Crucial Diagnostic Information Research Articles

Visualizing Macrophage Polarization through Fluorescent mRNA Profiling.

Macrophages, known for their phenotypic plasticity, play a critical role in maintaining homeostasis and inflammation-related pathogenesis. Although identifying diverse macrophage phenotypes holds promise for enhancing diagnoses and treatments of diseases mediated by macrophages, existing methodologies for differentiating macrophages often lack precision. They are limited by the cumbersome procedures that require large-scale equipment, such as flow cytometry and transcriptomic analysis. In this context, we have engineered fluorescent polyadenine (polyA)-mediated sticky flares that enable practical visualization of macrophages. This technology facilitates the highly sensitive detection of macrophage phenotypes through the specific recognition of intracellular mRNAs, permitting in situ imaging. Our approach demonstrates the potential for determining macrophage polarization status at the single-cell level within dynamic immune microenvironments, thereby providing crucial diagnostic and prognostic information that could guide the development of tailored treatments for macrophage-related diseases in personalized medicine.

Exploring Diagnostic and Therapeutic Odyssey in Pulmonary Arterial Hypertension: Insights from In-Depth Semi-Structured Interviews

Introduction: Establishing a diagnosis is paramount in medical practice as it shapes patients’ experiences and guides treatment. Patients grappling with rare diseases face a triple challenge: prolonged diagnostic journeys, limited responses to existing therapies, and the absence of effective monitoring tools. Genetic diagnosis often provides crucial diagnostic and prognostic information, opening up possibilities for genotype-targeted treatments and facilitating counselling and relative testing. The NIHR BioResource – Rare Diseases (NBR) Study and the Cohort Study in Idiopathic and Hereditary Pulmonary Arterial Hypertension (PAH Cohort study) aimed to enhance diagnosis and treatment for PAH, successfully identifying the genetic cause in 25% of idiopathic cases. However, the diagnostic and therapeutic odyssey in patients with PAH remains largely unexplored. Methods: Stakeholders from the NBR and PAH Cohort studies were recruited using purposive sampling. In-depth interviews and focus groups were recorded, transcribed, anonymised, and analysed thematically using MAXQDA software. Results: The study involved 53 interviews and focus groups with 63 participants, revealing key themes across five stages of the diagnostic odyssey: initial health concerns and interactions with general practitioners, experiences of misdiagnosis, relief upon receiving the correct diagnosis, and mixed emotions regarding genetic results and the challenges of living with the disease. Following the diagnosis, participants embarked on a therapeutic journey, facing various challenges, including the disease’s impact on professional and social lives, the learning curve associated with understanding the disease, shifts in communication dynamics with healthcare providers, therapeutic hurdles, and insurance-related issues. Building on these insights, we identified areas of unmet needs, such as improved collaboration with primary care providers and local hospitals, the provision of psychological support and counselling, and the necessity for ongoing patient education in the ever-evolving realms of research and therapy. Conclusions: The study highlights the significant challenges encountered throughout the diagnostic and therapeutic journey in PAH. To enhance patient outcomes, it is crucial to raise awareness of the disease, establish clear diagnostic pathways, and seamlessly integrate genetic diagnostics into clinical practice. Streamlining the diagnostic process can be achieved by utilising existing clinical infrastructure to support research and fostering better communication within the NHS. Moreover, there is an urgent need for more effective therapies alongside less burdensome drug delivery methods.

Diagnosing Polyomavirus Nephropathy Without a Biopsy: Validation of the Urinary Polyomavirus-Haufen Test in a Proof-of-Concept Study Including Uromodulin Knockout Mice.

