False Negatives Research Articles

Reliable molecular detection of small hive beetles

Invasive species require adequate reliable detection methods to mitigate their further spread and impact. However, the reliability of molecular detection methods is often hampered by both false positives (Error type I) and false negatives (Error type II). At present, the reliability of the four published molecular detection methods for small hive beetles (SHB), Aethina tumida, has not been rigorously evaluated considering their extensive genetic diversity. Here, we performed intra- and interlaboratory comparisons of the four available methods using SHB samples representing 78 regions from 27 countries on five continents, beetles from the same genus (Aethina concolor, A. inconspicua, A. flavicollis and A. major), as well as western honey bees, Apis mellifera, and ectoparasitic mites Varroa destructor. The data show that the Idrissou et al. (2018) and Li et al. (2018) methods avoid both false positives and false negatives probably due to lower sensitivity to nucleotide mismatches on the primer and probe’s target sequences. Further, the Li et al. (2018) method can be considered more sensitive because the fluorescent amplification curve crosses the threshold at lower Cq values compared to the Idrissou et al. (2018) one. In light of our data, the Li et al. (2018) method is the most reliable molecular diagnostic tool for SHB. We therefore recommend using this method as it will contribute to management efforts of this invasive species.

Don’t omit a urine culture when a urinalysis is suspicious for asymptomatic bacteriuria during pregnancy

Abstract Introduction/Objective Investigation of a clinical problem using steps of evidence-based practice (EBP) led to the development of a recommendation submitted for Choosing Wisely consideration. The prevalence of asymptomatic bacteriuria (ASB) in pregnancy is between 2-15%. ASB may be a concern in pregnancy because of its capacity for association with adverse outcomes, namely preterm delivery, low birthweight, and possible progression to pyelonephritis. Screening and treatment may reduce the risks of adverse outcomes, but data is not current. A point-of-care urine dipstick is low cost and commonly used at routine obstetrician clinic visits to screen for ASB. The urinalysis dipstick is associated with high false-positive and false-negative rates as compared to the gold standard urine culture. Some providers make the decision to treat ASB given a positive urinalysis screen without ordering a follow-up culture. Such practice could lead to overtreatment of the patient and poor antibiotic stewardship. Research question Is a urinalysis without culture sufficient for diagnosis and treatment of ASB during pregnancy? Methods/Case Report Laboratory Medicine Best Practices A-6 steps of Ask, Acquire, Appraise, Apply, Analyze, and Assess were utilized to develop an appropriate and relevant clinical question, collect literature, determine evidence value, assign ratings, implement into practice, and evaluate outcomes. During appraisal, six selected articles were graded according to study rating, practice, measures, and results, further including the final rating and effect size rating. Consistency and overall strength of the evidence were analyzed. Results (if a Case Study enter NA) Each evidence study demonstrated final ratings between 8 and 10, a rating of “Good,” and an effect size rating of “Substantial.” Consistency leads to a “High” overall strength among the body of evidence. Conclusion Obtain a urine culture if routine prenatal point-of-care dipstick urinalyses have findings consistent with asymptomatic bacteriuria. Urinalyses are limited as screening due to low sensitivity and specificity for bacteriuria. Positive urinalysis dipstick results should be neither the sole basis for diagnosis nor decision to treat.

Integrating Whole Slide Imaging and Artificial Intelligence Provides Higher Accuracy in Thyroid Cytology

