False Negatives Research Articles

Predicting Additional Metastases in Axillary Lymph Node Dissection After Neoadjuvant Chemotherapy: Ratio of Positive/Total Sentinel Nodes.

The aim of the study was to determine the clinical value of the sentinel lymph node ratio (SLN-R) in predicting additional positive lymph nodes during axillary lymph node dissection (ALND) in breast cancer patients following neoadjuvant chemotherapy (NAC). A cross-sectional study was performed at a single institution evaluating data from 1521 BC patients. Inclusion criteria comprised cT1/cT4, cN0/cN1 status with positive post-NAC axillary staging by SLN/TAD, respectively, and subsequent ALND. The study included 118 patients, divided into two groups based on the presence or absence of additional node metastasis at ALND: 39 in the residual disease group (RD) and 79 in the non-residual disease group (nRD). Univariate logistic regression analysis of SLN-R was conducted to assess its predictive value, yielding an odds ratio (OR) of 7.79 (CI 1.92-29.5, p = 0.003). An SLN-R cut-off point of <0.35 was identified using ROC curve analysis, with a false-negative rate of 10.2%, as a predictor for no additional metastasis at ALND following post-NAC SLN/TAD positivity. The study concludes that SLN-R is a valuable predictor for determining the omission of ALND in cases where SLN/TAD is positive after NAC. This metric, in combination with other clinical variables, could help develop a nomogram to spare patients from ALND.

Exploring methylene blue as an effective alternative to radiotracer for sentinel lymph node biopsy in early breast cancer in resource-limited settings

Background: Sentinel lymph node biopsy is the recommended approach in the evaluation of axilla during breast cancer surgery. This study evaluated the results of patients who underwent methylene blue sentinel lymph node biopsy. Methods: The study included 35 female patients with T1, T2 and T3 tumours. 5 ml of 1% methylene blue was injected into the periareolar region. The axillary sentinel lymph node was found and removed, and axillary dissection was performed. The sentinel lymph node and axillary dissection specimen were histopathologically examined, and the results were compared. Results: The sentinel lymph node was found in 33 (90%) patients. There were no complications associated with the use of methylene blue dye. Methylene blue-stained sentinel nodes were histologically negative for malignancy in 42% and positive in 58% of patients. Corresponding to sentinel nodes, the final axillary staging was negative in 60% and positive in 40% of patients. On diagnostic accuracy parameters, the sensitivity of the methylene blue method in early breast cancer (cT1-3N0M0) patients was 90.47%, and the specificity was 100%. The PPV of the technique under study was found to be 100% and NPV was 85.71%. The accuracy rate was 93%, and the false negative rate was identified as 9.5%. Conclusions: Sentinel lymph node biopsy by methylene blue is a method that can be applied with high accuracy. Methylene blue can be considered as an alternative to radio colloid dye in sentinel lymph node biopsy in a limited resource setting.

An evaluation of the first ten years of Newborn Screening for cystic fibrosis in Ireland

Abstract Ireland has the world’s highest incidence of cystic fibrosis (CF). Newborn screening for CF (NBSCF) improves outcomes but identifies carriers and may yield false positive/inconclusive results. This study aimed to evaluate the performance of the first 10 years of the Irish NBSCF programme. Data collected by the programme between 01st July 2011 and 30th June 2021 were analysed, including cases of CF, CF carriers and Cystic Fibrosis Screen Positive Inconclusive Diagnosis (CFSPID). Microsoft Excel was used to calculate sensitivity, specificity and positive predictive value (PPV). Results were compared to European Cystic Fibrosis Society (ECFS) standards. Overall 650,809 neonates were screened, with 290 cases of CF, 533 CF carriers and 21 CFSPIDs reported. NBSCF identified 284 (98%) of the children with CF, sensitivity was 97.93% (95% CI 96.29-99.57), specificity was 99.91% (95% CI 99.91-99.92%), PPV was 0.34 (95% CI 0.31-0.37). The observed incidence of CF was 1 in 2,203, still Europe’s highest. Of the six undetected cases, most were of Asian ethnicity, suggesting panel limitations for diverse ethnicities.The Irish NBSCF programme exceeded ECFS standards. Most false negatives were caused by CFTR mutations undetected by the genetic panel. Increasing the size of the genetic panel increases detection of CFSPID and carriers and is not currently recommended. Ongoing validity monitoring is vital as the Irish population diversifies. Key messages • The Irish Newborn Screening Programme for Cystic Fibrosis is effective. • Ongoing validity monitoring is vital as the Irish population diversifies.