Polyomavirus (PyV) nephropathy (PyVN) leads to kidney transplant dysfunction and loss. Since a definitive diagnosis requires an invasive kidney biopsy, a timely diagnosis is often hampered. In this clinical dilemma the PyV haufen-test, centering around the detection of 3-dimensional PyV aggregates in the urine, might provide crucial diagnostic information. A multistep experimental design was used. The hypothesis was that PyV-haufen form within the kidneys under high concentrations of uromodulin, a kidney-specific protein and that PyV-haufen are, therefore, kidney-specific disease biomarkers. The first investigative step showed colocalization of uromodulin with aggregated PyV (1) in 10 kidneys with PyVN by immunohistochemistry, (2) in urine samples containing PyV-haufen by electron microscopy/immunogold labeling (n = 3), and (3) in urine samples containing PyV-haufen by immunoprecipitation assays (n = 4). In the in vitro experiments of the next step, only high uromodulin concentrations (≥1.25 mg/mL) aggregated PyV, as is expected to occur within injured nephrons. In contrast, in voided urine samples (n = 59) uromodulin concentrations were below aggregation concentrations (1.2-19.6 µg/mL). In the third investigative step, none of 11 uromodulin-/- knockout mice (0%) with histologic signs of PyVN showed urinary PyV-haufen shedding, compared with 10 of 14 uromodulin+/+ wild-type mice (71%). PyV-haufen form within kidneys under high uromodulin concentrations. Thus, PyV-haufen detected in the urine are specific biomarkers for intrarenal disease (ie, definitive PyVN).

Consistent Learning-Based Breast Tumor Segmentation and Its Application in Sentinel Lymph Node Metastasis Prediction.

Accurate staging of lymph nodes provides crucial diagnostic information for breast cancer patients, where segmentation is of great importance by localizing and visualizing the breast tumor of interest. Nevertheless, current segmentation methods perform average when facing large span of tumor sizes, degraded image quality, blurred tumor boundaries, and resulting noise during manual annotation. Therefore, we develop a Multi-scale RepVGG-based Segmentation Network (MPSegNet) to segment breast tumor from MR images. In particular, we construct a consistent learning framework for the MPSegNet to alleviate the impact of noisy labels upon segmentation results. The rationale is that different views covering the same breast tumors are supposed to generate identical segmentation predictions. Then, we predict SLN metastasis given segmented breast tumors, where we evaluate the relationships between the predictive performance and tumor segmentations under different consistencies. The results show the superiority of our method over other state-of-the-art methods. A high consistency among multiple views can boost the segmentation performance during consistent learning. However, the optimal segmentation does not produce the best SLN metastatic prediction results, implying that the dependence of classification upon segmentation needs to be elaborately investigated further.Clinical Relevance- This study facilitates more accurate segmentation of breast tumors with consistent learning, and provides an initial analysis between tumor segmentation and subsequent prediction of SLN metastasis, which has potential significance for the precise medical care of breast cancer patients.

145. Harnessing the Inoculum Effect to Diagnose Molecular Mechanisms of Carbapenem Resistance

Abstract Background The global spread of carbapenem-resistant Enterobacterales (CRE) presents a major threat to public health. CRE employ two molecular mechanisms to evade carbapenems: 1) expression carbapenemases (CPases), which efficiently hydrolyze carbapenems or 2) disruption of porins, which reduces carbapenem influx to levels that can be hydrolyzed by certain beta-lactamases. Diagnosing mechanisms underlying carbapenem resistance is important for infection control and to administer appropriate treatments. Methods We measured Meropenem and Ertapenem minimum inhibitory concentrations (MICs) for 103 clinical Enterobacterales isolated from hospitals in Massachusetts and California using broth microdilution assays at 14 inocula spanning four orders of magnitude. They represented 30 isolates encoding CPases and intact porins, 46 isolates with disruptions in one or both of the porins responsible for carbapenem influx (OmpC and OmpF), 25 encoding CPases with disrupted porins, and two controls encoding intact porins and no CPases. Results We observed that the two mechanisms result in distinct profiles; first the carbapenem MICs of CPase-encoding strains show a strong inoculum dependence (Fig 1.A), whereas the MICs of porin deficient strains remain largely constant at all inocula (Fig 1.B). The synergistic action of these two mechanisms leads to high-level resistance that we termed “hyper-CRE” (Fig 1.C). Together these factors explain the level of resistance in nearly all our CRE isolates. To validate the hyper-CRE phenotype, we successfully employed CRISPR-based gene editing to show that knocking out the major porin in CPase-producing strains elevates their carbapenem resistance to hyper-CRE levels. We also determined 18% of our isolates changed susceptibility classification within the Clinical Laboratory and Standards Institute (CLSI) recommended inoculum range. This is worrisome for the treatment of infections with strains that are deemed susceptible via in vitro AST assays but are truly resistant in vivo. Conclusion Overall, our approach has demonstrated that measuring MICs at different inoculum can yield crucial diagnostic information about mechanisms of resistance which has important implications for patient care, infection control, and surveillance of emerging CPases. Disclosures All Authors: No reported disclosures.

