High Specificity Value Research Articles

Current role of musculoskeletal ultrasound in the detection of uratic deposits in asymptomatic hyperuricemia

The increasing prevalence of hyperuricemia, influenced by dietary changes, underscores the importance of population-based epidemiological research, particularly in regions lacking data. Beyond the well-documented complications of hyperuricemia on renal and articular fronts, elevated levels of serum uric acid are designated as a major risk factor for cardiovascular diseases, strokes, peripheral atheropathies, and renal failure. Long-standing asymptomatic hyperuricemia may pave the way for vascular and articular lesions. It is characterized by an elevation in plasma uric acid levels without clinical signs of gout, posing a therapeutic challenge due to the ambiguity surrounding the risk-benefit ratio of urate-lowering treatments (ULTs). While the necessity of treating asymptomatic hyperuricemia remains debated, the detection of urate deposits through imaging, particularly osteoarticular ultrasound, emerges as a key indicator. The ultrasound signs of uratic deposits exhibit high specificity and high positive predictive value, but a more limited sensitivity for early gout. These signs of uratic deposits primarily include the double contour sign, aggregates, and tophi. Various imaging modalities, including synovial fluid microscopy, computed tomography (CT), dual-energy computed tomography (DECT), and osteoarticular ultrasound, aid in identifying uric acid crystal deposits, with DECT emerging as an innovative method. Osteoarticular ultrasound provides a unique opportunity to better characterize individuals with hyperuricemia and gout, influencing the determination of therapeutic goals and clinical directions. Specific signs such as "double contour" and tophi, assessed by color Doppler, contribute to diagnostic precision. Osteoarticular ultrasound represents a potentially significant tool for the diagnosis, assessment, and monitoring of gout pathology, offering the possibility to decide on and evaluate treatment effectiveness. Despite its utility, the reproducibility of osteoarticular ultrasound requires thorough investigation. This tool proves crucial in the nuanced management of asymptomatic hyperuricemia, providing valuable insights into the patterns of urate crystal deposition and guiding therapeutic interventions.

A magnetic cross-linked alginate-biobased nanocomposite with anticancer and hyperthermia activities

A magnetic cross-linked alginate-biobased nanocomposite with anticancer and hyperthermia activities

Rapid point-of-care test for diagnosis of peritonitis in peritoneal dialysis patients.

Periplex® is a rapid point-of-care test based on the detection of interleukin-6 (IL-6) or matrix metalloproteinase-8 (MMP-8) to diagnose peritonitis in peritoneal dialysis (PD) patients. This single-centre study was conducted in Singapore General Hospital from 2019 to 2022. The study recruited PD patients suspected of having peritonitis. Periplex was performed at the presentation and recovery of peritonitis. Primary outcomes were sensitivity and specificity of Periplex at presentation. The positive and negative predictive values of tests were also performed. A total of 120 patients were included in the study. The mean age was 60.9 ± 14.9 years, 53% were male, 79% were Chinese and 47.5% had diabetes mellitus. Periplex was positive in all patients with peritonitis (n = 114); sensitivity of 100%; 95% confidence interval (CI): 100-100%. Periplex was falsely positive in three patients with non-infective eosinophilic peritonitis, resulting in a low specificity of 50%; 95% CI: 41.1-59.0%. Periplex had a positive predictive value of 97.4% and a negative predictive value of 100%. During recovery from peritonitis, Periplex had high specificity (93.6%) and negative predictive value (98.7%) to indicate the resolution of infection. MMP-8 was more sensitive than IL-6 in detecting peritonitis. Periplex was positive in all patients with peritonitis regardless of the types of PD solutions used. Periplex had high sensitivity, and positive and negative predictive values in the diagnosis of peritonitis can be considered as a screening tool for peritonitis. Given its high specificity and negative predictive value, it may also be used to document the resolution of peritonitis.

Comparative evaluation of lateral flow assays to diagnose chronic Trypanosoma cruzi infection in Bolivia.

