Estimates Of Sensitivity Research Articles

Sensitivity and specificity of relative apical sparing pattern of global longitudinal strain in the diagnosis of cardiac amyloidosis: a systematic review and meta-analysis

Abstract Background Cardiac amyloidosis (CA), characterized by a progressive infiltration of amyloid fibrils in cardiac tissue, presents a diagnostic challenge due to its resemblance to other cardiomyopathies by echocardiography. Although reduced left ventricular (LV) global longitudinal strain associated with a relative apical sparing pattern (RELAPS) has been associated with CA, its diagnostic accuracy has varied across studies. Purpose The aim of this systematic review and meta-analysis was to assess the diagnostic accuracy of using a quantitative RELAPS on 2D-echocardiography to differentiate patients with either transthyretin (ATTR) or light-chain (AL) cardiac amyloidosis from those with other causes of cardiomyopathies. Methods A comprehensive literature search was performed using PubMed, Scopus, Web of Science, and Cochrane databases from inception till May 2024. We included cohort original studies that reported the sensitivity and specificity of a quantitative RELAPS in differentiating ATTR-CA or AL-CA from other types of cardiomyopathies. RELAPS was defined as average apical longitudinal strain segments divided by the sum of average basal plus average mid longitudinal strains segments. Meta-analysis was performed using bivariate random effects regression to calculate sensitivity and specificity summary estimates. Subgroup analysis assessed the impact of different RELAPS cutoff values (1.0 and 0.9) on diagnostic performance. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results A total of 20 studies with 4308 patients were included in our meta-analysis, comprising 2,063 CA patients: 1,066 with ATTR-CA, 942 with AL-CA, and 55 unclassified CA patients. Eight studies used a cutoff of ≥0.9 to define the presence of RELAPS, while twelve studies used a cutoff of ≥1.0. The estimated sensitivity of RELAPS irrespective of the cutoff used for cardiac amyloidosis was 67% (95% confidence interval (CI): 59 - 75), with a specificity of 85% (95% CI: 78 - 90). Subgroup analysis revealed that a RELAPS cutoff >1.0 had higher specificity (87%, 95% CI: 79 - 93) but lower sensitivity (57%, 95% CI: 40 - 72), whereas a cutoff of 0.9 showed higher sensitivity (69%, 95% CI: 65 - 73) but lower specificity (83%, 95% CI: 75 - 90). Conclusion Our results confirm that the RELAPS pattern is an effective and simple diagnostic tool for detecting cardiac amyloidosis using echocardiography, demonstrating fairly good sensitivity and excellent specificity. Moreover, the choice of RELAPS cutoff value impacts diagnostic performance, highlighting its potential role in enhancing the accuracy of echocardiographic evaluations in suspected cases of cardiac amyloidosis. Forest Plot

Diagnostic accuracy of node-RADS for the detection of lymph node invasion: a systematic review and meta-analysis.

To conduct a meta-analysis assessing the diagnostic performance of the node reporting and data system (Node-RADS) for detecting lymph node (LN) invasion. We performed a systematic literature search of online scientific publication databases from inception up to July 31, 2024. We used the quality assessment of diagnostic accuracy studies-2 (QUADAS-2) to assess the study quality, and heterogeneity was determined by the Q-test and measured with I2 statistics. We employed the hierarchic summary ROC (HSROC) model to estimate the summary sensitivity and specificity. Subgroup analyses were conducted according to the imaging modality and cutoff values. A total of 13 studies involving 1341 participants met the inclusion criteria. Pooled summary estimates of sensitivity, specificity, and area under the curve of HSROC were 0.79 (95% CI: 0.66-0.88), 0.86 (95% CI: 0.80-0.90), and 0.90 (95% CI: 0.87-0.92). Subgroup analysis showed that the pooled sensitivity and specificity for CT were 0.74 (95% CI: 0.63-0.83) and 0.84 (95% CI: 0.74-0.91), whereas for MRI were 0.84 (95% CI: 0.59-0.95) and 0.88 (95% CI: 0.81-0.93), respectively. Node-RADS demonstrates the promising potential for the prediction of LN invasion, with high specificity but moderate sensitivity, particularly with optimal cutoff value ≥ 3. Indirect comparisons showed no significant difference between CT and MRI regarding overall diagnostic accuracy. Question Since the Node-RADS has been proposed, a number of studies have assessed its diagnostic performance for evaluating LN invasion. Findings Node-RADS demonstrated high specificity but moderate sensitivity, and cutoff ≥ 3 is the optimal threshold; indirect comparison suggested no significant difference between CT and MRI. Clinical relevance This study synthesized currently available evidence on studies of utilizing Node-RADS for assessing LNI in patients with various cancers, providing valuable insights for radiologists for utilizing this new risk scoring system in clinical practice.

