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Past reproductive effort allows males to assess their ability to acquire mates, but it also consumes resources that can reduce their future competitive ability. Few studies have examined how a male's reproductive history affects his subsequent mate choice, and, to date, no study has determined the relative contribution of past mating behavior and past ejaculate production because these two forms of investment are naturally highly correlated. Here, we disentangled the relative effects of past mating behavior and past ejaculate production in mosquitofish ( Gambusia holbrooki ) by experimentally preventing some males from ejaculating when trying to mate. We assessed the effect of mating behavior on mate choice by comparing males that had previously been with or without access to females and male rivals for 8 and 16 weeks and assessed the effect of ejaculation on mate choice by comparing males that either could or could not ejaculate when they had access to females for 16 weeks. Reproductive treatment did not affect male attractiveness, but it did affect male mate choice. Somewhat surprisingly, in five of the six treatment-by-age at testing combinations, males preferred a female in the vicinity of a male rival over a solitary female. This preference was marginally stronger for males that had previously engaged in mating behavior but were unaffected by past ejaculate production. We discuss the potential benefits to males of associating with another male when seeking mates. This is the first study to quantify the relative influence of pre- and post-copulatory reproductive investment on male mate choice., Competing Interests: We declare no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology.)

Background: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability., Objective: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers., Methods: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods., Results: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910)., Conclusions: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice., (©Sangwon Baek, Yeon joo Jeong, Yun-Hyeon Kim, Jin Young Kim, Jin Hwan Kim, Eun Young Kim, Jae-Kwang Lim, Jungok Kim, Zero Kim, Kyunga Kim, Myung Jin Chung. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.01.2024.)

Purpose: Cannulation of the major papilla is the most challenging part of endoscopic retrograde cholangiopancreatography (ERCP) for which physician-controlled wire-guided cannulation (PCWGC) and assistant-controlled wire-guided cannulation (ACWGC) are used as the cannulation techniques. PCWGC can reportedly save up to about 30% of the labor cost by reducing the number of assistants. This study aims to compare the safety and efficacy of PCWGC and ACWGC., Materials and Methods: Of the 2151 patients aged >20 years (4193 cases) who underwent ERCP at Yonsei University Medical Center between January 2015 and December 2016, 989 were included in this study., Results: Among efficacy outcomes, cannulation success rate, rate of precut sphincterotomy (PCWGC vs. ACWGC: 21.3% vs. 25.9%), bile duct cannulation time (PCWGC vs. ACWGC: median 3.0 minutes vs. 3.6 minutes), and total procedure time (PCWGC vs. ACWGC: median 13.6 minutes vs. 13.1 minutes) were not significantly different. Among safety outcomes, lower rates of post-ERCP pancreatitis were observed with PCWGC than with ACWGC (PCWGC vs. ACWGC: 5.8% vs. 8.8%, p =0.128). Among other post-ERCP adverse events (bleeding, perforation, and cholangitis), the difference was not significant between the groups. Radiation exposure (total dose area product, PCWGC vs. ACWGC: median 1979.9 µGym² vs. 2062.0 µGym², p =0.194) and ERCP cost excluding labor cost (PCWGC vs. ACWGC: $1576 vs. $1547, p =0.606) were not significantly different., Conclusion: Requiring less assistants, PCWGC showed comparable efficacy and safety to ACWGC. PCWGC can be considered as an alternative option, especially in facilities lacking manpower and resources., Competing Interests: The authors have no potential conflicts of interest to disclose., (© Copyright: Yonsei University College of Medicine 2024.)