Deep learning approach to identify abnormalities in blood cell images

Blood cell imaging provides crucial diagnostic information about a person's atypical problems. Due to their multi-level structures, Deep Learning models aid in extracting complicated insights from input images. The power of automatic feature extraction makes Convolutional Neural Networks (CNN) the most widely used deep learning approach for blood cell categorization. Blood cells are classified using a recurrent neural network (RNN), which captures long-term relationships between elements. The possibility of CNN-RNN-based models for blood cell image classification is investigated in this work. The results prove that CNN-LSTM based model outperforms in classifying the blood cell images with an accuracy of 92.1%.

Quantitative Chemical Imaging of Bone Tissue for Intraoperative and Diagnostic Applications.

Bone is difficult to image using traditional histopathological methods, leading to challenges in intraoperative pathological evaluation that is critical in guiding surgical treatment, particularly in orthopedic oncology. In this study, we demonstrate that a multimodal quantitative imaging approach that combines stimulated Raman scattering (SRS) microscopy, two-photon fluorescence (TPF) microscopy, and second-harmonic generation (SHG) microscopy can provide useful diagnostic information regarding intact bone tissue fragments from surgical excision or biopsy specimens. We imaged bone samples from 17 patient cases and performed quantitative chemical and morphological analyses of both mineral and organic components of bone. Our main findings show that carbonate content combined with morphometric analysis of bone organic matrix can separate several major classes of bone cancer-associated diagnostic categories with an average accuracy of 92%. This proof-of-principle study demonstrates that quantitative multimodal imaging and machine learning-based analysis of bony tissue can provide crucial diagnostic information for guiding clinical decisions in orthopedic oncology. Moreover, the general methodology of morphological and chemical imaging combined with machine learning can be readily extended to other tissue types for tissue diagnosis in intraoperative and other clinical settings.

The Wearable Physical Fitness Training Device Based on Fuzzy Theory

Mobile Edge Computing and Communication (MECC) can be deployed in close proximity with sensing devices and act as middleware between cloud and local networks. The health and fitness movement has become extremely popular recently. Endurance activities, such as marathons, triathlons, and cycling have also grown in popularity. However, with more people participating in these activities, more accidents and injuries occur—ranging from heat stroke, to heart attacks, shock, or hypoxia. All physical training activities include a risk of injury and accidents. Therefore, any research that offers a means of reducing injury risk will significantly contribute to the personal fitness field. Moreover, with the growing popularity of wearable devices and the rise of the MECC, the development and application of wearable devices that can connect to the MECC has become widespread, producing many new innovations. Although many wearable devices, such as wrist straps and smart watches, are available and able to detect individual physiological data, they cannot monitor the human body in a state of motion. Therefore, this study proposes a set of monitoring parameters for a novel wearable device connected to the MECC based on fitness management to assist fitness trainers in effective prompted strength training, and to offer timely warnings in the event of an injury risk. The data collected by the monitoring device using fuzzy theory include risk factor, body temperature, heart rate, and blood oxygen concentration. The proposed system can display in real-time the current physiological state of a wearer/user. The introduction of this device will hopefully enable trainers to immediately and effectively control and monitor the intensity of a training session, while increasing training safety, and offer crucial and immediate diagnostic information so that the correct treatment can be applied without delay in the event of injury.

