A hybrid deep learning model for forecasting lymphocyte depletion during radiation therapy.

PURPOSE: Recent studies have shown that severe depletion of the absolute lymphocyte count (ALC) induced by radiation therapy (RT) has been associated with poor overall survival of patients with many solid tumors. In this paper, we aimed to predict radiation-induced lymphocyte depletion in esophageal cancer patients during the course of RT based on patient characteristics and dosimetric features. METHODS: We proposed a hybrid deep learning model in a stacked structure to predict a trend toward ALC depletion based on the clinical information before or at the early stages of RT treatment. The proposed model consisted of four channels, one channel based on long short-term memory (LSTM) network and three channels based on neural networks, to process four categories of features followed by a dense layer to integrate the outputs of four channels and predict the weekly ALC values. Moreover, a discriminative kernel was developed to extract temporal features and assign different weights to each part of the input sequence that enabled the model to focus on the most relevant parts. The proposed model was trained and tested on a dataset of 860 esophageal cancer patients who received concurrent chemoradiotherapy. RESULTS: The performance of the proposed model was evaluated based on several important prediction metrics and compared to other commonly used prediction models. The results showed that the proposed model outperformed off-the-shelf prediction methods with at least a 30% reduction in the mean squared error (MSE) of weekly ALC predictions based on pretreatment data. Moreover, using an extended model based on augmented first-week treatment, data reduced the MSE of predictions by 70% compared to the model based on the pretreatment data. CONCLUSIONS: In conclusion, our model performed well in predicting radiation-induced lymphocyte depletion for RT treatment planning. The ability to predict ALC will enable physicians to evaluate individual RT treatment plans for lymphopenia risk and to identify patients at high risk who would benefit from modified treatment approaches.

The Effect of COVID-19 Pandemic on the Infants' Microbiota and the Probability of Development of Allergic and Autoimmune Diseases.

The human microbiota plays a significant role in various mechanisms of the body. The formation of a healthy microbiota, especially in early childhood, has a significant effect on maintaining human health. Since the onset of coronavirus disease 2019 (COVID-19), the disease has caused many changes in human life. According to the available information, many of these factors affect the composition and diversity of the body's microbiota, so this pandemic may alter and disrupt the microbiota and consequently increase the incidence of other diseases such as allergic and autoimmune disorders, especially in children and infants born in this era. In this review, the probable impact of the COVID-19 pandemic on body's microbiota and its relationship with the emergence of future diseases is discussed.

Radiation-Induced Lymphopenia Risks of Photon Versus Proton Therapy for Esophageal Cancer Patients.

PURPOSE: To assess possible differences in radiation-induced lymphocyte depletion for esophageal cancer patients being treated with the following 3 treatment modalities: intensity-modulated radiation therapy (IMRT), passive scattering proton therapy (PSPT), and intensity-modulated proton therapy (IMPT). METHODS AND MATERIALS: We used 2 prediction models to estimate lymphocyte depletion based on dose distributions. Model I used a piecewise linear relationship between lymphocyte survival and voxel-by-voxel dose. Model II assumes that lymphocytes deplete exponentially as a function of total delivered dose. The models can be fitted using the weekly absolute lymphocyte counts measurements collected throughout treatment. We randomly selected 45 esophageal cancer patients treated with IMRT, PSPT, or IMPT at our institution (15 per modality) to demonstrate the fitness of the 2 models. A different group of 10 esophageal cancer patients who had received PSPT were included in this study of in silico simulations of multiple modalities. One IMRT and one IMPT plan were created, using our standards of practice for each modality, as competing plans to the existing PSPT plan for each patient. We fitted the models by PSPT plans used in treatment and predicted absolute lymphocyte counts for IMRT and IMPT plans. RESULTS: Model validation on each modality group of patients showed good agreement between measured and predicted absolute lymphocyte counts nadirs with mean squared errors from 0.003 to 0.023 among the modalities and models. In the simulation study of IMRT and IMPT on the 10 PSPT patients, the average predicted absolute lymphocyte count (ALC) nadirs were 0.27, 0.35, and 0.37 K/µL after IMRT, PSPT, and IMPT treatments using Model I, respectively, and 0.14, 0.22, and 0.33 K/µL using Model II. CONCLUSIONS: Proton plans carried a lower predicted risk of lymphopenia after the treatment course than did photon plans. Moreover, IMPT plans outperformed PSPT in terms of predicted lymphocyte preservation.