Introduction: The incidence of postendoscopic retrograde cholangiopancreatography (ERCP) infections is reported to be up to 18% in patients with biliary obstruction. Antibiotic prophylaxis may reduce the risk of infectious complications after ERCP; however, the clinical value of prophylactic antibiotics in ERCP remains controversial., Methods: We conducted a double-blind, placebo-controlled, randomized trial to investigate whether the use of prophylactic antibiotics would reduce infectious complications after ERCP in patients with biliary obstruction. We randomly assigned patients in a 1:1 ratio to receive either a single dose of 1 g intravenous cefoxitin or normal saline as a placebo 30 minutes before undergoing ERCP. The primary outcome was the incidence of infectious complications after ERCP., Results: We enrolled 378 patients, and 189 patients were assigned to each group. The risk of infectious complications after ERCP was 2.8% (5 of 176 patients) in the antibiotic prophylaxis group and 9.8% (17 of 173 patients) in the placebo group (risk ratio, 0.29; 95% confidence interval [CI], 0.11-0.74, P = 0.0073). The incidence rates of bacteremia were 2.3% (4 of 176 patients) and 6.4% (11 of 173 patients), respectively (risk ratio, 0.36; 95% CI, 0.12-1.04; P = 0.0599). The incidence rate of cholangitis was 1.7% (3 of 176 patients) in the antibiotic prophylaxis group and 6.4% (11 of 173 patients) in the placebo group (risk ratio, 0.27; 95% CI, 0.08-0.87; P = 0.0267)., Discussion: Antibiotic prophylaxis before ERCP in patients with biliary obstruction resulted in a significantly lower risk of infectious complications, especially cholangitis, than placebo ( ClinicalTrials.gov trial number NCT02958059)., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Background: Systemic artery to pulmonary artery fistula (SA-PAF) is an uncommon disease which is often incidentally diagnosed during evaluation of hemoptysis patients. The aim of our study was to describe the cases of SA-PAF in our institution and to report the correlating clinical and radiological findings., Methods: We reviewed 231 chest computed tomography (CT) scans performed in our institution due to hemoptysis from January 2020 to February 2023. In patients diagnosed with SA-PAF had their electronic medical records and CT images analyzed., Results: In 231 patients, 19 (8.2%) of them had SA-PAF findings which was characterized by a peripheral nodular soft tissue opacity in the subpleural lung and traceable vascular structure in continuity with one or more peripheral pulmonary artery branches in CT. Etiology of each patient was categorized as either congenital (7, 36.8%), and acquired (12, 63.2%). The origins of SA-PAFs were 16 intercostal, two anterior mediastinal, and one costocervical artery. Eight of 19 patients did not show any associated intralobar imaging abnormalities, while bronchiectasis, cellular bronchiolitis, centrilobular emphysema, and pleura effusion were observed in 11 patients., Conclusions: SA-PAF is a benign vascular anomaly which is frequently overlooked when evaluating hemoptysis by either clinician or radiologists but is an important factor in the differential diagnosis of patients with hemoptysis., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-861/coif). The authors have no conflicts of interest to declare., (2023 Journal of Thoracic Disease. All rights reserved.)

The human microbiome exhibits intricate populations across the body, with the vaginal tract serving as an ecosystem characterized by the prevalence of the genus Lactobacillus . Disruptions in the vaginal microbiota, which are frequently linked to variables such as sexual activity, hormonal fluctuations, and excessive use of antibiotics, can result in vaginal dysbiosis and the development of diseases such as bacterial vaginosis (BV) and candidiasis. Lactobacillus species, owing to their capacity to create an acidic environment through the production of lactic acid, have a key function within this complex microbial community: they inhibit the growth of harmful microorganisms. This study aimed to investigate the genomic characteristics of L. rhamnosus LR6, a newly discovered strain isolated from the vaginal microbiota of 20 healthy women to assess its potential as a vaginal probiotic. We performed a comparative investigation of the genetic traits of L. rhamnosus using 45 publicly available genomes from various sources. We evaluated the genetic characteristics related to carbohydrate utilization, adhesion to host cells, and the presence of bacteriocin clusters. A comprehensive study was conducted by integrating in silico evaluations with experimental techniques to authenticate the physiological characteristics of strain LR6. We further used a rat model to assess the impact of L. rhamnosus LR6 administration on the changes in the gastrointestinal tract and the vaginal microbiome. The assessments revealed a significantly high inhibitory activity against pathogens, enhanced adherence to host cells, and high lactic acid production. Rat experiments revealed changes in both the fecal and vaginal microbiota; in treated rats, Firmicutes increased in both; Lactobacillaceae increased in the fecal samples; and Enterobacteriaceae decreased but Enterococcaceae , Streptococcaceae , and Morganellaceae increased in the vaginal samples. The study results provide evidence of the genetic characteristics and probiotic properties of LR6, and suggest that oral administration of L. rhamnosus LR6 can alter both gut and vaginal microbiome. Collectively, these findings establish L. rhamnosus LR6 as a highly promising candidate for improving vaginal health., Competing Interests: YC, SK, DS, MC, and SL employed by Cell Biotech, Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chung, Kang, Son, Lee, Chung and Lim.)

