Improvement In Sensitivity Research Articles

Ultrasound S-detect system can improve diagnostic performance of less experienced radiologists in differentiating breast masses. A retrospective dual-center study.

To compare the performance of radiologists when assisted by an S-detect system with that of radiologists or an S-detect system alone in diagnosing breast masses on US images in a dual-center setting. US images were retrospectively identified 296 breast masses (150 benign, 146 malignant) by investigators at two medical centers. Six radiologists from the two centers independently analyzed the US images and classified each mass into categories 2 to 5. The radiologists then re-reviewed the images with the assistance of the S-detect system. The diagnostic value of radiologists alone, S-detect system alone, and S-detect alone, and radiologists + S-detect were analyzed and compared. Radiologists had significantly decreased the average false negative rate (FNR) for diagnosing breast masses using S-detect system (- 10.7%) (P < 0.001) and increased the area under the receiver operating characteristic curve (AUC) from 0.743 to 0.788 (P < 0.001). Seventy-seven out of 888 US images from six radiologists in this study were changed positively (from false negative to true positive or from false positive to true negative) with the S-detect, while 39 out of 888 US images were altered negatively. Radiologists had better performance for the diagnosis of malignant breast masses on US images with an S-detect system than without. The study used a dual-center design and compared the performance of the radiologists alone, the S-detect alone, and the radiologists combined with the S-detect. The study reported an improvement in sensitivity and AUC particularly for low to intermediate-level radiologists, involved cases and radiologists from two different centers, and compared the diagnostic value of using S-detect system for masses of different sizes.

Blood biomarkers in Down syndrome: Facilitating Alzheimer's disease detection and monitoring.

Blood-based biomarkers continue to be explored for disease detection, monitoring of progression, and therapeutic outcomes as the diagnostic determination of Alzheimer's Disease in Down Syndrome (DS-AD) remains challenging in clinical settings. This perspective highlights the current status of this effort. Overall, amyloid (A), tau (T), and neurodegeneration (AT[N]) blood-based biomarkers have been shown to increase with disease pathology for individuals with DS. Phosphorylated tau biomarkers (p-tau217, p-tau181) have been consistently shown to track disease progression for DS-AD and are likely good candidates for use in clinical settings. Biomarkers of inflammation (glial fibrillary acidic protein) also show promise; however, additional work is needed. Findings from stability work of blood-based biomarkers conducted among non-DS also supportthe potential longitudinal utility of biomarkers such as neurofilament light chain and p-tau181 in DS. Gaps in our knowledge are highlighted, and a potential role for sex differences in biomarker outcomes is noted, along with recommendations for determining the appropriate context of use when translating biomarkers into clinical applications. HIGHLIGHTS: An overview of blood-based biomarkers for Alzheimer's disease (AD) was provided for consideration of their utility among individuals with Down syndrome when looking toward potential clinical applications. Longitudinal stability of many blood biomarkers and improvement in detection sensitivity make blood such as plasma a viable source for exploring AD pathology. Variability in reviewed findings regarding the application of blood biomarkers highlights the importance of understanding and defining the appropriate context of use, particularly when translating them into clinical practice.

Sensitivity improvement of the closed-loop resonant fiber-optic gyroscope interrogated with a broadband source

The resonant fiber-optic gyroscope (RFOG), interrogated with a broadband light source, represents an intriguing optical gyroscope that has been proposed as an extension of the traditional RFOG, which is driven by a narrow linewidth laser source. The implementation of a closed-loop detection system enhances the linearity and dynamic range of the gyroscope output. This paper is devoted to improving the sensitivity of the closed-loop broadband source-driven RFOG. First, a model for analysis and optimization of the feedback loop gain of a closed-loop broadband source-driven resonant fiber-optic gyroscope (RFOG) is developed, and the feedback loop gain is optimized. The experimental results indicate that the influence of the feedback loop noise on the gyro performance can be neglected. Subsequently, the noise contributions, including the shot noise, detector noise, and relative intensity noise, in the broadband source-driven RFOG system are investigated. A theoretical derivation and experimental verification of an expression for the reduction of the relative intensity noise by a fiber-optic ring resonator are presented. Finally, a packaged broadband source-driven RFOG prototype using a 175 m long fiber coil with an average diameter of 5.7 cm was constructed and evaluated. The experimental results demonstrate that the prototype achieves navigation-grade precision with an angular random walk of 0.003∘/h1/2 and a bias instability of 0.005°/h.

