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Prosthetics and Orthotics International Jun 2024The purpose of this scoping review was to investigate the effects of 3-dimensional (3D)-printed prostheses. Articles published up to August 19, 2023, were searched in...
The purpose of this scoping review was to investigate the effects of 3-dimensional (3D)-printed prostheses. Articles published up to August 19, 2023, were searched in the PubMed, Cochrane Library, Embase, and Scopus databases. The search terms used were "3D printed prosthesis," "3D printed prostheses," "3D printed prosthe*," "3D printed artificial arm," "3D printed artificial leg," "3D printing prosthesis," "3D printing prostheses," "3D printing prosthe*," "3D printing artificial arm," and "3D printing artificial leg." This review included studies that applied 3D-printed prostheses to upper- or lower-limb amputees. Case reports, conference abstracts, presentations, reviews, and unidentified articles were excluded from the analysis. A total of 937 articles were identified, 11 of which were included after confirming eligibility through the title, abstract, and full text. The results indicated that the 3D-printed prostheses demonstrated the ability to substitute for the functions of impaired limbs, similar to conventional prostheses. Notably, the production cost and weight were reduced compared with those of conventional prostheses, increasing patient satisfaction. The use of 3D-printed prostheses is expected to gain prominence in future clinical practice. However, concerns regarding the durability of 3D-printed prostheses have increased among users. Therefore, there is an ongoing need to explore highly durable materials that can withstand the weight of the user without breaking easily. In addition, advancements are required in technologies that enable the depiction of various skin tones and the production of smaller-sized prostheses suitable for clothing.
PubMed: 38896537
DOI: 10.1097/PXR.0000000000000366 -
Diagnostics (Basel, Switzerland) May 2024This survey represents the first endeavor to assess the clarity of the dermoscopic language by a chatbot, unveiling insights into the interplay between dermatologists...
This survey represents the first endeavor to assess the clarity of the dermoscopic language by a chatbot, unveiling insights into the interplay between dermatologists and AI systems within the complexity of the dermoscopic language. Given the complex, descriptive, and metaphorical aspects of the dermoscopic language, subjective interpretations often emerge. The survey evaluated the completeness and diagnostic efficacy of chatbot-generated reports, focusing on their role in facilitating accurate diagnoses and educational opportunities for novice dermatologists. A total of 30 participants were presented with hypothetical dermoscopic descriptions of skin lesions, including dermoscopic descriptions of skin cancers such as BCC, SCC, and melanoma, skin cancer mimickers such as actinic and seborrheic keratosis, dermatofibroma, and atypical nevus, and inflammatory dermatosis such as psoriasis and alopecia areata. Each description was accompanied by specific clinical information, and the participants were tasked with assessing the differential diagnosis list generated by the AI chatbot in its initial response. In each scenario, the chatbot generated an extensive list of potential differential diagnoses, exhibiting lower performance in cases of SCC and inflammatory dermatoses, albeit without statistical significance, suggesting that the participants were equally satisfied with the responses provided. Scores decreased notably when practical descriptions of dermoscopic signs were provided. Answers to BCC scenario scores in the diagnosis category (2.9 ± 0.4) were higher than those with SCC (2.6 ± 0.66, = 0.005) and inflammatory dermatoses (2.6 ± 0.67, = 0). Similarly, in the teaching tool usefulness category, BCC-based chatbot differential diagnosis received higher scores (2.9 ± 0.4) compared to SCC (2.6 ± 0.67, = 0.001) and inflammatory dermatoses (2.4 ± 0.81, = 0). The abovementioned results underscore dermatologists' familiarity with BCC dermoscopic images while highlighting the challenges associated with interpreting rigorous dermoscopic images. Moreover, by incorporating patient characteristics such as age, phototype, or immune state, the differential diagnosis list in each case was customized to include lesion types appropriate for each category, illustrating the AI's flexibility in evaluating diagnoses and highlighting its value as a resource for dermatologists.
PubMed: 38893694
DOI: 10.3390/diagnostics14111165 -
Diagnostics (Basel, Switzerland) May 2024The diagnosis and identification of melanoma are not always accurate, even for experienced dermatologists. Histopathology continues to be the gold standard, assessing...