Background/aims: Programmed death-ligand 1 (PD-L1) expression in tumor cells is associated with a poor biliary tract cancer (BTC) prognosis; tumor-infiltrating immune cells in the tumor microenvironment are associated with a better prognosis. The effect of PD-L1 expression on immune cells on survival is unclear. We investigated the relationship between PD-L1 expression in immune cells and BTC prognosis., Methods: PD-L1 expression was evaluated using an anti-PD-L1 22C3 mouse monoclonal primary antibody, and its relationships with clinical characteristics and prognosis were analyzed using the Cox proportional hazard model to investigate the prognostic performance of PD-L1 in BTC., Results: Among 144 analyzed cases, patients with positive PD-L1 expression in tumor cells and negative PD-L1 expression in immune cells showed poorer overall survival rates than those exhibiting other expressions (tumor cells: hazard ratio [HR]=1.023, p<0.001; immune cells: HR=0.983, p=0.021). PD-L1 expression in tumor cells was an independent predictor of poor overall survival (HR=1.024, p<0.001). In contrast, PD-L1 expression in immune cells was a predictive marker of good prognosis (HR=0.983, p=0.018)., Conclusions: PD-L1 expression in immune cells may be used as an independent factor to evaluate the prognosis of patients with BTC.

Background: Magnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI., Methods: This study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation., Results: A total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705-0.745), 0.936 (95% CI, 0.924-0.947), and 0.830 (95%CI, 0.792-0.868), respectively, at the image level and 0.947 (95% CI, 0.912-0.982), 0.691 (95% CI, 0.603-0.779), and 0.816 (95% CI, 0.776-0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493-0.780), 0.944 (95% CI, 0.933-0.955), and 0.731 (95% CI, 0.681-0.780), respectively, at the image level and 0.806 (95% CI, 0.729-0.883), 0.617 (95% CI, 0.523-0.711), and 0.711 (95% CI, 0.660-0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation., Conclusion: An AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lee, Jeon, Lee, Kang, Kim, Lee, Chung and Cha.)

Background/aim: We aimed to compare the clinicopathological outcomes in patients with locally advanced rectal cancer after short- or long-course concurrent chemoradiotherapy (CCRT) followed by delayed surgery., Patients and Methods: The records of 94 patients with cT3-4N0-2M0 rectal cancer who received CCRT between 2010 and 2017 were reviewed. Short-course radiotherapy (RT) was delivered with a median total dose of 25 Gy in five fractions (n=27), and long-course RT was delivered with a median total dose of 50.4 Gy in 28 fractions (n=67). The following concurrent chemotherapy regimens were administered: 5-fluorouracil plus leucovorin in 58 and capecitabine in 24; in 12 cases agents were unknown. The median interval between CCRT and surgery was 8 weeks. Adjuvant chemotherapy was administered after surgery in 80 patients (5-fluorouracil plus leucovorin, n=54; capecitabine, n=9; other, n=14; and unknown, n=3). Propensity-score matching analysis was conducted., Results: The median follow-up duration was 4.3 years. There were no statistically significant differences between the short- and long-course RT groups in sphincter preservation (85.2% vs. 92.5%, p=0.478), pathological complete remission (18.5% vs. 14.9%, p=0.905), downstaging (44.4% vs. 26.9%, p=0.159), and negative circumferential resection margin (92.6% vs. 89.6%, p=0.947) rates. No differences were found in survival outcomes between the short- and long-course groups at 3 years (overall survival: 91.8% vs. 88.1%, p=0.790; disease-free survival, 75.2% vs. 72.5%, p=0.420; locoregional relapse-free survival, 90.5% vs. 98.4%, p=0.180; and distant metastasis-free survival, 79.6% vs. 73.5%, p=0.490). Similar results were observed after PSM., Conclusion: Clinically, short-course CCRT may be a feasible alternative to long-course CCRT in patients with locally advanced rectal cancer., (Copyright © 2023, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in the emergence of new variants that are resistant to existing vaccines and therapeutic antibodies, has raised the need for novel strategies to combat the persistent global COVID-19 epidemic. In this study, a monoclonal anti-human angiotensin-converting enzyme 2 (hACE2) antibody, ch2H2, was isolated and humanized to block the viral receptor-binding domain (RBD) binding to hACE2, the major entry receptor of SARS-CoV-2. This antibody targets the RBD-binding site on the N terminus