Pliocene Warmth and Patterns of Climate Change Inferred From Paleoclimate Data Assimilation

AbstractAs the last time period when concentrations were near 400 ppm, the Pliocene Epoch (5.33–2.58 Ma) is a useful paleoclimate target for understanding future climate change. Existing estimates of global warming and climate sensitivity during the Pliocene rely mainly on model simulations. To reconstruct Pliocene climate and incorporate paleoclimate observations, we use data assimilation to blend sea‐surface temperature (SST) proxies with model simulations from the Pliocene Modeling Intercomparison Project 2 and the Community Earth System Models. The resulting reconstruction, “plioDA,” suggests that the mid‐Pliocene (3.25 Ma) was warmer than previously thought (on average 4.1°C warmer than preindustrial, 95% CI = 3.0°C–5.3°C), leading to a higher estimate of climate sensitivity (4.8°C per doubling of , 90% CI = 2.6°C–9.9°C). In agreement with previous work, the tropical Pacific zonal SST gradient during the mid‐Pliocene was moderately reduced (°C, 95% CI = –0.4°C). However, this gradient was more reduced during the early Pliocene (4.75 Ma, °C, 95% CI = –°C), a time period that is also warmer than the mid‐Pliocene (4.8°C above preindustrial, 95% CI = 3.6°C–6.2°C). PlioDA reconstructs a fresh North Pacific and salty North Atlantic, supporting Arctic gateway closure and contradicting the presence of Pacific Deep Water formation. Overall, plioDA updates our view of global and spatial climate change during the Pliocene, as well as raising questions about the state of ocean circulation and the drivers of differences between the early and mid‐Pliocene.

Causal Discovery and Reasoning for Continuous Variables with an Improved Bayesian Network Constructed by Locality Sensitive Hashing and Kernel Density Estimation

The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous attribute values. As a non-parametric distribution-free method, kernel density estimation (KDE) is applied in the conditional independence (CI) test. The skeleton of the BN is constructed utilizing the test based on mutual information and conditional mutual information, delineating potential relational connections between parents and children without imposing any distributional assumptions. In the searching stage of BN structure learning, the causal relationships between variables are achieved by using the conditional entropy scoring function and hill-climbing strategy. To further enhance the computational efficiency of our method, we incorporate a locality sensitive hashing (LSH) function into the KDE process. The method speeds up the calculations of KDE while maintaining the precision of the estimates, leading to a notable decrease in the time required for computing mutual information, conditional mutual information, and conditional entropy. A BN classifier (BNC) is established by using the computationally efficient BN learning method. Our experiments demonstrated that KDE using LSH has greatly improved the speed compared to traditional KDE without losing fitting accuracy. This achievement underscores the effectiveness of our method in balancing speed and accuracy. By giving the benchmark networks, the network structure learning accuracy with the proposed method is superior to other traditional structure learning methods. The BNC also demonstrates better accuracy with stronger interpretability compared to conventional classifiers on public datasets.

Current status of DECIGO and B-DECIGO

We present recent advancements in the Japanese space-based gravitational-wave detectors DECIGO (Deci-Hertz Gravitational-wave Observatory) and B-DECIGO, focusing on refined sensitivity estimation, binary black hole localization with B-DECIGO and DECIGO’s capability to measure the early universe’s reheating temperature. Through simulations and experiments involving back-linked interferometers, pre-stabilized lasers and optimized satellite orbits, we demonstrate key techniques that improve signal-to-noise ratios across a broad frequency range. Our work incorporates advanced parameter adjustments, including mirror geometry optimization and noise reduction techniques such as square completion and optical springs, leading to enhanced signal-to-noise ratios over a wide frequency range. These developments pave the way for B-DECIGO and DECIGO to explore high-redshift cosmology and early universe phenomena.

Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis.

Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for predicting PNI. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of radiomics models in predicting PNI in CRC. A comprehensive literature search was conducted across PubMed, Embase, and Web of Science for studies published up to July 28, 2024. Inclusion criteria focused on studies using radiomics models to predict PNI in CRC with sufficient data to construct diagnostic accuracy metrics. The quality of the included studies was assessed using QUADAS-2 and METRICS tools. Pooled estimates of sensitivity, specificity, and area under the curve (AUC) were calculated. Subgroup analyses were performed based on imaging modalities, segmentation methods, and other variables. Twelve studies comprising 2853 patients were included in the systematic review, with ten studies contributing to the meta-analysis. The pooled sensitivity and specificity for radiomics models in predicting PNI were 0.74 (95% CI: 0.63-0.82) and 0.85 (95% CI: 0.79-0.90), respectively, in the training cohorts. In the validation cohorts, the sensitivity was 0.65 (95% CI: 0.57-0.72), and specificity was 0.85 (95% CI: 0.81-0.89). The AUC was 0.87 (95% CI: 0.63-0.82) for the training cohorts and 0.84 (95% CI: 0.81-0.87) for the validation cohorts, indicating good diagnostic accuracy. The METRICS scores for the included studies ranged from 65.8 to 85.1%, with an overall average score of 67.25%, reflecting good methodological quality. However, significant heterogeneity was observed across studies, particularly in sensitivity and specificity estimates. Radiomics models show promise as a non-invasive tool for predicting PNI in CRC, with moderate to good diagnostic accuracy. However, the current study's limitations, including reliance on retrospective data, geographic concentration in China, and methodological variability, suggest that further research is needed. Future studies should focus on prospective designs, standardization of methodologies, and the integration of advanced machine-learning techniques to improve the clinical applicability and reliability of radiomics models in CRC management.

Association between SPISE and NAFLD in patients with type 2 diabetes

AimsNon-alcoholic fatty liver disease (NAFLD) is closely related to type 2 diabetes (T2D), with reduced insulin sensitivity being a key factor in their disrupted metabolic processes. The single point insulin sensitivity estimator (SPISE) is a novel index. This study aims to explore the association between SPISE and NAFLD in T2D population.MethodsThis study included a total of 2,459 patients with T2D. SPISE was calculated based on high density lipoprotein-cholesterol (HDL-c), triglycerides (TG), and body mass index (BMI). Participants were categorized into NAFLD and non-NAFLD groups based on the results of ultrasonographic diagnosis. The relationship between SPISE and NAFLD was analyzed separately for each gender.ResultsThe overall prevalence of NAFLD is 38.5%. In females and males, the SPISE was significantly reduced in the NAFLD group compared to the non-NAFLD group (both P < 0.05). The prevalence of NAFLD showed a significant reduction across quartiles of the SPISE in both genders (both P < 0.05).Additionally, univariate correlation analysis showed a negative correlation between SPISE and NAFLD (both P < 0.05). In multivariate regression analysis, a reduced SPISE was identified as an independent risk factor for NAFLD (odds ratios of 0.572 and 0.737, 95% CI of 0.477–0.687 and 0.587–0.926, respectively).Moreover, the area under the receiver operating characteristic (ROC) curve for SPISE was 0.209 in females and 0.268 in males (95% CI of 0.175–0.244 and 0.216–0.320, respectively). These results are more meaningful than those of other variables.ConclusionSPISE is significantly reduced in NAFLD patients with T2D. Compared to other indicators, SPISE demonstrates superior predictive value in diagnosing NAFLD, and it is independent of gender.

Whole-genome sequencing of cell-free DNA reveals DNA of tumor origin in plasma from patients with colorectal adenomas.

The presence of circulating tumor DNA (ctDNA) in patients with colorectal adenomas remains uncertain. Studies using tumor-agnostic approaches report ctDNA in 10-15% of patients, though with uncertainty as to whether the signal originates from the adenoma. To obtain an accurate estimate of the proportion of patients with ctDNA, a sensitive tumor-informed strategy is preferred, as it ensures the detected signal originates from the adenoma. Here, tumor-informed whole-genome sequencing-based ctDNA analysis (MRD-EDGESNV) was applied to two independent cohorts. Cohort 1, comprising 93 patients with stage III colorectal cancer (CRC) and 40 healthy individuals, was used to establish the signal threshold at 95% specificity. This threshold was then applied to Cohort 2, consisting of 22 patients with symptomatic and 20 with asymptomatic adenomas. In stage III, MRD-EDGESNV had an area under the curve of 0.98. ctDNA was detected in 50% and 25% of patients with symptomatic and asymptomatic adenomas, respectively. The median adenoma plasma tumor fraction was 5.9 × 10-5. These finding not only demonstrate the feasibility of ctDNA detection in patients with colorectal adenomas, but also provides an estimate of the necessary sensitivity required to detect these lesions, paving the way for future ctDNA-based screening strategies.

