: Retrospective cohort. SETTING: Ambulatory care. SUBJECTS: Patients (n = 578,461) seeking outpatient physical therapy (United States). METHODS: Potential predictor variables were extracted from the electronic medical record, and patients were placed into three different recurrent care categories. Logistic regression models were used to identify individual predictors of recurrent care seeking, and the least absolute shrinkage and selection operator (LASSO) was used to develop multivariate prediction models. RESULTS: The accuracy of models for different definitions of recurrent care ranged from 0.59 to 0.64 (c-statistic), and individual predictors were identified from multivariate models. Predictors of increased risk of recurrent care included receiving workers' compensation and Medicare insurance, having comorbid arthritis, being postoperative at the time of the first episode, age range of 44-64 years, and reporting night sweats or night pain. Predictors of decreased risk of recurrent care included lumbar pain, chronic injury, neck pain, pregnancy, age range of 25-44 years, and smoking. CONCLUSION: This analysis identified a preliminary predictive model for recurrence of care seeking of physical therapy, but model accuracy needs to improve to better guide clinical decision-making.

An assessment of health status and health behaviours in adolescents: main points and methods of the SOPKARD-Junior programme.

INTRODUCTION: The study involved preparing and implementation a model of complex screening programme for adolescents and comparison of anthropometric examinations between the population of the SOPKARD-Junior programme and representative sample of Polish children in the same age. MATERIAL AND METHODS: The screening programme in 14-15 year old pupils (n = 282) included: anthropometric, blood pressure, echocardiographic, electrocardiographic, carotid arteries, kidney and thyroid ultrasound examinations, as well as respiratory, dental and masticatory system, orthopaedic, psychological and psychiatric assessment. Blood and urine tests were also performed. The results of anthropometric examinations from the SOPKARD-Junior and OLAF programmes were used for comparative analysis. RESULTS: Statistically significant (p < 0.001) differences between young people from Sopot and their peers in the general Polish population were found in height (+3.61 cm for boys), body mass (+5.19 kg for boys and +3.99 kg for girls), body mass index (+0.99 kg/m2 for boys and +1.33 kg/m2 for girls), waist circumference (+4.52 cm for boys and +4.52 cm for girls) and hip circumference (+2.51 cm for boys). The highest attendance rate was achieved for examinations performed in school (e.g. anthropometric and blood pressure measurements - n = 268; 95%) and the lowest for the echocardiograpy performed in local hospital (n = 133; 47%). The mean score of the programme quality (scale 1-6) assessed by children was 4.63. CONCLUSIONS: The SOPKARD-Junior programme represents an attempt to develop a model of screening assessments for teenagers in Poland. Preliminary results of the SOPKARD-Junior programme indicate small differences in the biological development of Sopot youth in comparison with their peers from Polish population of the OLAF programme. The high attendance rate on research conducted at the school indicate that proposed health examinations in adolescents are acceptable and feasible.