of hACE2 and has a high binding affinity to outcompete the RBD. In vitro, ch2H2 antibody showed potent inhibitory activity against multiple SARS-CoV-2 variants, including the most antigenically drifted and immune-evading variant Omicron. In vivo, adeno-associated virus (AAV)-mediated delivery enabled a sustained expression of monoclonal antibody (mAb) ch2H2, generating a high concentration of antibodies in mice. A single administration of AAV-delivered mAb ch2H2 significantly reduced viral RNA load and infectious virions and mitigated pulmonary pathological changes in mice challenged with SARS-CoV-2 Omicron BA.5 subvariant. Collectively, the results suggest that AAV-delivered hACE2-blocking antibody provides a promising approach for developing broad-spectrum antivirals against SARS-CoV-2 and potentially other hACE2-dependent pathogens that may emerge in the future., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Duodenal ablation improves glycaemic control and weight loss, so it has been applied using hydrothermal catheters in obese and type 2 diabetes patients, indicating similar mechanisms and therapeutic effects as bariatric surgeries. Endoscopic photodynamic therapy is an innovative procedure that easily accessible to endocrine or gastrointestinal organs, so it is critical for the sprayed photosensitizer (PS) to long-term interact with target tissues for enhancing its effects. Surfactant-like PS was more stable in a wide range of pH and 2.8-fold more retained in the duodenum at 1 h than hydrophilic PS due to its amphiphilic property. Endoscopic duodenal ablation using surfactant-like PS was performed in high fat diet induced rat models, demonstrating body weight loss, enhanced insulin sensitivity, and modulation of incretin hormones. Locoregional ablation of duodenum could affect the profiles of overall intestinal cells secreting meal-stimulated hormones and further the systemic glucose and lipid metabolism, regarding gut-brain axis. Our strategy suggests a potential for a treatment of minimally invasive bariatric and metabolic therapy if accompanied by detailed clinical trials., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Cancer patients who are admitted to hospitals are at high risk of short-term deterioration due to treatment-related or cancer-specific complications. A rapid response system (RRS) is initiated when patients who are deteriorating or at risk of deteriorating are identified. This study was conducted to develop a deep learning-based early warning score (EWS) for cancer patients (Can-EWS) using delta values in vital signs., Methods: A retrospective cohort study was conducted on all oncology patients who were admitted to the general ward between 2016 and 2020. The data were divided into a training set (January 2016-December 2019) and a held-out test set (January 2020-December 2020). The primary outcome was clinical deterioration, defined as the composite of in-hospital cardiac arrest (IHCA) and unexpected intensive care unit (ICU) transfer., Results: During the study period, 19,739 cancer patients were admitted to the general wards and eligible for this study. Clinical deterioration occurred in 894 cases. IHCA and unexpected ICU transfer prevalence was 1.77 per 1000 admissions and 43.45 per 1000 admissions, respectively. We developed two models: Can-EWS V1, which used input vectors of the original five input variables, and Can-EWS V2, which used input vectors of 10 variables (including an additional five delta variables). The cross-validation performance of the clinical deterioration for Can-EWS V2 (AUROC, 0.946; 95% confidence interval [CI], 0.943-0.948) was higher than that for MEWS of 5 (AUROC, 0.589; 95% CI, 0.587-0.560; p < 0.001) and Can-EWS V1 (AUROC, 0.927; 95% CI, 0.924-0.931). As a virtual prognostic study, additional validation was performed on held-out test data. The AUROC and 95% CI were 0.588 (95% CI, 0.588-0.589), 0.890 (95% CI, 0.888-0.891), and 0.898 (95% CI, 0.897-0.899), for MEWS of 5, Can-EWS V1, and the deployed model Can-EWS V2, respectively. Can-EWS V2 outperformed other approaches for specificities, positive predictive values, negative predictive values, and the number of false alarms per day at the same sensitivity level on the held-out test data., Conclusions: We have developed and validated a deep learning-based EWS for cancer patients using the original values and differences between consecutive measurements of basic vital signs. The Can-EWS has acceptable discriminatory power and sensitivity, with extremely decreased false alarms compared with MEWS.