Abstract Introduction/Objective The aim of the study is to evaluate the potential advantages of integrating Whole Slide Imaging (WSI) and Artificial Intelligence (AI) to increase efficiency and diagnostic accuracy in thyroid cytology, especially in indeterminate lesions. Methods/Case Report A retrospective database search from 2000-2023 was performed, and the concordance with the pathologists was evaluated based on the manual or AI-based Region of Interest (ROI) identification, the best- suited AI, algorithm design, color enhancement, and combination of AI and Electronic Medical Records (EMR). Results (if a Case Study enter NA) The cohort included 3285 WSI and 121,384 ROI. In 109 WSI with manually diagnosed ROI, Deep learning successfully predicted benign and malignant cases, including 11 indeterminate cases, with zero false positives or negatives for 35% of cases. In the second study with 908 WSI, 4494 ROI were identified with Convolutional Neural Network (CNN). A higher area under the curve (AUC) resulted when EMR and Machine Learning algorithm (MLA) were integrated: EMR: 0.931, MLA: 0932, EMR+ MLA; 0.962, Sensitivity: 92.0%, Specificity 90.5%. In the next study with 145 WSI, the CNN-identified ROI was presented to the same pathologist after 117 days. Perfect concordance was seen between the two methods (K score 0.924, time to diagnosis;81.59 Second). In 360 WSI, YOLOV4 algorithm was trained to detect the ROI, and detection and classification rates were used to evaluate the accuracy. It had a precision of 81.84% (3.16% more than the single detection networks of 78.68%). In the last study with 964 WSI, color augmentation improved the AUC for indeterminate cases from 88.3% to 96.2%. Conclusion CNN is a better-suited AI for whole slide imaging due to its compatibility with image data and ability to automatically detect important features without human supervision. It also removes the component of subjective bias in interpretation. Color augmentation and integration of EMR can overcome the unique challenges in thyroid indeterminate cytology.

Modular Point-of-Need Tropane Alkaloid Detection at Regulatory Levels: Combining Solid-Liquid Extraction from Buckwheat with a Paper-Immobilized Liquid-Phase Microextraction and Immuno-Detection in Interconnectable 3D-Printed Devices.

Contamination with tropane alkaloids in cereals is expected to increase globally. However, current identification tools (e.g., liquid chromatography-mass spectrometry) for tropane alkaloids are time-consuming and expensive. Furthermore, their miniaturized alternatives lack sensitivity and robustness. Therefore, there is a pressing need for inexpensive and effective screening methods. Here, an on-site applicable modular workflow for tropane alkaloid detection in buckwheat is presented. The modular workflow combines paper microfluidics and interconnectable 3D-printed sample preparation tools and was evaluated for different tropane alkaloids, including atropine and scopolamine. Furthermore, integration with an indirect competitive lateral flow immunoassay (icLFIA) for atropine detection at relevant levels was demonstrated. In the modular workflow, to minimize matrix coextraction, tropane alkaloids were extracted from the milled buckwheat cereals by a mixture of alkaline aqueous and immiscible organic solvents (extraction recoveries: 66-79%). The tropane alkaloids were subsequently concentrated with a newly developed paper-immobilized liquid-phase microextraction (PI-LPME, extraction recoveries: 34-60%, concentration factor to immobilized solution in paper: 60-108×). After the PI-LPME, with an integrated 3D-printed setup, the tropane alkaloids were directly eluted (elution recoveries: 83-93%) and detected with the icLFIA. Digital read-out of the icLFIA, by employing a hand-held reader, enabled semiquantification of atropine (IC50 = 0.56 ng mL-1 in standard solutions). The modular workflow was validated by analyzing 24 blank and spiked buckwheat cereal samples with 5 and 10 μg kg-1 atropine. A cutoff value was established with an estimated false negative rate of 1% and estimated false positive rate of 0.68%. Therefore, the modular workflow can aid in fast, inexpensive, and on-site atropine detection by nonexperts, and when integrated with a scopolamine-specific icLFIA expanded toward scopolamine detection. Moreover, the developed sample extraction and concentration method (PI-LPME) is suitable for the analysis of many other compounds with pH-dependent polarity.

Next-generation technology in epilepsy diagnostic: First Moroccan pediatric case series and literature review.

Background: Pediatric epilepsy is the most prevalent neurological disorder among children, characterized by significant heterogeneity in terms of etiology, clinical presentation, and prognosis. In developed countries, genetic testing, particularly next-generation sequencing (NGS), has become standard practice for diagnosis. Methods/Observation: This paper presents the first pediatric Moroccan case series with epilepsy. The diagnosis was established using whole-exome sequencing which identified five variants. Moreover, we evaluated the effectiveness of different NGS technologies in epilepsy diagnosis by conducting a PubMed search with targeted keywords. Results: Whole-exome sequencing and whole-genome sequencing are more effective for epilepsy diagnosis than multi-gene panels. However, they also present significant challenges including false negatives and variants of unknown significance which complicate genetic interpretation and diagnostic process. Conclusion: Despite these limitations, the rapid accumulation of genetic data and advancements in bioinformatic tools are expected to address these issues, improving diagnostic accuracy. Keywords: Epilepsy, Next-generation sequencing, Neuropediatry, Genetics, Morocco.

