Microscope V16 Diagnostics Torre
Marika Szala
Microscopic examination of Giemsa-stained blood films remains a major form of diagnosis in malaria case management, and is a reference standard for research. However, as with other visualization-based diagnoses, accuracy depends on individual technician performance, making standardization difficult and reliability poor. Automated image recognition based on machine-learning, utilizing convolutional neural networks, offers potential to overcome these drawbacks. A prototype digital microscope device employing an algorithm based on machine-learning, the Autoscope, was assessed for its potential in malaria microscopy. Autoscope was tested in the Iquitos region of Peru in 2016 at two peripheral health facilities, with routine microscopy and PCR as reference standards. The main outcome measures include sensitivity and specificity of diagnosis of malaria from Giemsa-stained blood films, using PCR as reference.
Typical thick film microscope images. a A field-of-view image containing only two parasites, indicated by yellow circles with enlargements. b Malaria parasites ring forms. c Malaria parasite late stages
A high-performing algorithm could serve to standardize film interpretation across geography and across time, of particular relevance to clinical studies of drugs, vaccines and other diagnostics. It could also serve as a cross-checking on microscopist performance; machines do not tire, and cross-checking of slides, while recommended in any Quality Assurance (QA) programme, is rarely performed adequately [10]. In non-endemic areas, such as diagnosis of returned travelers, the device could supplement diagnosis provided by technicians who rarely see real parasites.
Typical thick film microscope images. a A field-of-view image containing only two parasites, indicated by yellow circles with enlargements. b Malaria parasites ring forms. c Malaria parasite late stages
Abstract:Microscopy plays a crucial role in the diagnosis of numerous diseases. However, the need for trained microscopists and pathologists, the complexity of pathology, and the accessibility and affordability of the technology can hinder the provision of rapid and high-quality diagnoses and healthcare. In this work, we present an affordable, 3D-printed, portable, robotic, mobile-based slide scanning microscope. The proposed device is composed of electronic, mechanical, and optical modules operated via smartphone with a control app. The device is connected and fully integrated with a telemedicine web platform, where digitized microscopy images can be remotely visualized and analyzed. The robotic scanner, which has approximately 1-µm resolution, has been evaluated in two clinical scenarios with histology and stool samples. The results showed sufficient image quality for performing a proper diagnosis in all cases under study.Keywords: robotic microscope scanner; mobile-based; 3D printed; telemedicine
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.
A total of 312 blood samples (189 sheep and 123 goats) were examined for Anaplasma infection during a 1 year period. Giemsa-stained blood smears were examined under the microscope. IFA and cELISA were used for the detection of Anaplasma spp. antibodies. PCR was used as a standard of truth and for the identification of Anaplasma species. Using cELISA assay, 47.4% (148) were positive (93 sheep and 55 goats) with a sensitivity and specificity of 91.9, and 86.9%, respectively. Using IFA, it was found that 57.4% (179)were positive (113 sheep and 66 goats) with a sensitivity and specificity of 100, and 93.3%, respectively. PCR assay identified A. ovis in 49 (25.3%) sheep and 30 (15.5%) goats, and A. phagocytophilumin 74 (38.1%) sheep and 41 (20.8%) goats.
Thin blood smears were prepared for microscopic examination accordingly the standard protocol [7].The slides were allowed to air-dry before being fixed with absolute methanol. Fixed smears were stained with 10% Giemsa (Cresent diagnostic, KSA) and examined by using compound microscope under oil immersion lens. About 25 fields were examined from each slide for the presence of Anaplasma and the number of infected erythrocytes. Anaplasma was identified on the basis of its morphology [8].
Anaplasmosis frequently occurs in tropical and subtropical regions, and it is a major problem to small ruminants [34]. Epidemiologic studies aimed to determine the prevalence of anaplasmosis uses different diagnostic tools, such as microscopic examination of stained blood smears, serological, and molecular tests. The reliability of the diagnostic tests is crucial for accurate diagnosis and estimation of the disease prevalence. Despite microscopic examination and serologic tests are practical and reliable diagnostics to detect Anaplasma spp. infection, they have limitations [1, 47]. The accuracy of stained blood smear examination can be hindered by the low number of infected cells, lack of expertise of the examiner, and/or the occurrence of intracellular artifacts [2, 3]. In the early acute phase of infection, serologic assays have limited value, due to the absence of detectable antibodies [5, 42].
Proper disease diagnosis requires reliable tests. Therefore, it is important to evaluate the existing diagnostic methods. The evaluation depends on several factors as; whether the test is suitable for the field and/or the laboratory settings; cost; and time required. Microscopic examination provides reliable results, but it is not suitable to diagnose carrier animals. cELISA is known for its ease of use, low cost, and for being quantitative. IFA is an economical and easy method to perform. In the present study, IFA was highly specific and sensitive, but it requires special laboratory settings such as fluorescent microscope. PCR is the most sensitive and reliable diagnostic tool that achieves simultaneous differentiation between different Anaplasma subspecies.
A cancer biomarker acts as a measurable biological molecule that can be found in blood and other tissues or body fluids, such as saliva and urine, indicating that cancer exists in the body [13, 14]. Cancer biomarkers may be proteins (secreted proteins or cell surface proteins) [15], carbohydrates [16], or nucleic acids (circulating tumor DNA, miRNA, etc.) [17] that are secreted by the body or cancer cells when cancer is present [18, 19]. The measurement of certain cancer biomarker levels enables early detection of cancer or tumor recurrence and helps monitor the efficacy of the therapy. Nevertheless, the use of biomarkers has been limited by several barriers, including low biomarker concentrations in body fluids, heterogeneity in the abundance and timing of biomarkers within patients, and the difficulty in carrying out prospective studies [20]. Nanotechnology offers high selectivity and sensitivity and the ability to conduct simultaneous measurements of multiple targets. Biosensors can be improved with nanoparticles/nanomaterials to provide specific targeting [21]. In addition, the use of nanoparticles provides an increased surface-to-volume ratio, which makes biosensors more sensitive in fulfilling the demands of specific biomolecular diagnostics [22]. Quantum dots (QDs), gold nanoparticles (AuNPs), and polymer dots (PDs) are three common nanoparticle probes used in diagnosing cancer [23, 24].
The third challenge is to develop NP-based devices with high sensitivity and that are easy to handle and cost-efficient. Most NP-based assays were prepared in academic laboratories, and many assays are unrealistic for clinical translation. For example, complicated confocal Raman microscopes were used to implement most studies based on SERS but are rarely present in hospitals or clinical laboratories. Successful development of NP-based POC (point of care) devices will greatly facilitate clinical application of nanotechnology in cancer diagnosis.
The recent progress in nanotechnology-based application in cancer diagnosis has been summarized in this review (Fig. 2). In the past 10 years, many efforts have been made to develop assays for cancer diagnosis based on nanotechnology. Compared with the currently available cancer diagnostics in the clinic, a variety of NP-based assays showed improvement in terms of selectivity and sensitivity or offered entirely new capacities that could not be achieved with traditional approaches. These advances will improve the survival rate of cancer patients by enabling early detection. In addition, these advances could be used to monitor cancer progress in response to treatment, which may contribute to the development of better strategies for cancer treatment.
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