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Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, and friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task to optimize the efficacy of treatments and health‐care. Clinicians evaluate the pressure ulcers by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. The purpose of this paper is to use a hybrid learning approach based on neural and Bayesian networks to design a computational system to automatic tissue identification in wound images.

A mean shift procedure and a region‐growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multi‐layer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes determined by clinical experts. This training procedure is driven by a k‐fold cross‐validation method. Finally, a Bayesian committee machine is formed by training a Bayesian network to combine the classifications of the k neural networks (NNs).

The authors outcomes show high efficiency rates from a two‐stage cascade approach to tissue identification. Giving a non‐homogeneous distribution of pattern classes, this hybrid approach has shown an additional advantage of increasing the classification efficiency when classifying patterns with relative low frequencies.

The methodology and results presented in this paper could have important implications to the field of clinical pressure ulcer evaluation and diagnosis.

The novelty associated with this work is the use of a hybrid approach consisting of NNs and Bayesian classifiers which are combined to increase the performance of a pattern recognition task applied to the real clinical problem of tissue detection under non‐controlled illumination conditions.

The purpose of this paper is to evaluate the performances of a resolution scheme able to follow the dynamics of brain tissue properties in combined ElectroEncefaloGraphic (EEG) – MagnetoEncefaloGraphic (MEG) techniques for the brain analysis, minimizing the computation burden.

The estimation process in combined EEG‐MEG is performed by a Moore‐Penrose pseudo‐inverse computation. This is affected by the uncertain knowledge of the living tissues' electric properties. In principle, it is possible to estimate those properties from the EEG‐MEG signals. The estimation process becomes in this case non‐linear. A resolution scheme is proposed, based on the exploitation of the different dynamics characterizing sources and tissues properties.

The proposed resolution scheme provides a reasonable estimate of the sources for a computationally affordable frequency of non‐liner estimations.

The proposed approach has not been tested yet on experimental data, and as such, its sensitivity to environmental uncertainty is not known yet.

The proposed strategy can be easily implemented to perform realistic measurement processing.

The paper presents a novel strategy to estimate tissues properties and EEG‐MEG signal sources based on the exploitation of their different dynamics, possibly taking advantages from an impedance tomography preliminary analysis for the tissue properties dynamics.

The purpose of this paper is to introduce a new tactile array sensor into the medical field to enhance current robotic minimally invasive surgery (RMIS) procedures that are still limited in scope and versatility. In this paper, a novel idea is proposed in which a tactile sensor array can measure rate of displacement in addition to force and displacement of any viscoelastic material during the course of a single touch. To verify this new array sensor, several experiments were conducted on a diversity of tissues from which it was concluded that this newly developed sensory offers definite and significant enhancements.

The proposed array sensor is capable of extracting force, displacement and displacement rate in the course of a single touch on tissues. Several experiments have been conducted on different tissues and the array sensor to verify the concept and to verify the output of the sensor.

It is shown that this new generation of sensors are required to distinguish the difference in hardness degrees of materials with viscoelastic behavior.

In this paper, a new generation of tactile sensors is proposed that is capable of measuring indentation time in addition to force and displacement. This idea is completely unique and has not been submitted to any conference or journal.

In the following case, the identification of a burn victim was aided by examination of the soft tissue areas on the alveolar surfaces, the sites of recent dental extractions and the evaluation of the degree of healing of these extraction sites. A review of the ante‐mortem radiographs and dental records of a suspected person who might be the burn victim revealed a history of recent extractions at the sites noted on the burn victim. This information in addition to the routine odontologic forensic landmarks aided in concluding a positive identification of the burn victim.

The purpose of this study is to determine the contribution of expert reports, which were prepared as a result of examining the evidence sent to Istanbul Criminal Laboratory, to the conclusion of judicial cases of burglary, homicide, and wounding in the provinces of Marmara Region between the years 2004‐2005.

In this research, 6,249 judicial cases (murder, wounding, burglary) that occurred within the borders of Marmara Region during 2004‐2005 and were submitted to Istanbul Criminal Police Laboratory (KPL) have been subjected to evaluation according to the years (2004, 2005), the type of the case (murder, wounding, or burglary), whether any sexual assaults also occurred, the existence of the biological evidence (blood, saliva, skin residue, hair, tissue, semen, blood and similar biological material), and the conclusion of cases.

When analyzing the crime types, it was seen that wounding and burglary were committed the most, respectively in 2004 and 2005. Out of total committed crimes in this period, homicide held the lowest percentage. The most evaluated biological evidence was blood. Sexual assaults realized together with violent crimes were on an average of 0.8 percent. By analyzing the biological evidence, the success in identifying the perpetrators of the cases was only 16 percent, which has to be evaluated carefully.

Finding evidence at the crime scene and its proper investigation and submission to courts are extremely important. From this aspect, the expert reports of the Criminal Laboratories have an important level of impact on the conclusion of the cases. Commencing with the evidence collected from homicide, wounding and burglary crimes, which were committed in Marmara Region, it is necessary to evaluate the current situation and offer proposals for increasing its effectiveness.

One of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model.

The proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based on a priori algorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed.

The performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273.

This paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.