A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults

Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

A New Epileptic Seizure Prediction Framework Based on Electroencephalography Signals

This research seeks to evaluate how effectively seizures can be predicted and managed in epilepsy using a specialized deep learning model based on Long Short-Term Memory (LSTM) neural networks. The model leverages non-invasive scalp electroencephalography (EEG) recordings for predicting seizures. To develop and assess the proposed LSTM neural network model, a comprehensive dataset was gathered. The model emphasizes achieving high sensitivity and reducing false alarms to improve its real-time applicability. The evaluation involved various metrics to measure accuracy, sensitivity, and rates of false positives and false negatives. The effectiveness of the proposed LSTM neural network model was outstanding, with accuracy rates ranging from 99.07% to 99.95%. Notably, the sensitivity score of 1 confirmed precise prediction for all seizure cases. The model demonstrated minimal false positive and false negative rates, highlighting its reliability in predicting seizures. This study emphasizes the promising potential of the proposed LSTM neural network model in providing advanced warning for seizures. The high accuracy and sensitivity rates suggest its usefulness in enabling timely preventive measures for patients, ultimately reducing the occurrence of seizures. This innovative approach holds significance in enhancing the overall management and quality of life for individuals dealing with epilepsy.

Diagnostic Accuracy of Beta-2 Transferrin Gel Electrophoresis for Detecting Cerebrospinal Fluid Rhinorrhea.

Unilateral thin clear rhinorrhea (UTCR) may represent a variety of pathologies including cerebrospinal fluid (CSF) rhinorrhea. Beta-2 transferrin (B2Tf) gel electrophoresis (GE) has become the preferred testing modality due to reportedly high sensitivity (87%-100%) and specificity (71%-100%). However, there have been relatively few studies assessing its diagnostic accuracy. The purpose of this single-institution study was to determine the accuracy of B2Tf GE in detecting CSF rhinorrhea. A single-center retrospective review was conducted from 2016 and 2024 for all patients who presented with UTCR and underwent B2Tf GE. Institutional review board approval was obtained. The gold standard for diagnostic confirmation of true and false positives (TP, FP) as well as false negatives (FN) was endoscopic exploration. The gold standard for true negative (TN) was response to medical therapy. A total of 105 patients underwent 149 B2Tf GE tests. 40 (38.1%) patients were diagnosed with CSF rhinorrhea. Of the 149 B2-Tf GE tests, there were 51 TPs, 72 TNs, 20 FPs, and 6 FNs yielding 89.5% sensitivity, 78.3% specificity, 71.8% positive predictive value, and 92.3% negative predictive value, respectively. Of the false results the most common causes for error were purulent sinusitis (n = 6, 23.1%), possible mucous contamination from nose-blowing during collection (n = 3, 11.5%), patient collection error (n = 3, 11.5%), and blood contamination (n = 1, 3.8%). Although these single-institutional data demonstrate test accuracy within ranges previously reported in the literature, they also demonstrate diagnostic limitations. Future studies should explore reasons for erroneous B2Tf GE results and how these may change clinical decision-making. IV Laryngoscope, 2024.

Anomaly detection and confidence interval‐based replacement in decay state coefficient of ship power system

AbstractThe anomaly detection and predictive replacement of the degradation decay state coefficient (Desc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first‐order, and second‐order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z‐score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.

Assessing the Factors Leading to Missed Breast Cancer Diagnoses in Mammography Among Pakistani Women: A Prospective Cross-Sectional Study.