Bolivia has the highest incidence of Chagas disease (CD) worldwide. Caused by the parasite Trypanasoma cruzi, CD is generally a chronic condition. Diagnosis is logistically and financially challenging, requiring at least two different laboratory-based serological tests. Many CD cases are missed; in Bolivia it is estimated just 6% of individuals chronically infected with T. cruzi get diagnosed. Achieving control on the way to elimination of CD requires a radical simplification of the current CD testing pathways, to overcome the barriers to accessing CD treatment. We aimed to generate unbiased performance data of lateral flow assays (LFAs) for T. cruzi infection in Bolivia, to evaluate their usefulness for improving T. cruzi diagnosis rates in a precise and efficient manner. This retrospective, laboratory-based, diagnostic evaluation study sought to estimate the sensitivity/specificity of 10 commercially available LFAs for T. cruzi, using the current CD diagnostic algorithm employed in Bolivia as the reference test method. All tests were blinded at the study site and performed by three operators. In total, 470 serum samples were tested, including 221 and 249 characterized as CD-positive/-negative, respectively. The LFAs were scored according to their relative importance using a decision-tree-based algorithm, with the mean decrease in Gini index as the scoring metric. The estimates of sensitivities ranged from 62.2-97.7% (95% confidence interval (CI) lower bound 55.0-94.7%); for specificities the range was 78.6-100% (95% CI lower bound 72.0-97.5%); 5/10 and 6/10 tests had sensitivity >90% and specificity >95%, respectively. Four LFAs showed high values of both sensitivity (93-95%) and specificity (97-99%). The agreement between 6 LFAs and the reference tests was almost perfect (Kappa 0.83-0.94). Most LFAs evaluated thus showed performances comparable with current laboratory-based diagnostic methods.

Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models

Colorectal cancer (CRC), also known as colorectal cancer, is a significant disease marked by high fatality rates, ranking as the third leading cause of global mortality. The main objective of this study was to assess the accuracy of predictive models in predicting both mortality events and the probability of disease recurrence. A retrospective analysis was conducted on a cohort of 284 individuals diagnosed with colorectal cancer between 2001 and 2017. Demographic and clinical data, including gender, disease stage, age at diagnosis, recurrence status, and treatment details, were meticulously recorded. We rigorously evaluated various predictive models, including Decision Trees, Random Forests, Random Survival Forests (RSF), Gradient Boosting, mboost, Deep Learning Neural Network (DLNN), and Cox regression. Performance metrics, such as sensitivity, positive predictive value (PPV), specificity, area under the receiver operating characteristic curve (ROC area), and overall accuracy, were calculated for each model to predict mortality and disease recurrence. The analysis was performed using R version 4.1.3 software and the Python programming language. For mortality prediction, the mboost model demonstrated the highest sensitivity at 96.9% (95% CI: 0.83-0.99) and an ROC area of 0.88. It also exhibited high specificity at 80% (95% CI: 0.59-0.93), a positive predictive value of 86.1% (95% CI: 0.70-0.95), and an overall accuracy of 89% (95% CI: 0.78-0.96). Random Forests showed perfect sensitivity of 100% (95% CI: 0.85-1) but had low specificity at 0% (95% CI: 0-0.52) and poor overall accuracy (50%). On the other hand, DLNN had the lowest performance metrics for mortality prediction, with a sensitivity of 24% (95% CI: 0.222-0.268), specificity of 75% (95% CI: 0.73-0.77), and a lower positive predictive value of 42% (95% CI: 0.38-0.45). The Gradient Boosting model showed the best performance in predicting recurrence, achieving perfect sensitivity of 100% (95% CI: 0.87-1) and high specificity at 92.9% (95% CI: 0.76-0.99). It also had a high positive predictive value of 93.3% (95% CI: 0.77-0.99). Gradient Boosting, with an ROC area of 96.4%, and mboost, with an ROC area of 75%, demonstrated remarkable performance. DLNN had the lowest performance metrics for recurrence prediction, with sensitivity at 1.75% (95% CI: 0.01-0.02), specificity at 98% (95% CI: 0.97-0.98), and a lower positive predictive value at 52.6% (95% CI: 0.39-0.65). In summary, the mboost model demonstrated outstanding performance in predicting mortality, achieving exceptional results across various evaluation metrics. Random Forests exhibited perfect sensitivity but showed poor specificity and overall accuracy. The DLNN model displayed the lowest performance metrics for mortality prediction. In terms of recurrence prediction, the Gradient Boosting model outperformed other models with perfect sensitivity, high specificity, and positive predictive value. The DLNN model had the lowest performance metrics for recurrence prediction. Overall, the results emphasize the effectiveness of the mboost and Gradient Boosting models in predicting mortality and recurrence in colorectal cancer patients.