Inferring diagnoses from prescription data: a machine-learning approach

Abstract Estimating disease prevalence is a fundamental while challenging public-health goal: in the absence of solid diagnostic data, it typically requires expensive and time intensive studies. Analyzing existing medical records is an alternative, complementary approach. In particular, prescription records offer a glimpse into the health status of populations, prescribing patterns, and operational dynamics. We asked whether these could be used to infer diagnostics and, therefore, estimate disease prevalence. We used the Portuguese electronic medical prescriptions dataset (ADD NUMBERS) and trained a machine learning model that captures the probability that specific drugs are prescribed for the same or related medical conditions. We 1) constructed a manually-curated dataset of diseases and associated medications; 2) used this dataset to train embeddings models; 3) tested these models against human-classified data; 4) used the trained/tested model to uncover comorbidities associated with a) antibiotic prescription (to validate the model), and b) the impact of COVID-19 in prescription patterns of chronic-diseases. The model was able to accurately identify drugs used for targeting ten specific diseases, with few false positives and false negatives. Our analysis showed that the COVID-19 pandemic had almost no effect on the number of prescriptions for some chronic diseases (such as diabetes and depression), but, importantly, had a strong impact on the number of new diagnoses, with new users of diabetes medication in 2019 dropping by more than 16.8% in 2020. Medical prescriptions offer a promising tool to quickly assess disease prevalence, underdiagnosis, and allow a broad overview of the medical status of the population, especially in the face of health emergencies that strongly pressure the medical system. Key messages • We propose a new approach to infer diagnostics from prescription data with several promising applications. • We propose a new approach to infer diagnostics from prescription data with several promising applications.

FlawCheck: Detecting Smart Contract Vulnerabilities Based on Symbolic Execution

ABSTRACTSmart contracts are turing‐complete computer programs running on blockchains. Like traditional programs, smart contracts are also vulnerable. However, unlike traditional programs, it is very difficult to modify smart contracts once they are deployed on the blockchain. Therefore, reducing potential vulnerabilities in contracts before deployment to the blockchain is very important. The existing smart contract detection tools mostly fail to fully consider the complex control flow relationships within smart contracts, leading to false positives and false negatives. To address these problems, we propose FlawCheck, a vulnerability detection tool based on symbolic execution. First, it compiles smart contract source code into bytecode. Then, it disassembles bytecode into an opcode sequence, building the dependencies of contract control flow. Next, it performs preliminary analysis on the information generated during the simulated execution on the Ethereum virtual machine to identify suspicious vulnerability paths. Finally, it detects these suspicious paths by using symbolic execution. We verified that FlawCheck can detect five types of smart contract vulnerabilities. Experimental results on a dataset of 13016 real contracts shows that FlawCheck has higher accuracy than other tools.

Comparison of angiography-derived index of microcirculatory resistance measurements between low and high frame-rate acquisitions: the prospective ADMIRAL-study