Sensitivity improvement of fiber optic interferometric hydrophone based on composite structure

Sensitivity improvement of fiber optic interferometric hydrophone based on composite structure

Detection of suicidality from medical text using privacy-preserving large language models.

Attempts to use artificial intelligence (AI) in psychiatric disorders show moderate success, highlighting the potential of incorporating information from clinical assessments to improve the models. This study focuses on using large language models (LLMs) to detect suicide risk from medical text in psychiatric care. To extract information about suicidality status from the admission notes in electronic health records (EHRs) using privacy-sensitive, locally hosted LLMs, specifically evaluating the efficacy of Llama-2 models. We compared the performance of several variants of the open source LLM Llama-2 in extracting suicidality status from 100 psychiatric reports against a ground truth defined by human experts, assessing accuracy, sensitivity, specificity and F1 score across different prompting strategies. A German fine-tuned Llama-2 model showed the highest accuracy (87.5%), sensitivity (83.0%) and specificity (91.8%) in identifying suicidality, with significant improvements in sensitivity and specificity across various prompt designs. The study demonstrates the capability of LLMs, particularly Llama-2, in accurately extracting information on suicidality from psychiatric records while preserving data privacy. This suggests their application in surveillance systems for psychiatric emergencies and improving the clinical management of suicidality by improving systematic quality control and research.

Ultrasensitive and Stretchable Strain Sensors Based on Laser-Induced Graphene With ZnO Nanoparticles.

Laser-induced graphene (LIG) has attracted considerable attention for its use in flexible and stretchable sensors, owing to its electrical/mechanical properties and scalable fabrication processes. Although laser scanning facilitates the formation of LIG and its strain sensor, the strain-sensing sensitivity enhancement of LIG remains limited by the material's properties and structural design. In this study, we demonstrate a substantial improvement in sensitivity that was achieved by fabricating a LIG using ZnO nanoparticle (NP)-assisted photothermal enhancement. The results show that ZnO NPs selectively reduce the threshold fluence needed to convert polyimide (PI) into LIG. By transferring the LIG formed on PI to poly(dimethylsiloxane), we fabricate a stretchable strain sensor with ultrahigh sensitivity and a gauge factor of 1214 at 10% strain, which is approximately 60 times higher than the gauge factor without ZnO NPs. Using the selective graphenization properties of LIG, a flexible, dual-sided integrated sensor sheet that is equipped with flexible strain and ultraviolet (UV) sensors is demonstrated. This sheet enables simultaneous monitoring of UV intensity and joint bending angles of sports wearable devices. We validated the developed sensors by attaching them to a runner's body to monitor and simulate forefoot and heel strikes, demonstrating the sensor's ultrahigh sensitivity and long-term stability without the need for a camera. These findings highlight the potential of the proposed method for developing multifunctional sensor applications with ultrahigh sensitivity and stability.

Fluorogenic RNA aptamer based artificial membraneless organelles for small molecule and cell sensing

Artificial membraneless organelle with molecularly crowded and confined microenvironment shows great potential for improving functional RNA properties and developing biosensing. However, the performance of current artificial membraneless organelles are impaired with polycationic components. In this work, we demonstrate the construction of a purely fluorescent RNA aptamer based artificial membraneless organelle (FRAME) for improving RNA performance and biosensing. The fluorescent RNA aptamer in FRAME exhibited higher enzymatic resistance, thermostability, photostability and binding affinity than that dispersed in solution. The FRAME can be easily designed as a biosensor for tetracycline detection in lake water and tap water, which eliminated the adverse effects of polycationic components and demonstrated about 42.8-fold sensitivity improvement than the biosensor prepared with Poly-L-Lysine and RNA. By introducing epithelial cell adhesion molecule (EpCAM) aptamer to specific recognition of tumor cells, the FRAME is easily engineered as an ultra-sensitive probe to precisely identify cancer cell phenotypes. We believe that the proposed FRAME system would provide a universal platform for biosensing.