The diagnosis and identification of melanoma are not always accurate, even for experienced dermatologists. Histopathology continues to be the gold standard, assessing specific parameters such as the Breslow index. However, it remains invasive and may lack effectiveness. Therefore, leveraging mathematical modeling and informatics has been a pursuit of diagnostic methods favoring early detection. Fractality, a mathematical parameter quantifying complexity and irregularity, has proven useful in melanoma diagnosis. Nonetheless, no studies have implemented this metric to feed artificial intelligence algorithms for the automatic classification of dermatological lesions, including melanoma. Hence, this study aimed to determine the combined utility of fractal dimension and unsupervised low-computational-requirements machine learning models in classifying melanoma and non-melanoma lesions. We analyzed 39,270 dermatological lesions obtained from the International Skin Imaging Collaboration. Box-counting fractal dimensions were calculated for these lesions. Fractal values were used to implement classification methods by unsupervised machine learning based on principal component analysis and iterated K-means (100 iterations). A clear separation was observed, using only fractal dimension values, between benign or malignant lesions (sensibility 72.4% and specificity 50.1%) and melanoma or non-melanoma lesions (sensibility 72.8% and specificity 50%) and subsequently, the classification quality based on the machine learning model was ≈80% for both benign and malignant or melanoma and non-melanoma lesions. However, the grouping of metastatic melanoma versus non-metastatic melanoma was less effective, probably due to the small sample size included in MM lesions. Nevertheless, we could suggest a decision algorithm based on fractal dimension for dermatological lesion discrimination. On the other hand, it was also determined that the fractal dimension is sufficient to generate unsupervised artificial intelligence models that allow for a more efficient classification of dermatological lesions.
PubMed: 38893659
DOI: 10.3390/diagnostics14111132 -
International Journal of Molecular... May 2024Nanotechnology is revolutionizing fields of high social and economic impact. such as human health preservation, energy conversion and storage, environmental... (Review)
Review
Nanotechnology is revolutionizing fields of high social and economic impact. such as human health preservation, energy conversion and storage, environmental decontamination, and art restoration. However, the possible global-scale application of nanomaterials is raising increasing concerns, mostly related to the possible toxicity of materials at the nanoscale. The possibility of using nanomaterials in cosmetics, and hence in products aimed to be applied directly to the human body, even just externally, is strongly debated. Preoccupation arises especially from the consideration that nanomaterials are mostly of synthetic origin, and hence are often seen as "artificial" and their effects as unpredictable. Melanin, in this framework, is a unique material since in nature it plays important roles that specific cosmetics are aimed to cover, such as photoprotection and hair and skin coloration. Moreover, melanin is mostly present in nature in the form of nanoparticles, as is clearly observable in the ink of some animals, like cuttlefish. Moreover, artificial melanin nanoparticles share the same high biocompatibility of the natural ones and the same unique chemical and photochemical properties. Melanin is hence a natural nanocosmetic agent, but its actual application in cosmetics is still under development, also because of regulatory issues. Here, we critically discuss the most recent examples of the application of natural and biomimetic melanin to cosmetics and highlight the requirements and future steps that would improve melanin-based cosmetics in the view of future applications in the everyday market.
Topics: Melanins; Humans; Hair Color; Animals; Cosmetics; Nanoparticles; Skin Pigmentation; Nanostructures; Nanotechnology
PubMed: 38892049
DOI: 10.3390/ijms25115862 -
BMC Medical Imaging Jun 2024Leishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that...
BACKGROUND
Leishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that affect the skin and the internal organs. Around, 700,000 to 1,000,000 of the newly infected cases and 26,000 to 65,000 deaths are reported worldwide annually. The disease exhibits three clinical presentations, namely, the cutaneous, muco-cutaneous and visceral Leishmaniasis which affects the skin, mucosal membrane and the internal organs, respectively. The relapsing behavior of the disease limits its diagnosis and treatment efficiency. The common diagnostic approaches follow subjective, error-prone, repetitive processes. Despite, an ever pressing need for an accurate detection of Leishmaniasis, the research conducted so far is scarce. In this regard, the main aim of the current research is to develop an artificial intelligence based detection tool for the Leishmaniasis from the Geimsa-stained microscopic images using deep learning method.
METHODS
Stained microscopic images were acquired locally and labeled by experts. The images were augmented using different methods to prevent overfitting and improve the generalizability of the system. Fine-tuned Faster RCNN, SSD, and YOLOV5 models were used for object detection. Mean average precision (MAP), precision, and Recall were calculated to evaluate and compare the performance of the models.
RESULTS
The fine-tuned YOLOV5 outperformed the other models such as Faster RCNN and SSD, with the MAP scores, of 73%, 54% and 57%, respectively.
CONCLUSION
The currently developed YOLOV5 model can be tested in the clinics to assist the laboratorists in diagnosing Leishmaniasis from the microscopic images. Particularly, in low-resourced healthcare facilities, with fewer qualified medical professionals or hematologists, our AI support system can assist in reducing the diagnosing time, workload, and misdiagnosis. Furthermore, the dataset collected by us will be shared with other researchers who seek to improve upon the detection system of the parasite. The current model detects the parasites even in the presence of the monocyte cells, but sometimes, the accuracy decreases due to the differences in the sizes of the parasite cells alongside the blood cells. The incorporation of cascaded networks in future and the quantification of the parasite load, shall overcome the limitations of the currently developed system.
Topics: Deep Learning; Humans; Microscopy; Azure Stains; Leishmaniasis; Leishmania
PubMed: 38890604
DOI: 10.1186/s12880-024-01333-1 -
ACS Applied Materials & Interfaces Jun 2024Nociceptor is an important receptor in the organism's sensory system; it can perceive harmful stimuli and send signals to the brain in order to protect the body in time....