Abstract Background Angiography-derived index of microcirculatory resistance (angio-IMR) is a novel, wire-free, angiography-based method (QFR-IMR®) to assess coronary microvascular dysfunction. If a frame-rate of 15 frames per second (fps) is recommended, there is an interest in decreasing this frame-rate to reduce X-ray exposure. However, the impact of frame-rate reduction on angio-IMR quantification has not been investigated. Purpose To compare angio-IMR at 7.5 fps (angio-IMRlow) to angio-IMR at 15 fps (angio-IMRhigh) in a multicentric study. Methods From October 2023 to February 2024, we conducted a multicentre prospective study including consecutive patients referred for an invasive coronary angiogram with a final diagnosis of ischemia with no obstructive coronaries arteries (INOCA) (positive stress test with normal coronary angiogram) or acute coronary syndrome (ACS). Main exclusion criteria were creatinine clearance&amp;lt;45ml/kg/1.73m2, factors that might substantially impact the coronary angiograms quality (e.g: aortic stenosis, supraventricular tachycardia), and TIMI flow &amp;lt;3. In ACS patients, angio-IMR was measured after revascularisation. The same two acquisitions with different angles (≥25°) were recorded at a rate of 7.5 fps and a rate of 15 fps to measure angio-IMR in the selected vessel (culprit coronary vessel in ACS patients or left anterior descending artery in patients with INOCA). Angio-IMR was measured with dedicated software (QFR-IMR® RE, Medis Medical Imaging). Correlation between angio-IMRlow and angio-IMRhigh was assessed by linear regression and Bland-Altman test. The diagnostic accuracy of angio-IMRlow to predict microcirculatory dysfunction (defined as angio-IMRhigh≥ 25 for INOCA and ≥ 40 for ACS) was also assessed. Statistical significance was awarded by p≤0.05. Results Of 87 consecutive eligible patients, 75 were included in the study, comprising 57 with ACS and 18 with INOCA (mean age 61±12 years, 72% male). Angio-IMRlow (median=35; [IQR:28-48]) was slightly but significantly higher (p=0.04) than angio-IMRhigh (median=34; [IQR:25-45]). Angio-IMRlow and angio-IMRhigh were significantly correlated (r=0.90; p&amp;lt;0.0001) (Figure 1). Using Bland-Altman analysis, mean difference between the two measurements was 2.2 (95% CI 0.1-4.4) with a significant higher value with angio-IMRlow (p=0.04). Moreover, as value of Angio-IMRhigh increases, the difference between angio-IMRlow and angio-IMRhigh becomes more significant (Figure 2). Using angio-IMRhigh to diagnose microcirculatory dysfunction as reference, angio-IMRlow achieved positive and negative predictive values of 83% and 85% respectively with 6 false positives, and 6 false negatives. Conclusion Correlation between angio-IMRhigh and angio-IMRlow is acceptable. Lowering frame acquisition rate reduces radiation exposure without relevantly impact on the diagnostic accuracy of angio-IMR, which can contribute to a widespread use of angio-IMR.Correlation between angioIMRlow and highBland-Altman plot

Echocaridographic assessment of pulmonary hypertension: improving the probability

Abstract Introduction Pulmonary hypertension (PH) is a cardio-pulmonary disorder characterised by raised pulmonary artery pressure (mPAP &amp;gt;20mmHg) leading to right ventricular failure, and death if left untreated. Transthoracic echocardiography (TTE) assessment of PH uses a probability-based approach, combining peak tricuspid regurgitant velocity with other supporting signs. We evaluated the latest ESC TTE PH guidelines in a real-world UK dataset and identified markers that may improve the sensitivity of the existing algorithm. Method Retrospective analysis of 447 patients referred for initial PH assessment. This was a select population of patients who underwent both TTE and right heart catheter (RHC) (mean time to RHC = 40±30 days). In addition to current TTE PH probability markers we analysed right ventricular free wall strain (RVFWS). Measurements were made by a single operator using Tomtec Arena 2020. Crosstabulation, ROC curve analysis and T-tests were used to compare variables between PH and no PH. Results Mean sample age was 64±15 yrs (61% female). 6.9% (n=4) of the no PH group had pulmonary mid-systolic notching, which may be due to pseudo-notching attributable to incomplete Doppler envelope. Sensitivity and specificity of current ESC TTE probability guidelines were 83%, and 65% respectively (95% C.I.). However, within the low probability group (i.e. those felt to be less likely to have PH) 62% (n=63) had confirmed PH at RHC (mPAP = 28±9). The addition of RVFWS as an additional marker to the existing supporting signs of PH (Table 1) resulted in an improvement of the sensitivity of the algorithm to 92% with a mild reduction in specificity (63%). The number of false negatives were reduced by 51%. Of these, the majority were in the low TTE probability group (n = 32). Conclusions Using current PH TTE probability algorithms classifies a significant proportion of those with PH as having a low TTE PH probability. The addition of RVFWS improves the sensitivity of the existing algorithm to 92% and reduces false negatives by 51%.

