Negative Rate Research Articles

INTERCEPT Program of circulating tumor DNA (ctDNA) testing for minimal residual disease (MRD) in colorectal cancer (CRC): Results from a prospective clinical cohort.

27 Background: Minimal residual disease, defined by ctDNA+ in the absence of macroscopic of disease, represents a new and clinically poorly defined subset of patients (pts). While observational cohorts have been reported on the prognosis of MRD, there is little information on the impact of returning and acting on ctDNA results on the subsequent ability to detect radiographic disease. Here we provide real-world evidence form a large prospective cohort where ctDNA testing was routinely performed and returned to pts and providers, providing opportunities to understand the prognostic performance of ctDNA in practice, including the rate of false negatives defined as radiographic recurrence without detectable ctDNA. Methods: The INTERCEPT program enrolled pts undergoing curative intent surgery for stages II-IV CRC at MD Anderson Cancer Center. A tumor-informed MRD assay (Signatera) was drawn post-operatively and every 3 months as per established reimbursement guidelines. Pts enrolled between Jan-Dec 2022 were included with follow up through Aug, 2023. Post-op timepoint was defined as any test within 6.5 months of surgery to include adjuvant therapy. A false negative result was defined as a negative test but with radiographic detection of recurrence within the subsequent 4 months. Results: A total of 1,140 pts were included, with 44.7% female and 39% with advanced disease. At least one ctDNA+ result was obtained in 338 patients (29.6%). When limited to pts with a post-operative/post-adjuvant timepoint (n= 520) and a median follow up of 10.4m, the 12m DFS was 94.2% for ctDNA-, and 54.3% for pts with at least 1 ctDNA+ test (median DFS of 14.9m); HR 17.67 (95% CI: 10.08 – 30.99), p<0.0001. When segregated by stage, ctDNA remained the most important predictor of outcomes [Table], such that pts with stage IV ctDNA- had better prognosis than pts with stage II ctDNA+ (38.9% vs 87.7% 12mDFS, p=.002). For 15 pts, we detected a false negative ctDNA, representing a rate of 1.3% (15/1140) corresponding to a negative predictive value (NPV) of 98.1%. Conclusions: In this first prospective real-world ctDNA program, MRD testing had a high NPV (98.1%) with very few false negative cases. The prognostic importance of ctDNA was confirmed and exceeded the risk prediction of stage alone, underlying and supporting the importance of introducing the test in practice and the need to investigate interventional studies, as are ongoing in the INTERCEPT program (crcmrd.org). [Table: see text]

Assessment of the diagnostic performance of intradermal tuberculin test and post-mortem inspection for the diagnosis of bovine tuberculosis according to WOAH guidelines

Bovine tuberculosis (bTB) constitutes a global challenge for public and animal health with still some deficiencies regarding its diagnosis. This study aimed to estimate the accuracy of the single intradermal tuberculin test (SIT) and post-mortem inspection for different diagnostic objectives following WOAH guidelines. Tissue samples from 59 microbiological culture/PCR-positive and 58 microbiological culture/PCR-negative cattle were evaluated. The diagnostic sensitivity and specificity, the positive and negative probability indices as well as the positive and negative predictive values (PPV and NPV) of each technique were estimated for different pretest probabilities. The SIT with strict interpretation demonstrated moderate precision in confirming the absence of infection in populations historically free of bTB, with a 12.1% rate of false positives, but also detecting positive animals in the early stage of the eradication programs, with a 13.6% rate of false negatives. The diagnostic performance for ruling out bTB was notably high (NPV > 90%) in animals with a pre-test probability (PTP) below 42%. Post-mortem inspection constituted an interesting alternative tool to confirm suspected and positive cases for SIT, particularly in areas with bTB prevalence exceeding 19%, where implementing SIT and eradication measures may be impractical. In these areas, the likelihood that animals with tuberculosis-like lesions are affected by the disease surpasses 90%. Similarly, in herds with a PTP below 25%, the absence of bTB could be confidently ruled out with over 90% certainty. These findings highlight the effectiveness of SIT and post-mortem inspection as valuable techniques for current eradication programs and controlling bTB in high-prevalence areas where molecular techniques may not be feasible.

An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy.

Neural correlates of word processing influenced by painful primes.

