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Background: Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy. Novel diagnostic techniques based on digital image analysis by Artificial Intelligence (AI) tools are a suitable alternative for schistosomiasis diagnosis., Methodology: Digital images of 24 urine sediment samples were acquired in non-endemic settings. S. haematobium eggs were manually labeled in digital images by laboratory professionals and used for training YOLOv5 and YOLOv8 models, which would achieve automatic detection and localization of the eggs. Urine sediment images were also employed to perform binary classification of images to detect erythrocytes/leukocytes with the MobileNetv3Large, EfficientNetv2, and NasNetLarge models. A robotized microscope system was employed to automatically move the slide through the X-Y axis and to auto-focus the sample., Results: A total number of 1189 labels were annotated in 1017 digital images from urine sediment samples. YOLOv5x training demonstrated a 99.3% precision, 99.4% recall, 99.3% F-score, and 99.4% mAP0.5 for S. haematobium detection. NasNetLarge has an 85.6% accuracy for erythrocyte/leukocyte detection with the test dataset. Convolutional neural network training and comparison demonstrated that YOLOv5x for the detection of eggs and NasNetLarge for the binary image classification to detect erythrocytes/leukocytes were the best options for our digital image database., Conclusions: The development of low-cost novel diagnostic techniques based on the detection and identification of S. haematobium eggs in urine by AI tools would be a suitable alternative to conventional microscopy in non-endemic settings. This technical proof-of-principle study allows laying the basis for improving the system, and optimizing its implementation in the laboratories., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Rubio Maturana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In a clinical context, conventional optical microscopy is commonly used for the visualization of biological samples for diagnosis. However, the availability of molecular techniques and rapid diagnostic tests are reducing the use of conventional microscopy, and consequently the number of experienced professionals starts to decrease. Moreover, the continuous visualization during long periods of time through an optical microscope could affect the final diagnosis results due to induced human errors and fatigue. Therefore, microscopy automation is a challenge to be achieved and address this problem. The aim of the study is to develop a low-cost automated system for the visualization of microbiological/parasitological samples by using a conventional optical microscope, and specially designed for its implementation in resource-poor settings laboratories. A 3D-prototype to automate the majority of conventional optical microscopes was designed. Pieces were built with 3D-printing technology and polylactic acid biodegradable material with Tinkercad/Ultimaker Cura 5.1 slicing softwares. The system's components were divided into three subgroups: microscope stage pieces, storage/autofocus-pieces, and smartphone pieces. The prototype is based on servo motors, controlled by Arduino open-source electronic platform, to emulate the X-Y and auto-focus (Z) movements of the microscope. An average time of 27.00 ± 2.58 seconds is required to auto-focus a single FoV. Auto-focus evaluation demonstrates a mean average maximum Laplacian value of 11.83 with tested images. The whole automation process is controlled by a smartphone device, which is responsible for acquiring images for further diagnosis via convolutional neural networks. The prototype is specially designed for resource-poor settings, where microscopy diagnosis is still a routine process. The coalescence between convolutional neural network predictive models and the automation of the movements of a conventional optical microscope confer the system a wide range of image-based diagnosis applications. The accessibility of the system could help improve diagnostics and provide new tools to laboratories worldwide., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Dantas de Oliveira et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it., Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide., Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application., Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Maturana, de Oliveira, Nadal, Serrat, Sulleiro, Ruiz, Bilalli, Veiga, Espasa, Abelló, Suñé, Segú, López-Codina, Clols and Joseph-Munné.)

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Rubio, de Oliveira, Nadal, Bilalli, Zarzuela, Espasa, Sulleiro, Bosh, Veiga, Abelló, López-Codina, Pumarola, Sayrol and Joseph-Munne.)