Predictive Modeling of Semi-Submersible Floater Motion Using Bi-LSTM Model

Abstract Floating offshore wind turbines (FOWTs) are emerging as a promising renewable energy solution, tapping into the vast wind resources in deep-sea locations. Accurate predictions of heave, pitch, sway, roll, yaw, and surge are essential to ensuring the structural integrity and power generation efficiency of FOWTs. Traditional methods relying on potential flow theory to predict FOWTs’ hydrodynamic responses involve simplifications that may not capture real-world complexities. Although computational fluid dynamics (CFD) simulations offer accuracy, they are computationally expensive. This study explores the use of Bi-directional long-short-term memory (Bi-LSTM) neural networks as an alternative to address computational challenges in predicting FOWTs’ hydrodynamic responses. These networks excel at handling time series data, capturing temporal correlations crucial for understanding FOWT floater motion dynamics. They also effectively capture bidirectional dependencies, enhancing their suitability for this application. The model considers various input parameters, including wave amplitudes, periods, steepness, directions, and floater design characteristics such as stifness, pre-tension, and the free length of mooring lines. The Bi-LSTM model is trained using a subset of the CFD dataset generated for the scaled OC5 semi-submersible platform with mooring, reserving the remainder for validation and testing. The comparison between simulated and predicted motion demonstrated a strong correlation in surge but noticeable discrepancies in heave and pitch. FFT analysis highlighted consistent frequency components but variations in magnitude, suggesting areas for model sensitivity improvement and further investigation into wave interaction dynamics. The correlation coefficients revealed a strong positive correlation for the surge motion, while the heave and pitch motions showed slightly lower correlation coefficients, suggesting better performance in predicting the surge.

A Novel Sensitivity Improvement Method for the Electromagnetic Micro-Debris Detector Using Nanocrystalline Iron Cores

A Novel Sensitivity Improvement Method for the Electromagnetic Micro-Debris Detector Using Nanocrystalline Iron Cores

Validation of the Online Collaborative Ocular Tuberculosis Study Calculator for Tubercular Uveitis

This was the first study, to the authors' knowledge, to statistically evaluate the predictive accuracy of Collaborative Ocular Tuberculosis Study (COTS) calculator in guiding initiation of antitubercular therapy (ATT) in patients with clinically suspicious tubercular uveitis (TBU) in an international cohort. To evaluate the accuracy of a score of 4 or greater on the online COTS calculator in recommending ATT initiation. This study was an evaluation of a diagnostic test or technology. Data input required for the COTS calculator were extracted from the COTS-1 study dataset, which comprised retrospective, observational records of patients with TBU who were monitored for 12 months after treatment. Patients were recruited from international ophthalmic centers. In the absence of a traditional criterion standard, the 12-month treatment response to ATT was used to classify patients as disease positive or negative. The accuracy of clinicians at the ATT decision-making stage in the COTS-1 study was set against COTS calculator scores of 4 or greater. Diagnostic accuracy metrics, including sensitivity, specificity, positive predictive value (PPV), precision, recall, and F1 score, were computed. Data collected from January 2004 to December 2014 were analyzed. COTS calculator to guide initiation of ATT in patients with TBU. Comparison of accuracy between clinician judgment and the COTS calculator, analyzed at varying scores and further stratified by tuberculosis endemicity. Of the 492 participants (mean [SD] age, 42.3 [19.0] years; 233 male [47.3%]), application of the COTS calculator identified 225 (45.7%) with high or very high probability to start ATT (score = 4 or 5) and 111 (22.5%) with very high probability alone (score = 5). COTS-5 exhibited the highest specificity (88.7%; 95% CI, 81.4%-93.8%) compared with clinician judgment (29.6%; 95% CI, 21.4%-38.8%), and clinician judgment led in sensitivity (95.5%; 95% CI, 92.9%-97.4%) compared with COTS-5 (26%; 95% CI, 21.6%-30.7%). COTS-4 and COTS-5 balanced specificity (64.3%; 95% CI, 54.9%-73.1%) and sensitivity (48.8%; 95% CI, 43.7%-54%). PPV and sensitivity were consistently higher in the endemic group for all 3 tests. Results of this diagnostic study suggest that the COTS calculator (score ≥4) was more specific than clinician judgment for ATT initiation. Although clinician judgment is a good first step to identify all potential true positives (with high sensitivity), a second consultation with COTS-5 (with high PPV) may lead to less false positives. This tool, apt for high-prevalence, low-resource settings, recommends ATT more selectively for genuine TBU cases. Large prospective studies are essential to explore potential improvements in the calculator's sensitivity.

