A generalized ghost detection and segmentation method for double-joint photographic experts group compression Sepideh Azarianpour, Amir Reza Sadri Journal of Medical Signals & Sensors 2019 9(4):211-220 Background: The versatility of digital photographs and vast usage of image processing tools have made the image manipulation accessible and ubiquitous. Thus, there is an urgent need to develop digital image forensics tools, specifically for joint photographic experts group (JPEG) format which is the most prevailing format for storing digital photographs. Existing double JPEG methods needs improvement to reduce their sensitivity to the random grid shifts which is highly common in manipulation scenario. Also, a fully automatic pipeline, in terms of segmentation followed by the classifier is still required. Methods: First, a low-pass filter (with some modifications) is used to distinguish between high-textured and low-textured areas. Then, using the inconsistency values between the quality-factors, a grayscale image, called the ghost image, is constituted. To automate the whole method, a novel segmentation method is also proposed, which extracts the ghost borders. In the last step of the proposed method, using Kolmogorov–Smirnov statistic, the distance between two separated areas (ghost area and the rest of the image) is calculated and compared with a predefined threshold to confirm the presence of forgery/authenticity. Results: In this study, a simple yet efficient algorithm to detect double-JPEG compression is proposed. This method reveals the sub-visual differences in the quality factor in the different parts of the image. Afterward, forgery borders are extracted and are used to assess authenticity score. In our experiments, the average specificity of our segmentation method exceeds 92% and the average precision is 75%. Conclusion: The final binary results for classification are compared with six state-of-the-art methods. According to several performance metrics, our method outperforms the previously proposed ones.

An ensemble method for diagnosis of Parkinson's disease based on voice measurements Razieh Sheibani, Elham Nikookar, Seyed Enayatollah Alavi Journal of Medical Signals & Sensors 2019 9(4):221-226 Background: Parkinson's disease (PD) is the most common destructive neurological disorder after Alzheimer's disease. Unfortunately, there is no specific test such as electroencephalography or blood test for diagnosing the disease. In accordance with the previous studies, about 90% of people with PD have some types of voice abnormalities. Therefore, voice measurements can be used to detect the disease. Methods: This study presents an ensemble-based method for identifying patients and healthy samples by class label prediction based on voice frequency characteristics. It includes three stages of data preprocessing, internal classification and ultimate classification. The outcomes of internal classifiers next to primary feature vector of samples are considered the ultimate classifier inputs. Results: According to the results, the proposed method achieved 90.6% of accuracy, 95.8% of sensitivity, and 75% of specificity, admissible compared to those of other relevant studies. Conclusion: Current experimental outcomes provide a comparative analysis of various machine learning classifiers and confirm that using ensemble-based methods has improved medical diagnostic tasks.

A comparison between ultrasonic bath and direct sonicator on osteochondral tissue decellularization Farin Forouzesh, Mohsen Rabbani, Shahin Bonakdar Journal of Medical Signals & Sensors 2019 9(4):227-233 Background: Decellularization techniques have been widely used in tissue engineering recently. However, applying these methods which are based on removing cells and maintaining the extracellular matrix (ECM) encountered some difficulties for dense tissues such as articular cartilage. Together with chemical agents, using physical methods is suggested to help decellularization of tissues. Methods: In this study, to improve decellularization of articular cartilage, the effects of direct and indirect ultrasonic waves as a physical method in addition to sodium dodecyl sulfate (SDS) as chemical agents with 0.1% and 1% (w/v) concentrations were examined. Decellularization process was evaluated by nucleus staining with hematoxylin and eosin (H and E) and by staining glycosaminoglycans (GAG) and collagen. Results: The H and E staining indicated that 1% (w/v) SDS in addition to ultrasonic bath for 5 h significantly decreased the cell nucleus residue to lacuna ratio by 66%. Scanning electron microscopy showed that using direct sonication caused formation of micropores on the surface of the sample which results in better penetration of decellularization material and better cell attachment after decellularization. Alcian Blue and Picrosirius Red staining represented GAG and collagen, respectively, which maintained in ECM structure after decellularization by ultrasonic bath and direct sonicator. Conclusion: Ultrasonic bath can help better penetration of the decellularization material into the cartilage. This improves the speed of the decellularization process while it has no significant defect on the structure of the tissue.