Assessing the robustness and implications of econometric estimates of climate sensitivity

Assessing the robustness and implications of econometric estimates of climate sensitivity

An estimate of the Arctic climate sensitivity based on the Arrhenius’s rule

Estimating the magnitude of the Arctic equilibrium climate sensitivity is key for understanding the climate dynamics of this polar region. Using a linear regression model, we find that the Arctic equilibrium climate sensitivity is 3.61°C. To the best of our knowledge, this is the first time that an estimate of the Arctic equilibrium climate sensitivity is provided. The obtained results confirm that this region is more sensitive to climate change than the rest of the world. The Arctic sensitivity is almost one and a half times that of the global sensitivity. In this paper, we have used the estimated Arctic equilibrium climate sensitivity to obtain projections of the Arctic temperature under different forcing scenarios.

An innovative method for sensitive spectrophotometric estimation of doxorubicin hydrochloride in injection and biological fluids by complex formation with dysprosium

An innovative method for sensitive spectrophotometric estimation of doxorubicin hydrochloride in injection and biological fluids by complex formation with dysprosium

Prediction accuracy of discrete choice experiments in health-related research: a systematic review and meta-analysis

Discrete choice experiments (DCEs) are increasingly used to inform the design of health products and services. It is essential to understand the extent to which DCEs provide reliable predictions outside of experimental settings in real-world decision-making situations. We aimed to compare the prediction accuracy of stated preferences with real-world choices, as modelled from DCE data. We searched six databases for health-related studies that used DCE to assess external validity and reported on predicted versus real-world choices, up to July 2024. A generalised linear mixed model was used for a meta-analysis to jointly pool the sensitivity and specificity. Heterogeneity was assessed using the I 2 statistic, and sources of heterogeneity using meta-regression. This study is registered with PROSPERO (CRD42023451545). We identified 14 relevant studies, of which 10 were included in the meta-analysis. Most studies were conducted in high-income countries (11/14, 79%) from the European region (9/14, 64%) and analysed using mixed logit models (5/14, 36%). Pooled sensitivity and specificity estimates were 89% (95% CI:77-95, I 2=97%) and 52% (95% CI:32-72, I 2=95%), respectively. The area under the SROC curve (AUC) was 0.81 (95% CI:0.77-0.84). Our meta-regression found that DCEs for prevention-related choices had higher sensitivity than treatment-related choices. DCEs conducted under clinical settings and analysed using the heteroskedastic multinomial logit model, incorporating systematic preference heterogeneity and random opt-out utility, had higher specificity than non-clinical settings and alternative models. DCEs are valuable for capturing health-related preferences and possess reasonable external validity to predict health-related behaviours, particularly for opt-in choices. Contextual factors (e.g., type of intervention, study setting, analysis method) influenced the predictive accuracy. JJO is supported by an Australian National Health and Medical Research Council Emerging Leadership Investigator Grant (GNT1193955). EBG is supported by the Dutch Research Council (NWO-Talent-Scheme-Vidi-Grant No, 09150171910002). YZ is supported by an Australian Government Research Training Program (RTP) scholarship.

Predictive Value of the PaO2/FIO2 Ratio for Mortality in Patients with Acute Respiratory Distress Syndrome: A Systematic Review and Meta-analysis.