Objective To determine the frequency of false-negative mammograms, and identify the factors contributing to missed breast cancer diagnoses in Pakistani women. Materials and methods This descriptive, prospective cross-sectional study was conducted at a tertiary care hospital from December 15, 2020, to December 10, 2023, including 150 women aged 30 to 60 who underwent bilateral mammography and concurrent breast ultrasound. The study analyzed the frequency and causes of false negatives, categorizing them into patient-related, tumor-related, technical-related, and provider-related factors. Stratification was performed based on age groups and Breast Imaging Reporting and Data System (BI-RADS) scores, and statistical significance was assessed using Chi-square tests. Results The study found a 5.1% frequency of false-negative mammograms. Lesion-related factors were seen in 59 (39.7%) patients; patient-related factors were seen in 40 (26.7%) patients; provider-related factors were seen in 29 (19.3%) patients; and technical-related factors were seen in 22 (26.7%) patients. Conclusion Dense breast tissue significantly contributes to missed breast cancer diagnoses in Pakistani women. While lesion-related, provider-related, and technical-related factors uniformly affect mammography outcomes, addressing patient-specific challenges - particularly in younger women with dense breasts - is crucial. The study suggests incorporating supplementary imaging modalities, like ultrasound, in routine screening for better detection, potentially informing national breast cancer screening guidelines in Pakistan.

Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies.

Feline chronic enteropathy is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histological evaluation of lymphocyte numbers and distribution in small intestinal biopsies is crucial for classification and grading. However, conventional histological methods for lymphocyte quantification have low interobserver agreement, resulting in low diagnostic reliability. This study aimed to develop and validate an artificial intelligence (AI) model to detect intraepithelial and lamina propria lymphocytes in hematoxylin and eosin-stained small intestinal biopsies from cats. The median sensitivity, positive predictive value, and F1 score of the AI model compared with the majority opinion of 11 veterinary anatomic pathologists, were 100% (interquartile range [IQR] 67%-100%), 57% (IQR 38%-83%), and 67% (IQR 43%-80%) for intraepithelial lymphocytes, and 89% (IQR 71%-100%), 67% (IQR 50%-82%), and 70% (IQR 43%-80%) for lamina propria lymphocytes, respectively. Errors included false negatives in whole-slide images with faded stain and false positives in misidentifying enterocyte nuclei. Semiquantitative grading at the whole-slide level showed low interobserver agreement among pathologists, underscoring the need for a reproducible quantitative approach. While semiquantitative grade and AI-derived lymphocyte counts correlated positively, the AI-derived lymphocyte counts overlapped between different grades. Our AI model, when supervised by a pathologist, offers a reproducible, objective, and quantitative assessment of feline intestinal lymphocytes at the whole-slide level, and has the potential to enhance diagnostic accuracy and consistency for feline chronic enteropathy.

Evaluation of the Clinical Impact of a Smartphone Application for Cataract Detection.

Background Approximately 10 million people in India suffer from bilateral blindness, with cataracts accounting for roughly 70% of these cases. However, there is a severe scarcity of ophthalmologists in India (12,000 across the country), which makes routine cataract screening very difficult, particularly in rural areas. To tackle this problem, we investigated the use of an artificial intelligence (AI)-based application for cataract screening at All India Institute of Medical Sciences (AIIMS), Bibinagar, that can be used by nursing officers and other healthcare professionals as a primary screening tool. Ophthalmologists from AIIMS Bibinagar additionally validate the results of this application. Purpose The aim of this study was to assess the clinical performance of a smartphone-based cataract screening application that uses an AI module to identify cataracts in photos taken with the device's camera. The study compared the application's results with diagnoses made by ophthalmologists using a slit lamp. Methods At AIIMS Bibinagar, 495 patients participated in a prospective clinical trial. The AI-based screening solution examined smartphone images that were taken in accordance with a set protocol to identify whether cataracts were present. The results of the application were then compared with the diagnoses made by ophthalmologists based on slit-lamp tests. Results The study included 990 eye images. The AI screening application demonstrated an overall accuracy of 90.01% for cataract detection. Specific metrics include a sensitivity of 89.50%, specificity of 89.73%, precision of 91.43%, and an F1 score of 90.36%. The positive predictive value (PPV) was approximately 91.3%, based on 485 true positives and 46 false positives. The negative predictive value (NPV) was approximately 87.6%, based on 402 true negatives and 57 false negatives. Conclusions The smartphone-based cataract screening application proves to be an effective tool for community-level cataract screening in remote areas where access to expensive equipment and specialized ophthalmic care is limited. Its high accuracy and efficiency make it a valuable option for low-resource settings and suitable for home screening, particularly in the post-COVID era.

Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba Do Sul Middle Valley, Southeast Brazil

Soil erosion is a global issue—with gully erosion recognized as one of the most important forms of land degradation. The purpose of this study is to compare and contrast the outcomes of four machine learning models, Classification and Regression (CART), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), used for mapping susceptibility to soil gully erosion. The controlling factors of gully erosion in the Piraí Drainage Basin, Paraíba do Sul Middle Valley were analysed by image interpretation in Google Earth and gully erosion samples (n = 159) were used for modelling and spatial prediction. The XGBoost and RF models achieved identical results for the area under the receiver operating characteristic curve (AUROC = 88.50%), followed by the SVM and CART models, respectively (AUROC = 86.17%; AUROC = 85.11%). In all models analysed, the importance of the main controlling factors predominated among Lineaments, Land Use and Cover, Slope, Elevation and Rainfall, highlighting the need to understand the landscape. The XGBoost model, considering a smaller number of false negatives in spatial prediction, was considered the most appropriate, compared to the Random Forest model. It is noteworthy that the XGBoost model made it possible to validate the hypothesis of the study area, for susceptibility to gully erosion and identifying that 9.47% of the Piraí Drainage Basin is susceptible to gully erosion. Furthermore, replicable methodologies are evidenced by their rapid applicability at different scales.

Unveiling a Surgical Revolution: The Use of Conventional Histology versus Ex Vivo Fusion Confocal Microscopy in Breast Cancer Surgery.

Ex vivo fusion confocal microscopy (EVFCM) enables the rapid examination of breast tissue and has the potential to reduce the surgical margins and the necessity for further surgeries. Traditional methods, such as frozen section analysis, are limited by the distortion of tissue and artefacts, leading to false negatives and the need for additional surgeries. This study on observational diagnostic accuracy evaluated the ability of EVFCM to detect breast cancer. A total of 36 breast tissue samples, comprising 20 non-neoplastic and 16 neoplastic cases, were analysed using EVFCM and compared to the results obtained from routine histopathology. A Mohs surgeon experienced in EVFCM (evaluator A) and two breast pathologists unfamiliar with EVFCM (evaluators B and C) performed blinded analyses. EVFCM showed high concordance with the histopathology and the detection of neoplasia, with significant kappa values (p < 0.001). Evaluator A achieved 100% sensitivity and specificity. Evaluators B and C achieved a sensitivity of >87%, a specificity of >94%, positive predictive values of >95%, and negative predictive values of 81% and 94%, respectively. EVFCM therefore offers a promising technique for the assessment of margins in breast-conserving surgery. Its widespread adoption could significantly reduce re-excisions, lower healthcare costs, and improve cosmetic and oncological outcomes.

Bio inspired technological performance in color Doppler ultrasonography and echocardiography for enhanced diagnostic precision in fetal congenital heart disease

The aim of this experiment is to investigate the bioinspired diagnostic performance of color Doppler ultrasound (CDUS) and two-dimensional (2D) echocardiography (ECG) for fetal congenital heart disease (FCHD). The research subjects were 33 expectant mothers with a diagnosis of FCHD at Xiangyang No. 1 People's Hospital between January 2017 and January 2021. The accuracy, sensitivity, and specificity of the two detection techniques were computed to ascertain and compare the diagnostic efficiency after CDUS and ECG examinations of all pregnant women. According to the findings, the prenatal CDUS detection rate was 92.61% higher than the 2D ECG detection rate (64.32%). The CDUS had an accuracy of 93.94%, sensitivity of 93.55%, and specificity of 100%, detecting 29 true positives, 0 false positives, 2 false negatives, and 2 true negatives. At 84.85% accuracy, 88.89% sensitivity, and 80% specificity, the 2D ECG identified 16 true positives, 3 false positives, 2 false positives, and 12 true negatives. There was a statistically significant (P < 0.05) difference between the accuracy, sensitivity, and specificity of 2D ECG and CDUS. In summary, CDUS was more effective than 2D ECG in diagnosing prenatal FCHD, and it also had a lower rate of missed and incorrect diagnoses.