Bridging piezoelectric and electrostatic effects: a novel piezo-MEMS pitch/roll gyroscope with sub 10°/h bias instability

This paper proposes a novel piezo-MEMS pitch/roll gyroscope that co-integrates piezoelectric and electrostatic effects, for the first time achieves electrostatic mode-matching operation for piezoelectric gyroscopes. Movement of operated out-of-plane (OOP) mode (n = 3) and in-plane (IP) mode (n = 2) are orthogonal, ensuring that the OOP amplitude is not significantly limited by parallel plates set at nodes of IP mode. Therefore, a large OOP driving amplitude actuated by piezoelectric and frequency tuning in the IP sense mode trimmed by electrostatic can be achieved together with a low risk of pull-in, hence releases the trade-off between the tuning range and the linear actuation range. At a tuning voltage of 66 V, the frequency split decreased from 171 Hz to 0.1 Hz, resulting in a 167x times improvement in sensitivity. The mode-matched gyroscope exhibits an angle random walk (ARW) of 0.41°/√h and a bias instability (BI) of 8.85°/h on a test board within a customized vacuum chamber, marking enhancements of 68x and 301x, respectively, compared to its performance under mode-mismatch conditions. The BI performance of the presented pitch/roll gyroscope is comparable to that of the highest-performing mechanically trimmed piezo-MEMS yaw gyroscopes known to date, while offering the unique advantage of lower cost, better mode-matching resolution, and the flexibility of real-time frequency control.

Video-feedback intervention to promote positive parenting and sensitive discipline in early elementary education (VIPP-School): a randomized controlled trial

ABSTRACT The VIPP-SD (Video-feedback Intervention to promote Positive Parenting and Sensitive Discipline) program has been shown to promote positive interactions between parents and children with behavior problems. This study evaluated an adapted version for teachers (VIPP-School) through a parallel-group randomized controlled trial involving 58 teacher–child dyads from kindergarten to second grade. Teacher–child dyads were randomly assigned to VIPP-School (n = 28) or an active control group (n = 30). Intention-to-treat analyses showed improvements in teacher sensitivity (d = 0.87) and supportive teacher classroom interactions (d = 0.53) in the VIPP-School condition compared to the control condition. VIPP-School had no significant effect on teachers’ observed sensitive discipline (although changes were in the expected direction), nor on reported child externalizing behavior, overall relationship quality, school happiness or teacher self-efficacy. These findings show that the intervention, originally developed for families, could be effectively applied to early elementary education. The limited number of sessions increases the feasibility of VIPP-School.