The golden batch-driven root cause analysis for anomalies in bioreactor fermentation process

Bioreactors are essential for the production of biopharmaceuticals and bioproducts, requiring continuous monitoring to ensure quality assurance. Manual processes in manufacturing plants often lead to anomalies such as out-of-trend and out-of-spec incidents, necessitating extensive root cause analysis that typically takes 2–8 weeks. This paper introduces an innovative methodology that uses the golden batch profile as a benchmark to identify deviations and root causes in subsequent industrial batches. The methodology involves normalizing the data and calculating the variances of a specified batch from the golden batch profile. By examining the contribution of each critical process parameter to these variances, the study highlights their importance in root cause analysis. The application of this methodology to the IndPenSim dataset demonstrated its effectiveness by significantly reducing false positives and negatives compared to traditional PCA-based methods. Emphasis on the deviations of critical quality attributes and critical process parameters from the specified batch compared to the golden batch profile offers valuable insights into industrial process analysis. This approach not only enhances anomaly detection accuracy but also improves the efficiency and reliability of biopharmaceutical and bioproduct manufacturing processes.

Diagnosing heart failure with preserved ejection fraction in patients with unexplained dyspnoea using left ventricular pressure-volume loops at rest and on exertion

Abstract Background To date, diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, especially in euvolaemic patients presenting with unexplained dyspnoea on exertion and reduced exercise capacity. A stepwise approach was introduced with the HFA-PEFF diagnostic algorithm*, in which a relevant number of patients has to undergo invasive workup for a final judgement. Purpose The present study aimed to examine a population of patients with unexplained dyspnoea on exertion, using the diagnostic methods endorsed by the HFA-PEFF algorithm as well as left ventricular (LV) pressure-volume loops at rest, on exertion, and during caval occlusion, in order to identify and describe different phenotypes of HFpEF. Methods We prospectively included patients with unexplained dyspnoea on exertion, a left ventricular ejection fraction (LVEF) of ≥ 50%, and without an obvious cause of the dyspnoea. All patients underwent a comprehensive invasive haemodynamic workup. After invasive rule-out of a haemodynamically relevant coronary artery stenosis, we performed a right heart catheterization at rest (R) and on exertion (E) with an arm ergometer. Real-time continuous pressure-volume loops were obtained using a 7-French conductance catheter at rest, on exertion, and under temporal preload reduction via partial occlusion of the vena cava inferior. Results Thirty patients were included. Two patients had to be excluded from further analysis (one due to a technical failure of the conductance catheter and one with severe hypertension (systolic blood pressure &amp;gt; 200 mmHg) during the assessment). The 28 patients in this analysis had a mean age of 64.4±9.7 years and 57% were female. Eleven patients fulfilled invasively easily obtainable diagnostic criteria of HFpEF (mean pulmonary capillary wedge pressure (mPCWP) at rest ≥ 15 mmHg, exertional mPCWP ≥ 25 mmHg, LV end-diastolic pressure (LVEDP) at rest ≥ 16 mmHg, or exertional LVEDP ≥ 25 mmHg) according to the current guideline*. An additional 15 patients had a normal mPCWP and LVEDP, but fulfilled diagnostic criteria for HFpEF measured by the conductance catheter (time constant of LV relaxation tau &amp;gt; 48 ms and/ or chamber stiffness b &amp;gt; 0.27). We referred to this group as "early HFpEF". Patients with early HFpEF had a significantly higher systolic blood pressure, a worse LV relaxation time, and were more likely to exhibit chronotropic insufficiency compared to the HFpEF group (Table 1). HFpEF patients were characterised by significantly higher natriuretic peptides, LVEDP on exertion, and LV stiffness compared to patients with early HFpEF (pressure-volume loops are depicted in Figure 1). Conclusion Invasive haemodynamic testing for HFpEF in patients with unexplained dyspnoea on exertion via right heart catheterization without obtaining LV pressure-volume loops had a false negative rate of 58%. Better detection, the prognosis, and treatment of this "early HFpEF" population warrants further research.Table 1Figure 1