Requirement specification and modeling a wearable smart blanket system for monitoring patients in ambulance Sorayya Rezayi, Ali Asghar Safaei, Niloofar Mohammadzadeh Journal of Medical Signals & Sensors 2019 9(4):234-244 Background: Nowadays, the role of smart systems and developed tools such as wearable systems for monitoring the patients and controlling their conditions consistently has increased significantly. The present research sought to identify the factors which are essential for designing a wearable smart blanket system and modeling the proposed systems. Methods: To this aim, the requirements for creating the proposed system in ambulance were described after determining the features related to wearable systems by conducting on a comparative study. First, some studies were performed to identify the wearable system development. Then, the elicited questionnaire was given to the physicians and medical informatics specialists. Finally, the extracted requirements were implemented for modeling a smart blanket system. Results: Based on the results, the wearable smart blanket system includes some specific characteristics such as monitoring the important signs, communicating with the surroundings, processing the signals instantly, and storing all important signs. In addition, they should involve some nonfunctional characteristics such as easy installment and function, interactivity, error fault tolerance, low energy consumption, and the accuracy of sign stability. Then, based on the requirements and data elements extracted from the questionnaire, the system was modeled as a detailed design of the proposed technical blanket system. Based on the results, the architecture of the designed system could provide expected scenarios by using the Active Review for Intermediate Design-oriented scenario-based evaluation method. Conclusion: Today, smart systems and tools have considerably developed in terms of monitoring the patients and controlling their conditions. Therefore, wearable systems can be implemented for monitoring the health status of patients in ambulance.

Assessment of manganese-zinc ferrite nanoparticles as a novel magnetic resonance imaging contrast agent for the detection of 4T1 breast cancer cells Tayebe Sobhani, Daryoush Shahbazi-Gahrouei, Mahboubeh Rostami, Maryam Zahraei, Amin Farzadniya Journal of Medical Signals & Sensors 2019 9(4):245-251 Background: The aim of the study was to evaluate the potential of manganese-zinc ferrite nanoparticles (MZF NPs) as a novel negative magnetic resonance imaging (MRI) contrast agents for 4T1 (mouse mammary carcinoma) and L929 (murine fibroblast) cell lines. Methods: MZF NPs and its suitable coating, polyethylene glycol (PEG) via covalent bonding, were investigated under in vitro condition. The cytotoxicity of MZF NPs was tested by 3-(4,5-dimethyl thiazolyl-2)-2,5-diphenyltetrazolium bromide assay after 12 and 24 h of incubation. To evaluate the potential of MZF NPs as T2MRI nanocontrast agent, images were obtained from phantom containing different Fe concentrations and T2relaxivity (r2) was measured. The viability of both 4T1 breast cancer and L929 murine fibroblast cell lines incubated with different Fe concentrations. Results: In vitro T2-weighted MRI showed that signal intensity of 4T1 cells was lower than that of L929 as control cells. T2-weighted MRI showed that signal intensity of MZF NPs enhanced with increasing concentration of NPs. The values of 1/T2relaxivity (r2) for coated MZF NPs with PEG found to be 85.5 mM−1s−1 which is higher than that of commercially clinical used (Sinerem) MRI contrast agent. Conclusion: The results showed that MZF NPs have potential to detect breast cancer cells (4T1) and also have high contrast resolution between normal (L929) and cancerous cells (4T1) which is a suitable nanoprobe for T2-weighted MR imaging contrast agents.

