The anti-cancer mechanisms of curcumin have been reported to include suppressions of angiogenesis and tumor proliferation. The main goal of this research is to increase the solubility of curcumin by cold atmospheric plasma (CAP) and assess the effects of modified curcumin by charging with tri-polyphosphate chitosan nanoparticles for MCF-7, MDA-MB-231 breast cancer cells, and human fibroblast cells. Curcumin modification was done by CAP and its solubility was evaluated by spectrophotometry. After loading modified curcumin into nano-chitosan-TPP, nanocurcumin was characterized by scanning electron microscopy. Cellular viability and apoptosis of treated cells were assessed by MTT and Annexin V. The changes of messenger RNA expression of TP5353 and VEGF genes were analyzed by real-time PCR. CAP was able to transform the curcumin to possess hydrophilic characteristics after 90 seconds. The mean diameter of Curcumin loaded chitosannanoparticles (NPs) were determined as 48 nm. MTT results showed that the IC 50 of nano Cur-chitosan-TPP was effectively decreased compared to free curcumin in MCF-7 (15 μg/mL at 72 hours vs 20 μg/mL at 48 hours). Additionally, nano Cur-chitosan-TPP had no significant effect on normal cells (Human dermal fibroblas: HDF), whereas it also decreased the viability of triple negative breast cancer cell line (MDA-MB-231). Realtime PCR results showed that expression level of TP53 gene was upregulated (P = .000), whereas VEGF gene downregulated (P = .000) in treated MCF-7 cells. Curcumin loaded chitosan nanoparticles have led to an induction of apoptosis (79.93%) and cell cycle arrest (at S and G2M). Modified-curcumin-tri-polyphosphate chitosan nanoparticles using CAP can be considered as a proper candidate for breast cancer treatment.

Purpose: Cardiac arrhythmia is one of the most common heart diseases that can have serious consequences. Thus, heartbeat arrhythmias classification is very important to help diagnose and treat. To develop the automatic classification of heartbeats, recent advances in signal processing can be employed. The Hidden Markov Model (HMM) is a powerful statistical tool with the ability to learn different dynamics of the real time-series such as cardiac signals. Materials and Methods: In this study, a hierarchy of HMMs named Layered HMM (LHMM) was presented to classify heartbeats from the two-channel electrocardiograms. For training in the first layer, the morphology of the heartbeats was used as observations, while observations in the second layer were the inference results of the first layer. The performance of the proposed LHMM was evaluated in classifying three types of heartbeat arrhythmias (Atrial premature beats (A), Escape beats (E), Left bundle branch block beats (L)) using fifteen records of the MIT-BIH arrhythmia database. Furthermore, the obtained results of the proposed model were compared with other HMM generalizations. Results: The best average accuracy was achieved 97.10±1.63%. The best sensitivity of 96.8±1.24%, 98.85±0.52%, and 95.64±1.41 were obtained for A, E, and L, respectively. Furthermore, the results of the proposed method were better than other HMM generalizations. Conclusion: Extracting information from time-series dynamics by HMM-based methods has good classification results. The proposed model shows that applying a two-layered HMM can lead to better extraction of information from the observations; therefore, the classification performance of cardiac arrhythmias has been improved using LHMM.

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Objective. Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings. Approach. In this study, a hierarchy of HMMs named Layered HMM was presented to detect respiratory events from PSG recordings. The recordings of 210 PSGs from Massachusetts General Hospital’s database were used for this study. To develop detection algorithms, extracted feature signals from airflow, movements over the chest and abdomen, and oxygen saturation in blood (SaO2) were chosen as observations. The respiratory disturbance index (RDI) was estimated as the number of apneas, hypopneas, and RERAs per hour of sleep. Main results. The best F1 score of the event by event detection algorithm was between 0.22±0.16 and 0.70±0.08 for different groups of sleep apnea severity. There was a strong correlation between the estimated and the PSG-derived RDI (R2=0.91, p<0.0001). The best recall of RERA detection was achieved 0.45±0.27. Significance. The results showed that the layered structure can improve the performance of the detection of respiratory events during sleep.