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The process of patients waiting for diagnostic examinations after an abnormal screening mammogram is inefficient and anxiety-inducing. Artificial intelligence (AI)-aided interpretation of screening mammography could reduce the number of recalls after screening. We proposed a same-day diagnostic workup to alleviate patient anxiety by employing an AI-aided interpretation to reduce unnecessary diagnostic testing after an abnormal screening mammogram. However, the potential unintended consequences of introducing this workflow in a high-volume breast imaging center are unknown. Using discrete event simulation, we observed that implementing the AI-aided screening mammogram interpretation and same-day diagnostic workflow would reduce daily patient volume by 4%, increase the time a patient would be at the clinic by 24%, and increase waiting times by 13-31%. We discuss how changing the hours of operation and introducing new imaging equipment and personnel may alleviate these negative impacts.

Lung cancer screening (LCS) is effective in reducing mortality, particularly when patients adhere to follow-up recommendations standardized by the Lung CT Screening Reporting & Data System (Lung-RADS). Nevertheless, patient adherence to recommended intervals varies, potentially diminishing benefit from screening. We conducted a systematic review and meta-analysis of patient adherence to Lung-RADS-recommended screening intervals. We systematically searched MEDLINE, EMBASE, Web of Science, the Cochrane Central Register of Controlled Trials, and major radiology and oncology conference archives between April 28, 2014, and December 17, 2020. Eligible studies mentioned patient adherence to the recommendations of Lung-RADS. The review protocol was registered with PROSPERO (CRD42020189326). We identified 24 eligible studies for qualitative summary, of which 21 were suitable for meta-analysis. The pooled adherence rate was 57% (95% confidence interval: 46%-69%) for defined adherence (e.g., an annual incidence screen was performed within 15 mo) among 6689 patients and 65% (95% confidence interval: 55%-75%) for anytime adherence among 5085 patients. Large heterogeneity in adherence rates between studies was observed (I2 = 99% for defined adherence, I2 = 98% for anytime adherence). Heterogeneous adherence rates were associated with Lung-RADS scores, with significantly higher adherence rates among Lung-RADS 3 to 4 than Lung-RADS 1 to 2 (p < 0.05). Patient adherence to Lung-RADS-recommended screening intervals is suboptimal across clinical LCS programs in the United States, especially among patients with results of Lung-RADS categories 1 to 2. To improve adherence rates, future research may focus on implementing tailored interventions after identifying barriers to LCS. We also propose a minimum standardized set of data elements for future pooled analyses of LCS adherence on the basis of our findings.

We present an interpretable end-to-end computer-aided detection and diagnosis tool for pulmonary nodules on computed tomography (CT) using deep learning-based methods. The proposed network consists of a nodule detector and a nodule malignancy classifier. We used RetinaNet to train a nodule detector using 7,607 slices containing 4,234 nodule annotations and validated it using 2,323 slices containing 1,454 nodule annotations drawn from the LIDC-IDRI dataset. The average precision for the nodule class in the validation set reached 0.24 at an intersection over union (IoU) of 0.5. The trained nodule detector was externally validated using a UCLA dataset. We then used a hierarchical semantic convolutional neural network (HSCNN) to classify whether a nodule was benign or malignant and generate semantic (radiologist-interpretable) features (e.g., mean diameter, consistency, margin), training the model on 149 cases with diagnostic CTs collected from the same UCLA dataset. A total of 149 nodule-centered patches from the UCLA dataset were used to train the HSCNN. Using 5-fold cross validation and data augmentation, the mean AUC and mean accuracy in the validation set for predicting nodule malignancy achieved 0.89 and 0.74, respectively. Meanwhile, the mean accuracy for predicting nodule mean diameter, consistency, and margin were 0.59, 0.74, and 0.75, respectively. We have developed an initial end-to-end pipeline that automatically detects nodules ≥ 5 mm on CT studies and labels identified nodules with radiologist-interpreted features automatically.

Purpose: The purpose of the article is to investigate the contribution of calprotectin and factors in toll-like receptor 4/nuclear factor-κB/myeloid differentiation factor 88 (TLR4/NF-κB/MyD88) pathway in patients with idiopathic acute anterior uveitis (IAAU). Methods: In total, 72 patients with IAAU and 56 healthy individuals were enrolled. Serum calprotectin, TLR-4, and MyD88 were determined. Best-corrected visual acuity, uveitis activity grading, and macular thickness measured by optical coherence tomography were performed. Results: Serum calprotectin, TLR4, and MyD88 levels were higher in IAAU group than those in healthy individuals. Serum calprotectin level was positively correlated with uveitis activity grading and macular thickness. Receiver operating characteristic curve analysis showed serum calprotectin had larger area under curve than serum TLR4 and MyD88. Conclusions: The calprotectin and TLR4/NF-κB/MyD88 signal might contribute to the pathogenesis of IAAU and serum calprotectin might be a specific biomarker for the measurement of ocular inflammation in IAAU. [ABSTRACT FROM AUTHOR]