The role of metformin in modulating cardiometabolic risk in obese pediatric populations with metabolic syndrome: A systematic review

Introduction: Obesity significantly increases the risk of developing cardiometabolic complications, which are precursors to cardiovascular diseases (CVD). In children and adolescents, metabolic dysfunctions such as insulin resistance, dyslipidemia, and hypertension are key contributors to early-onset cardiovascular risk. Metformin, a well-established antidiabetic agent, has shown potential benefits in addressing these metabolic imbalances. However, the long-term impact of metformin on cardiovascular outcomes in obese pediatric patients remains unclear. Aim: This systematic review aims to evaluate the effect of metformin on cardiometabolic parameters in obese children and adolescents with metabolic syndrome, focusing on insulin sensitivity, body mass index (BMI), blood pressure, and lipid profiles. Methods: A systematic review was conducted by searching databases including PubMed, Cochrane Library, Embase, and Web of Science. The inclusion criteria targeted randomized controlled trials (RCTs), cohort studies, systematic reviews, and meta-analyses published between 2004 and 2024. Pediatric patients aged 6-18 years with obesity and metabolic syndrome were included. The primary outcomes assessed were BMI, insulin sensitivity, lipid profiles, blood pressure, and cardiovascular outcomes. Results: A total of 23 studies, including 10 RCTs and 1 meta-analysis, were reviewed, involving 777 patients. Metformin demonstrated consistent improvements in insulin sensitivity and BMI in obese children and adolescents. Several studies reported modest reductions in BMI, improved insulin resistance, and better lipid profiles. However, variability in results was observed regarding long-term cardiovascular outcomes. While metformin reduced systolic blood pressure and carotid intimal-medial thickness (CIMT), the long-term impact on preventing major cardiovascular events remained inconclusive. Discussion: Metformin’s benefits on cardiometabolic risk factors in obese children are primarily attributed to its effects on insulin sensitivity, lipid metabolism, and inflammatory pathways. Its ability to activate AMP-activated protein kinase (AMPK) leads to improved metabolic profiles and reduced inflammation and is crucial for mitigating obesity-related cardiovascular risks. Despite these positive effects, the evidence for long-term prevention of cardiovascular events is limited and requires further exploration. Conclusions: Metformin offers significant short-term benefits in improving BMI, insulin sensitivity, and cardiometabolic parameters in obese children and adolescents. However, its long-term efficacy in preventing cardiovascular events remains uncertain. Further studies are necessary to establish its role in long-term cardiovascular protection in pediatric populations

MEMS capacitive pressure sensor: analysis, theoretical modeling, simulation and performance comparison of the effect of a conical notch

Abstract Micro-Electro-Mechanical Systems (MEMS) pressure sensors are widely used in various applications due to their high sensitivity, low power consumption, and compactness. This work involves the design and simulation of a MEMS-based Touch Mode Capacitive Pressure Sensor (TMCPS). The proposed sensor is based on a substrate with an integrated conical notch featuring a circular diaphragm, aiming to enhance the key performance parameters of the sensor. The integration of a conical notch in the substrate increases the touch area between the diaphragm and substrate compared to the literature, ensuring increased capacitance and capacitive sensitivity. In this work the mathematical analysis of deflection of a circular diaphragm employing thin plate theory, capacitance, and capacitive sensitivity, along with step-by-step explanations, is provided. The results are obtained from MATLAB simulations. The deflection of the diaphragm is validated through Finite Element Analysis (FEA) using COMSOL Multiphysics. The proposed work demonstrates a significant improvement in sensor sensitivity compared to the existing literature.

Exercise-changed gut mycobiome as a potential contributor to metabolic benefits in diabetes prevention: an integrative multi-omics study