Salivary Biomarkers for Oral Cancer Detection: An Exploratory Systematic Review.

Different efforts have been made to find better and less invasive methods for the diagnosis and prediction of oral cancer, such as the study of saliva as a source of biomarkers. The aim of this study was to perform a scoping review about salivary molecules that have been assessed as possible biomarkers for the diagnosis of oral squamous cell carcinoma (OSCC). A search was conducted using EBSCO, PubMed (MEDLINE), Scopus, and Web of Science. The research question was as follows: which molecules present in saliva have utility to be used as biomarkers for the early detection of oral cancer? Sixty-two studies were included. Over 100 molecules were assessed. Most of the markers were oriented towards the early diagnosis of OSCC and were classified based on their ability for detecting OSCC and oral potentially malignant disorders (OPMDs), OSCC outcome prediction, and the prediction of the malignant transformation of OPMDs. TNF-α, IL-1β, IL-6 IL-8, LDH, and MMP-9 were the most studied, with almost all studies reporting high sensitivity and specificity values. TNF-α, IL-1β, IL-6 IL-8, LDH, and MMP-9 are the most promising salivary biomarkers. However, more studies with larger cohorts are needed before translating the use of these biomarkers to clinical settings.

Digital Pathology in the Detection of Infectious Microorganisms: An Evaluation of Its Strengths and Weaknesses Across a Panel of Immunohistochemical and Histochemical Stains Routinely Used in Diagnostic Surgical Pathology.

The diagnosis of some infectious diseases requires their identification in tissue specimens. As institutions adopt digital pathology for primary diagnosis, the limits of microorganism detection from digital images must be delineated. To assess the reliability of microorganism detection from digitized images of histochemical and immunohistochemical stains commonly used in pathology. Original glass slides from 620 surgical pathology cases evaluated for the presence of infectious microorganisms were digitized. Immunohistochemical stains included those for herpes simplex virus (n = 100), cytomegalovirus (n = 100), Helicobacter pylori (n = 100), and spirochetes (n = 80). Histochemical stains included mucicarmine for Cryptococcus spp (n = 20), Grocott methenamine silver for fungi (n = 100), Giemsa for H pylori (n = 100), and Ziehl-Neelsen for acid-fast bacilli (n = 20). The original diagnosis based on the glass slides was regarded as the reference standard. Six pathologists reviewed the digital images. Digital review was generally associated with high (ie, ≥90%) specificity and positive predictive value owing to a low percentage of false-positive reads, whereas a high percentage of false negatives contributed to low sensitivity and negative predictive value for many stains. Fleiss κ showed substantial interobserver agreement in the interpretation of Grocott methenamine silver and immunostains for herpes simplex virus, H pylori, and cytomegalovirus; moderate agreement for spirochete, Ziehl-Neelsen, and mucicarmine; and poor agreement for Giemsa. Digital immunohistochemistry generally outperforms histochemical stains for microorganism detection. Digital interpretation of Ziehl-Neelsen and mucicarmine stains is associated with low scores for interrater reliability, accuracy, sensitivity, and negative predictive value such that it should not substitute for conventional review of glass slides.