A Novel Method for Constructing Classification Models by Combining Different Biomarker Patterns.

Different biomarker patterns, such as those of molecular biomarkers and ratio biomarkers, have their own merits in clinical applications. In this study, a novel machine learning method used in biomedical data analysis for constructing classification models by combining different biomarker patterns (CDBP)is proposed. CDBP uses relative expression reversals to measure the discriminative ability of different biomarker patterns, and selects the pattern with the higher score for classifier construction. The decision boundary of CDBP can be characterized in simple and biologically meaningful manners. The CDBP method was compared with eight state-of-the-art methods on eight gene expression datasets to test its performance. CDBP, with fewer features or ratio features, had the highest classification performance. Subsequently, CDBP was employed to extract crucial diagnostic information from a rat hepatocarcinogenesis metabolomics dataset. The potential biomarkers selected by CDBP provided better classification of hepatocellular carcinoma (HCC)and non-HCC stages than previous works in the animal model. The statistical analyses of these potential biomarkers in an independent human dataset confirmed their discriminative abilities of different liver diseases. These experimental results highlight the potential of CDBP for biomarker identification from high-dimensional biomedical datasets and demonstrate that it can be a useful tool for disease classification.

Exploring trends in reported orofacial lesions in Dubai: A single-centre retrospective study.

Patients often present in dental settings with orofacial lesions, which can be challenging to diagnose clinically due to their heterogenous characteristics. This can have serious implications for patient health, especially for head and neck tumours, which can undergo rapid metastatic spread (Kalele, Kulkarni, Patil, and Nayyar 2016). Histological examinations provide crucial diagnostic information and can indicate the likelihood of a malignant transformation, which impacts prognosis and optimal treatment planning (Logan and Goss, 2010). Histo-diagnostic data can also generate epidemiological information to guide regional healthcare policies.

Evaluation of Perceptual and Multi Sub-Band Energy Features for Classification of Normal, Pathological and Noisy Phonocardiogram

Murmurs in the heart sound signal convey crucial diagnostic information on valvular defects. It is a significant challenge to identify murmurs in heart sound signals due to its narrow bandwidth and the short time duration. In the clinical examination, these sounds are categorized based on human auditory perception. In this paper, we explore the popular perceptual features mel frequency cepstral coefficients (MFCC) and rhythm pattern (RP), and various multiple sub-band energy (MSBE) features for evaluation and quantification of murmurs and noises. After a comprehensive analysis of MFCC at a different number of coefficient values, it is observed that four MFCC coefficients are optimal and more discriminative than other conventional features. This is further compared with the proposed MSBE feature. The proposed feature performs better achieving an average M Acc score of 68.27%, 79.83% and 94.71% for respective classes. M Acc is the mean value of sensitivity and specificity. The estimation of sub-band energy values from log magnitude spectrogram and across larger frequency intervals improve the quantification of low amplitude high-frequency components introduced into the heart sound signals.

Supervised learning for early and accurate battery terminal voltage collapse detection

Rechargeable batteries are critical components in many electrical systems nowadays. One has to ensure reliable diagnosis and assessment of the installed batteries for smooth and safe operations. Assessment of the remaining capacity of a battery is crucial diagnostic information. A battery management system (BMS) needs to reliably report the ability of the battery to supply power or the lack thereof. If the BMS fails to do so at an early stage, this may compromise the health of the entire electric system. When a battery nears a region where the battery state-of-charge (SOC) is low, there is a risk of an abrupt drop in the terminal voltage. An early detection of such a region is crucial; otherwise, the BMS may not have enough time to react. To address this issue, our work provides a novel supervised learning approach towards an early detection of Li-ion battery terminal voltage collapse. No knowledge of initial SOC or battery model parameters is required. This is particularly important as batteries lose their capacity to store charge over time. The efficacy of the proposed approach is demonstrated by an early and accurate detection of terminal voltage collapse over a set of discharge tests conducted using multiple batteries.

