Probing the characteristics and biofunctional effects of disease-affected cells and drug response via machine learning applications

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.

Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost

PURPOSE

Watch and wait strategy is a safe and effective alternative to surgery in patients with locally advanced rectal cancer (LARC) who have achieved pathological complete response (pCR) after neoadjuvant therapy (NAT); present restaging methods do not meet clinical needs. This study aimed to construct a machine learning (ML) model to predict pCR preoperatively.

METHODS

LARC patients who received NAT were included to generate an extreme gradient boosting-based ML model to predict pCR. The group was divided into a training set and a tuning set at a 7:3 ratio. The SHapley Additive exPlanations value was used to quantify feature importance. The ML model was compared with a nomogram model developed using independent risk factors identified by conventional multivariate logistic regression analysis.

RESULTS

Compared with the nomogram model, our ML model improved the area under the receiver operating characteristics from 0.72 to 0.95, sensitivity from 43 to 82.2%, and specificity from 87.1 to 91.6% in the training set, the same trend applied to the tuning set. Neoadjuvant radiotherapy, preoperative carbohydrate antigen 125 (CA125), CA199, carcinoembryonic antigen level, and depth of tumor invasion were significant in predicting pCR in both models.

CONCLUSION

Our ML model is a potential alternative to the existing assessment tools to conduct triage treatment for patients and provides reference for clinicians in tailoring individual treatment: the watch and wait strategy is used to avoid surgical trauma in pCR patients, and non-pCR patients receive surgical treatment to avoid missing the optimal operation time window.

Laser tweezers as a biophotonic tool to investigate the efficacy of living sickle red blood cells in response to optical deformation

A laser tweezer technique based on single and/or dual-laser beams is proposed as a biophotonic tool to trap single cells and investigate their biophysical and biomechanical characteristics. Optical deformability and changes in size and cellular morphology of living and nonliving cells can be measured using the proposed technique. Representative results of red blood cell (RBC) optical deformability of 20 homozygous patients with sickle cell disease, including follow-up patients after treating with hydroxyurea (HU) for at least 3 months and 20 healthy control groups, are presented and compared. Shape recovery of deformed RBCs and relaxation time are recorded for each RBC. Results showed that healthy blood and patients treated with HU demonstrate significantly higher optical deformability and degree of optical elongation with morphological change of RBCs than untreated patients. Moreover, the healthy control group and patients treated with HU exhibited faster relaxation time for RBCs than untreated patients. A trapping power that reaches 180 mW caused no observable photo-damage at a wavelength 1064 nm.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12551-021-00790-0.

Phytosynthesized nanoparticles as a potential cancer therapeutic agent

Plants are the well-known sources for the hyper-accumulation and reduction of metallic ions. Analysis of various plant extracts has justified the presence of different types of phytochemicals that possess the stabilization and reduction functionalities of precursors to form nanoparticles. Such characteristics make plants as an attractive source for synthesizing eco-friendly nanoparticles (NPs) with potentially less toxicity to the body. Recently, phytosynthesized nanoparticles have been explored for targeted inhibition and diagnosis of cancer cells without affecting non-cancerous healthy cells. The aim of this review is to discuss the characteristic performance of NPs synthesized from various plant sources for the diagnosis and inhibition of cancer. The mode of action of phytosynthesized nanoparticles for anti-cancer applications are also discussed.