Validation of the Italian Version of the Web Screening Questionnaire for Common Mental Disorders.

The ever-increasing spread of Internet-based systems for common mental disorders has generated the need for brief online screening methods. This study aims to test the psychometric properties of the Web Screening Questionnaire (WSQ) to examine its suitability for screening for common mental health problems among a community sample of Italian adults. A total of 1282 subjects (F = 819; mean age = 42.05) answered the WSQ. Its discriminant characteristics were examined with other validated selected scales for measuring mental health widely used in the Italian population using sensitivity, specificity, and area under the curve (AUC), as well as positive (PPV) and negative predictive values (NPV). Most of the WSQ subscales exhibited moderate to high specificity values. Specifically, the scales of 'agoraphobia' (0.947; 95%CI [0.934, 0.960]), 'anxiety' (0.959; 95%CI [0.946, 0.970]), and 'panic disorder' (0.973; 95%CI [0.964, 0.981]) showed the highest values whilst the 'obsessive-compulsive' dimension had the lowest value at 0.838, 95%CI [0.815, 0.861]. With exceptions observed for 'depression' (0.716; 95%CI [642, 798]) and 'alcohol abuse' (0.760; 95%CI [560, 920]), instead, the WSQ demonstrated critical sensitivity values (<0.6) in all dimensions. The WSQ was appropriate for discriminating between people with and without a psychiatric condition, as it helps to confirm the absence of disorders. However, further diagnostic procedures are required, in case of a positive WSQ screening result.

Graph Neural Network and BERT Model for Antimalarial Drug Predictions Using Plasmodium Potential Targets

Malaria continues to pose a significant global health burden despite concerted efforts to combat it. In 2020, nearly half of the world’s population faced the risk of malaria, underscoring the urgency of innovative strategies to tackle this pervasive threat. One of the major challenges lies in the emergence of the resistance of parasites to existing antimalarial drugs. This challenge necessitates the discovery of new, effective treatments capable of combating the Plasmodium parasite at various stages of its life cycle. Advanced computational approaches have been utilized to accelerate drug development, playing a crucial role in every stage of the drug discovery and development process. We have witnessed impressive and groundbreaking achievements, with GNNs applied to graph data and BERT from transformers across diverse NLP text analysis tasks. In this study, to facilitate a more efficient and effective approach, we proposed the integration of an NLP based model for SMILES (i.e., BERT) and a GNN model (i.e., RGCN) to predict the effect of antimalarial drugs against Plasmodium. The GNN model was trained using designed antimalarial drug and potential target (i.e., PfAcAS, F/GGPPS, and PfMAGL) graph-structured data with nodes representing antimalarial drugs and potential targets, and edges representing relationships between them. The performance of BERT-RGCN was further compared with that of Mordred-RGCN to evaluate its effectiveness. The BERT-RGCN and Mordred-RGCN models performed consistently well across different feature combinations, showcasing high accuracy, sensitivity, specificity, MCC, AUROC, and AUPRC values. These results suggest the effectiveness of the models in predicting antimalarial drugs against Plasmodium falciparum in various scenarios based on different sets of features of drugs and potential antimalarial targets.

Morphometric analysis for determination of larval instars in Dermestes frischii Kugelann and Dermestes undulatus Brahm (Coleoptera: Dermestidae).

Dermestes frischii Kugelann, 1792 and Dermestes undulatus Brahm, 1790 are the most abundant species worldwide at outdoor or indoor crime scenes during the dry and skeletal stages of decomposition. The attribution of larval age in these beetles is problematic due to the variable number of instars, which is influenced by environmental factors. In this study, a morphometric approach was used to look for potential morphological features as evidence of larval stages. Breeding and monitoring were performed for both species in an incubator with a preset temperature of 28°C ± 0.5 without a photoperiod. Morphometric measurements were made on 10 larvae per instar for each species using length, width, and thickness parameters. Linear discriminant analysis was then used to generate decision boundaries that clearly separated larval stages. The cross-validation procedure demonstrated that the morphometric approach successfully discriminated adjacent larval stages in both species with high values of sensitivity and specificity. This less-invasive approach could improve the ability to estimate minPMI in forensic studies of Dermestidae beetles. Future studies may extend this approach to other species and establish good practices for collecting and storing specimens for morphometric analysis.