ABSTRACT Background The importance of gut microbes in mediating the benefits of lifestyle intervention is increasingly recognized. However, compared to the bacterial microbiome, the role of intestinal fungi in exercise remains elusive. With our established randomized controlled trial of exercise intervention in Chinese males with prediabetes (n = 39, ClinicalTrials.gov:NCT03240978), we investigated the dynamics of human gut mycobiome and further interrogated their associations with exercise-elicited outcomes using multi-omics approaches. Methods Clinical variations and biological samples were collected before and after training. Fecal fungal composition was analyzed using the internal transcribed spacer 2 (ITS2) sequencing and integrated with paired shotgun metagenomics, untargeted metabolomics, and Olink proteomics. Results Twelve weeks of exercise training profoundly promoted fungal ecological diversity and intrakingdom connection. We further identified exercise-responsive genera with potential metabolic benefits, including Verticillium, Sarocladium, and Ceratocystis. Using multi-omics approaches, we elucidated comprehensive associations between changes in gut mycobiome and exercise-shaped metabolic phenotypes, bacterial microbiome, and circulating metabolomics and proteomics profiles. Furthermore, a machine-learning algorithm built using baseline microbial signatures and clinical characteristics predicted exercise responsiveness in improvements of insulin sensitivity, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% CI: 0.85–0.97) in the discovery cohort and of 0.79 (95% CI: 0.74–0.86) in the independent validation cohort (n = 30). Conclusions Our findings suggest that intense exercise training significantly remodels the human fungal microbiome composition. Changes in gut fungal composition are associated with the metabolic benefits of exercise, indicating gut mycobiome is a possible molecular transducer of exercise. Moreover, baseline gut fungal signatures predict exercise responsiveness for diabetes prevention, highlighting that targeting the gut mycobiome emerges as a prospective strategy in tailoring personalized training for diabetes prevention.

Evaluating XGBoost’s predictive accuracy on surgical site Infections in cardiac surgery

Abstract Background Surgical site infections (SSIs) pose a significant threat in cardiac surgery (CS), profoundly impacting patient prognosis. SSI incidence varies widely, reported between 3.5% and 26.8%. The study used a decade-long dataset from SSI surveillance in a specialized hospital in Italy, applying machine learning (ML) techniques to predict the SSI incidence among CS patients. Methods Data collected from 2013-2023 through the surveillance system of SSI in patients undergoing CS were used to train a predictive ML algorithm (XGBoost). Data included information on demographics, risk factors, surgery variables (incision site, prophylaxis, etc.), and the infection outcome derived from follow-up interviews at 30 days post-surgery, or 90 days for patients with prosthetic materials, according to the ECDC case definition of SSI. We used the R libraries “caret”, “smotefamily” and “xgboost” to train the algorithm. A train-test split of 70-30 was applied. Both downsampling of the majority class (no SSI) and oversampling of SSI cases with SMOTE were used in the training set to address class imbalance. Results A total of 10,534 subjects (65.9% males, mean age 68.3 years) who underwent CS (64.3% with prosthetic materials) were included, among which 533 SSIs were identified (mean incidence of 5.06%), with 430 cases (80.7%) occurring after discharge (38.7% deep SSI) and 103 (19.3%) before discharge (55.5% deep SSI). The trained XGBoost algorithm achieved an AUC of 0.62, sensitivity of 69%, and specificity of 50% in the prediction of SSI on the test dataset. Conclusions These findings suggest that while the XGBoost model provides a fair predictive capability, there is significant room for improvement in sensitivity and specificity. The use of artificial intelligence offers a promising opportunity to identify patients at risk of SSI. However, further research and comprehensive data are needed to refine the predictive model and effectively improve prevention measures. Key messages • Predominantly occurring post-discharge, SSIs in cardiac surgery necessitate enhanced prevention strategies, highlighting the critical phase after hospital discharge where surveillance is paramount. • Artificial intelligence may enhance post-discharge SSI surveillance, promising to identify patients at increased risk. Further research is needed to refine these AI tools for optimal sensitivity.

Deep learning improves detection of aortic stenosis in b-mode ultrasound: results of a pivotal multicenter validation study using artificial intelligence