Detection of Aspergillus flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms

Plant diseases cause economic losses and health risks, such as aflatoxins linked to liver cancer. These toxins, produced by fungi like Aspergillus flavus in figs, are often detected late through invasive methods or visual inspection. Since Spain, particularly Extremadura, is a key fig producer, alternative detection methods are essential to preventing aflatoxins in the food chain. The aim of this research is the early detection of Aspergillus flavus fungus using non-invasive techniques with hyperspectral imaging and applying artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a fully connected neural network was used to analyze the spectral signature associated with each pixel; secondly, the wavelet transform was applied to each spectral signature. The resulting images were fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process that was performed. The results are promising, with 83% accuracy, 82.75% recall, and 83.25% F1-measure for the fully connected neural network. The high F1-measure demonstrates that the model’s performance is good. The model has a low incidence of false positives for samples that contain aflatoxin, while a higher number of false positives appears in healthy controls. Due to the presence of false negatives, this class also has a high recall. The convolutional neural network results, accuracy, recall, and F1 are 77.25%, indicating moderate model performance. Only class 3, with higher aflatoxin concentration, achieves high precision and low false positive incidence. Healthy controls exhibit a high presence of false negatives. In conclusion, we demonstrate the effectiveness of pixel-level analysis in identifying the presence of the fungus and the viability of the non-invasive techniques applied in improving food safety. Although further research is needed, in this study, the fully connected neural network model shows good performance with lower energy consumption.

Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model

Abstract Background Although statistical models have been commonly used to identify patients at risk of cardiovascular disease (CVD) for preventive therapy, these models tend to over-recommend therapy [1]. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all individuals, as modelling in this context is currently inadequate. Aims To develop and validate a novel deep learning model, the Transformer-based Risk assessment survival model (TRisk) model, for prediction of 10-year risk of CVD in both the general population and individuals with diabetes. Methods An open cohort of 3 million adults aged 25 to 84 years free of CVD and statin use was identified using linked primary and secondary electronic health records from 291 and 98 general practices in England, which were used for TRisk model development and external validation, respectively. Comparison against two commonly recommended statistical models (QRISK3 [2] and SCORE2 [3]) and a deep learning derivation of QRISK3 was conducted. Additional analyses compared discriminatory performance in other age groups, by sex, and across categories of socioeconomic status. Similar analyses were conducted in a subgroup of 59 thousand patients with diabetes. Results TRisk demonstrated superior discrimination (C index in the general population: 0.910; 95% confidence interval [CI]: 0.906 to 0.913). TRisk’s performance was found to be less sensitive to population age range than the benchmark models and outperformed other models also in all stratified analyses. All models were overall well-calibrated. In decision curve analyses, TRisk demonstrated greater net benefit than benchmark models across the range of relevant thresholds. In impact analyses, at the widely recommended 10% risk threshold and the higher 15% threshold for the general population, TRisk reduced both the total number of patients classified at high risk (by 27% and 39% respectively) and the number of false negatives as compared with currently recommended strategies. TRisk similarly outperformed other models in patients with diabetes. Compared with the widely recommended treat-all policy approach for patients with diabetes, TRisk at a 10% risk threshold would lead to deselection of 24% of individuals with a small fraction of false negatives (0.2% of cohort). Conclusion TRisk enabled a more targeted selection of individuals at risk of CVD compared to benchmark statistical and deep learning models, in both the general population and patients with diabetes. Incorporation of TRisk into routine clinical care would allow a reduction in the number of treatment-eligible patients in general population and subgroup with diabetes by approximately one-third and one-quarter respectively while preventing at least as many events as with currently adopted approaches.Models' discrimination on both cohortsImpact analyses for both cohorts

Breast Lesion Detection for Ultrasound Images Using MaskFormer.