The administration of painful primes has been shown to influence the perception of successively presented semantic stimuli. Painful primes lead to more negative valence ratings of pain-related, negative, and positive words than no prime. This effect was greater for pain-related than negative words. The identities of this effect's neural correlates remain unknown. In this EEG experiment, 48 healthy subjects received noxious electrical stimuli of moderate intensity. During this priming, they were presented with adjectives of variable valence (pain-related, negative, positive, and neutral). The triggered event-related potentials were analyzed during N1 (120-180 ms), P2 (170-260 ms), P3 (300-350 ms), N400 (370-550 ms), and two late positive complex components (LPC1 [650-750 ms] and LPC2 [750-1000 ms]). Larger event-related potentials were found for negative and pain-related words compared to positive words in later components (N400, LPC1, and LPC2), mainly in the frontal regions. Early components (N1, P2) were less affected by the word category but were by the prime condition (N1 amplitude was smaller with than without painful stimulation, P2 amplitude was larger with than without painful stimulation). Later components (LPC1, LPC2) were not affected by the prime condition. An interaction effect involving prime and word category was found on the behavioral level but not the electrophysiological level. This finding indicates that the interaction effect does not directly translate from the behavioral to the electrophysiological level. Possible reasons for this discrepancy are discussed.

A space–time gauge theory for dynamic plasticity: Non-linear micro-inertia and its consequences

Exploiting the invariance of energy density under local translation in space–time, a gauge theoretic model is developed for dynamic viscoplasticity in polycrystalline solids. The invariance is preserved using minimally replaced space–time gauge covariant operators. Translation in time leads to a new definition of gauge covariant temporal derivative; hence the classical notion of velocity is modified. Using a space–time pseudo-Riemannian metric, we derive an evolution equation for the equivalent plastic strain, which is expressed via the compensating fields thus giving it a geometric meaning. Dissipative terms and some higher order temporal and spatial derivatives naturally appear in the model. Also established is a correspondence of the compensating field due to spatial translation with Kröner’s multiplicative decomposition of the deformation gradient. Using this framework, we explicate on the geometric interpretation of certain internal variables often used in classical viscoplasticity models. In order to assess how the theory performs, we carry out numerical simulations of uniaxial and 3D continuum viscoplasticity problems. We specifically explore the role of a nonlinear micro-inertia term in phenomena involving anomalous strain rate sensitivity, e.g. negative strain rate sensitivity, strain rate locking, and in possible oscillations in the stress–strain response.

Persistence of antibodies 5 years after hepatitis B vaccination in preterm birth children: A retrospective cohort study using real-world data.

Previous studies did not provide substantial evidence for long-term immune persistence after the hepatitis B vaccine (HepB) in preterm birth (PTB) children. Consequently, there is ongoing controversy surrounding the booster immunization strategy for these children. Therefore, we conducted a retrospective cohort study to evaluate the disparities in immune persistence between PTB children and full-term children. A total of 1027 participants were enrolled in this study, including 505 PTB children in the exposure group and 522 full-term children in the control group. The negative rate of hepatitis B surface antibody (HBsAb) in the PTB group was significantly lower than that in the control group (47.9% vs. 41.4%, p = .035). The risk of HBsAb-negative in the exposure group was 1.5 times higher than that in the control group (adjusted odds ratio [aOR] = 1.5, 95% confidence interval [CI]: 1.1-2.0). The geometric mean concentration (GMC) of HBsAb was much lower for participants in the exposure group compared to participants in the control group (9.3 vs. 12.4 mIU/mL, p = .029). Subgroup analysis showed that the very preterm infants (gestational age <32 weeks) and the preterm low birth weight infants (birth weight <2000 g) had relatively low GMC levels of 3.2 mIU/mL (95% CI: 0.9-11.1) and 7.9 mIU/mL (95% CI: 4.2-14.8), respectively. Our findings demonstrated that PTB had a significant impact on the long-term persistence of HBsAb after HepB vaccination. The very preterm infants (gestational age <32 weeks) and the preterm low birth weight infants (birth weight <2000 g) may be special populations that should be given priority for HepB booster vaccination.

