Background: The aim of the present study was to investigate the use of complementary and alternative medicine (CAM) and the factors that influence their use in patients with breast cancer.
Patients And Methods: This descriptive and cross-sectional study was carried out with 135 breast cancer patients on chemotherapy.
Results: 30.4% of patients admitted using one or more CAM methods. The most common method was herbal therapy (97.6%). There were statistically significant differences among CAM users and non-users in terms of time elapsed since initial diagnosis, current stage of the disease, and current type of therapy. As the time since the initial diagnosis increased, so did the percentage of CAM users. Those patients with advanced stage cancer or relapsed disease who were receiving palliative therapy used CAM methods more than those receiving adjuvant therapy. As far as quality of life was concerned, symptoms such as nausea and vomiting, dyspnea, and diarrhea were more common among CAM users.
Conclusion: It is important and necessary that health professionals working in oncology clinics are made aware of the common use of CAM methods so that they can provide the necessary communication between patients and other health professionals on these treatment modalities.
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http://dx.doi.org/10.1159/000240988 | DOI Listing |
J Inorg Biochem
January 2025
Yusuf Hamied Department of Chemistry, Lensfield Rd, Cambridge CB2 1EW, UK.
By introducing new-to-nature transformations, artificial metalloenzymes hold great potential for expanding the biosynthetic toolbox. The chemistry of an active cofactor in these enzymes is highly dependent on how the holoprotein is assembled, potentially limiting the choice of organometallic complexes amenable to incorporation and ability of the protein structure to influence the metal centre. We have previously reported a method utilising ligand exchange as a means to introduce ruthenium-arene fragments into a four-helix bundle protein.
View Article and Find Full Text PDFTransl Oncol
January 2025
State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address:
Background: Accurate estimation of recurrence risk for cervical cancer plays a pivot role in making individualized treatment plans. We aimed to develop and externally validate an end-to-end deep learning model for predicting recurrence risk in cervical cancer patients following surgery by using multiparametric MRI images.
Methods: The clinicopathologic data and multiparametric MRI images of 406 cervical cancer patients from three institutions were collected.
BMC Oral Health
January 2025
Division of Fixed Prosthodontics, Conservative Dentistry Department, Faculty of Dentistry, Alexandria University, Alexandria, Egypt.
Background: Increasing demand for durable and aesthetically pleasing dental restorations, including laminates, inlays, onlays, and crowns, has led to advancements in all-ceramic systems, particularly with the development of advanced lithium disilicate materials. However, limited data on the fit accuracy and fracture resistance of these materials restricts their wider application in clinical restorative practices.
Aim Of The Study: This in vitro study aims to compare the marginal and internal fit, assess the fracture resistance, and evaluate the failure modes of crowns fabricated from advanced and conventional lithium disilicate materials.
Comput Methods Programs Biomed
December 2024
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Zhejiang, Hangzhou, China.
Background: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.
Objective: This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).
Turk Kardiyol Dern Ars
January 2025
Department of Cardiology, Istanbul Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Türkiye.
Objective: Although left ventricular hypertrophy frequently accompanies end-stage renal disease, heart failure (HF) with reduced ejection fraction (EF) is also observed in a subset of patients. In those patients kidney transplantation (KT) is generally avoided due to an increased risk of mortality in addition to the risks associated with HF. This prospective study was designed to follow patients with HF who were being prepared for KT.
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