This study evaluates the performance of the MaskFormer model for segmenting and classifying breast lesions using ultrasound images, addressing ultrasound's limitations. Ultrasound used for breast cancer detection faces challenges like low image contrast and difficulty in the detection of small or multiple lesions, further complicated by variability based on operator skill. Initial experiments with U-Net and other CNN-based models revealed constraints, such as early plateauing in model loss, which indicated suboptimal learning and performance. In contrast, MaskFormer demonstrated continuous improvement, achieving higher precision in breast lesion segmentation and significantly reducing both false positives and false negatives. Comparative analysis showed MaskFormer's superior performance, with the highest precision and recall rates for malignant lesions and an overall mean average precision (mAP) of 0.943. The model's ability to detect a diverse range of breast lesions, including those potentially missed by the human eye, especially by less experienced practitioners, underscores its potential. These findings suggest that integrating AI models like MaskFormer could greatly enhance ultrasound performance for breast cancer detection, providing reliable, operator-independent image analysis and potentially improving patient outcomes on a global scale.

MRI and CT imaging characteristics in parotid tumors with false-negative fine-needle aspirations

BackgroundsPreoperative imaging, particularly with magnetic resonance imaging (MRI) and computed tomography (CT) scans, plays a crucial role in distinguishing between benign and malignant parotid gland tumors, while the reliability of Ultrasound-Guided Fine Needle Aspiration (FNA) in diagnosing these masses remains a topic of debate.MethodsThis two-center retrospective analysis was conducted on 347 patients with parotid gland tumors who had FNA and preoperative imaging (CT or MRI). All patients underwent surgery and final histopathological examination was available, along with complete medical records between January 2008 and May 2023.ResultsAmong the 347 patients, 318 (92%) had benign and 10 (3%) had malignant tumors based on FNA, with 19 (5%) unsatisfactory specimens. Final histological diagnosis revealed 303 (87%) benign and 44 (13%) malignant lesions, with a false-negative rate of 10.6% for FNA. Multivariate analysis identified irregular shape and invasion as independent predictors of malignancy in patient with benign or unsatisfactory FNA results. The odds ratio for irregular shape was 3.06 and for invasion was 12.73.ConclusionImaging characteristics, such as irregular shape and invasion may indicate towards malignant parotid tumors, even in patients with false-negative benign findings in FNA.

National-Scale Detection of New Forest Roads in Sentinel-2 Time Series

The Norwegian Environment Agency is responsible for updating a map of undisturbed nature, which is performed every five years based on aerial photos. Some of the aerial photos are already up to five years old when a new version of the map of undisturbed nature is published. Thus, several new nature interventions may have been missed. To address this issue, the timeliness and mapping accuracy were improved by integrating Sentinel-2 satellite imagery for the detection of new roads across Norway. The focus on new roads was due to the fact that most new nature interventions include the construction of new roads. The proposed methodology is based on applying U-Net on all the available summer images with less than 10% cloud cover over a five-year period, with an aggregation step to summarize the predictions. The observed detection rate was 98%. Post-processing steps reduced the false positive rate to 46%. However, as the false positive rate was still substantial, the manual verification of the predicted new roads was needed. The false negative rate was low, except in areas without vegetation.

Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes.

Children with type 1 diabetes (T1D) frequently have nocturnal hypoglycemia, daytime physical activity being the most important risk factor. The risk for late post-exercise hypoglycemia depends on various factors and is difficult to anticipate. The availability of continuous glucose monitoring (CGM) enabled the development of various machine learning approaches for nocturnal hypoglycemia prediction for different prediction horizons. Studies focusing on nocturnal hypoglycemia prediction in children are scarce, and none, to the best knowledge of the authors, investigate the effect of previous physical activity. The primary objective of this work was to assess the risk of hypoglycemia throughout the night (prediction horizon 9 h) associated with physical activity in children with T1D using data from a structured setting. Continuous glucose and physiological data from a sports day camp for children with T1D were input for logistic regression, random forest, and deep neural network models. Results were evaluated using the F2 score, adding more weight to misclassifications as false negatives. Data of 13 children (4 female, mean age 11.3 years) were analyzed. Nocturnal hypoglycemia occurred in 18 of a total included 66 nights. Random forest using only glucose data achieved a sensitivity of 71.1% and a specificity of 75.8% for nocturnal hypoglycemia prediction. Predicting the risk of nocturnal hypoglycemia for the upcoming night at bedtime is clinically highly relevant, as it allows appropriate actions to be taken-to lighten the burden for children with T1D and their families.