Abstract Background Aortic stenosis (AS) is a highly prevalent and morbid condition, with moderate or severe AS present in 9% of patients over 75 years. Despite improved diagnostic and therapeutic methods, AS remains underdiagnosed, particularly in Black patients and underserved communities. Echocardiography (echo) is effective for detecting AS but requires expertise to quantify and interpret valve gradients and valve area. We hypothesized that a deep learning (DL) algorithm could be trained to detect AS severity using B-mode (non-Doppler) echo alone, the modality most likely to be available in point-of-care ultrasound (POCUS) studies. Here, we present the results of a multicenter pivotal trial to test whether artificial intelligence (AI) can help increase the detection of AS from B-mode echoes. Methods A convolutional neural network (CNN) was trained to classify the severity of aortic stenosis from 3 B-mode echo views of the aortic valve (parasternal long- and short-axis and apical 5-chamber). The training dataset had over 500,000 cine loops from 29,527 echoes in 26,981 patients. A fully crossed multireader multicase study was conducted using 220 echoes never seen during algorithm development. 5 level II-trained cardiologists (referred to as POCUS readers as they only reviewed B-mode clips) were presented with these echoes twice at least a month apart, once with and again without AI interpretation of AS severity. Readers identified 2 dichotomous severity levels (either "suggestive of moderate to severe AS" or not), with ground truth established by 3 level III-trained cardiology echo experts. While experts had access to full studies (including Doppler) to adjudicate severity, POCUS readers had no Doppler or measurements of any kind available. Sensitivity and specificity were compared for the 5 POCUS readers when aided by the AI algorithm vs unaided, with similar tests of inter-reader agreement. Results There was a statistically significant improvement in sensitivity (+5.5%, 95% CI [1.5%, 9.5%], p=0.007) with stable specificity (-0.3%, 95% CI [-3.6%, 3.0%], p=NS) when the 5 POCUS readers received the assistance of the AI software. Fig 1 shows 2,500 bootstrapping assessments to better show consistency of these findings. In addition, the POCUS inter-reader agreement (overall percent agreement) improved by approximately 7% (p&amp;lt;0.001), becoming comparable to that of expert readers (Fig 2). Conclusions In a comprehensive multireader pivotal trial, an AI algorithm improved the performance of POCUS cardiologist readers to identify significant AS from limited B-mode echoes as might be obtained in screening or outreach settings. Sensitivity for detection of moderate-severe AS increased significantly by 5.5% with stable specificity. Inter-reader agreement improved to resemble that of expert cardiologists given access to full study information, including Doppler. AI may have a role in assisting detection of significant AS from limited B-mode echoes.Fig 1Fig 2

Association between dyspnea, chest pain, nt-probnp and cardiac impairment in people with cystic fibrosis

Abstract Background Cystic fibrosis (CF) is an autosomal recessive disease affecting multiple organs. Emerging evidence indicates an elevated risk of cardiovascular complications in people with CF (pwCF), with some studies implicating CF-related cardiomyopathy as an underlying mechanism. Whether screening for cardiac symptoms and N-terminal pro B-type natriuretic peptide (NT-proBNP) levels may aid in identifying cardiac impairment in pwCF remains unknown. Purpose To assess prevalence of dyspnea and chest pain in pwCF and investigate whether symptoms and NT-pro-BNP are predictive of cardiac impairment. Methods We interviewed 104 adult pwCF and 104 age- and sex-matched controls from the general population about symptoms of dyspnea and chest pain. All participants were examined with transthoracic echocardiography and NT-proBNP. Cardiac impairment was defined as left ventricular (LV) ejection fraction (LVEF) &amp;lt;50%, LV global longitudinal strain (GLS) &amp;lt; 16% or presence of diastolic dysfunction. Results Chest pain was more frequent in pwCF than in controls: (29% vs. 1%, p&amp;lt;0.001), although primarily characterized as atypical and non-exertional chest pain (27%). PwCF also suffered more from dyspnea than controls (89% vs. 18%, p&amp;lt;0.001) (Figure 1) and more pwCF had abnormal cardiac function compared to controls (44% versus 22%, p&amp;lt;0.001). Median NT-proBNP in pwCF was 50.0 pg/ml (40.7; 100.8) and increasing NT-proBNP level was associated with decreasing FEV1 (L) (estimate: -0.008, p&amp;lt;0.001). NT-proBNP level was not associated with neither dyspnea nor chest pain (p=0.60 and p=0.15, respectively). PwCF with dyspnea did not differ from those without on clinical or echocardiographic parameters. However, pwCF with chest pain had lower absolute GLS compared to pwCF without (17.4% vs.18.5%, p=0.026). Neither dyspnea nor chest pain was more frequent in pwCF with cardiac impairment versus normal cardiac function (54% vs. 39%, p=0.17 and 29% vs. 28%, p=0.91, respectively). Decreasing absolute GLS was associated with increased odds of chest pain (OR 1.25, 95% CI 1.02; 1.53, p=0.030) (Figure 2). Neither dyspnea or chest pain alone, nor in combination with NT-proBNP were able to predict abnormal cardiac function. While dyspnea and chest pain combined showed a slight improvement in sensitivity and positive predictive value for diagnosing cardiac impairment, the combination of symptoms and increased NT-proBNP did not enhance diagnostic accuracy. Conclusions In this cross-sectional study, dyspnea and atypical, non-exertional chest pain were common symptoms in adult pwCF. While abnormal cardiac function was prevalent in pwCF, neither symptoms, NT-proBNP nor a combination of these were associated with increased odds of cardiac impairment. Dyspnea and chest pain, though prevalent, are multifactorial in pwCF and do not seem to serve as sensitive markers of cardiac impairment, even when combined with NT-proBNP level.