Research progress on human papillomavirus-negative cervical cancer: A review.

Cervical cancer is the fourth most common cancer in women worldwide. The vast majority of cervical cancers are associated with human papillomavirus (HPV) infection, but a small proportion of cervical cancers occur independently of HPV infection, with different subtypes having varying rates of occurrence. Despite the presence of false negatives in current testing, improving the accuracy of detection is crucial for studying the pathogenesis of HPV-negative cervical cancer and improving the prognosis of these patients. Existing research suggests that HPV-negative cervical cancer has a different pathogenesis from HPV-positive cervical cancer, although the exact mechanism is not yet clear. It is currently believed to be associated with the immune microenvironment, certain tumor gene mutations, and some long noncoding RNAs. This article provides an overview of the latest research progress on HPV-negative cervical cancer, including possible reasons, pathogenesis, pathological features, and clinical characteristics, aiming to provide new insights for diagnosis, treatment, and prognosis improvement.

Optimization of testing protocols to screen for COVID-19: a multi-objective model.

In this paper we develop a new multi-objective simulated annealing (MOSA) algorithm to generate optimal testing protocols for infectious diseases, using the COVID-19 pandemic as our context. A SEIR (susceptible-exposed-infected-recovered) epidemiological model is embedded as the computational platform for our MOSA algorithm to optimize testing protocols for screening across three joint objectives: minimum cost of test materials, minimum total infections over the testing horizon, and minimum number of false negatives over the horizon. We demonstrate the application of this optimization tool to recommend screening protocols for K-12 school districts in the U.S.State of North Carolina. Our approach is scalable by population coverage and can be employed at the level of individual school districts or regional collections of districts, individual schools or collections of schools across a district, business sites, or nursing homes, among other congregate settings where individuals may be screened prior to gaining entry to the site. The algorithm can be solved two ways, generating either independent optimal protocols across individual testing locations, or a common protocol covering all locations in the collection of testing sites. Our findings can be used to inform policy decisions to guide the development of effective testing strategies for controlling the spread of COVID-19 or other pandemic diseases in a wide range of congregate settings across various geographic regions.

Frozen Section Analysis of Sentinel Nodes in Patients With Oral Squamous Cell Carcinoma

Frozen section (FS) analysis of sentinel nodes offers potential on-table diagnosis and treatment for occult metastasis in oral squamous cell cancer. Systematic analysis of FS during sentinel node biopsy has not been illuminated in the literature. To systematically review pooled data from studies using FS analysis in evaluating sentinel nodes in patients with cT1-T2 N0 oral squamous cell cancer. An academic librarian led the search of CENTRAL, CINAHL, Cochrane Database of Systematic Reviews, Embase, and MEDLINE for studies published in English between January 2000 and January 2023. Two authors independently screened cohort studies, case series, and randomized clinical trials, in which FS analysis was used to evaluate sentinel nodes in patients with cT1-T2 N0 oral squamous cell cancer. Data were extracted by 2 reviewers. Reporting quality was estimated using the Diagnostic Precision Study Quality Assessment Tool. Data analysis was performed between April and July 2023, and the meta-analysis was completed using the bivariate random-effects model. The primary outcome was the pooled sensitivity of FS sentinel node analysis. Secondary outcomes included evaluation of the FS technique, rate of occult metastasis, false-negative rate, and survival. Seventeen articles with 878 patients met the eligibility criteria. Although protocols varied, confirmatory serial step sectioning was performed in all studies. Occult metastasis was found in 263 of 878 patients (30%), and FS analysis identified 173 cases (65.8%). Following serial sectioning, an additional 90 positive results were identified, leading to 47 patients undergoing staged completion neck dissection. The pooled sensitivity of FS was 0.71 (95% CI, 0.60-0.80), the diagnostic odds ratio was 110, and the false-negative rate was 34.2%. The Cochrane Q value was 15.62 (df = 16; P = .48) and τ2 = 0.36. In this systematic review and meta-analysis, evaluated studies showed various techniques, in which pooled sensitivity reached 0.71, providing a benchmark for comparison to other 1-stop approaches. Due to the high false-negative rate of approximately one-third of patients, intraoperative FS must always be supplemented by serial sectioning. On-table diagnosis remains a key objective for sentinel node biopsy, and FS detection may be improved by standardizing protocols.