This paper aims to develop a novel miniature 3-axis force sensor which can detect the interaction forces during tissue palpation in MIS (minimally invasive surgery). MIS offers many significant merits compared with traditional open surgery, the wound to the patients and the postoperative pains are alleviated and reduced dramatically. However, the inherent drawback due to lack of force feedback still exists while conducting some operation procedures. For example, tissue palpation performed easily during open surgery could not be realized in an MIS manner.

The force sensor is based on the resistive-based sensing method that utilizes strain gauges to measure the strain when the external loads are acting on the tip of the sensor. A novel flexible tripod structure with bending and compression deformations is designed to discriminate the magnitudes and directions of the three orthogonal force components. A linear characteristic matrix is derived to disclose the relationship between the sensitivity and the geometric parameters of the structure, and a straightforward geometric parameterized optimization method considering the sensitivity isotropy is proposed to provide the sensor structure with high sensitivity and adequate stiffness.

The sensor prototype can perform force measurement with sensing ranges of ± 3.0 N in axial direction and ± 1.5 N in radial direction, and the resolutions are 5 per cent and 1 per cent, respectively. It is concluded that this force sensor is compatible with MIS instruments and the ex-vivo experiment shows that the sensor can be used to perform tissue palpation during MIS procedures.

This paper is intended to address the significant role of force sensing and force feedback during MIS operations, and presents a new application of the resistive-based sensing method in MIS. A tripod structure is designed and a straightforward optimization method considering the sensitivity isotropy of the sensor is proposed to determine geometric parameters suited for the given external loads.

The purpose of this paper is to present a system for automatic recognition of defects detected in non-conductive polymer composites using pulsed terahertz imaging.

On the beginning, non-destructive evaluation of composites using electromagnetic waves in terahertz frequency is shortly introduced. Next automatic defects recognition (ADR) algorithm is proposed, focussing on new features calculation. Dimensionality of features space is reduced by using principal component analysis. Finally, results of basalt fiber reinforced composite materials inspection and identification using artificial neural networks is presented and discussed.

It is possible to develop ADR system for non-destructive evaluation of dielectric materials using pulsed terahertz technique. New set of features in time and frequency domains is proposed and verified.

ADR in non-destructive testing is utilized in case of digital radiography and ultrasonic testing. Terahertz inspection with pulsed excitation is reported as a source of many useful information about the internal structure of the dielectric material. Up to now ADR based on terahertz non-destructive evaluation systems was not utilized.

The purpose of this study is to investigate the effect of a 15-week dietary intake of cactus flour on metabolic parameters, body weight and dietary intake of rats.

Male Wistar rats were divided into four experimental groups (n = 8-10): control or westernized diets added or not of cactus flour. The following parameters were evaluated during the period of dietary manipulation: body weight, food intake, glycemic and lipid profile (oral glucose tolerance test, metabolic parameters, hepatic and muscular glycogen dosage), visceral and body fat (relative weight to body weight). Data were analyzed using Graphpad Prism®5, p = 0.05.

Animals fed on a Western-style diet together with flour cactus presented lower weight gain (335.7 ± 20.0, p = 0.05) over the evaluated period, even when the volume of food intake was not different among the groups. The addition of cactus flour to a Western-style diet appears to lower glucose levels at 30 and 60 min (p = 0.05), as shown in the glucose tolerance curve. There was a downward trend does fat stores, cholesterol levels and triglycerides. Therefore, it was concluded that this addition cactus flour is effective even when the diet is hyperlipidic, demonstrating its ability to attenuate risk parameters for the occurrence of metabolic syndromes such as sub fraction high cholesterol levels and glucose tolerance.

The addition of functional foods to diets may work to improve the harmful effects of this type of diet. Opuntia ficus indica has high nutritional value and has hypoglycemic and hypolipemic properties besides being antioxidant.

Acute and chronic obstructive pulmonary disease (COPD) is a common and progressive lung disease that makes breathing difficult over time and can even lead to death. Despite this, there is no definitive treatment for it yet. This study aims to evaluate the studies on single and combined herbal interventions affecting COPD.

In this study, all articles published in English up to 2020 were extracted from the Web of Science (WoS) database and collected using Boolean tools based on keywords, titles and abstracts. Finally, the data required for bibliographic analysis, such as the author(s), publication year, academic journal, institution, country of origin, institution, financial institution and keywords were extracted from the database.

A total of 573 articles were analyzed. The number of papers in the lung disease field showed an upward trend from 1984 to 2021, and there was a surge in paper publications in 2013. China, Korea and Brazil published the highest number of studies on COPD, and Chinese medical universities published the most papers. Three journals that received the highest scores in this study were the Journal of Ethnopharmacology, International Immunopharmacology and Plos One. In the cloud map, expression, activation and expression were the most frequently researched subjects. In the plus and author keywords, acute lung injury was the most commonly used word. Inflammation, expression of various genes, nitric oxide-dependent pathways, NFkappa B, TNFalpha and lipopolysaccharide-dependent pathways were the mechanisms underlying COPD. Scientometric analysis of COPD provides a vision for future research and policymaking.

This study aimed to evaluate the studies on single and combined herbal interventions affecting COPD.