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Scientific Reports Dec 2023Chlamydophila pneumoniae is a cause of community-acquired pneumonia (CAP) and responsible for 1-2% of cases in paediatric patients. In Mexico, information on this...
Chlamydophila pneumoniae is a cause of community-acquired pneumonia (CAP) and responsible for 1-2% of cases in paediatric patients. In Mexico, information on this microorganism is limited. The aim of this study was to detect C. pneumoniae using two genomic targets in a real-time PCR and IgM/IgG serology assays in paediatric patients with CAP at a tertiary care hospital in Mexico City and to describe their clinical characteristics, radiological features, and outcomes. A total of 154 hospitalized patients with diagnosis of CAP were included. Detection of C. pneumoniae was performed by real-time PCR of the pst and arg genes. Complete blood cell count, C-reactive protein measurement and IgM and IgG detection were performed. Clinical-epidemiological and radiological data from the patients were collected. C. pneumoniae was detected in 25 patients (16%), of whom 88% had underlying disease (P = 0.014). Forty-eight percent of the cases occurred in spring, 36% in girls, and 40% in children older than 6 years. All patients had cough, and 88% had fever. Interstitial pattern on chest-X-ray was the most frequent (68%), consolidation was observed in 32% (P = 0.002). IgM was positive in 7% and IgG in 28.6%. Thirty-six percent presented complications. Four percent died. A high proportion showed co-infection with Mycoplasma pneumoniae (64%). This is the first clinical report of C. pneumoniae as a cause of CAP in Mexican paediatric patients, using two genomic target strategy and serology. We found a frequency of 16.2% with predominance in children under 6 years of age. In addition; cough and fever were the most common symptoms. Early detection of this pathogen allows timely initiation of specific antimicrobial therapy to reduce development of complications. This study is one of the few to describe the presence of C. pneumoniae in patients with underlying diseases.
Topics: Female; Child; Humans; Child, Preschool; Pneumonia, Mycoplasma; Chlamydophila pneumoniae; Pathology, Molecular; Cough; Mexico; Tertiary Care Centers; Mycoplasma pneumoniae; Community-Acquired Infections; Immunoglobulin G; Immunoglobulin M
PubMed: 38052876
DOI: 10.1038/s41598-023-48701-5 -
International Journal of Medical... 2020The advantages of atomic force microscopy (AFM) in biological research are its high imaging resolution, sensitivity, and ability to operate in physiological conditions.... (Review)
Review
The advantages of atomic force microscopy (AFM) in biological research are its high imaging resolution, sensitivity, and ability to operate in physiological conditions. Over the past decades, rigorous studies have been performed to determine the potential applications of AFM techniques in disease diagnosis and prognosis. Many pathological conditions are accompanied by alterations in the morphology, adhesion properties, mechanical compliances, and molecular composition of cells and tissues. The accurate determination of such alterations can be utilized as a diagnostic and prognostic marker. Alteration in cell morphology represents changes in cell structure and membrane proteins induced by pathologic progression of diseases. Mechanical compliances are also modulated by the active rearrangements of cytoskeleton or extracellular matrix triggered by disease pathogenesis. In addition, adhesion is a critical step in the progression of many diseases including infectious and neurodegenerative diseases. Recent advances in AFM techniques have demonstrated their ability to obtain molecular composition as well as topographic information. The quantitative characterization of molecular alteration in biological specimens in terms of disease progression provides a new avenue to understand the underlying mechanisms of disease onset and progression. In this review, we have highlighted the application of diverse AFM techniques in pathological investigations.
Topics: Biomechanical Phenomena; Cell Adhesion; Cytoskeleton; Diabetes Mellitus, Type 2; Extracellular Matrix; Humans; Image Processing, Computer-Assisted; Inflammation; Membrane Proteins; Microscopy, Atomic Force; Molecular Imaging; Pathology, Clinical; Pathology, Molecular
PubMed: 32308537
DOI: 10.7150/ijms.41805 -
American Journal of Clinical Pathology Aug 2020To review the response to the coronavirus disease 2019 (COVID-19) pandemic in a forensics center that integrates an academic department of pathology with multiple...