Alignment of noncoding ribonucleic acids with pseudoknots using context-sensitive hidden Markov model Nayyer Mostaghim Bakhshayesh, Mousa Shamsi, Mohammad Hossein Sedaaghi, Hossein Ebrahimnezhad Journal of Medical Signals & Sensors 2019 9(4):252-258 Up to now, various signal processing techniques have been used to predict protein-coding genes that are unsuitable for predicting ribonucleic acids (RNAs). Modeling a gene network can be employed in various fields, such as the discovery of new drugs, reducing the side effects of treatment methods, further identifying genetic diseases and treatments for genetic disorders by influencing the activity of effectual genes, preventing the growth of unwanted tissues via growth weakening and cell reproduction, and also for many other applications in the fields of medicine and agriculture. The main purpose of this study was to design a suitable algorithm based on context-sensitive hidden Markov models (csHMMs) for the alignment of secondary structures of RNAs, which can identify noncoding RNAs. In this model, several RNA families are compared, and their existing similarities are measured. An expectation–maximization algorithm is used to estimate the model's parameters. This algorithm is the standard algorithm to maximize HMM parameters. The alignment results for RNAs belonging to the hepatitis delta virus family showed an accuracy of 83.33%, a specificity of 89%, and a sensitivity of 97%, and RNAs belonging to the purine family showed an accuracy of 65%, a specificity of 76%, and a sensitivity of 76%. The results show that csHMMs, in addition to aligning the primary sequences of RNAs, would align the secondary structures of RNAs with high accuracy.

Alpha-wave characteristics in psychophysiological insomnia Mohammad Rezaei, Hiwa Mohammadi, Habibolah Khazaie Journal of Medical Signals & Sensors 2019 9(4):259-266 Individuals with psychophysiological insomnia (Psych-Insomnia) would show raised cortical arousal through their initiating sleep. Frequent changes in the alpha activity can be indicative of visual cortical activation, even without visual stimulation or retinal input. Therefore, we aimed to investigate alpha-wave characteristics in Psych-Insomnia before and after sleep onset. In a case–control study, 11 individuals with Psych-Insomnia (age: 44.00 ± 13.27) and 11 age-, sex-, and body mass index-matched healthy individuals (age: 41.64 ± 15.89) were recruited for this study. An overnight polysomnography monitoring was performed. Alpha characteristics were calculated from wake before sleep onsets (WBSOs), wake after sleep onset, rapid eye movement, and nonrapid eye movement in the both groups. They include the alpha power and alpha frequency and their variability in the central region. In the WBSO, alpha activity and variability were higher in the Psych-Insomnia individuals compared to healthy individuals. In both groups, alpha frequency variability was observed at approximately 1 Hz. Alpha-wave synchronization in Psych-Insomnia individuals was higher than the group with normal sleep. Individuals with Psych-Insomnia have a lot of imagination in the wake before sleep, which can be caused by stress, everyday concerns, and daily concerns.

Bromelain inhibitory effect on colony formation: An In vitro Study on human AGS, PC3, and MCF7 cancer cells Farzane Raeisi, Elham Raeisi, Esfandiar Heidarian, Daryoush Shahbazi-Gahroui, Yves Lemoigne Journal of Medical Signals & Sensors 2019 9(4):267-273 Bromelain is dotted with anticancer properties on various cancer cell lines. Anticancer pathways of bromelain, as well related intervening signalization are under investigation. Investigating the inhibitory potential of bromelain on AGS, PC3, and MCF7 cells proliferation and colony formation. The bromelain inhibitory potential on AGS, PC3, and MCF7 cells proliferation at various bromelain concentrations was assessed by MTT; thereby, bromelain potency on colony formation impediment was evaluated using clonogenic assays at determined 50% inhibitory concentrations (IC50) on four different cell densities (10, 50, 100, and 200 cells per well). Bromelain inhibits AGS, PC3, and MCF7 cells proliferation in such a dose-dependent manner. Determined IC50to AGS, PC3, and MCF7 cells were 65, 60 and 65μg/ml respectively. At IC50, bromelain significantly suppressed the AGS, PC3, and MCF7 cells colony formation at four treated densities (10, 50, 100 and 200 cells per well). Plating efficiency percentage and cell surviving fraction were decreased after bromelain treatment to AGS, PC3, and MCF7 human cancer cells as a function of initial cell density. The 50, 50 or 100, and 10 or 50 cells per well were considered to be optimum number of initial cell density for AGS, PC3, and MCF7 cells. Cell proliferative and colony formation inhibition are two pathways to in vitro bromelain anticancer effects. The current study displayed a dose-dependent inhibitory effect of bromelain, as well impeding colony formation AGS, PC3, and MCF7 human cancer cells.

Erratum: The ellipselet transform Journal of Medical Signals & Sensors 2019 9(4):274-274