A Prospective Study of Machine Learning-Assisted Radiation Therapy Planning for Patients Receiving 54 Gy to the Brain.

PURPOSE: The capacity for machine learning (ML) to facilitate radiation therapy (RT) planning for primary brain tumors has not been described. We evaluated ML-assisted RT planning with regard to clinical acceptability, dosimetric outcomes, and planning efficiency for adults and children with primary brain tumors. METHODS AND MATERIALS: In this prospective study, children and adults receiving 54 Gy fractionated RT for a primary brain tumor were enrolled. For each patient, one ML-assisted RT plan was created and compared with 1 or 2 plans created using standard ("manual") planning procedures. Plans were evaluated by the treating oncologist, who was blinded to the method of plan creation. The primary endpoint was the proportion of ML plans that were clinically acceptable for treatment. Secondary endpoints included the frequency with which ML plans were selected as preferable for treatment, and dosimetric differences between ML and manual plans. RESULTS: A total of 116 manual plans and 61 ML plans were evaluated across 61 patients. Ninety-four percent of ML plans and 93% of manual plans were judged to be clinically acceptable (P = 1.0). Overall, the quality of ML plans was similar to manual plans. ML plans comprised 34.5% of all plans evaluated and were selected for treatment in 36.1% of cases (P = .82). Similar tumor target coverage was achieved between both planning methods. Normal brain (brain minus planning target volume) received an average of 1 Gy less mean dose with ML plans (compared with manual plans, P < .001). ML plans required an average of 45.8 minutes less time to create, compared with manual plans (P < .001). CONCLUSIONS: ML-assisted automated planning creates high-quality plans for patients with brain tumors, including children. Plans created with ML assistance delivered slightly less dose to normal brain tissues and can be designed in less time.

What is life after psychosis like? Stories of three individuals diagnosed with schizophrenia.

WHAT IS KNOWN ABOUT THE SUBJECT?: Schizophrenia is known as a highly debilitating mental illness, characterized by hallucination, delusions and paranoia. Literature on recovery from schizophrenia has only been emerging in the last three decades. WHAT DOES THE PAPER ADDS TO EXISTING KNOWLEDGE?: It adds to the growing literature on the first-hand accounts of recovery from schizophrenia. It highlights the strengths of persons with schizophrenia. WHAT ARE THE IMPLICATIONS FOR MENTAL HEALTH NURSING?: Having a diagnosis of schizophrenia may not be as bleak as it seems. For mental health professionals to see the person behind their patients with schizophrenia. ABSTRACT: "You are not a typical case of schizophrenia." This was the statement by my psychiatrist that sparked the idea of compiling a collection of recovery narratives. Because of its fluid and subjective nature, personal recovery from schizophrenia needs to be understood in the cultural, societal and familial context of its storyteller. Autobiographic lived experience narratives serve to give an unadulterated account of the strength and tenacity of persons in recovery. In this lived experience narrative paper, you will find stories of three unique individuals living with a diagnosis of schizophrenia. Kurtle (name changed) tells the story of the multiple hats she wears in life. Valerie opens up about her past to shed light on her sense of meaning and purpose today. Ying Ying shares her family dynamics and how it has shaped her as a person. These stories aim to (1) change the way mental health professional relate to their patients, (2) emphasize the critical roles that mental health professionals play in their patients' recovery, and (3) remind mental health professionals to look beyond their patients' illness and see the persons behind the illness. Perhaps, then, recovery will become a typical phenomenon among persons living with schizophrenia.

Engineering protein and cell adhesivity using PEO-terminated triblock polymers.

Previous studies on customizing cell culture environments have utilized a variety of microfabrication-based tools to control the spatial localization of adhesive proteins and subsequently mammalian cells. Others have used various methods to immobilize nonadhesive PEO-based polymers on surfaces to inhibit protein absorption and cell adhesion. In this study, we report the application of a well-characterized, commercially available, PEO-terminated triblock polymer (Pluronic F108) to create micropatterned nonadhesive domains on a variety of biomaterials that deter cell adhesion for up to 4 weeks in culture. The Pluronic can be applied using microfluidic tools or photolithographic techniques, and can be adsorbed to a variety of common surfaces including tissue culture polystyrene, methylated glass, silicone, and polylactic-co-glycolic acid. The effectiveness of the Pluronic in inhibiting cell adhesion in the presence of collagen I is also quantified. Finally, these patterning techniques are generalized to control tissue organization on a variety of common biomaterials. This simple method for micropatterning PEO and, therefore, proteins and cells should prove useful as a tool for biomolecular surface engineering.