The improvement of sleep quality in patients with cancer has a positive therapeutic effect on them. However, there are no specific treatment guidelines for treating sleep disturbance in cancer patients. We investigated the effect of forest therapy on the quality of sleep in patients with cancer. This study was conducted on nine patients (one male, eight female; mean age, 53.6 ± 5.8 years) with gastrointestinal tract cancer. All patients participated in forest therapy for six days. They underwent polysomnography (PSG) and answered questionnaires on sleep apnea (STOP BANG), subjective sleep quality (Pittsburgh Sleep Quality Index, PSQI), sleepiness (Stanford and Epworth Sleepiness Scales), and anxiety and depression (Hospital Anxiety and Depression Scale) to evaluate the quality of sleep before and after forest therapy. Sleep efficiency from the PSG results was shown to have increased from 79.6 ± 6.8% before forest therapy to 88.8 ± 4.9% after forest therapy ( = 0.027) in those patients, and total sleep time was also increased, from 367.2 ± 33.4 min to 398 ± 33.8 min ( = 0.020). There was no significant difference in the STOP BANG score, PSQI scores, daytime sleepiness based on the results of the Stanford and Epworth Sleepiness Scales, and depression and anxiety scores. Based on the results of this study, we suggest that forest therapy may be helpful in improving sleep quality in patients with gastrointestinal cancers.
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http://dx.doi.org/10.3390/ijerph16142449 | DOI Listing |
Cells
December 2024
Department of Herbal Pharmacology, College of Korean Medicine, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure-activity relationship (QSAR) modeling, structure-activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson-Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors.
View Article and Find Full Text PDFDescription "Lyrical Stillness" is a poem I wrote during my radiation oncology rotation, during which I was provided the opportunity to learn more about multidisciplinary cancer care while delving deeper into radiotherapy. Cancer can be an overwhelming disease process physically, psychologically, and emotionally. Patients undergoing treatment often experience stress and anxiety with the uncertainty of their prognoses.
View Article and Find Full Text PDFClin Pharmacol Drug Dev
January 2025
Department of Pharmacometrics Modeling, A2-Ai LLC, Ann Arbor, MI, USA.
Certepetide (aka LSTA1 and CEND-1) is a novel cyclic tumor-targeting internalizing arginyl glycylaspartic acid peptide being developed to treat solid tumors. Certepetide is designed to overcome existing challenges in treating solid tumors by delivering co-administered anticancer drugs into the tumor while selectively depleting immunosuppressive T cells, enhancing cytotoxic T cells in the tumor microenvironment, and inhibiting the metastatic cascade. A population pharmacokinetic (PK) analysis was conducted to characterize the concentration-time profile of patients with metastatic exocrine pancreatic cancer receiving certepetide in combination with nab-paclitaxel and gemcitabine, and to investigate the effects of clinically relevant covariates on PK parameters.
View Article and Find Full Text PDFCrit Care
January 2025
Department of Pediatric, West China Second University Hospital, Sichuan University, Chengdu, China.
Background: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research.
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BMC Psychiatry
January 2025
Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Background: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders.
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