How have breeders adapted rice flowering to the growing region?

Flowering time is a crucial rice trait that influences its adaptation to various environments, cropping schedules, and agronomic characteristics. Rice breeders have exploited spontaneous mutations in heading date genes to regulate the flowering time. In the present study, we investigated how breeders in Fukui Prefecture regulated days to heading while developing promising rice varieties. Genome-wide association studies (GWAS) identified Hd1, Hd16, and Hd18 as the major genes controlling days to heading in the population. However, we suspected that this highly bred population might exhibit genomic stratification, which could lead to spurious or false correlations in the GWAS. Thus, we also conducted correlation and partial correlation analyses, which uncovered another key heading date gene, Hd17, that GWAS failed to detect because of its linkage disequilibrium with the major effect gene Hd16. Examination of haplotype frequencies across different breeding periods revealed that the early-heading Hd16 (Hd16(E)) and late-heading Hd17 (Hd17(L)) were increasingly co-selected in the Hd1 functional population. Varieties carrying this Hd16(E)/Hd17(L) combination exhibited days to heading in the range of 70-80, which corresponds to the peak temperature and sunshine period and is also optimal for grain quality and yield components in the Fukui environment. The present study highlights that it is imperative to remain vigilant for Type I (false positives) and Type II (false negatives) errors when performing GWAS on highly bred populations and to implement appropriate countermeasures by accounting for gene-by-gene interactions established through the breeding process. We also discuss the effectiveness of Hd16(E), which is not used outside Japan for subtle days to heading control but is widely used in Japan at certain latitudes.

Predicting Deep Venous Thrombosis Using Artificial Intelligence: A Clinical Data Approach

Deep venous thrombosis is a critical medical condition that occurs when a blood clot forms in a deep vein, usually in the legs, and can lead to life-threatening complications such as pulmonary embolism if not detected early. Hospitalized patients, especially those with immobility or post-surgical recovery, are at higher risk of developing deep venous thrombosis, making early prediction and intervention vital for preventing severe outcomes. In this study, we evaluated the following eight machine learning models to predict deep venous thrombosis risk: logistic regression, random forest, XGBoost, artificial neural networks, k-nearest neighbors, gradient boosting, CatBoost, and LightGBM. These models were rigorously tested using key metrics, including accuracy, precision, recall, F1-score, specificity, and receiver operating characteristic curve, to determine their effectiveness in clinical prediction. Logistic regression emerged as the top-performing model, delivering high accuracy and an outstanding receiver operating characteristic curve score, which reflects its strong ability to distinguish between patients with and without deep venous thrombosis. Most importantly, the model’s high recall underscores its ability to identify nearly all true deep venous thrombosis cases, significantly reducing the risk of false negatives—a critical concern in clinical settings, where delayed or missed diagnoses can result in life-threatening complications. Although models such as random forest and eXtreme Gradient Boosting also demonstrated competitive performances, logistic regression proved the most reliable across all metrics. These results suggest that machine learning models, particularly logistic regression, have great potential for early deep venous thrombosis detection, enabling timely clinical interventions and improved patient outcomes.

Examining the false-negative rate of a negative axillary node ultrasound-guided core needle biopsy in breast cancer patients undergoing upfront surgery

BackgroundAxillary assessment in breast cancer is key to determining an upfront surgery or neoadjuvant chemotherapy (NAC) approach. We investigated the false-negative rate (FNR) of axillary-node ultrasound-guided core-needle biopsy (US-CNBx) and the surgical management of pN ​+ ​patients. MethodsThis single-institution study from 2010 to 2020 included patients with benign findings on US-CNBx and upfront surgery. Statistical analyses were performed via t-tests and chi-squared tests. Results95 axillae met inclusion, 23 were pN+, resulting in a US-CNBx FNR of 24.2 ​%. pN ​+ ​patients more frequently had cT2-T3 tumors vs pN0 patients (43.5 ​% vs 27.8 ​%, p ​= ​0.03). Of the 23 ​pN ​+ ​patients, 9 underwent breast-conserving surgery (BCS) and 14 underwent mastectomy. In those with BCS, 7 had 1–2 positive nodes, 2 had ≥3 nodes; 3 received an ALND. In those with mastectomies, 12 had 1–2 positive nodes, 2 had ≥3 positive nodes; 6 received an ALND. ConclusionIn this cohort, US-CNBx had a FNR of 24.2 ​%. pN ​+ ​patients had a greater frequency of cT2–cT3 tumors, therefore clinicians should be cognizant of potential occult nodal disease despite negative CNBx when deciding management.

