Psychological Aspects of Breast Cancer.

Breast cancer is the most commonly diagnosed cancer in women. Associated psychological symptoms include stress, adjustment difficulties, anxiety, depression, impaired cognitive function, sleep disturbances, altered body image, sexual dysfunction, and diminished overall well-being. Distress screening and assessment identifies women who will benefit from therapeutic interventions. Addressing these symptoms improves compliance with treatment and outcomes including disease-related outcomes, psychological symptoms, and quality of life. The most effective treatments include teaching coping skills such as expressing emotion, along with other structured cognitive behavioral, interpersonal, and mindfulness approaches. Patients should be provided these psychosocial supports throughout their cancer journey.

Detecting biomarkers associated with antipsychotic-induced extrapyramidal syndromes by using machine learning techniques.

BACKGROUND: Antipsychotic-associated extrapyramidal syndromes (EPS) are a common side effect that may result in discontinuation of treatment. Although some clinical features of individuals who develop specific EPSs are well defined, no specific laboratory parameter has been identified to predict the risk of developing EPS. METHODS: Three hundred and ninety hospitalizations of patients under antipsychotic medication were evaluated. Machine learning techniques were applied to laboratory parameters routinely collected at admission. RESULTS: Random forests classifier gave the most promising results to show the importance of parameters in developing EPS. Albumin has the maximum importance in the model with 4.28% followed by folate with 4.09%. The mean albumin levels of EPS and non-EPS group was 4,06 ± 0,40 and 4,24 ± 0,37 (p = 0,027) and folate level was 6,42 ± 3,44 and 7,95 ± 4,16 (p = 0,05) respectively. Both parameters showed lower levels in EPS group. CONCLUSIONS: Our results suggest that relatively low albumin and folate levels may be associated with developing EPS. Further research is needed to determine cut-off levels for these candidate markers to predict EPS.