Discovering fluorescence of existing compounds, which are generally regarded as non-fluorescent, is of important academic and technical significance. This article reports the fluorescence of common compounds containing pyrrolidone ring(s) and their oxidized hydrolyzates. Poly( N-vinylpyrrolidone) (PVP), polymerized from a very weak fluorescent monomer N-vinyl-2-pyrrolidone (NVP), exhibits strong intrinsic fluorescence. Moreover, the fluorescence of its 'hydrolyzate' is dramatically enhanced by about 1000 times. The 'hydrolyzate' of N-methyl-pyrrolidone (NMP) also exhibits significantly enhanced fluorescence. By studying the chemical structures and fluorescence of the hydrolyzates, the enhanced fluorescence is attributed to the formation of secondary amine oxide. The much stronger fluorescence of the polymers compared to the corresponding small molecular compounds is ascribed to the 'aggregation-induced emission' (AIE) effect of the luminophores. PVP and its oxidized hydrolyzate also show some phenomena different to the common AIE effect. The fluorescence of PVP and its oxidized hydrolyzate shows stimuli response to metal ions and pH values. This study introduces novel fluorescent materials for various potential applications. [ABSTRACT FROM AUTHOR]

The process of patients waiting for diagnostic examinations after an abnormal screening mammogram is inefficient and anxiety-inducing. Artificial intelligence (AI)-aided interpretation of screening mammography could reduce the number of recalls after screening. We proposed a same-day diagnostic workup to alleviate patient anxiety by employing an AI-aided interpretation to reduce unnecessary diagnostic testing after an abnormal screening mammogram. However, the potential unintended consequences of introducing this workflow in a high-volume breast imaging center are unknown. Using discrete event simulation, we observed that implementing the AI-aided screening mammogram interpretation and same-day diagnostic workflow would reduce daily patient volume by 4%, increase the time a patient would be at the clinic by 24%, and increase waiting times by 13-31%. We discuss how changing the hours of operation and introducing new imaging equipment and personnel may alleviate these negative impacts., (©2024 AMIA - All rights reserved.)

Determining factors influencing patient participation in and adherence to cancer screening recommendations is key to successful cancer screening programs. However, the collection of variables necessary to anticipate patient behavior in cancer screening has not been systematically examined. Using lung cancer screening as a representative example, we conducted an exploratory analysis to characterize the current representations of 18 demographic, health-related, and psychosocial variables collected as part of a conceptual model to understand factors for lung cancer screening participation and adherence. Our analysis revealed a lack of standardization in controlled terminologies and common data elements for these variables. For example, only eight (44%) demographic and health-related variables were recorded consistently in the electronic health record. Multiple survey instruments could collect the remaining variables but were highly inconsistent in how variables were represented. This analysis suggests opportunities to establish standardized data formats for psychological, cognitive, social, and environmental variables to improve data collection., (©2022 AMIA - All rights reserved.)

It has been a long-standing challenge to develop organic molecular capsules for selective anion binding in water. Here, selective recognition of aqueous fluoride was achieved through triple protonation of a hemicryptophane ( L ), which is composed of a fluorescent cyclotriveratrylene (CTV) cap and tris(2-aminoethyl)amine (tren) as the anion binding site. Fluoride encapsulation by [3H- L ] 3+ was evidenced by 1 H NMR, 19 F NMR, LC-MS, and X-ray crystallography. In addition, [3H- L ] 3+ exhibited a 'turn-on' fluorescence signal ( λ em = 324 nm) upon fluoride addition. An apparent association constant K A = (7.5 ± 0.4) × 10 4 M -1 and a detection limit of 570 nM fluoride were extracted from the fluorescence titration experiments in citrate buffer at pH 4.1. To the best of our knowledge, [3H- L ] 3+ is the first example of a metal-free molecular capsule that reports on fluoride binding in purely aqueous solutions with a fluorescence response. Finally, the protonated capsule was supported on silica gel, which enabled adsorptive removal of stoichiometric fluoride from water and highlights real-world applications of this organic host-guest chemistry., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

We present an interpretable end-to-end computer-aided detection and diagnosis tool for pulmonary nodules on computed tomography (CT) using deep learning-based methods. The proposed network consists of a nodule detector and a nodule malignancy classifier. We used RetinaNet to train a nodule detector using 7,607 slices containing 4,234 nodule annotations and validated it using 2,323 slices containing 1,454 nodule annotations drawn from the LIDC-IDRI dataset. The average precision for the nodule class in the validation set reached 0.24 at an intersection over union (IoU) of 0.5. The trained nodule detector was externally validated using a UCLA dataset. We then used a hierarchical semantic convolutional neural network (HSCNN) to classify whether a nodule was benign or malignant and generate semantic (radiologist-interpretable) features (e.g., mean diameter, consistency, margin), training the model on 149 cases with diagnostic CTs collected from the same UCLA dataset. A total of 149 nodule-centered patches from the UCLA dataset were used to train the HSCNN. Using 5-fold cross validation and data augmentation, the mean AUC and mean accuracy in the validation set for predicting nodule malignancy achieved 0.89 and 0.74, respectively. Meanwhile, the mean accuracy for predicting nodule mean diameter, consistency, and margin were 0.59, 0.74, and 0.75, respectively. We have developed an initial end-to-end pipeline that automatically detects nodules ≥ 5 mm on CT studies and labels identified nodules with radiologist-interpreted features automatically.