OBJECTIVES
To review the response to the coronavirus disease 2019 (COVID-19) pandemic in a forensics center that integrates an academic department of pathology with multiple regional county medical examiners' offices.
METHODS
Faculty and staff were asked to volunteer stories, data, and photographs describing their activities from March through May 2020. The information was assembled into a narrative summary.
RESULTS
Increased deaths challenged capacity limits in a hospital morgue and a large urban medical examiner's office (MEO) successfully managed by forensic teams and monitored by an institutional command center. Autopsies of suspected and proven cases of COVID-19 were performed in both facilities. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing of decedents was performed in a MEO serving a large urban area. Scene investigators worked directly with families to meet needs unique to a pandemic. Artful photographs of decedent's hands and/or tattoos were offered to those unable to have in-person viewings. Pathologists and social workers were available to families of the deceased and created novel solutions to facilitate the grieving process.
CONCLUSIONS
Forensic pathology is important to successfully navigating emerging diseases like the COVID-19 pandemic. Direct conversations with families are common in forensic pathology and serve as a model for patient- and family-centered care.
Topics: Betacoronavirus; COVID-19; Coronavirus Infections; Forensic Pathology; Health Personnel; Humans; Pandemics; Pneumonia, Viral; SARS-CoV-2
PubMed: 32556078
DOI: 10.1093/ajcp/aqaa106 -
Scientific Reports Apr 2023Out-of-distribution (OOD) generalization, especially for medical setups, is a key challenge in modern machine learning which has only recently received much attention....
Out-of-distribution (OOD) generalization, especially for medical setups, is a key challenge in modern machine learning which has only recently received much attention. We investigate how different convolutional pre-trained models perform on OOD test data-that is data from domains that have not been seen during training-on histopathology repositories attributed to different trial sites. Different trial site repositories, pre-trained models, and image transformations are examined as specific aspects of pre-trained models. A comparison is also performed among models trained entirely from scratch (i.e., without pre-training) and models already pre-trained. The OOD performance of pre-trained models on natural images, i.e., (1) vanilla pre-trained ImageNet, (2) semi-supervised learning (SSL), and (3) semi-weakly-supervised learning (SWSL) models pre-trained on IG-1B-Targeted are examined in this study. In addition, the performance of a histopathology model (i.e., KimiaNet) trained on the most comprehensive histopathology dataset, i.e., TCGA, has also been studied. Although the performance of SSL and SWSL pre-trained models are conducive to better OOD performance in comparison to the vanilla ImageNet pre-trained model, the histopathology pre-trained model is still the best in overall. In terms of top-1 accuracy, we demonstrate that diversifying the images in the training using reasonable image transformations is effective to avoid learning shortcuts when the distribution shift is significant. In addition, XAI techniques-which aim to achieve high-quality human-understandable explanations of AI decisions-are leveraged for further investigations.
Topics: Humans; Neural Networks, Computer; Machine Learning; Supervised Machine Learning
PubMed: 37055519
DOI: 10.1038/s41598-023-33348-z -
Emerging Infectious Diseases Dec 2022The US President's Emergency Plan for AIDS Relief (PEPFAR) supports molecular HIV and tuberculosis diagnostic networks and information management systems in low- and... (Review)
Review
The US President's Emergency Plan for AIDS Relief (PEPFAR) supports molecular HIV and tuberculosis diagnostic networks and information management systems in low- and middle-income countries. We describe how national programs leveraged these PEPFAR-supported laboratory resources for SARS-CoV-2 testing during the COVID-19 pandemic. We sent a spreadsheet template consisting of 46 indicators for assessing the use of PEPFAR-supported diagnostic networks for COVID-19 pandemic response activities during April 1, 2020, to March 31, 2021, to 27 PEPFAR-supported countries or regions. A total of 109 PEPFAR-supported centralized HIV viral load and early infant diagnosis laboratories and 138 decentralized HIV and TB sites reported performing SARS-CoV-2 testing in 16 countries. Together, these sites contributed to >3.4 million SARS-CoV-2 tests during the 1-year period. Our findings illustrate that PEPFAR-supported diagnostic networks provided a wide range of resources to respond to emergency COVID-19 diagnostic testing in 16 low- and middle-income countries.