Innovative deep learning techniques for monitoring aggressive behavior in social media posts

The study aims to evaluate and compare the performance of various machine learning (ML) classifiers in the context of detecting cyber-trolling behaviors. With the rising prevalence of online harassment, developing effective automated tools for aggression detection in digital communications has become imperative. This research assesses the efficacy of Random Forest, Light Gradient Boosting Machine (LightGBM), Logistic Regression, Support Vector Machine (SVM), and Naive Bayes classifiers in identifying cyber troll posts within a publicly available dataset. Each ML classifier was trained and tested on a dataset curated for the detection of cyber trolls. The performance of the classifiers was gauged using confusion matrices, which provide detailed counts of true positives, true negatives, false positives, and false negatives. These metrics were then utilized to calculate the accuracy, precision, recall, and F1 scores to better understand each model’s predictive capabilities. The Random Forest classifier outperformed other models, exhibiting the highest accuracy and balanced precision-recall trade-off, as indicated by the highest true positive and true negative rates, alongside the lowest false positive and false negative rates. LightGBM, while effective, showed a tendency towards higher false predictions. Logistic Regression, SVM, and Naive Bayes displayed identical confusion matrix results, an anomaly suggesting potential data handling or model application issues that warrant further investigation. The findings underscore the effectiveness of ensemble methods, with Random Forest leading in the cyber troll detection task. The study highlights the importance of selecting appropriate ML algorithms for text classification tasks in social media contexts and emphasizes the need for further scrutiny into the anomaly observed among the Logistic Regression, SVM, and Naive Bayes results. Future work will focus on exploring the reasons behind this occurrence and the potential of deep learning techniques in enhancing detection performance.

A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques

This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on “Early Heart Disease Prediction using Boosting Techniques”, this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost’s superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction.

Microstructure evolution and ablation mechanisms of Csf/ZrB2-SiC composites at different heat fluxes under air plasma flame

In this study, short carbon fibre (Csf) reinforced ZrB2-SiC composites were fabricated via direct ink writing followed by precursor infiltration and pyrolysis. The ablation behaviour of the composites was investigated at heat fluxes of 4–6 MW/m2 under an air plasma flame, which revealed excellent ablation resistance with negative linear ablation rates of − 0.1 to − 0.5 µm/s. The results showed that ZrO2-SiO2 oxide layer was formed on the ablation surface after ablation, which provided effective protection to the internal materials. With an increase in heat flux, ZrO2 precipitated from the SiO2 melt first in the form of nano-tetragonal ZrO2 (t-ZrO2), owing to the vaporisation of SiO2. During cooling, the nano t-ZrO2 particles were retained, and some of them reacted with SiO2 to form ZrSiO4, thus improving the thermal stability of the SiO2-ZrO2 oxide layer. In contrast, t-ZrO2 agglomerated and grew owing to capillary force and finally transformed into monoclinic ZrO2 (m-ZrO2). Moreover, a higher heat flux led to the size of precipitation ZrO2 increases, meanwhile increasing the content of t-ZrO2 and ZrSiO4 in the oxide layer.

CT-guided core needle biopsies of head and neck tumors: a comprehensive monocenter analysis of safety and outcomes.

Although core needle biopsy is an important tool in minimally invasive tissue sampling and diagnostics for head and neck masses, comprehensive data about safety and outcomes is lacking. To retrospectively evaluate the diagnostic performance and safety of computed tomography (CT)-guided percutaneous core needle biopsy of head and neck masses. This retrospective single-center study included patients from 04/2007 to 12/2021, and a total of 156 core needle biopsies were evaluated. The initial histopathological results were compared with the long-term final diagnosis to evaluate the diagnostic yield of CT-guided core needle biopsies. The patients' age, sex, and history of malignancy, as well as procedural complications and radiation exposure were collected. A total of 156 biopsies of 150 patients (mean age 56years ± 17; 89 men) were evaluated. 57.3% (86/150) of patients had a history of malignancy. 55.1% (86/156) of the lesions were accessed by an infrahyoid needle approach. 92.9% (145/156) of biopsies yielded conclusive results. There were no false positives and 4 false negatives, resulting in a total false negative rate of 2.7% (4/145) and a total diagnostic yield of 90.4% (141/156). There were nine puncture-related complications (9/156-5.7%). None of the complications required further reintervention. The average dose length product was 311.3mGy × cm. CT-guided core needle biopsies of head and neck masses showed excellent results with high diagnostic yield and clinical safety. General anesthesia for open biopsy carries a higher risk for elderly patients, and fine needle aspiration has a poor reputation in terms of its diagnostic yield. This study focuses on safety and diagnostic yield of CT-guided core needle biopsies. • CT-guided core needle biopsy in head and neck tumors was a reliable and safe procedure. • The most common cause for an inconclusive biopsy result was a shortage of tissue collected during the biopsy. • During our study period of nearly 15years, the radiation exposure of head and neck biopsies decreased.

