This therapy process study investigates the use of guided affective imagery for tumor patients. The therapeutic access to tumor patients is generally described as complex and challenging because of a disturbed emotion regulation and a defensive focus on reality. After autologous blood stem cell transplantation, 29 patients were treated with psychotherapy, including two daydreaming imagery sessions. Three text-analytical measures--Affective Dictionary Ulm, Regressive Imagery Dictionary, and Computerized Referential Activity for verbatim session transcripts--as well as the Quality of Life Questionnaire and the Karnofsky Performance Status were administered. Results show that guided affective imagery was able to enhance the psychotherapeutic process in tumor patients by activating the primary process, decreasing anxiety, and increasing referential activity. The positive emotional shift during imagery was achieved by the patients irrespective of their oncological severity status. Study limitations and future directions for research are discussed.
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http://dx.doi.org/10.1080/10503300701832433 | DOI Listing |
Sci Rep
December 2024
Department of Gastroenterology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang city, Jiangxi province, China.
P2X7 receptor (P2X7R) plays a role in regulating tumor progression, but it is unclear whether P2X7R affects the pathological characteristics of patients with gastric cancer and the activity of gastric cancer cells. Therefore, this study preliminarily investigated the relationship between P2X7R and clinicopathological features of patients with gastric cancer, and further explored the effect of P2X7R on the proliferation, migration and invasion of gastric cancer cells through functional experiments. The results showed that P2X7R was highly expressed in gastric cancer tissues and gastric cancer cells.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
School of Pharmacy, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Cuproptosis, a newly identified form of cell death, has drawn increasing attention for its association with various cancers, though its specific role in colorectal cancer (CRC) remains unclear. In this study, transcriptomic and clinical data from CRC patients available in the TCGA database were analyzed to investigate the impact of cuproptosis. Differentially expressed genes linked to cuproptosis were identified using Weighted Gene Co-Expression Network Analysis (WGCNA).
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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