Policymakers need clear, fast assessment of the real spread of the COVID-19 epidemic in each of their respective countries. Standard measures of the situation provided by the governments include reported positive cases and total deaths. While total deaths indicate immediately that countries like Italy and Spain had the worst situation as of mid-April, 2020, reported cases alone do not provide a complete picture of the situation. Different countries diagnose differently and present very distinctive reported case fatality ratios. Similar levels of reported incidence and mortality might hide a very different underlying pictures. Here we present a straightforward and robust estimation of the diagnostic rate in each European country. From that estimation we obtain a uniform, unbiased incidence of the epidemic. The method to obtain the diagnostic rate is transparent and empirical. The key assumption of the method is that the infection fatality ratio of COVID-19 in Europe is not strongly country-dependent. We show that this number is not expected to be biased due to demography nor to the way total deaths are reported. The estimation protocol is dynamic, and it has been yielding converging numbers for diagnostic rates in all European countries as from mid-April, 2020. Using this diagnostic rate, policy makers can obtain Effective Potential Growth updated every day, providing an unbiased assessment of the countries at greater risk of experiencing an uncontrolled situation. The method developed has been and will be used to track possible improvements in the diagnostic rate in European countries as the epidemic evolves., Competing Interests: The authors have declared that no competing interests exist.

Bone marrow obtained by iliac crest aspiration is a common source for harvesting mesenchymal stem cells, other progenitor cells, and associated cytokine/growth factors. Recent studies have reported good to excellent outcomes with the use of bone marrow aspirate concentrate (BMAC) for pain relief in the treatment of focal chondral lesions and osteoarthritis of the knee. However, the harvesting and processing technique are crucial to achieve satisfactory results. Several studies have examined outcomes after BMAC injection, with encouraging results, but there is a lack of consensus in terms of the frequency of injection, the amount of BMAC that is injected, and the timing of BMAC injections. The purpose of this Technical Note was to describe a standardized bone marrow aspiration harvesting technique and processing method.

Chronic exertional compartment syndrome is characterized by exertional pain and elevated intracompartmental pressures affecting the leg in physically active young people. In patients who have failed conservative measures, fasciotomy is the treatment of choice. This study presents a new method for performing fasciotomy using high-resolution ultrasound (US) guidance and reports on the clinical outcomes in a group of these patients. Over a 3-year period, 7 consecutive patients with a total of 9 involved legs presented clinically with anterior compartment chronic exertional compartment syndrome, which was confirmed by intracompartmental pressure measurements before and after exercise. After a US examination, fasciotomy under US guidance was performed. Preoperative and postoperative pain and activity levels were assessed as well as number of days needed to “return to play.” All patients had a decrease in pain, and all except 1 returned to presymptomatic exercise levels with a median return to play of 35 days.

Regenerative therapies using adult stem cells have attracted great interest in the recent years and offer a promising alternative to current surgical practices. In this report, we evaluated the safety and efficacy of an autologous cell-based treatment of osteoarthritis using mesenchymal stromal cells expanded from bone marrow aspirates that were administered intra-articularly. Ten 2-year old ewes were divided in two groups (for analysis at 6 and 12 months, respectively). Full thickness articular cartilage defects of approximately 60mm(2) were created arthroscopically in the medial femorotibial condyles and a meniscal tear in the anterior horn of the medial meniscus in the 20 hind legs. Intra-articular injection of 4 mL of either treatment (a suspension of cells) or control (same as treatment, without cells) were applied one month after generating a chronic condition similar to human pathology. Animals were monitored radiographically, by MRI and ultrasound scanning; and macroscopic and histological analyses were conducted at 6 and 12 months. Furthermore a full necropsy was performed at 12 months post-treatment. The intra-articular injection of autologous MSC was safe, as judged by the lack of local or systemic adverse effects during the clinical follow-up and by a full necropsy performed at 12 months post-treatment. Evidence of regeneration of articular cartilage and meniscus was case-dependent but statistically significant improvement was found in specific macroscopic and histological parameters. Such parameters included colour, rigidity, cell distribution and hyaline quality of the refill tissue as well as the structure of subchondral bone., (Copyright © 2014 Elsevier B.V. All rights reserved.)