Melanoma Skin Cancer Identification with Explainability Utilizing Mask Guided Technique

Melanoma is a highly prevalent and lethal form of skin cancer, which has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the identification of diseases using medical imaging. The paper introduces a computational model for classifying melanoma skin cancer images using convolutional neural networks (CNNs) and vision transformers (ViT) with the HAM10000 dataset. Both approaches utilize mask-guided techniques, employing a specialized U2-Net segmentation module to generate masks. The CNN-based approach utilizes ResNet50, VGG16, and Xception with transfer learning. The training process is enhanced using a Bayesian hyperparameter tuner. Moreover, this study applies gradient-weighted class activation mapping (Grad-CAM) and Grad-CAM++ to generate heatmaps to explain the classification models. These visual heatmaps elucidate the contribution of each input region to the classification outcome. The CNN-based model approach achieved the highest accuracy at 98.37% in the Xception model with a sensitivity and specificity of 95.92% and 99.01%, respectively. The ViT-based model approach achieved high values for accuracy, sensitivity, and specificity, such as 92.79%, 91.09%, and 93.54%, respectively. Furthermore, the performance of the model was assessed through intersection over union (IOU) and other qualitative evaluations. Finally, we developed the proposed model as a web application that can be used as a support tool for medical practitioners in real-time. The system usability study score of 86.87% is reported, which shows the usefulness of the proposed solution.

Influence of a protected riparian corridor on the benthic aquatic macroinvertebrate assemblages of a small northern Michigan (USA) agricultural stream

The biotic integrity of streams is frequently compromised by anthropogenic disturbance; thus, it is important to study the potential mitigating effects of undisturbed riparian zones on streams within disturbed watersheds. In this study, two first-order northern Michigan streams located 1 km apart in adjacent agricultural watersheds, but with differing riparian land use, were compared to observe the effects of intact forest within the immediate 100 m stream corridor. Both streams had high specific conductance values associated with agricultural streams. The stream with a primarily forested riparian corridor, however, had greater canopy cover, faster stream velocity, greater coarse substrate, lower percentage of organic sediment, lower water temperatures, higher dissolved oxygen, higher taxonomic richness, higher relative biomass of shredders, lower relative biomass of gathering collectors, and more pollution intolerant taxa than the stream with a primarily agricultural corridor. Moreover, the benthic macroinvertebrate assemblages were highly distinct from each other, with the riparian forested stream dominated by aquatic insects like mayflies and caddisflies, and the agricultural stream by non-emergent isopods and amphipods. These results demonstrate the effects of a buffer zone on stream substrate composition, water physicochemistry, and benthic assemblages, and indicate the importance of preserving riparian zones within agricultural watersheds.

The MOG antibody non-P42 epitope is predictive of a relapsing course in MOG antibody-associated disease

BackgroundMyelin oligodendrocyte glycoprotein (MOG) IgG seropositivity is a prerequisite for MOG antibody-associated disease (MOGAD) diagnosis. While a significant proportion of patients experience a relapsing disease, there is currently no biomarker...

Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery

Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery is critical for surgical planning and patient counseling. Due to the relatively small number of cases of ASD surgery, machine learning applications have been limited to traditional models (e.g., logistic regression or standard neural networks) and coarse clinical variables. We present the novel application of advanced models (CNN, LLM, GWAS) using complex data types (radiographs, clinical notes, genomics) for ASD outcome prediction. Methods: We developed a CNN trained on 209 ASD patients (1549 radiographs) from the Stanford Research Repository, a CNN pre-trained on VinDr-SpineXR (10,468 spine radiographs), and an LLM using free-text clinical notes from the same 209 patients, trained via Gatortron. Additionally, we conducted a GWAS using the UK Biobank, contrasting 540 surgical ASD patients with 7355 non-surgical ASD patients. Results: The LLM notably outperformed the CNN in predicting pulmonary complications (F1: 0.545 vs. 0.2881), neurological complications (F1: 0.250 vs. 0.224), and sepsis (F1: 0.382 vs. 0.132). The pre-trained CNN showed improved sepsis prediction (AUC: 0.638 vs. 0.534) but reduced performance for neurological complication prediction (AUC: 0.545 vs. 0.619). The LLM demonstrated high specificity (0.946) and positive predictive value (0.467) for neurological complications. The GWAS identified 21 significant (p &lt; 10−5) SNPs associated with ASD surgery risk (OR: mean: 3.17, SD: 1.92, median: 2.78), with the highest odds ratio (8.06) for the LDB2 gene, which is implicated in ectoderm differentiation. Conclusions: This study exemplifies the innovative application of cutting-edge models to forecast outcomes in ASD, underscoring the utility of complex data in outcome prediction for neurosurgical conditions. It demonstrates the promise of genetic models when identifying surgical risks and supports the integration of complex machine learning tools for informed surgical decision-making in ASD.

A diagnostic framework to identify vestibular involvement in multi-sensory neurological disease.

Identifying vestibular causes of dizziness and unsteadiness in multi-sensory neurological disease can be challenging, with problems typically attributed to central or peripheral nerve involvement. Acknowledging vestibular dysfunction as part of the presentation provides an opportunity to access targeted vestibular rehabilitation, for which extensive evidence exists. A diagnostic framework was developed and validated to detect vestibular dysfunction, benign paroxysmal positional vertigo or vestibular migraine. The specificity and sensitivity of the diagnostic framework was tested in patients with primary mitochondrial disease. Adults with a confirmed diagnosis of primary mitochondrial disease were consented, between September 2020 and February 2022. Participants with and without dizziness or unsteadiness underwent remote physiotherapy assessment and had in-person detailed neuro-otological assessment. The six framework question responses were compared against objective neuro-otological assessment or medical notes. The output was binary, with sensitivity and specificity calculated. Seventy-four adults completed the study: age range 20-81 years (mean 48 years, ±SD 15.05 years); ratio 2:1 female to male. The framework identified a vestibular diagnosis in 35 participants, with seven having two diagnoses. The framework was able to identify vestibular diagnoses in adults with primary mitochondrial disease, with a moderate (40-59) to very high (90-100) sensitivity and positive predictive value, and moderate to high (60-74) to very high (90-100) specificity and negative predictive value. Overall, the clinical framework identified common vestibular diagnoses with a moderate to very high specificity and sensitivity. This presents an opportunity for patients to access effective treatment in a timely manner, to reduce falls and improve quality of life.

A phase II trial of TAS-102 in patients with colorectal cancer with ctDNA-defined minimal residual disease post-adjuvant therapy: Results from the MD Anderson INTERCEPT Program.