Evaluation of combination of ALA-PDT and interferon for cervical low-grade squamous intraepithelial lesion (LSIL)

BackgroundCervical LSIL is a precancerous disease which requires regular follow-up. High risk patients need active interventions. Interferon and topical PDT have been used in the treatment of cervical LSIL. The aim of this study was to evaluate the combination use of topical PDT and interferon in the treatment of cervical LSIL. Materials and methodsA prospective study was carried out involving 159 women with cervical LSIL and high risk human papillomaviruses (hr-HPV) infection. Patients were divided into three groups. Group 1-receiving interferon suppository only, Group 2-receiving 19 mg/cm2 ALA plus post PDT interferon, and Group 3-receiving 38 mg/cm2 ALA plus post PDT interferon. The primary endpoint was pathological regression. The secondary endpoints were the HPV negative conversion rate and the adverse effects of treatment. ResultsAt 6–12 months after PDT, for Group 1, the effective rate, CR rate and HPV negative conversion rate was 48.3 %, 43.3 % and 24.0 %, respectively. For Group 2, the effective rate, CR rate and HPV negative conversion rate were 89.3 %, 71.4 %, and 72.4 %, respectively. For Group 3, the effective rate, CR rate and HPV negative conversion rate were 91.5 %, 66.1 %, and 64.4 %, respectively, significantly higher than those of interferon only group. Two ALA dose group study showed similar efficacy. No patient experienced serious adverse effects. ConclusionsALA-PDT combined with interferon therapy was feasible and tolerable. Two ALA dose groups showed similar outcomes in treating cervical LSIL.

The Effects of Strain Rate and Anisotropy on the Formability and Mechanical Behaviour of Aluminium Alloy 2024-T3

The present study focuses on the mechanical behaviour and formability of the aluminium alloy 2024-T3 in sheet form with a thickness of 0.8 mm. For this purpose, tensile tests at quasi-static and intermediate strain rates were performed using a universal testing machine, and high strain rate experiments were performed using a split Hopkinson tension bar (SHTB) facility. The material’s anisotropy was investigated by considering seven different specimen orientations relative to the rolling direction. Digital image correlation (DIC) was used to measure specimen deformation. Based on the true stress–strain curves, the alloy exhibited negative strain rate sensitivity (NSRS). Dynamic strain aging (DSA) was investigated as a possible cause. However, neither the strain distribution nor the stress–strain curves gave further indications of the occurrence of DSA. A higher deformation capacity was observed in the high strain rate experiments. The alloy displayed anisotropic mechanical properties. Values of the Lankford coefficient lower than 1, more specifically, varying between 0.45 and 0.87 depending on specimen orientations and strain rate, were found. The hardening exponent was not significantly dependent on specimen orientation and only moderately affected by strain rate. An average value of 0.183 was observed for specimens tested at a quasi-static strain rate. Scanning electron microscopy (SEM) revealed a typical ductile fracture morphology with fine dimples. Dimple sizes were hardly affected by specimen orientation and strain rate.

Unveiling Stars: How Graphical Displays of Online Consumer Ratings Affect Consumer Perception and Judgment

Prior research has indicated that consumers’ decisions are significantly influenced by online reviews. However, existing research has focused mainly on attributes (e.g., average ratings) that are not fully controlled by firms; only limited research has investigated how controllable attributes (e.g., review display formats) affect consumers. Drawing on visual perception research, the authors examine the effectiveness of two prominent graphical display formats used by major e-commerce platforms: one that displays rating distributions in a proportional format (e.g., Amazon) and one that does so in a simple format (e.g., Google). The results indicate that due to the changes in graphs’ reference points caused by the shrunken x-axis in simple bar graphs, consumers respond more positively to an item when its rating distribution is displayed in a graphically simple (vs. proportional) format. This effect is moderated by the distribution's peak value (i.e., the share of the most frequent rating) and imbalance score (i.e., the difference between the share of positive and negative ratings). Furthermore, even an item's future ratings are influenced by the graphical format in which its prior ratings are displayed. The contributions to the marketing literature are discussed, and insights that can aid managers in making more informed decisions are provided.