Sonographic features of the testicular adrenal rests tumors in patients with congenital adrenal hyperplasia: a single-center experience and literature review

PurposeTesticular adrenal rests tumor (TART) is a rare kind of benign tumor in the testis. It usually occurred secondary to congenital adrenal hyperplasia (CAH), a hormonal disorder caused by hydroxylase deficiency. As the first-line examination method, ultrasound provides crucial diagnostic information for TART, although misdiagnosis to malignancy is quite common because of its rare prevalence. We aimed to summarize the sonographic manifestations of TART to improve the diagnostic accuracy and specificity.MethodsEight CAH patients with TART identified by ultrasound in our medical center were retrospectively reviewed. Clinical and hormonal profile, semen analysis and treatment choices were collected. Sonographic examinations were performed at the first evaluation and interpreted by experienced radiologists individually. All patients received regular follow-up, and 5 patients undertook repeated scrotal ultrasound. A literature review of TART in CAH patients was conducted, with 123 patients from 23 articles since 1990 included.ResultsA total of 8 patients aged between 4 to 27 years old were enrolled. 7 of 8 (87.5%) patients exhibited bilateral testicular lesions. The sizes of the testicular lesions were between 0.18 ml to 5.68 ml, and all showed a clear boundary. 10/15 (66.7%) lesions were homogenously hypoechoic, 4/15 (26.7%) were heterogeneously iso-hypoechoic, and 1/15 (6.7%) were homogenously isoechoic. 10/15 (66.7%) lesions were hyper-vascular. The longitudinal follow-up of 5 patients showed testicular lesions changed in terms of size, echogenicity, and vascularity after steroid treatment. A potential correlation may exist between ACTH levels and tumor size (p = 0.066). From the literature review, 100/123 (81%) patients got bilateral lesions, and 95% of them were located near the mediastinum. 80/103 (78%) lesions exhibited a clear boundary, and predominant lesions (74%) were hypoechogenic. Vascularity was with great diversity. Seventy-nine lesions of 44 patients were followed-up by scrotal ultrasound, among which 29 (37%) remained unchanged, 29(37%) shrank, and 21(27%) disappeared.ConclusionsKey sonographic characteristics of TART are: resembled lesions on both testes, located near the mediastinum, clear boundary, and changed in size or echogenicity after steroid treatment. These features can help radiologists to make an accurate diagnosis of TART.

The RB1 Story: Characterization and Cloning of the First Tumor Suppressor Gene.

The RB1 gene is the first described human tumor suppressor gene and plays an integral role in the development of retinoblastoma, a pediatric malignancy of the eye. Since its discovery, the stepwise characterization and cloning of RB1 have laid the foundation for numerous advances in the understanding of tumor suppressor genes, retinoblastoma tumorigenesis, and inheritance. Knowledge of RB1 led to a paradigm shift in the field of cancer genetics, including widespread acceptance of the concept of tumor suppressor genes, and has provided crucial diagnostic and prognostic information through genetic testing for patients affected by retinoblastoma. This article reviews the long history of RB1 gene research, characterization, and cloning, and also discusses recent advances in retinoblastoma genetics that have grown out of this foundational work.

Thermopneumatic suction integrated microfluidic blood analysis system.

Blood tests provide crucial diagnostic information regarding several diseases. A key factor that affects the precision and accuracy of blood tests is the interference of red blood cells; however, the conventional methods of blood separation are often complicated and time consuming. In this study, we devised a simple but high-efficiency blood separation system on a self-strained microfluidic device that separates 99.7 ± 0.3% of the plasma in only 6 min. Parameters, such as flow rate, design of the filter trench, and the relative positions of the filter trench and channel, were optimized through microscopic monitoring. Moreover, this air-difference-driven device uses a cost-effective and easy-to-use heater device that creates a low-pressure environment in the microchannel within minutes. With the aforementioned advantages, this blood separation device could be another platform choice for point-of-care testing.