Topics: Humans; COVID-19 Testing; Pathology, Molecular; Pandemics; SARS-CoV-2; COVID-19; HIV Infections
PubMed: 36502414
DOI: 10.3201/eid2813.220789 -
Nature Medicine May 2024Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions....
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.
Topics: Humans; Deep Learning; Neoplasms, Unknown Primary; Female; Male; Aged; Middle Aged; ROC Curve; Adult; Cytodiagnosis; Aged, 80 and over; Ascites; Cytology
PubMed: 38627559
DOI: 10.1038/s41591-024-02915-w -
Journal of Medical Internet Research Apr 2023There is increasing interest in the use of artificial intelligence (AI) in pathology to increase accuracy and efficiency. To date, studies of clinicians' perceptions of... (Review)
Review
BACKGROUND
There is increasing interest in the use of artificial intelligence (AI) in pathology to increase accuracy and efficiency. To date, studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting the need for further research regarding how to integrate it into clinical practice.
OBJECTIVE
The aim of the study was to determine contextual factors that may support or constrain the uptake of AI in pathology.
METHODS
To go beyond a simple listing of barriers and facilitators, we drew on the approach of realist evaluation and undertook a review of the literature to elicit stakeholders' theories of how, for whom, and in what circumstances AI can provide benefit in pathology. Searches were designed by an information specialist and peer-reviewed by a second information specialist. Searches were run on the arXiv.org repository, MEDLINE, and the Health Management Information Consortium, with additional searches undertaken on a range of websites to identify gray literature. In line with a realist approach, we also made use of relevant theory. Included documents were indexed in NVivo 12, using codes to capture different contexts, mechanisms, and outcomes that could affect the introduction of AI in pathology. Coded data were used to produce narrative summaries of each of the identified contexts, mechanisms, and outcomes, which were then translated into theories in the form of context-mechanism-outcome configurations.
RESULTS
A total of 101 relevant documents were identified. Our analysis indicates that the benefits that can be achieved will vary according to the size and nature of the pathology department's workload and the extent to which pathologists work collaboratively; the major perceived benefit for specialist centers is in reducing workload. For uptake of AI, pathologists' trust is essential. Existing theories suggest that if pathologists are able to "make sense" of AI, engage in the adoption process, receive support in adapting their work processes, and can identify potential benefits to its introduction, it is more likely to be accepted.
CONCLUSIONS
For uptake of AI in pathology, for all but the most simple quantitative tasks, measures will be required that either increase confidence in the system or provide users with an understanding of the performance of the system. For specialist centers, efforts should focus on reducing workload rather than increasing accuracy. Designers also need to give careful thought to usability and how AI is integrated into pathologists' workflow.
Topics: Humans; Artificial Intelligence; Narration; Machine Learning; Pathology
PubMed: 37093631
DOI: 10.2196/38039 -
Forensic Science, Medicine, and... Mar 2021Modern technologies enable the exchange of information about the expansion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the continually... (Review)
Review
Modern technologies enable the exchange of information about the expansion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the continually increasing number of the coronavirus disease 2019 (COVID-19) cases almost in real time. The gravity of a current epidemiological situation is represented by the mortality rates, which are scrupulously updated daily. Performing autopsies on patients with either suspected or confirmed COVID-19 is of high importance since these might not only improve clinical management but also reduce the risk of SARS-CoV-2 infection expansion. The following paper aimed to present the most crucial aspects of SARS-CoV-2 infection from the point of view of forensic experts and pathologists, recommendations and safety precautions regarding autopsies, autopsy room requirements, possible techniques, examinations used for effective viral detection, recommendations regarding burials, and gross and microscopic pathological findings of the deceased who died due to SARS-CoV-2 infection. Autopsies remain the gold standard for determining the cause of death. Therefore, it would be beneficial to perform autopsies on patients with both suspected and confirmed COVID-19, especially those with coexisting comorbidities.