Contrast-enhanced breast imaging: Current status and future challenges

BackgroundContrast-enhanced breast MRI and recently also contrast-enhanced mammography (CEM) are available for breast imaging. The aim of the current overview is to explore existing evidence and ongoing challenges of contrast-enhanced breast imaging. MethodsThis narrative provides an introduction to the contrast-enhanced breast imaging modalities breast MRI and CEM. Underlying principle, techniques and BI-RADS reporting of both techniques are described and compared, and the following indications and ongoing challenges are discussed: problem-solving, high-risk screening, supplemental screening in women with extremely dense breast tissue, breast implants, neoadjuvant systemic therapy (NST) response monitoring, MRI-guided and CEM- guided biopsy. ResultsTechnique and reporting for breast MRI are standardised, for the newer CEM standardisation is in progress. Similarly, compared to other modalities, breast MRI is well established as superior for problem-solving, screening women at high risk, screening women with extremely dense breast tissue or with implants; and for monitoring response to NST. Furthermore, MRI-guided biopsy is a reliable technique with low long-term false negative rates. For CEM, data is as yet either absent or limited, but existing results in these settings are promising. ConclusionContrast-enhanced breast imaging achieves highest diagnostic performance and should be considered essential. Of the two contrast-enhanced modalities, evidence of breast MRI superiority is ample, and preliminary results on CEM are promising, yet CEM warrants further study.

Fuzzy Inference System Development for Turbogenerator Failure Diagnosis on Floating Production Offloading and Storage Platform

This paper introduces a novel approach for diagnosing failures within a turbogenerator mineral lube oil system, employing a fuzzy inference system (FIS) model. The study leverages real operational data collected from supervisory monitoring sensors across four turbogenerators over a three-year operational span, resulting in a dataset comprising 40,456,663 input parameters. The failure modes were established through expert knowledge, using the Failure Mode, Effect, and Criticality Analysis (FMECA) documentation as the basis. Initially, the model’s universe variables were constructed using the sensor calibration range, and then the fuzzy membership functions were formulated based on the operational thresholds inherent to each measured parameter. The fault identification mechanism is underpinned by an inference system employing predefined rules, extrapolated from expert judgments encapsulating failure typologies specific to the turbogenerators’ mineral lube oil system, as delineated in the FMECA. The FIS model demonstrates notable efficacy in failure diagnosis with an overall performance evaluation of the system yielding satisfactory outcomes, having a 98.35% true positive rate for failure classification, coupled with a 99.99% true negative rate for accurate classification during normal system operation. These results highlight the visibility of the FIS model in diagnosing failures within the turbogenerator mineral lube oil system, thereby showcasing its potential for enhancing operational reliability and maintenance efficiency.

Comparative analysis of machine learning algorithms for breast cancer prediction

Breast cancer is a global health concern, and early diagnosis is crucial for successful treatment. The objective of this paper is to conduct a comparative analysis of machine-learning algorithms for the prediction of breast cancer. This study used the Wisconsin Diagnostic Breast Cancer Dataset. Data preparation, technique selection, and performance evaluation are included in the study. The inquiry begins by comparing malignant and benign instances according to input factors and diagnostic outcomes. Finding components having an inverse relationship to the diagnosis is prioritized. Next, a careful approach is used to choose attributes to improve the dataset for model construction. The preprocessed data trains and optimizes four well-known machine learning algorithms: Random Forest, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression. The models are evaluated for accuracy, precision, recall, F1-score, and ROC curve. This study aimed to evaluate numerous breast cancer prediction systems to determine their strengths and weaknesses. To provide openness and replicability, the study uses the Jupyter Notebook platform, Python, and data analytic tools. The logistic regression model has a test accuracy percentage of 99.26%, surpassing all other models examined in this study. Furthermore, it has a minimum false positive rate (FPR) of 1 and a false negative rate (FNR) of 4. The model exhibits a higher level of precision in comparison to the studies examined in the literature review. This study is crucial for early diagnosis and therapy development. The effects include lower healthcare expenses, better patient outcomes, and better diagnostics. Machine learning has shown promise in fighting breast cancer, boosting its relevance in healthcare.

