Search Results (18)

(1) Background: This study validates multichannel impedance/pH (MII/pH) sensors to measure oesophageal impedance transit (EZT). (2) Methods: EZT involved patients rapidly drinking 200 mL of saline during their MII/pH test. During the EZT study, the oesophageal pH sensor was used to exclude gastric acid reflux occurring and interfering with the oesophageal transit. EZTs were compared between (i) asymptomatic and symptomatic patients with dysphagia and (ii) barium swallow study outcomes for normal oesophageal transit and retention. Statistical t-tests, chi-squared tests, receiver operating characteristic curves with Youden’s J Index and regression analysis were conducted. (3) Results: A total of 458 patients (265 females) undertook the transit test during their MII/pH test. Prolonged EZT was found in patients with symptomatic dysphagia (t-statistics 4.28–4.43, p < 0.001) with the cut-off threshold at 1 min in the distal oesophagus for dysphagia symptoms (sensitivity 0.81, specificity 0.75). EZT was significantly higher in patients with retention on the BS test (t-statistics 7.29–8.91, p < 0.001), with the distal oesophageal cut-off threshold at 3.7 min being predictive for retention (sensitivity 0.79, specificity 0.93). Increased EZT in the distal oesophagus showed a direct positive correlation to higher dysphagia severity (r = 0.67, p < 0.001). (4) Conclusions: MII/pH sensors provide a platform to measure oesophageal transit, which was able to explain dysphagia from poor oesophageal clearance and predict the BS test outcome. Full article

Synthetic aperture radar (SAR) is emerging as a valuable dataset for monitoring crops globally. Unlike optical remote sensing, SAR can provide earth observations regardless of solar illumination or atmospheric conditions. Several methods that utilize SAR to identify agriculture rely on computationally expensive algorithms, such as machine learning, that require extensive training datasets, complex data pre-processing, or specialized software. The coefficient of variation (CV) method has been successful in identifying agricultural activity using several SAR sensors and is the basis of the Cropland Area algorithm for the upcoming NASA-Indian Space Research Organization (ISRO) SAR mission. The CV method derives a unique threshold for an AOI by optimizing Youden’s J-Statistic, where pixels above the threshold are classified as crop and pixels below are classified as non-crop, producing a binary crop/non-crop classification. Training this optimization process requires at least some existing cropland classification as an external reference dataset. In this paper, general CV thresholds are derived that can discriminate active agriculture (i.e., fields in use) from other land cover types without requiring a cropland reference dataset. We demonstrate the validity of our approach for three crop types: corn/soybean, wheat, and rice. Using data from the European Space Agency’s (ESA) Sentinel-1, a C-band SAR instrument, nine global AOIs, three for each crop type, were evaluated. Optimal thresholds were calculated and averaged for two AOIs per crop type for 2018–2022, resulting in 0.53, 0.31, and 0.26 thresholds for corn/soybean, wheat, and rice regions, respectively. The crop type average thresholds were then applied to an additional AOI of the same crop type, where they achieved 92%, 84%, and 83% accuracy for corn/soybean, wheat, and rice, respectively, when compared to ESA’s 2021 land cover product, WorldCover. The results of this study indicate that the use of the CV, along with the average crop type thresholds presented, is a fast, simple, and reliable technique to detect active agriculture in areas where either corn/soybean, wheat, or rice is the dominant crop type and where outdated or no reference datasets exist. Full article

The receiver operating characteristic (ROC) curve is a valuable statistical tool in medical research. It assesses a biomarker’s ability to distinguish between diseased and healthy individuals. The area under the ROC curve ($AUC$) and the Youden index (J) are common summary indices used to evaluate a biomarker’s diagnostic accuracy. Simultaneously examining $AUC$ and J offers a more comprehensive understanding of the ROC curve’s characteristics. In this paper, we utilize a semiparametric density ratio model to link the distributions of a biomarker for healthy and diseased individuals. Under this model, we establish the joint asymptotic normality of the maximum empirical likelihood estimator of $(AUC,J)$ and construct an asymptotically valid confidence region for $(AUC,J)$. Furthermore, we propose a new test to determine whether a biomarker simultaneously exceeds prespecified target values of $AU{C}_0$ and $J_0$ with the null hypothesis $H_0:AUC\le AU{C}_0$ or $J\le J_0$ against the alternative hypothesis $H_a:AUC>AU{C}_0$ and $J>J_0$. Simulation studies and a real data example on Duchenne Muscular Dystrophy are used to demonstrate the effectiveness of our proposed method and highlight its advantages over existing methods. Full article