Diagnostic Performances of 18F-Fluorocholine PET/CT as First-Line Functional Imaging Method for Localization of Hyperfunctioning Parathyroid Tissue in Primary Hyperparathyroidism

Rationale and ObjectivesThis study evaluated the diagnostic performance of 18F-fluorocholine (FCH) PET/CT as the first-line functional imaging method for preoperative localization of hyperfunctioning parathyroid glands (HPGs) in patients with primary hyperparathyroidism (PHPT). Materials and MethodsThis retrospective single-centre study included 80 consecutive patients with PHPT, referred for FCH PET/CT between January 2018 and July 2022, and who subsequently underwent surgery. The diagnostic performance of FCH PET/CT was compared to histological results for per-lesion analysis, and to postoperative resolution of biochemical PHPT for per-patient analysis. Results18F-FCH-PET/CT revealed 95 positive foci in 77/80 patients and was negative in 3/80 patients. Postoperative resolution of HPT was obtained in 67/80 patients (84%). Per-lesion analysis showed 80 true positives, 5 true negatives, 11 false negatives and 8 false positives. Seven PET positive foci could not be compared with histology. In a first per-lesion analysis, excluding these 7 anomalies, sensitivity and positive predictive value (PPV) of FCH PET/CT were 88% (95% CI: 79-94) and 91% (95% CI: 87-94), respectively. In a second per-lesion analysis considering the 7 anomalies as false positives (maximum bias analysis), PPV was 84% (95% CI: 80%-87%). By per-patient analysis, FCH PET/CT correctly identified and located all pathological glands in 56/80 (70%, 95% CI: 59-80) patients. Conclusion18F-Fluorocholine PET/CT appears to be an effective pre-surgical imaging method for localization of hyperfunctioning parathyroid tissue in patients with PHPT.

Evaluating the Effectiveness of a Large Language Model in the Psychological Assessment of Potential Liver Transplant Candidates: A Feasibility Study

Introduction: The use of artificial intelligence (AI) in medical settings has shown promise in various domains including diagnostics, treatment recommendation, and patient management. Recent advances have explored the potential of AI in pre-surgical assessments, but its application in transplant psychology remains unexplored. The objective of this study is to assess the effectiveness of AI in the context of screening potential liver transplant donors and recipients. To assess the feasibility of using ChatGPT-4 to screen potential liver transplant donors and recipients. Methods: This study utilizes a cross-sectional research design to evaluate the feasibility of using ChatGPT-4 in the preliminary screening of living liver donors and liver transplant candidates. The study aims to determine the accuracy and reliability of ChatGPT-4 in assessing hypothetical scenarios involving potential donors and recipients. Results: The analysis showed no found no significant differences between ChatGPT-4 and the expert panel in assessing liver transplant candidates, demonstrating an overall accuracy of 83.58%, sensitivity of 56.10%, and specificity of 80.49%. Additionally, the Cohen’s Kappa statistic of 0.68 (95% CI: 0.52-0.83) indicated substantial agreement between ChatGPT-4 and the psychologists’ evaluations. The absence of false positives (0%) and a low false negative rate (8%) emphasize ChatGPT-4's cautious and accurate decision-making capabilities. Conclusion: The findings of this study demonstrate that ChatGPT-4 has the potential to serve as an effective screening tool for liver transplant candidates, complementing the work of human experts and enhancing the overall efficiency of the transplant process. While challenges remain, the integration of AI into the liver transplantation workflow could lead to significant improvements in candidate evaluation and patient outcomes, paving the way for the broader application of AI in clinical practice.