A reinforcement learning approach for finding optimal policy of adaptive radiation therapy considering uncertain tumor biological response.

Recent studies have shown that a tumor's biological response to radiation varies over time and has a dynamic nature. Dynamic biological features of tumor cells underscore the importance of using fractionation and adapting the treatment plan to tumor volume changes in radiation therapy treatment. Adaptive radiation therapy (ART) is an iterative process to adjust the dose of radiation in response to potential changes during the treatment. One of the key challenges in ART is how to determine the optimal timing of adaptations corresponding to tumor response to radiation. This paper aims to develop an automated treatment planning framework incorporating the biological uncertainties to find the optimal adaptation points to achieve a more effective treatment plan. First, a dynamic tumor-response model is proposed to predict weekly tumor volume regression during the period of radiation therapy treatment based on biological factors. Second, a Reinforcement Learning (RL) framework is developed to find the optimal adaptation points for ART considering the uncertainty in biological factors with the goal of achieving maximum final tumor control while minimizing or maintaining the toxicity level of the organs at risk (OARs) per the decision-maker's preference. Third, a beamlet intensity optimization model is solved using the predicted tumor volume at each adaptation point. The performance of the proposed RT treatment planning framework is tested using a clinical non-small cell lung cancer (NSCLC) case. The results are compared with the conventional fractionation schedule (i.e., equal dose fractionation) as a reference plan. The results show that the proposed approach performed well in achieving a robust optimal ART treatment plan under high uncertainty in the biological parameters. The ART plan outperformed the reference plan by increasing the mean biological effective dose (BED) value of the tumor by 2.01%, while maintaining the OAR BED within +0.5% and reducing the variability, in terms of the interquartile range (IQR) of tumor BED, by 25%.

Genetic diversity through social heterosis can increase virulence in RNA viral infections and cancer progression.

In viral infections and cancer tumours, negative health outcomes often correlate with increasing genetic diversity. Possible evolutionary processes for such relationships include mutant lineages escaping host control or diversity, per se, creating too many immune system targets. Another possibility is social heterosis where mutations and replicative errors create clonal lineages varying in intrinsic capability for successful dispersal; improved environmental buffering; resource extraction or effective defence against immune systems. Rather than these capabilities existing in one genome, social heterosis proposes complementary synergies occur across lineages in close proximity. Diverse groups overcome host defences as interacting 'social genomes' with group genetic tool kits exceeding limited individual plasticity. To assess the possibility of social heterosis in viral infections and cancer progression, we conducted extensive literature searches for examples consistent with general and specific predictions from the social heterosis hypothesis. Numerous studies found supportive patterns in cancers across multiple tissues and in several families of RNA viruses. In viruses, social heterosis mechanisms probably result from long coevolutionary histories of competition between pathogen and host. Conversely, in cancers, social heterosis is a by-product of recent mutations. Investigating how social genomes arise and function in viral quasi-species swarms and cancer tumours may lead to new therapeutic approaches.

Total Antioxidant Capacity, Catalase Activity and Salivary Oxidative Parameters in Patients with Temporomandibular Disorders.

OBJECTIVES: Temporomandibular disorders (TMD) are characterized by pain or discomfort in the temporomandibular joint, periauricular region, masticatory muscles, and neck on one or both sides. It may also be associated with joint sounds, restricted mandibular movements and mandibular deviation. Oxidative agents may have a deleterious role in the pathogenesis of joint diseases, and oxidative stress can lead to TMD. The aim of this study was to assess the oxidative stress biomarkers in the saliva of TMD patients and healthy controls. MATERIALS AND METHODS: This case-control study was conducted on 30 patients with TMDs (5 males and 25 females) with a mean age of 30.7±13.2 years, and 30 healthy controls (5 males and 25 females) with a mean age of 29.16±11.2 years. Saliva samples were collected according to the standard protocol and the total antioxidant capacity of the saliva (non-enzymatic), catalase activity, and malondialdehyde (MDA) levels were measured using the ferric reducing ability of plasma, Aebi's method, and high-performance liquid chromatography, respectively. Finally, The MDA levels were analyzed by the Mann-Whitney test. Other quantitative parameters were analyzed by independent t-test. RESULTS: TMD patients had significantly higher salivary levels of MDA compared to the control group (P=0.001). But there were no significant differences in catalase (P=0.49) and total antioxidant capacity (P=0.22) of TMD patients and healthy controls. CONCLUSION: It seems that oxidative stress may be involved in the pathogenesis of TMDs.