Background: Tuberculosis (TB) and sarcoidosis are two common granulomatous diseases involving lymph nodes. Differential diagnosis is not always easy because pathogen demonstration in tuberculosis is not always possible and both diseases share clinical, radiological and histological patterns. The aim of our study was to identify factors associated with each diagnosis and set up a predictive score for TB. Methods: All cases of lymph node tuberculosis and sarcoidosis were retrospectively reviewed. Demographics, clinical characteristics, laboratory and imaging data, and microbiological and histological results were collected and compared. Results: Among 441 patients screened, 192 patients were included in the final analysis. The multivariate analysis showed that weight loss, necrotic granuloma, normal serum lysozyme level and hypergammaglobulinemia were significantly associated with TB. A risk score of TB was built based on these variables and was able to discriminate TB versus sarcoidosis with an AUC of 0.85 (95% CI: 0.79–0.91). Using the Youden’s J statistic, its most discriminant value (−0.36) was associated with a sensitivity of 80% and a specificity of 75%. Conclusions: We developed a score based on weight loss, necrotic granuloma, normal serum lysozyme level and hypergammaglobulinemia with an excellent capacity to discriminate TB versus sarcoidosis. This score needs still to be validated in a multicentric prospective study. Full article

Laboratory methods for detecting specific pathogens in oral fluids are widely reported, but there is little research on the oral fluid sampling process itself. In this study, a fluorescent tracer (diluted red food coloring) was used to test the transfer of a target directly from pigs or indirectly from the environment to pen-based oral fluid samples. Pens of ~30, ~60, and ~125 14-week-old pigs (32 pens/size) on commercial swine farms received one of two treatments: (1) pig exposure, i.e., ~3.5 mL of tracer solution sprayed into the mouth of 10% of the pigs in the pen; (2) environmental exposure, i.e., 20 mL of tracer solution was poured on the floor in the center of the pen. Oral fluids collected one day prior to treatment (baseline fluorescence control) and immediately after treatment were tested for fluorescence. Data were evaluated by receiver operating characteristic (ROC) analysis, with Youden’s J statistic used to set a threshold. Pretreatment oral fluid samples with fluorescence responses above the ROC threshold were removed from further analysis (7 of 96 samples). Based on the ROC analyses, oral fluid samples from 78 of 89 pens (87.6%), contained red food coloring, including 43 of 47 (91.5%) pens receiving pig exposure and 35 of 42 (83.3%) pens receiving environmental exposure. Thus, oral fluid samples contain both pig-derived and environmental targets. This methodology provides a safe and quantifiable method to evaluate oral fluid sampling vis-à-vis pen behavior, pen size, sampling protocol, and target distribution in the pen. Full article

The current contribution aimed to evaluate the capacity of the naive Bayes classifier to predict the progression of dengue fever to severe infection in children based on a defined set of clinical conditions and laboratory parameters. This case-control study was conducted by reviewing patient files in two public hospitals in an endemic area in Mexico. All 99 qualifying files showed a confirmed diagnosis of dengue. The 32 cases consisted of patients who entered the intensive care unit, while the 67 control patients did not require intensive care. The naive Bayes classifier could identify factors predictive of severe dengue, evidenced by 78% sensitivity, 91% specificity, a positive predictive value of 8.7, a negative predictive value of 0.24, and a global yield of 0.69. The factors that exhibited the greatest predictive capacity in the model were seven clinical conditions (tachycardia, respiratory failure, cold hands and feet, capillary leak leading to the escape of blood plasma, dyspnea, and alterations in consciousness) and three laboratory parameters (hypoalbuminemia, hypoproteinemia, and leukocytosis). Thus, the present model showed a predictive and adaptive capacity in a small pediatric population. It also identified attributes (i.e., hypoalbuminemia and hypoproteinemia) that may strengthen the WHO criteria for predicting progression to severe dengue. Full article

Background: Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images. Methods: We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan–Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response. Results: The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results. Conclusions: This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities. Full article