Background Despite the controversy regarding its clinical utility, the PaO2/FIO2 ratio has been used to define the severity of acute respiratory distress syndrome (ARDS). This systematic review and meta-analysis (SRMA) details summary estimates of the predictive performance of PaO2/FIO2 ratio in predicting mortality in patients with ARDS. Methods To clarify the integrated diagnostic accuracy, we included studies in which the study population comprised patients with ARDS in any clinical setting, included adult patients (≥18 years old), and evaluated mortality. The MEDLINE and Cochrane Central Registry of Controlled Trials databases were searched for articles in English. We performed SRMA on the accuracy of the diagnostic prognostic tests using the Quality Assessment of Diagnostic Accuracy Studies-2 tool to evaluate the risk of bias. We obtained summary point estimates of sensitivity and specificity and calculated the area under the receiver operating characteristic (AUROC) curve of the summary receiver operating characteristic curve with 95% confidence intervals (CIs). Results Twenty-eight trials with 38270 patients were included in the quality assessment. Most of the studies were conducted in intensive-care units. Overall, the risk of bias is high. For PaO2/FIO2 of 100 and 200 the pooled sensitivity, specificity, and AUROC were 44.8% (95% CI, 38.1%-51.7%), 70.6% (95% CI, 65.9%-74.9%), 0.60 (0.58-0.64) and 83.9% (95% CI, 78.9%-87.8%), 26.1% (95% CI, 20.8%-32.1%), 0.64 (0.60-0.69), respectively. Conclusion The PaO2/FIO2 ratio alone did not have impressive prediction accuracy for mortality in patients with ARDS and might not be able to be used solely as a clinical prognostic tool.

Diagnostic accuracy of Agile-4 score for liver cirrhosis in patients with metabolic dysfunction-associated steatotic liver disease. A systematic review and meta-analysis of diagnostic test accuracy studies.

A novel noninvasive score, Agile-4 score, combining liver stiffness measurements, aspartate aminotransferase/alanine aminotransferase, platelet count, diabetes status and sex has been developed for the identification of cirrhosis in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). We assessed the performance of Agile-4 for ruling-in/out liver cirrhosis in MASLD patients. We searched Medline, Cochrane library, Web of science, Scopus and Echosens website up to May 2024. Eligible studies assessed the accuracy of Agile-4 for ruling-in (≥0.565) and ruling-out (<0.251) liver cirrhosis, using biopsy as the reference standard, at predefined thresholds. We calculated pooled sensitivity and specificity estimates for both Agile-4 thresholds alongside 95% confidence intervals following bivariate random-effect models. We assessed the risk of bias using Quality Assessment of Diagnostic Accuracy Studies-2 tool. We included seven studies with 6037 participants. An Agile-4 score ≥0.565 yielded a pooled specificity of 0.93 (95% CI, 0.86-0.97). Similarly, an Agile-4 score <0.251 excluded cirrhosis with a summary sensitivity of 0.90 (0.80-0.95). Assuming a cirrhosis prevalence of 30%, the positive predictive value (PPV) for ruling-in cirrhosis was 80%, while the negative predictive value for ruling-out cirrhosis was 95%. Most studies were at high or unclear risk for bias due to concerns regarding patient selection and the blinding status of Agile-4 score interpretation in relation to biopsy results. Agile-4 score performs well for ruling-in/out liver cirrhosis in MASLD patients. Owing to the relatively low PPV, sequential application of the Agile-4 after fibrosis-4 index (FIB-4) testing might further enhance its performance.

Faecal immunochemical tests for patients with symptoms suggestive of colorectal cancer: An updated systematic review and multiple-threshold meta-analysis of diagnostic test accuracy studies.

Extending faecal immunochemical tests for haemoglobin (FIT) to all primary care patients with symptoms suggestive of colorectal cancer (CRC) could identify people who are likely to benefit from colonoscopy and facilitate earlier treatment. The aim of this work was to investigate the diagnostic accuracy of FIT across different analysers at different thresholds, as a single test or in duplicate (dual FIT). This systematic review and meta-analysis searched 10 sources (December 2022). Diagnostic accuracy studies of HM-JACKarc, OC-Sensor, FOB Gold, QuikRead go, NS-Prime and four Immunodiagnostik (IDK) tests in primary care patients were included. Risk of bias was assessed (QUADAS-2). Statistical syntheses produced summary estimates of sensitivity and specificity at any chosen threshold for CRC, inflammatory bowel disease and advanced adenomas separately. Sensitivity analyses investigated reference standard and population type (high, low or all-risk). Subgroup analyses investigated patient characteristics (e.g. anaemia, age, sex, ethnicity). Thirty-seven studies were included. At a threshold of 10 μg/g, pooled results for sensitivity and specificity (95% credible intervals) for CRC, respectively, were: HM-JACKarc (n = 16 studies) 89.5% (84.6%-93.4%) and 82.8% (75.2%-89.6%); OC-Sensor (n = 11 studies) 89.8% (85.9%-93.3%) and 77.6% (64.3%-88.6%); FOB Gold (n = 3 studies), 87.0% (67.3%-98.3%) and 88.4% (81.7%-94.2%). There were limited or no data on the other tests, dual FIT and relating to patient characteristics. Test sensitivity at a threshold of 10 μg/g highlights a requirement for adequate safeguards in test-negative patients with ongoing symptoms. Further research is needed into the impact of patient characteristics and dual FIT.