Nociceptor is an important receptor in the organism's sensory system; it can perceive harmful stimuli and send signals to the brain in order to protect the body in time. The injury degree of nociceptor can be divided into three stages: self-healing injury, treatable injury, and permanent injury. However, the current studies on nociceptor simulation are limited to the self-healing stage due to the limitation of the untunable resistance switching behavior of memristors. In this study, we constructed Al/2DP/Ag memristor arrays with adjustable memory behaviors to emulate the nociceptor of biological neural network of all three stages. For this purpose, a PDMS/AgNWs/ITO/PET pressure sensor was assembled to mimic the tactile perception of the skin. The memristor arrays can not only simulate all the response of nociceptor, i.e., the threshold, relaxation, no adaptation, and sensitization with the self-healing injury, but can also simulate the treatable injury and the permanent injury. These behaviors are both demonstrated with a single memristor and in the form of pattern mapping of the memristor array.
PubMed: 38889049
DOI: 10.1021/acsami.4c05112 -
Skin Research and Technology : Official... Jun 2024
Topics: Humans; Artificial Intelligence; Search Engine; Dermatitis
PubMed: 38887862
DOI: 10.1111/srt.13725 -
Scientific Data Jun 2024Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a...
Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation.
Topics: Dermoscopy; Humans; Skin Neoplasms; Spain; Neural Networks, Computer; Artificial Intelligence; Machine Learning
PubMed: 38886204
DOI: 10.1038/s41597-024-03387-w -
Bioactive Materials Sep 2024Endogenous regeneration is becoming an increasingly important strategy for wound healing as it facilitates skin's own regenerative potential for self-healing, thereby... (Review)
Review
Endogenous regeneration is becoming an increasingly important strategy for wound healing as it facilitates skin's own regenerative potential for self-healing, thereby avoiding the risks of immune rejection and exogenous infection. However, currently applied biomaterials for inducing endogenous skin regeneration are simplistic in their structure and function, lacking the ability to accurately mimic the intricate tissue structure and regulate the disordered microenvironment. Novel biomimetic biomaterials with precise structure, chemical composition, and biophysical properties offer a promising avenue for achieving perfect endogenous skin regeneration. Here, we outline the recent advances in biomimetic materials induced endogenous skin regeneration from the aspects of structural and functional mimicry, physiological process regulation, and biophysical property design. Furthermore, novel techniques including reprograming, flexible electronic skin, artificial intelligence, single-cell sequencing, and spatial transcriptomics, which have potential to contribute to the development of biomimetic biomaterials are highlighted. Finally, the prospects and challenges of further research and application of biomimetic biomaterials are discussed. This review provides reference to address the clinical problems of rapid and high-quality skin regeneration.
PubMed: 38883311
DOI: 10.1016/j.bioactmat.2024.04.011 -
Journal of Microorganism Control 2024Cutibacterium acnes is an opportunistic pathogen recognized as a contributing factor to acne vulgaris. The accumulation of keratin and sebum plugs in hair follicles...
Cutibacterium acnes is an opportunistic pathogen recognized as a contributing factor to acne vulgaris. The accumulation of keratin and sebum plugs in hair follicles facilitates C. acnes proliferation, leading to inflammatory acne. Although numerous antimicrobial cosmetic products for acne-prone skin are available, their efficacy is commonly evaluated against planktonic cells of C. acnes. Limited research has assessed the antimicrobial effects on microorganisms within keratin and sebum plugs. This study investigates whether an antibacterial toner can penetrate keratin and sebum plugs, exhibiting bactericidal effects against C. acnes. Scanning electron microscopy and next-generation sequencing analysis of the keratin and sebum plug suggest that C. acnes proliferate within the plug, predominantly in a biofilm-like morphology. To clarify the potential bactericidal effect of the antibacterial toner against C. acnes inside keratin and sebum plugs, we immersed the plugs in the toner, stained them with LIVE/DEAD BacLight Bacterial Viability Kit to visualize microorganism viability, and observed them using confocal laser scanning microscopy. Results indicate that most microorganisms in the plugs were killed by the antibacterial toner. To quantitatively evaluate the bactericidal efficacy of the toner against C. acnes within keratin and sebum, we immersed an artificial plug with inoculated C. acnes type strain and an isolate collected from acne-prone skin into the toner and obtained viable cell counts. The number of the type strain and the isolate inside the artificial plug decreased by over 2.2 log and 1.2 log, respectively, showing that the antibacterial toner exhibits bactericidal effects against C. acnes via keratin and sebum plug penetration.
Topics: Sebum; Anti-Bacterial Agents; Humans; Keratins; Acne Vulgaris; Biofilms; Microbial Viability; Propionibacteriaceae; Propionibacterium acnes; Hair Follicle; Microscopy, Electron, Scanning
PubMed: 38880618
DOI: 10.4265/jmc.29.2_63