Background: While non-alcoholic fatty liver disease (NAFLD) is a wide-spread liver disease, only some patients progress towards steatohepatitis and cirrhosis. Aim: We comparatively analyzed the methacetin breath test (MBT) for the microsomal function of the liver and the octanoate breath test (OBT) for mitochondrial activity, in detecting patients with steatohepatitis and estimating fibrosis. Methods: 81 patients with histologically proven NAFLD (SAF score) were evaluated. The parameters used for both breath tests were the dose/h and the cumulative dose recovery at multiple timepoints. The statistical association between histological diagnosis and breath test results used Independent Samples t Test. The accuracy for diagnosis was evaluated using area under the receiver operator characteristic (AUROC) and the sensitivity and specificity were assessed using the Youden J method. Results: Both MBT and OBT were able to differentiate patients with simple steatosis from NASH and to stratify patients with significant fibrosis and cirrhosis (p-values < 0.001 for most analyzed timepoints). The best parameter for NASH diagnosis was OBT dose at 30 min. In the case of significant fibrosis, the most accurate test was MBT cumulative dose at 30 min. Conclusions: Both MBR and OBT tests are potentially useful tools in assessing patients with NAFLD. Full article

Background: Machine-learning techniques are useful for creating prediction models in clinical practice. This study aimed to construct a prediction model of postoperative 30-day mortality based on an automatically extracted electronic preoperative evaluation sheet. Methods: We used data from 276,341 consecutive adult patients who underwent non-cardiac surgery between January 2011 and December 2020 at a tertiary center for model development and internal validation, and another dataset from 63,384 patients between January 2011 and October 2021 at another center for external validation. Postoperative 30-day mortality was 0.16%. We developed an extreme gradient boosting (XGB) prediction model using only variables from preoperative evaluation sheets. Results: The model yielded an area under the curve of 0.960 and an area under the precision and recall curve of 0.216, which were 0.932 and 0.122, respectively, in the external validation set. The optimal threshold calculated by Youden’s J statistic had a sensitivity of 0.885 and specificity of 0.914. In an additional analysis with balanced distribution, the model showed a similar predictive value. Conclusion: We presented a machine-learning prediction model for 30-day mortality after non-cardiac surgery using preoperative variables automatically extracted from electronic medical records and validated the model in a multi-center setting. Our model may help clinicians predict postoperative outcomes. Full article

Recent studies have shown that using international guidelines to diagnose metabolic syndrome (MetS) may underestimate its prevalence in different Asian populations. This study aims to determine the validity of anthropometric indicators and appropriate cut-off values to predict MetS for Vietnamese adults. We analyzed data on 4701 adults across four regions of Vietnam. Four conventional and five novel anthropometric indexes were calculated. The area under a receiver operating characteristic (ROC) curve (AUC) and Youden’s J statistic were applied to evaluate the diagnostic ability and optimal cut-off values. Regardless of diagnostic criteria and gender, Abdominal volume index (AVI), Body roundness index (BRI), and Waist-height ratio (WHtR) had the highest AUC values, followed by Body mass index (BMI) and Waist-hip ratio (WHR). However, it was seen that differences among the AUC values of most indices were minor. In men, using International Diabetes Federation (IDF) criteria, the threshold of indices was 3.86 for BRI, 16.20 for AVI, 0.53 for WHtR, 22.40 for BMI, and 0.90 for WHR. In women, the threshold for these figures were 3.60, 12.80, 0.51, 23.58, and 0.85, respectively. It is recommended that health personnel in Vietnam should apply appropriate thresholds of anthropometry, which are lower than current international guidelines, for MetS screening to avoid under-diagnosis. Full article

To prevent the COVID-19 pandemic that threatens human health, vaccination has become a useful and necessary tool in the response to the pandemic. The vaccine not only induces antibodies in the body, but may also cause adverse effects such as fatigue, muscle pain, blood clots, and myocarditis, especially in patients with chronic disease. To reduce unnecessary vaccinations, it is becoming increasingly important to monitor the amount of anti-SARS-CoV-2 S protein antibodies prior to vaccination. A novel SH-SAW biosensor, coated with SARS-CoV-2 spike protein, can help quantify the amount of anti-SARS-CoV-2 S protein antibodies with 5 μL of finger blood within 40 s. The LoD of the spike-protein-coated SAW biosensor was determined to be 41.91 BAU/mL, and the cut-off point was determined to be 50 BAU/mL (Youden’s J statistic = 0.94733). By using the SH-SAW biosensor, we found that the total anti-SARS-CoV-2 S protein antibody concentrations spiked 10–14 days after the first vaccination (p = 0.0002) and 7–9 days after the second vaccination (p = 0.0116). Furthermore, mRNA vaccines, such as Moderna or BNT, could achieve higher concentrations of total anti-SARS-CoV-2 S protein antibodies compared with adenovirus vaccine, AZ (p < 0.0001). SH-SAW sensors in vitro diagnostic systems are a simple and powerful technology to investigate the local prevalence of COVID-19. Full article

