Conceptualizing problems with symptoms, function, health behavior, health-seeking skills, and financial strain in breast cancer survivors using hierarchical clustering

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

The role of integrated psychological support in breast cancer patients: a randomized monocentric prospective study evaluating the Fil-Rouge Integrated Psycho-Oncological Support (FRIPOS) program

PURPOSE

This study examined the effects of Fil-Rouge Integrated Psycho-Oncological Support (FRIPOS) in a group of women with breast cancer compared with a group receiving treatment as usual (TAU).

METHODS

The research design was a randomized, monocentric, prospective study with three time points of data collection: after the preoperative phase (T0), in the initial phase of treatments (T1), and 3 months after the start of treatments (T2). The FRIPOS group (N = 103) and the TAU group (N = 79) completed a sociodemographic questionnaire, the Symptom Checklist-90-R (SCL-90-R) at T0; the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ) C30 and EORTC QLQ-BR23 at T1; and SCL-90-R, EORTC QLQ-C30, and EORTC QLQ-BR23 at T2.

RESULTS

A series of independent and paired t tests showed that patients in the FRIPOS group performed better on all scales related to symptomatic manifestations and on some quality of life scales (fatigue, dyspnea, and sleep disturbances) at T2. In addition, a series of ten multiple regressions were performed to predict each SCL subscale at T2 from the SCL score at T0 and the EORTC QLQ-C30 scores at T2. In nine of ten regression models (all except somatization), both FRIPOS group membership and QoL subscale contributed significantly to prediction.

CONCLUSIONS

This study suggests that patients in the FRIPOS group have more benefits in emotional, psychological, and collateral symptoms than patients in the TAU group and that these improvements are due to integrated psycho-oncology care.

Unmet needs of 1210 Chinese breast cancer survivors and associated factors: a multicentre cross-sectional study

Background

Breast cancer survivors (BCSs) often have potential unmet needs. Identification of the specific needs of BCSs is very significant for medical service provision. This study aimed to (1) investigate the unmet needs and quality of life (QoL) of BCSs in China, (2) explore the diverse factors associated with their unmet needs, and (3) assess the association between their unmet needs and QoL.

Methods

A multicentre, cross-sectional survey was administered to 1210 Chinese BCSs. The Cancer Survivor Profile-Breast Cancer and the Functional Assessment of Cancer Therapy-Breast scale were administered to survivors who gave informed consent to participate. Data were analysed using t-test, ANOVA, multiple regression analysis, and Pearson correlations.

Results

The 1192 participants completed questionnaires (response rate 98.51%). Our study reveals that the most prevalent unmet needs were in the ‘symptom burden domain’. The unmet needs of BCSs depend on eleven factors; age, time since diagnosis, education level, occupation, payment, family income status, stage of cancer, treatment, family history of cancer, pain, and physical activities. To ensure the provision of high-quality survivorship care and a high satisfaction level, more attention should be paid to actively identifying and addressing the unmet needs of BCSs. The problem areas identified in the Cancer Survivor Profile for breast cancer were negatively associated with all subscales of QoL except the health behaviour domain, with the correlation coefficient ranging from − 0.815 to − 0.011.

Conclusion

Chinese BCSs exhibit a high demand for unmet needs in this study, and the most prevalent unmet needs were in the ‘symptom burden domain’. There was a significant association between patients’ unmet needs (as defined in the Cancer Survivor Profile for breast cancer) and QoL. Future research should focus on enhancements to survivorship or follow-up care to address unmet needs and further improve QoL.

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.