Topics: Air Filters; Autopsy; Burial; COVID-19; COVID-19 Testing; Cadaver; Clothing; Cremation; Disease Reservoirs; Embalming; Forensic Pathology; Humans; Infection Control; Infectious Disease Transmission, Patient-to-Professional; Lung; Middle East Respiratory Syndrome Coronavirus; Personal Protective Equipment; Radiography; SARS-CoV-2; Specimen Handling; Tomography, X-Ray Computed
PubMed: 33394313
DOI: 10.1007/s12024-020-00341-1 -
Computational Intelligence and... 2023Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital...
Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital histopathology increases diagnosis precision and provides better image quality and more detail for the pathologist with multiple viewing options and team annotations. As a result of the benefits above, faster treatment is available, increasing therapy success rates and patient recovery and survival chances. However, the present manual examination of these images is tedious and time-consuming for pathologists. Therefore, reliable automated techniques are needed to effectively classify normal and malignant cancer images. This paper applied a deep learning approach, namely, EfficientNet and its variants from B0 to B7. We used different image resolutions for each model, from 224 × 224 pixels to 600 × 600 pixels. We also applied transfer learning and parameter tuning techniques to improve the results and overcome the overfitting problem. We collected the dataset from the Lung and Colon Cancer Histopathological Image LC25000 image dataset. The dataset acquisition consists of 25,000 histopathology images of five classes (lung adenocarcinoma, lung squamous cell carcinoma, benign lung tissue, colon adenocarcinoma, and colon benign tissue). Then, we performed preprocessing on the dataset to remove the noisy images and bring them into a standard format. The model's performance was evaluated in terms of classification accuracy and loss. We have achieved good accuracy results for all variants; however, the results of EfficientNetB2 stand excellent, with an accuracy of 97% for 260 × 260 pixels resolution images.
Topics: Humans; Adenocarcinoma; Algorithms; Colonic Neoplasms; Lung Neoplasms; Lung
PubMed: 37876944
DOI: 10.1155/2023/7282944 -
Veterinary Pathology Jan 2022Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary... (Review)
Review
Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary pathology. The DM workflow encompasses specimen preparation, whole-slide image acquisition, slide retrieval, and the workstation, each of which has the potential (depending on the technical parameters) to introduce limitations and artifacts into microscopic examination by pathologists. Performing validation studies according to guidelines established in human pathology ensures that the best-practice approaches for patient care are not deteriorated by implementing DM. Whereas current publications on validation studies suggest an overall high reliability of DM, each laboratory is encouraged to perform an individual validation study to ensure that the DM workflow performs as expected in the respective clinical or research environment. With the exception of validation guidelines developed by the College of American Pathologists in 2013 and its update in 2021, there is no current review of the application of methods fundamental to validation. We highlight that there is high methodological variation between published validation studies, each having advantages and limitations. The diagnostic concordance rate between DM and LM is the most relevant outcome measure, which is influenced (regardless of the viewing modality used) by different sources of bias including complexity of the cases examined, diagnostic experience of the study pathologists, and case recall. Here, we review 3 general study designs used for previous publications on DM validation as well as different approaches for avoiding bias.
Topics: Animals; Humans; Microscopy; Pathologists; Pathology, Veterinary; Reproducibility of Results; Specimen Handling
PubMed: 34433345
DOI: 10.1177/03009858211040476