Background. We aimed to assess the validity of the Mat-O-Covid Job Exposure Matrix (JEM) on SARS-CoV-2 using compensation data from the French National Health Insurance compensation system for occupational-related COVID-19. Methods. Deidentified compensation data for occupational COVID-19 in France were obtained between August 2020 and August 2021. The case acceptance was considered as the reference. Mat-O-Covid is an expert-based French JEM on workplace exposure to SARS-CoV-2. Bi- and multivariable models were used to study the association between the exposure assessed by Mat-O-Covid and the reference, as well as the area under the curve (AUC), sensitivity, specificity, predictive values, and likelihood ratios. Results. In the 1140 cases included, there was a close association between the Mat-O-Covid index and the reference (p < 0.0001). The overall predictivity was good, with an AUC of 0.78 and an optimal threshold at 13 per thousand. Using Youden’s J statistic resulted in 0.67 sensitivity and 0.87 specificity. Both positive and negative likelihood ratios were significant: 4.9 [2.4–6.4] and 0.4 [0.3–0.4], respectively. Discussion. It was possible to assess Mat-O-Covid’s validity using data from the national compensation system for occupational COVID-19. Though further studies are needed, Mat-O-Covid exposure assessment appears to be accurate enough to be used in research. Full article

Background. Postoperative hypocalcemia is one of the most common complications after total thyroidectomy. Parathormone (PTH) and calcium levels, measured several hours after surgery, have been suggested as valuable markers for detecting patients at risk for post-thyroidectomy hypocalcemia. We aimed to determine if early post-surgery PTH and calcium levels can be used for the early identification of patients at risk for symptomatic hypocalcemia. Methods. PTH and calcium were measured before surgery and at 10 min and 4 h post-thyroidectomy, in 77 patients. Performance characteristics of PTH and calcium levels and their post/pre-surgery ratios were calculated. Results. Four-hour calcium was a sensitive (93.75%) but not specific (67.61%) indicator of patients at risk for symptomatic hypocalcemia. The 4-h/pre-surgery PTH ratio was the most accurate (90.81%) and the most specific (94.37%) test to identify patients at risk. Serum calcium at 4-h, 4-h/pre-surgery PTH ratio, and PTH at 10 min post-surgery had the higher diagnostic odds ratios (50.86, 32.85, and 29.04, respectively). The 4-h/pre-surgery PTH ratio also had the highest (0.694) Youden’s J statistic. Conclusions. Low serum calcium levels 4 h after thyroidectomy and the 4-h/pre-surgery PTH ratio could be valuable additions to everyday clinical practice in post-thyroidectomy patients. Full article

Campylobacter jejuni is one of the leading causes of foodborne illness worldwide. C. jejuni is commonly found in poultry. It is the most frequent cause of contamination and thus resulting in not only public health concerns but also economic impacts. To test for this bacterial contamination in food processing plants, this study attempted to employ a simple and rapid detection assay called loop-mediated isothermal amplification (LAMP). The best cutoff value for the positive determination of C. jejuni calculated using real-time LAMP quantification cycle (C_q) was derived from the receiver operating characteristic (ROC) curve modeling. The model showed an area under curve (AUC) of 0.936 (95% Wald CI: 0.903–0.970). Based on Youden’s J statistic, the optimal cutoff value which had the highest sensitivity and specificity from the model was calculated as 18.07. The LAMP assay had 96.9% sensitivity, 95.8% specificity, and 93.9 and 97.9% positive and negative predictive values, respectively, compared to a standard culture approach for C. jejuni identification. Among all non-C. jejuni strains, the LAMP assay gave each of 12.5% false-positive results to C. coli and E. coli (1 out of 8 samples). The assay can detect C. jejuni at the lowest concentration of 10³ CFU/mL. Our results suggest a preliminary indicator for the application of end-point LAMP assays, such as turbidity and UV fluorescence tests, to detect C. jejuni in field operations. The LAMP assay is an alternative screening test for C. jejuni contamination in food samples. The method provides a rapid detection, which requires only 9 min with a cutoff value of C_q. We performed the extraction of DNA from pure cultures and the detection of C. jejuni using the LAMP assay within 3 h. However, we were not able to reduce the time for the process of enrichment involved in our study. Therefore, we suggest that alternative enrichment media and rapid DNA extraction methods should be considered for further study. Compared to other traditional methods, our proposed assay requires less equipment and time, which is applicable at any processing steps in the food production chain. Full article

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CV_thr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values. Full article