Adjunctive Diagnostic Methods for Skin Cancer Detection: A Review of Electrical Impedance-Based Techniques

Synergistic use of handcrafted and deep learning features for tomato leaf disease classification

This research introduces a Computer-Aided Diagnosis-system designed aimed at automated detections & classification of tomato leaf diseases, combining traditional handcrafted features with advanced deep learning techniques. The system's process encompasses preprocessing, feature extraction, feature fusion, and classification. It utilizes enhancement filters and segmentation algorithms to isolate with Regions-of-Interests (ROI) in images tomato leaves. These features based arranged in ABCD rule (Asymmetry, Borders, Colors, and Diameter) are integrated with outputs from a Convolutional Neural Network (CNN) pretrained on ImageNet. To address data imbalance, we introduced a novel evaluation method that has shown to improve classification accuracy by 15% compared to traditional methods, achieving an overall accuracy rate of 92% in field tests. By merging classical feature engineering with modern machine learning techniques under mutual information-based feature fusion, our system sets a new standard for precision in agricultural diagnostics. Specific performance metrics showcasing the effectiveness of our approach in automated detection and classifying of tomato leaf disease.

Anticancer therapeutic potential of multimodal targeting agent- "phosphorylated galactosylated chitosan coated magnetic nanoparticles" against N-nitrosodiethylamine-induced hepatocellular carcinoma

Superparamagnetic iron oxide nanoparticles (SPIONs) are extensively used as carriers in targeted drug delivery and has several advantages in the field of magnetic hyperthermia, chemodynamic therapy and magnet assisted radionuclide therapy. The characteristics of SPIONs can be tailored to deliver drugs into tumor via "passive targeting" and they can also be coated with tissue-specific agents to enhance tumor uptake via "active targeting". In our earlier studies, we developed HCC specific targeting agent- "phosphorylated galactosylated chitosan"(PGC) for targeting asialoglycoprotein receptors. Considering their encouraging results, in this study we developed a multifunctional targeting system- "phosphorylated galactosylated chitosan-coated magnetic nanoparticles"(PGCMNPs) for targeting HCC. PGCMNPs were synthesized by co-precipitation method and characterized by DLS, XRD, TEM, VSM, elemental analysis and FT-IR spectroscopy. PGCMNPs were evaluated for in vitro antioxidant properties, uptake in HepG2 cells, biodistribution, in vivo toxicity and were also evaluated for anticancer therapeutic potential against NDEA-induced HCC in mice model in terms of tumor status, electrical properties, antioxidant defense status and apoptosis. The characterization studies confirmed successful formation of PGCMNPs with superparamagnetic properties. The internalization studies demonstrated (99-100)% uptake of PGCMNPs in HepG2 cells. These results were also supported by biodistribution studies in which increased iron content (296%) was noted inside the hepatocytes. Further, PGCMNPs exhibited no in vivo toxicity. The anticancer therapeutic potential was evident from observation that PGCMNPs treatment decreased tumor bearing animals (41.6%) and significantly (p ≤ 0.05) lowered tumor multiplicity. Overall, this study indicated that PGCMNPs with improved properties are efficiently taken-up by hepatoma cells and has therapeutic potential against HCC. Further, this agent can be tagged with 32P and hence can offer multimodal cancer treatment options via radiation ablation as well as magnetic hyperthermia.

Integrated Sensors for Soft Medical Robotics

Minimally invasive procedures assisted by soft robots for surgery, diagnostics, and drug delivery have unprecedented benefits over traditional solutions from both patient and surgeon perspectives. However, the translation of such technology into commercialization remains challenging. The lack of perception abilities is one of the obstructive factors paramount for a safe, accurate and efficient robot-assisted intervention. Integrating different types of miniature sensors onto robotic end-effectors is a promising trend to compensate for the perceptual deficiencies in soft robots. For example, haptic feedback with force sensors helps surgeons to control the interaction force at the tool-tissue interface, impedance sensing of tissue electrical properties can be used for tumor detection. The last decade has witnessed significant progress in the development of multimodal sensors built on the advancement in engineering, material science and scalable micromachining technologies. This review article provides a snapshot on common types of integrated sensors for soft medical robots. It covers various sensing mechanisms, examples for practical and clinical applications, standard manufacturing processes, as well as insights on emerging engineering routes for the fabrication of novel and high-performing sensing devices.

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

Emerging trends and hot spots on electrical impedance tomography extrapulmonary applications

Objective

Electrical impedance tomography (EIT) develops rapidly in technology and applications. Nowadays EIT is used in multiple clinical and experimental scenarios including pulmonary, brain, and tissue monitoring, etc. The present study explores the research trends and hotspots on EIT extrapulmonary application research by bibliometrics analysis.

Approach

Publications on EIT extrapulmonary applications between 1987 and 2021 were retrieved from the Web of Science Core Collection database. For precise screening, search strategy "electrical impedance tomography" plus "hemodynamic" or "brain" or "nerve" or "cancer" or "venous" or "vessel" or "tumor" or "veterinary" or "tissue" or "cell" or "wearable" or "application" and excluding "lung", "ventilation" "respiratory", "pulmonary", "algorithm", "current", "voltage" or "electrode" were used. CiteSpace and VOSviewer were used to analyze the publication features, collaboration, keywords co-occurrence, and co-cited reference.

Main results

A total of 506 articles were finally identified. The global publication numbers on extrapulmonary applications gradually increased yearly in the past 30 years. The US, UK, and China contributed most three publications concerning EIT extrapulmonary applications. "tissues", "conductivity", "model" were research hotspots, and "cutaneous melanoma", "microstructure", "diagnosis" were recent topics (Portions of this research have previously been presented in poster form).

Significance

Overall, EIT extrapulmonary applications bibliometrics analysis provides a unique insight into research focus, current trends, and future directions.

Impact of Age and Sex on Electrical Impedance Values in Healthy Oral Mucosa

Background: Electrical impedance (EI) is a property of all living tissues and represents the resistance to the electric current flow through a living tissue. EI depends on the structure and chemical composition of the tissue. The aim of this study was to determine the influence of age, sex, and electrode pressure on the EI values of healthy oral mucosa. The study involved 101 participants with healthy oral mucosa who were divided into three age groups. EI was measured in seven anatomical regions. Results: Significant differences between different age groups were found. Younger participants (20–40 years) had significantly higher EI values than the older participants (60+). Significantly higher EI values were found in women at all localisations at all measured frequencies, except on the hard palate. EI values measured with higher sub-pressure were significantly lower than values measured with lower sub-pressure at all frequencies and localisations, except the tongue dorsum, tongue border, and sublingual mucosa. Conclusions: This study found that EI values in healthy oral mucosa depend on age and sex and may also depend on the pressure of the measuring device. These factors should be kept in mind when EI is used as a diagnostic method for different oral lesions.