Searching across-cohort relatives in 54,092 GWAS samples via encrypted genotype regression.

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier.

Comparison between transthoracic echocardiography and transoesophageal echocardiography in the diagnosis of acute aortic dissection from an emergency perspective. A systematic review and meta-analysis.

Acute aortic dissection (AAD) is a life-threatening medical condition with high early fatality. Therefore, a prompt and precise diagnosis, which can be achieved through invasive and non-invasive techniques is vital. Echocardiography, unlike MRI and CT, is accessible in emergency units and bedside-compatible. The recommended echocardiographic techniques for AAD are transthoracic and transoesophageal echocardiography (TTE and TOE). Therefore, our review compares their diagnostic roles in AAD. Studies relevant to our topic were attained through a database search and manual scrutiny of references lists of articles obtained from the electronic databases. The Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) has been used for quality assessment. All quantitative analyses were performed using either STATA 16 or Comprehensive Meta-Analyst software. The search strategy yielded 1,798 articles, of which only 11 were eligible for inclusion. Our subgroup analysis showed that conventional TTE had a sensitivity and specificity of 85.35% and 84.51% for the diagnosis of Stanford type A AAD and was 45.89% sensitive and 87.05% specific for the diagnosis of type B AAD. However, the subgroup analysis shows that contrast-enhancement of TTE results in a sensitivity and specificity of 93.30% and 97.60% for diagnosis of type A AAD, and 83.60% and 94.50% for diagnosis of type B AAD, respectively. On the other hand, conventional TOE was 93.64% sensitive and 95.50% specific for the diagnosis of type A AAD, 99.80% sensitive and 99.87% specific for the diagnosis of type B AAD. Moreover, our analyses show that TTE has pooled false negative and positive rates of 28.6% and 18.6%, while TOE has shown false negative and positive rates of 2.4% and 4.3%, respectively. TOE is the more favorable diagnostic tool for AAD diagnosis than TTE. However, it cannot be used as a stand-alone diagnostic tool since misdiagnosis cases are being reported. Contrast-enhanced TTE can also diagnose AAD since it provides similar results to conventional TOE.

Bank lending policies and monetary policy: some lessons from the negative interest era

Abstract What is the long-term impact of negative interest rates on bank lending? To answer this question, we construct a unique summary measure of negative rate exposure by individual banks based on exclusive survey data and banks’ balance sheets and couple it with the credit register of Spain and firms’ balance sheets to identify this impact on the supply of credit to firms. We find that only when deposit rates reached the zero lower bound did affected banks (relative to non-affected banks) decrease their supply, especially when undercapitalized and lending to risky firms. The adverse effects of the negative rates on banks’ intermediation capacity only took place after a protracted period of time.

Coastal evolution and future projections in Conde County, Brazil: A multi-decadal assessment via remote sensing and sea-level rise scenarios

Global sea levels, having risen by approximately 20 cm since the mid-19th century, necessitate a critical examination of their impacts on shoreline dynamics. This research evaluates the historical (1985–2022) and future shoreline changes in Conde County, Paraíba State, Brazil, an area of significant touristic interest. Employing Landsat satellite imagery, the study utilized the Digital Shoreline Analysis System (DSAS) and a Kalman filter algorithm for cloud removal, while also assessing land use and land cover changes using data from the MapBiomas Project for 2000, 2010, and 2020. These analyses informed projections of potential inundation under various sea-level rise (SLR) scenarios: 1, 2, 5, and 10 m. Key findings revealed a negative average coastline change rate of −0.27 m/year from 1985 to 2022, indicative of erosive trends likely accelerated by human activities. Long-term projections for 2032 and 2042 anticipate continued erosion in areas identified as highly vulnerable. The SLR scenario analysis underscores the urgent need for adaptive climate measures; while a 1- or 2-meter SLR presents limited immediate effects, a 5-meter rise could lead to significant inundation across key sectors, including urban and agricultural landscapes. The projected severity of a 10-meter SLR necessitates immediate, comprehensive interventions to safeguard both natural and human systems.