Meta-analysis on the Diagnostic Value of SDC2 Methylation in Colorectal Cancer Screening Among Chinese

To systematically evaluate the diagnostic value of syndecan-2 (SDC2) gene methylation status in the detection of Colorectal cancer (CRC) and advanced adenomas (AA) through meta-analysis. Databases including PubMed, Web of Science were searched from 2020 to June 14, 2024. Relevant studies were selected based on predefined inclusion and exclusion criteria. The true positive (TP), false positive (FP), false negative (FN), and true negative (TN) results were calculated for each study. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A bivariate meta-analysis model was used to pool the sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR), and a summary receiver operating characteristic (SROC) curve was constructed. Spearman’s correlation coefficient, Cochran-Q test, and I² test were used to evaluate heterogeneity. Sensitivity analysis was performed to explore the potential sources of heterogeneity. 24 literatures were included for meta-analysis. The pooled sensitivity was 0.77 for CRC and 0.52 for AA, and the pooled specificity was 0.93 for CRC and 0.91 for AA. The pooled PLR was 10.63 for CRC and 5.57 for AA, and the pooled NLR was 0.25 for CRC and 0.53 for AA. The DOR was 43.21 for CRC and 10.47 for AA. The SROC AUC was 0.91 for CRC and 0.83 for AA. The methylation level of the SDC2 gene in stool samples has significant diagnostic value for colorectal cancer and is considered an ideal biomarker for the early detection of this disease.

Characterizing the Transformation and Diagnosis of Atypical Lipomatous Tumor to Dedifferentiated Liposarcoma: Single Institutional Outcomes.

Atypical lipomatous tumor (ALT) in the extremities is a locally aggressive adipocytic tumor with the potential risk of transformation into dedifferentiated liposarcoma (DDLS). Studies seldom differentiate whether DDLS was diagnosed on initial biopsy, final resected specimen, or subsequent recurrence. Our study seeks to characterize how and when patients received their ALT or DDLS diagnoses to better understand the relationship between the two neoplasms. We performed a retrospective review of patients diagnosed with ALT or DDLS of the extremities. Clinical characteristics, including the method of diagnosis of an ALT or DDLS, time between diagnoses, and tumor recurrence was recorded. Univariate/multivariate analysis was performed to identify risk factors. Forty-five patients were diagnosed with ALT after core needle biopsy (CNB) and 41 of them received marginal en bloc excision. Three (7.3%) of these patients had a heterogeneous tumor on final resection, pathology revealed both ALT and DDLS. Four patients (8.2%) were diagnosed with DDLS from CNB and received negative margin en bloc excision. One of these tumors was identified as heterogeneous ALT/DDLS after resection. Fifty-three patients received marginal en bloc resection without CNB after a benign lipomatous mass was suspected on CT/MRI. Among these, one (1.9%) had a tumor with a heterogeneous composition of both ALT and DDLS on pathology. There were 11 (11.7%) ALT recurrences and 1 (1.0%) DDLS recurrence after ALT resection. Obtaining a proper diagnosis whether ALT or DDLS is critical. Our cohort found that amongst those concerning lipomatous lesions biopsied, 7.84% will show biopsy proven DDLS. Additionally, 6.67% of the biopsies will be false negatives and show DDLS on final pathology. Furthermore, our local recurrence for ALT was 11.7% recurring as ALT and 1.0% recurring as DDLS.