Efficient Finite-Difference Estimation of Second-Order Parametric Sensitivities for Stochastic Discrete Biochemical Systems

Biochemical reaction systems in a cell exhibit stochastic behaviour, owing to the unpredictable nature of the molecular interactions. The fluctuations at the molecular level may lead to a different behaviour than that predicted by the deterministic model of the reaction rate equations, when some reacting species have low population numbers. As a result, stochastic models are vital to accurately describe system dynamics. Sensitivity analysis is an important method for studying the influence of the variations in various parameters on the output of a biochemical model. We propose a finite-difference strategy for approximating second-order parametric sensitivities for stochastic discrete models of biochemically reacting systems. This strategy utilizes adaptive tau-leaping schemes and coupling of the perturbed and nominal processes for an efficient sensitivity estimation. The advantages of the new technique are demonstrated through its application to several biochemical system models with practical significance.

Sensitivity Analysis in Photodynamics: How Does the Electronic Structure Control cis-Stilbene Photodynamics?

The techniques of computational photodynamics are increasingly employed to unravel reaction mechanisms and interpret experiments. However, misinterpretations in nonadiabatic dynamics caused by inaccurate underlying potentials are often difficult to foresee. This work focuses on revealing the systematic errors in the nonadiabatic simulations due to the underlying potentials and suggests a thrifty approach to evaluate the sensitivity of the simulations to the potential. This issue is exemplified in the photochemistry of cis-stilbene, where similar experimental outcomes have been differently interpreted based on the electronic structure methods supporting nonadiabatic dynamics. We examine the predictions of cis-stilbene photochemistry using trajectory surface hopping methods coupled with various electronic structure methods (OM3-MRCISD, SA2-CASSCF, XMS-SA2-CASPT2, and XMS-SA3-CASPT2) and assess their ability to interpret experimental observations. While the excited-state lifetimes and calculated photoelectron spectra show consistency with experiments, the reaction quantum yields vary significantly: either completely suppressing cyclization or isomerization. Intriguingly, analyzing stationary points on the potential energy surface does not hint at any major discrepancy, making the electronic structure methods seemingly reliable when treated separately. We show that performing an ensemble of simulations with different potentials provides an estimate of the electronic structure sensitivity. However, this ensemble approach is costly. Thus, we propose running nonadiabatic simulations with an external bias at a resource-efficient underlying potential (semiempirical or machine-learned) for the sensitivity analysis. We demonstrate this approach using a semiempirical OM3-MRCISD method with a harmonic bias toward cyclization.

Validation of the effectiveness of pig farm repopulation protocol following African swine fever outbreaks in the Philippines

The African swine fever (ASF) epidemic has severely challenged the Philippines’ swine industry since 2019. The National African Swine Fever Prevention and Control Program (NASFPCP), launched in 2021, aims to provide guidance for managing ASF through surveillance, monitoring, and swine repopulation. This study evaluates the effectiveness of post-outbreak disinfection protocols and government-mandated measures for repopulation standard. Surveillance data from three repopulation phases—(I) depopulation, cleaning, and disinfection; (II) downtime (20 days); and (III) sentinel animals (40 days)—were collected from February 2020 to December 2021 in the province of Batangas. Time-to-detection of positive events were analyzed for different farm types, seasons, or location using survival analysis modeling. Probability of detecting infected farms at different sampling times was estimated in terms of sensitivity of the sampling time. Data from 145 swine farms, including 99 backyard and 46 commercial farms, revealed positive rates of 10.1 and 8.7%, respectively. The failure rate during repopulation surveillance was 9.66%, whereas 90.34% farms remained ASF negative. Sensitivity estimate increased from 18–21 to 89% by day 27, with sentinel animals on that day exhibiting the highest estimated sensitivity. This highlights the importance of sentinel pigs in the NASFPCP for effective ASF control in the Philippines. Survival analysis showed no statistically significant differences in the results between either farm type, season, or municipality level. Geographic mapping of surveyed farms and those with positive detections identified high-risk locations including San Juan and Lipa City as key areas of concern. Enhancing targeted surveillance is critical for improving an early ASF detection and national response in the Philippines.