136 Background: Circulating tumor DNA (ctDNA) has emerged as a novel marker with high specificity and positive predictive value for identifying radiographically occult microscopic or minimal residual disease (MRD) in colorectal cancer (CRC) patients (pts). An additional widely acknowledged unmet need is the development of therapeutic strategies to eradicate MRD and increase the cure rate of the disease. Ongoing trials are evaluating MRD as a biomarker for determining the intensity of post-operative adjuvant therapy. However, the feasibility and efficacy of therapy in pts who are MRD+ during surveillance (after all curative intent treatments including adjuvant chemotherapy) is unknown. Methods: In this proof-of-concept phase II study, pts with stages II-IV CRC noted to be MRD+ during surveillance (ctDNA+ but no radiographic evidence of disease on imaging in last 30 days) received 6 months (m) of TAS-102. Pts were identified through the MD Anderson INTERCEPT program that integrates MRD testing with a tissue informed assay (Signatera) into standard of care for CRC pts. The primary endpoint was 6-m ctDNA clearance and secondary objectives included 3-m ctDNA clearance, and disease-free survival (DFS). Assuming H0 = 5% spontaneous, HA = 30% ctDNA clearance with TAS 102 &amp; 1-sided α = 0.05, 15 patients are required for β = 85%. Results: Between 7/22/22 and 9/7/23,15 pts with MRD were enrolled. Baseline characteristics included a median age of 62 years (range: 37-73), Caucasian (77%), male (77%), resected Stage IV (92%) and median ctDNA level of 1.55 MTM/ml (range 0.04-28.72). At data cutoff, 11 pts had 3-m ctDNA levels and 10 had a decrease in their ctDNA level (91%) including 5 with undetectable levels (45%). At 6-m, 60% of patients had ctDNA levels at or below prior levels; three maintained undetectable ctDNA at 6-m: of those, two pts had subsequent radiographic progression following therapy completion. Median DFS was 9.4-m in the full cohort. Conclusions: We demonstrate the feasibility of identifying and enrolling MRD+ pts through screening during surveillance onto therapeutic trials and show the ability of an experimental therapy to convert patients to MRD negative status, a necessary but not sufficient step for disease eradication. Preliminary results suggest that treatment with TAS-102 is effective in inducing ctDNA clearance in the short term in most pts, but with no evidence of persistent clearance. Updated results including 6-m ctDNA levels and survival data will be presented. Clinical trial information: NCT05343013 .

68Ga]Ga-FAPI-46 PET/CT for locoregional lymph node staging in urothelial carcinoma of the bladder prior to cystectomy: initial experiences from a pilot analysis.

[68Ga]Ga-FAPI-46 PET/CT is a novel hybrid imaging method that previously showed additional diagnostic value in the assessment of distant urothelial carcinoma lesions. We hypothesized that patients with bladder cancer benefit from [68Ga]Ga-FAPI-46 PET/CT prior to radical cystectomy for locoregional lymph node staging. Eighteen patients underwent [68Ga]Ga-FAPI-46 PET/CT for evaluation of lymph node (LN) status in predefined LN regions. Two hundred twenty-nine intraoperatively removed LN served as histopathological reference standard. Urothelial carcinoma (UC) spread was found in ten LN in seven different regions (14.3%). Hereby, [68Ga]Ga-FAPI-46 PET/CT was positive in four out of seven regions (57.1%) and showed significantly increased FAPI uptake compared to non-pathological regions. In the remaining three out of seven (42.9%) regions, [68Ga]Ga-FAPI-46 PET/CT was rated negative since no pathological increased FAPI uptake was detected or the proximity of the urinary tract prevented a differentiation from physiological uptake. CT was inconspicuous in these three regions. In total, two FAP-positive LN regions were found without histopathological counterpart. Overall, sensitivity, specificity, positive predictive value, and negative predictive value were 57.1%, 95.2%, 66.7%, and 93.0% for PET imaging. In summary, this innovative [68Ga]Ga-FAPI-46 PET/CT method showed high specificity and negative predictive value in patients with bladder UC with a future potential to optimize therapy planning.

Comparison of Different Diagnostic Methods of Helicobacter Pylori Infection in Children