Background: Compared with chest X-ray (CXR) imaging, which is a single image projected from the front of the patient, chest digital tomosynthesis (CDTS) imaging can be more advantageous for lung lesion detection because it acquires multiple images projected from multiple angles of the patient. Various clinical comparative analysis and verification studies have been reported to demonstrate this, but there is no artificial intelligence (AI)-based comparative analysis studies. Existing AI-based computer-aided detection (CAD) systems for lung lesion diagnosis have been developed mainly based on CXR images; however, CAD-based on CDTS, which uses multi-angle images of patients in various directions, has not been proposed and verified for its usefulness compared to CXR-based counterparts., Background and Objective: This study develops and tests a CDTS-based AI CAD system to detect lung lesions to demonstrate performance improvements compared to CXR-based AI CAD., Methods: We used multiple (e.g., five) projection images as input for the CDTS-based AI model and a single-projection image as input for the CXR-based AI model to compare and evaluate the performance between models. Multiple/single projection input images were obtained by virtual projection on the three-dimensional (3D) stack of computed tomography (CT) slices of each patient's lungs from which the bed area was removed. These multiple images result from shooting from the front and left and right 30/60 ∘ . The projected image captured from the front was used as the input for the CXR-based AI model. The CDTS-based AI model used all five projected images. The proposed CDTS-based AI model consisted of five AI models that received images in each of the five directions, and obtained the final prediction result through an ensemble of five models. Each model used WideResNet-50. To train and evaluate CXR- and CDTS-based AI models, 500 healthy data, 206 tuberculosis data, and 242 pneumonia data were used, and three three-fold cross-validation was applied., Results: The proposed CDTS-based AI CAD system yielded sensitivities of 0.782 and 0.785 and accuracies of 0.895 and 0.837 for the (binary classification) performance of detecting tuberculosis and pneumonia, respectively, against normal subjects. These results show higher performance than the sensitivity of 0.728 and 0.698 and accuracies of 0.874 and 0.826 for detecting tuberculosis and pneumonia through the CXR-based AI CAD, which only uses a single projection image in the frontal direction. We found that CDTS-based AI CAD improved the sensitivity of tuberculosis and pneumonia by 5.4% and 8.7% respectively, compared to CXR-based AI CAD without loss of accuracy., Conclusions: This study comparatively proves that CDTS-based AI CAD technology can improve performance more than CXR. These results suggest that we can enhance the clinical application of CDTS. Our code is available at https://github.com/kskim-phd/CDTS-CAD-P., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Background and Objective: The cone-beam computed tomography (CBCT) provides three-dimensional volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease. However, it lacks the sensitivity to detect soft tissue lesions owing to reconstruction constraints. Consequently, only physicians with expertise in CBCT reading can distinguish between inherent artifacts or noise and diseases, restricting the use of this imaging modality. The development of artificial intelligence (AI)-based computer-aided diagnosis methods for CBCT to overcome the shortage of experienced physicians has attracted substantial attention. However, advanced AI-based diagnosis addressing intrinsic noise in CBCT has not been devised, discouraging the practical use of AI solutions for CBCT. We introduce the development of AI-based computer-aided diagnosis for CBCT considering the intrinsic imaging noise and evaluate its efficacy and implications., Methods: We propose an AI-based computer-aided diagnosis method using CBCT with a denoising module. This module is implemented before diagnosis to reconstruct the internal ground-truth full-dose scan corresponding to an input CBCT image and thereby improve the diagnostic performance. The proposed method is model agnostic and compatible with various existing and future AI-based denoising or diagnosis models., Results: The external validation results for the unified diagnosis of sinus fungal ball, chronic rhinosinusitis, and normal cases show that the proposed method improves the micro-, macro-average area under the curve, and accuracy by 7.4, 5.6, and 9.6% (from 86.2, 87.0, and 73.4 to 93.6, 92.6, and 83.0%), respectively, compared with a baseline while improving human diagnosis accuracy by 11% (from 71.7 to 83.0%), demonstrating technical differentiation and clinical effectiveness. In addition, the physician's ability to evaluate the AI-derived diagnosis results may be enhanced compared with existing solutions., Conclusion: This pioneering study on AI-based diagnosis using CBCT indicates that denoising can improve diagnostic performance and reader interpretability in images from the sinonasal area, thereby providing a new approach and direction to radiographic image reconstruction regarding the development of AI-based diagnostic solutions. Furthermore, we believe that the performance enhancement will expedite the adoption of automated diagnostic solutions using CBCT, especially in locations with a shortage of skilled clinicians and limited access to high-dose scanning., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Background: Peripherally inserted central catheters (PICCs) have been widely used as one of the representative central venous lines (CVCs) due to their long-term intravascular access with low infectivity. However, PICCs have a fatal drawback of a high frequency of tip mispositions, increasing the risk of puncture, embolism, and complications