Accuracy of cell-free fetal DNA in detecting chromosomal anomalies in women experiencing miscarriage: systematic review and meta-analysis.

To report the diagnostic accuracy of cell-free fetal DNA (cfDNA) in detecting fetal chromosomal anomalies in women experiencing miscarriage. PubMed, MEDLINE, EMBASE and Cochrane databases were searched from inception to June 2024. The inclusion criteria were women experiencing miscarriage (defined as pregnancy loss before 20 weeks of gestation) who underwent cfDNA screening for trisomies 21, 18and13, other autosomal aneuploidies, sex-chromosome aneuploidies and/or copy-number variants (CNVs). The index test was cfDNA screening for each of the chromosomal anomalies listed. The reference standard was cytogenetic analysis of pregnancy tissue. The quality of the studies was assessed using the revised tool for the quality assessment of diagnostic accuracy studies. Summary estimates of sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio were computed using the hierarchical summary receiver-operating-characteristics model. Seven studies (887 women) were included in the systematic review and meta-analysis. cfDNA had a sensitivity and specificity of 100% (95% CI, 81.5-100%) and 100% (95% CI, 99.1-100%), respectively, for trisomy 21, 100% (95% CI, 54.1-100%) and 100% (95% CI, 99.0-100%), respectively, for trisomy 18, and 88.9% (95% CI, 51.8-99.7%) and 100% (95% CI, 99.1-100%), respectively, for trisomy 13. The respective values for other autosomal trisomies were 75.8% (95% CI, 65.7-84.2%) and 99.4% (95% CI, 97.9-99.9%), while those for CNVs were 60.0% (95% CI, 14.7-94.7%) and 100% (95% CI, 97.4-100%). Failure of cfDNA testing was reported in 7.3% (95% CI, 5.7-9.2%) of cases. cfDNA has high diagnostic accuracy in detecting fetal trisomies 21, 18 and 13 in women experiencing miscarriage, while its accuracy for other autosomal aneuploidies and CNVs is only moderate. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Disentangling the effects of intermittent faecal shedding and imperfect test sensitivity on the microscopy-based detection of gut parasites in stool samples.

Gut-parasite transmission often involves faecal shedding, and detecting parasites in stool samples remains the cornerstone of diagnosis. However, not all samples drawn from infected hosts contain parasites (because of intermittent shedding), and no test can detect the target parasites in 100% of parasite-bearing samples (because of imperfect sensitivity). Disentangling the effects of intermittent shedding and imperfect sensitivity on pathogen detection would help us better understand transmission dynamics, disease epidemiology, and diagnostic-test performance. Using paediatric Giardia infections as a case-study, here we illustrate a hierarchical-modelling approach to separately estimating the probabilities of host-level infection (Ψ); stool-sample-level shedding, given infection (θ); and test-level detection, given infection and shedding (p). We collected 1-3 stool samples, in consecutive weeks, from 276 children. Samples (413 overall) were independently examined, via standard sedimentation/optical microscopy, by a senior parasitologist and a junior, trained student (826 tests overall). Using replicate test results and multilevel hierarchical models, we estimated per-sample Giardia shedding probability at [Formula: see text] and observer-specific test sensitivities at [Formula: see text] and [Formula: see text]. Gender-specific infection-frequency estimates were [Formula: see text] and [Formula: see text]. Had we used a (hypothetical) Perfect Test with 100% narrow-sense sensitivity (pPT = 1.0), the average probability of detecting Giardia in a sample drawn from an infected child (Ψ = 1.0) would have been Pr(d|i,PT) = Ψ×θ×pPT≈1.0×0.44×1.0≈0.44. Because no test can be >100% sensitive, Pr(d|i) (which measures clinical sensitivity) can only be brought above ~ 0.44 by tinkering with the availability of Giardia in stool samples (i.e., θ); for example, drawing-and-pooling 3 replicate samples would yield [Formula: see text]. By allowing separate estimation (and modelling) of pathogen-shedding probabilities, the approach we illustrate provides a means to study pathogen transmission cycles and dynamics in unprecedented detail. Separate estimation (and modelling) of true test sensitivity, moreover, may cast new light on the performance of diagnostic tests and procedures, whether novel or routine-practice.