Comparison of DNA extraction procedures for detection of Mycoplasma bovis directly from extended bovine semen straw samples using a commercial M. bovis PCR

BackgroundMycoplasma bovis is a global pathogen of cattle but was detected for the first time in New Zealand in 2017, triggering a response under their Biosecurity Act as an “unwanted organism”. Following a lengthy eradication campaign, the Ministry of Primary Industries (MPI) now requires all bovine semen destined for export to New Zealand to be screened with an M. bovis-specific real-time PCR (rtPCR) compliant with amended import health standard (IHS) test requirements aimed at preventing the accidental importation of M. bovis. The standard stipulates that semen samples cannot be centrifuged prior to DNA extraction. To comply with these strict requirements, one of the listed tests was validated together with different DNA preparation steps and compared with existing in-house procedures. DNA was extracted from semen straws using the current in-house semi-automated platform procedures for processing culture, tissue and body fluid sample submissions and was compared with the stipulated test requirements. DNA from centrifuged and unspun semen samples spiked with M. bovis was also compared.ResultsThe rtPCR had a sensitivity and specificity of 100% (95% confidence interval = 79–100% and 74–100%, respectively) when testing DNA from other Mycoplasma species or bovine semen spiked with the latter, with a high level of repeatability for within- and between- run replicates. The consistent limit of detection was 0.001 pg/µl M. bovis DNA and between 5.3 × 102 and 7.5 × 102 CFU/ml M. bovis when artificially spiked in semen. DNA extracted using the KingFisher Flex was detected with lower Cq values than the Maxwell 16, but the comparable improvements in sensitivity were mainly associated with non-centrifuged samples (p < 0.001). None of the procedures tested impeded the detection sensitivity of M. bovis in the presence of competitor organisms Acholeplasma laidlawii, Mycoplasma bovigenitalium and Ureaplasma diversum, confirming M. bovis specificity of the polC target.ConclusionsUnder the experimental conditions applied, this rtPCR test efficiently detected M. bovis in extended bovine semen straw samples from DNA extracted using both semi-automated extraction platforms, irrespective of prior centrifugation of extended semen.

Improvement of angle measurement sensitivity using second harmonic wave interference

This paper presents the angular displacement measurement based on second harmonic generation (SHG). In our previous study, the angular resolution of sub-arcsecond was achieved. In this paper, three kinds of sensors based on SHG are reported. In these three sensors, the angular displacement is determined from the intensity of the SHG signal since the SHG is sensitive to the angle of the incident light. Two of these sensors in this study utilize the interference of SHG to improve the sensitivity performance. Comparing with the sensors which have been studied before, interference type sensors in this study allow the femtosecond laser to pass through the nonlinear optical crystal twice, which leads to a significant sensitivity improvement. The principles were verified by both simulation and experiment. In the simulation part, the sensitivities of these three kinds of sensors are compared. In the experiment, the sensitivities were confirmed to be consistent with the simulation. Additionally, the resolution of the presented systems is evaluated and sub-0.1-arcsecond is achieved.