Background: Helicobacter Pylori Infection (HPI) is usually acquired during childhood, without specific treatment this infection rarely resolves spontaneously. Therefore, correct diagnosis is necessary for the effective eradication and there is no single test can be regarded as the gold standard for the identification of H. pylori.Objective: To compare the sensitivity, specificity, and accuracy of the stool antigen test, serologic test, rapid urease test, and histology for the diagnosis of H. pylori infection.Materials and Methods: This cross-sectional descriptive study was conducted at the Department of Pediatric Gastroenterology &amp; Nutrition, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka from April 2019 to October 2021 on 54 children aged less than 18 years suspected to be H. pylori infection, who attended the outpatient as well as admitted. In a typical data sheet, a complete clinical history, exam results, and investigation reports were documented. Results were expressed as sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.Results: The gastro-duodenal biopsy and Rapid Urease Test (RUT) (100%) have the highest sensitivity, followed by serology (95%), and Saturation (SAT) (70%). Highest specificity was found in gastro-duodenal biopsy 88.2% followed by RUT 67.6% &amp; SAT 41.2% and only 32.4% by serology. The gastro-duodenal biopsy had the highest diagnosis accuracy (92.6%), followed by RUT (79.6%), serology (55.6%) and SAT (51.9%).Conclusion: In the study, the gastro-duodenal biopsy and rapid urease test both showed high sensitivity, specificity, and negative predictive value; as a result, the combination of these tests constituted the best alternative for making the diagnosis. Because of their high sensitivity, serological tests may be useful non-invasive screening methods. KYAMC Journal Volume: 14, No: 03, October 2023: 130-137.

Comparison of screening methods for computer adaptive tests to predict reading and math performance

AbstractThe present study compared the diagnostic accuracy of a single computer adaptive test (CAT), Star Reading or Star Math, and a combination of the two in a gated screening framework to predict end‐of‐year proficiency in reading and math. Participants included 13,009 students in Grades 3–8 who had at least one fall screening score and end‐of‐year state test score in reading and math. First, diagnostic accuracy statistics were evaluated for a single screening measure to predict proficiency on end‐of‐year tests. Second, a gated screening framework was simulated to examine the diagnostic accuracy of a combination of screening measures (i.e., scores from the CATs and the end‐of‐year test). The diagnostic accuracy of each screening method was compared. Results suggest that diagnostic accuracy did not improve for the gated screening method when compared to the single screening method. The gated screening method tended to yield low sensitivity values (M = 0.42, range = 0.35–0.48) and high specificity values (M = 0.97, range = 0.95–0.99). The only condition to reach acceptable sensitivity and specificity (&gt;0.70) was a single reading screener predicting reading outcomes. Sample specific cut‐scores from receiver operating curve (ROC) analyses led to improved diagnostic accuracy outcomes relative to all other methods.

Diagnostic value of non-invasive indicators of myocardial function in patients after myocardial infarction

Aim. To study the diagnostic value of myocardial function parameters to assess their predictive ability in relation to left ventricular (LV) global contractility in patients after myocardial infarction (MI).Material and methods. The final analysis included 97 patients with MI aged 55,9±8,6 years. We performed standard two-dimensional echocardiography and speckle tracking echocardiography with analysis of following myocardial function parameters: global work index (GlobalWI, mm Hg %); global constructive work (GlobalCW, mm Hg %); global wasted work (GlobalWW, mm Hg %); global work efficiency (GlobalWE). The probability of ejection fraction (EF) reduction was analyzed using ROC curves using three criteria. The function parameters with the maximum sum of sensitivity and specificity were selected as the cut-off point.Results. On days 7-9 after MI, depending on the EF, patients were divided into 3 groups: "REF" — patients with EF &lt;40%); "MREF" — with EF from 40 to 49%, and "PEF" — patients with EF ³50%. By the 24th week, GlobalWI increased by 23% in the PEF group and by 33% in the MREF group. In the REF group, there was a gradual decrease in GlobalWI until the end of the study. In the PEF group, GlobalCW increased by 21% by week 24. By the end of follow-up, an increase in GlobalWW of 15% was observed in the REF group. GlobalWI, GlobalCW and GlobalWW were found to have the highest sensitivity values (60%) for reduced EF, ranging from 4049% at 24 weeks from the index event. GlobalWW had the highest specificity value (80%). With regard to a decrease in EF &lt;40% by week 24, the GlobalWW had the highest sensitivity (50%) and specificity (70%).Conclusion. Parameters of myocardial function have diagnostic and prognostic value for assessing LV systolic function after MI. Already in the acute period, myocardial function parameters can be used to determine the risk of decreased LV global contractility.