Exfoliative cytology for diagnosing basal cell carcinoma and other skin cancers in adults.

Exfoliative cytology for diagnosing basal cell carcinoma and other skin cancers in adults.

Patch-based system for Classification of Breast Histology images using deep learning

In this work, we proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images. Presence of limited images necessitated extraction of patches and augmentation to boost the number of training samples. Thus patches of suitable sizes carrying crucial diagnostic information were extracted from the original images. The proposed classification system works in two different modes: one patch in one decision (OPOD) and all patches in one decision (APOD). The proposed PBC first predicts the class label of each patch by OPOD mode. If that class label is the same for all the extracted patches and that is the class label of that image, then the output is considered as correct classification. In another mode that is APOD, the class label of each extracted patch is extracted as done in OPOD and a majority voting scheme takes the final decision about class label of the image. We have used ICIAR 2018 breast histology image dataset for this work which comprises of 4 different classes namely normal, benign, in situ and invasive carcinoma. Experimental results show that our proposed OPOD mode achieved a patch-wise classification accuracy of 77.4% for 4 and 84.7% for 2 histopathological classes respectively on the test set obtained by splitting the training dataset. Also, our proposed APOD technique achieved image-wise classification accuracy of 90% for 4-class and 92.5% for 2-class classification respectively on the split test set. Further, we have achieved accuracy of 87% on the hidden test dataset of ICIAR-2018.

Doing their best: strategies used by South African clinicians in working with psychiatric inpatients across a language barrier

Background and objectivesSouth Africa has 11 official languages, but most psychiatrists can speak only English and Afrikaans and there are no formal interpreter posts in the mental healthcare system. As a result clinicians communicate with patients who have limited English language proficiency (LEP) without the use of interpreters. We present case material, constituting recordings of interactions between clinicians and LEP patients in a public psychiatric institution. The aim is to have a better understanding of how these clinical encounters operated and what communicative strategies clinicians used.DesignWe used the Roter interaction analysis system (RIAS) to evaluate clinicians’ conversational strategies and to analyze interactions between clinicians and patients.ResultsClinicians showed a high degree of tenacity in trying to engage patients in the clinical conversation, build rapport, and gather crucial diagnostic information. However, patients often responded briefly and monosyllabically, or kept quiet. In psychiatry where commonality of language cannot be assumed, it is not possible to determine the clinical significance of these responses.DiscussionClinicians went to great lengths to understand LEP patients. It is also clear that patients were often not optimally understood. Clinicians would try to gain valid information in a polite manner, but would abandon these attempts repeatedly as it became clear that proper communication was not possible.ConclusionsOur findings suggest that in the absence of interpreter services, the communication between clinicians and LEP patients is sparse and yields limited clinical information. The lack of proper language services stands in the way of optimal clinical care and requires urgent attention.

P31 Routine analysis of pleural aspirates for AFB in patients with pleural effusion of unknown cause is of limited use

The investigation of pleural effusion of unknown cause may include analysis of pleural aspirate for presence of acid-fast bacilli (AFB) by smear and culture. We reviewed data on all pleural...

(iii) The basic science of nuclear medicine

Abstract Functional imaging techniques in nuclear medicine provide crucial diagnostic information in a variety of clinical settings that are encountered regularly in orthopaedic practice. It is important that the orthopaedic surgeon is aware of the modalities on offer and appreciates the relative strengths and limitations of these techniques. This article will describe the physical principles underlying nuclear medicine imaging with single-photon and positron-emitting radiotracers. It will then go on to illustrate the clinical applications of these techniques as applied to pathological conditions of the bone, with particular emphasis on bone scintigraphy and hybrid imaging techniques, such as single-photon emission computed tomography/computed tomography (SPECT/CT) and positron emission tomography/computed tomography (PET/CT).