such as cardiac arrhythmias. To automatically and precisely detect it, various attempts have been made by using the latest deep learning (DL) technologies. However, even with these approaches, it is still practically difficult to determine the tip location because the multiple fragments phenomenon (MFP) occurs in the process of predicting and extracting the PICC line required before predicting the tip., Objective: This study aimed to develop a system generally applied to existing models and to restore the PICC line more exactly by removing the MFs of the model output, thereby precisely localizing the actual tip position for detecting its misposition., Methods: To achieve this, we proposed a multi-stage DL-based framework post-processing the PICC line extraction result of the existing technology. Our method consists of the following three stages: 1. Existing PICC line segmentation network for a baseline, 2. Patch-based PICC line refinement network, 3. PICC line reconnection network. The proposed second and third-stage models address MFs caused by the sparseness of the PICC line and the line disconnection due to confusion with anatomical structures respectively, thereby enhancing tip detection., Results: To verify the objective performance of the proposed MFCN, internal validation and external validation were conducted. For internal validation, learning (130 samples) and verification (150 samples) were performed with 280 data, including PICC among Chest X-ray (CXR) images taken at our institution. External validation was conducted using a public dataset called the Royal Australian and New Zealand College of Radiologists (RANZCR), and training (130 samples) and validation (150 samples) were performed with 280 data of CXR images, including PICC, which has the same number as that for internal validation. The performance was compared by root mean squared error (RMSE) and the ratio of single fragment images (Ratio SFI ) (i.e., the rate at which model predicts PICC as multiple sub-lines) according to whether or not MFCN is applied to seven conventional models (i.e., FCDN, UNET, AUNET, TUNET, FCDN-HT, UNET-ELL, and UNET-RPN). In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45 %. The RMSE improved over 63% from an average of 27.54 mm (17.16 to 35.80 mm) to 9.77 mm (9.11 to 10.98 mm). In external validation, when MFCN was applied, the MFP incidence rate decreased by an average of 32% and the RMSE decreased by an average of 65%. Therefore, by applying the proposed MFCN, we observed the consistent detection performance improvement of PICC tip location compared to the existing model., Conclusion: In this study, we applied the proposed technique to the existing technique and demonstrated that it provides high tip detection performance, proving its high versatility and superiority. Therefore, we believe, in countries and regions where radiologists are scarce, that the proposed DL approach will be able to effectively detect PICC misposition on behalf of radiologists., Competing Interests: Declaration of competing interest All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript, (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Placental transmogrification of the lung is a very rare benign lung disease with a characteristic finding being alveoli resembling chorionic villi of the placenta. The purpose of this study was to assess the computed tomography (CT) findings of placental transmogrification of the lung in six patients and their relation to the histopathologic findings., Methods: Six patients with histopathologically proven placental transmogrification of the lung from 2004 to 2021 were included. Their CT findings were analyzed and their imaging features were compared with pathology specimens., Results: In four of six cases, CT showed variable sized cystic lesions confined to a unilateral lung. One case presented nodule and cystic lesion together. The other case showed solitary pulmonary nodule without cystic lesion. Moreover, nodular interlobular septal thickening and clustered interstitial nodules were observed in all six cases. In four of the six cases, these nodules merged into dense nodular consolidation. Three cases showed dilated pulmonary vasculatures of the involved lung., Conclusions: On CT, placental transmogrification of the lung typically presents as cystic lesion confined to a unilateral lung. Pulmonary nodule with or without associated cystic lesion can also be seen. Nodular interlobular septal thickening and clustered interstitial nodules were observed in all cases. This might be attributable to the proliferation of chorionic villi-like structures in interstitium which are found in histopathologic specimens., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-733/coif). The authors have no conflicts of interest to declare., (2023 Journal of Thoracic Disease. All rights reserved.)

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ  =  0.992, CI 0.990-0.994). Image quality was improved with DLM compared to ASiR-V and FBP., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: FAM83H has been implicated in cancer progression, and PD1 is an important target for anti-cancer immune checkpoint therapy. Recent studies suggest an association between FAM83H expression and immune infiltration. However, studies on the roles of FAM83H and its relationship with PD1 in breast carcinomas have been limited., Methods: Immunohistochemical expression of FAM83H and PD1 and their prognostic significance were evaluated in 198 breast carcinomas., Results: The expression of FAM83H in cancer cells was significantly associated with the presence of PD1-positive lymphoid cells within breast carcinoma tissue. Individual and co-expression patterns of nuclear FAM83H and PD1 were significantly associated with shorter survival of breast carcinomas in univariate analysis. In multivariate analysis, the expression of nuclear FAM83H (overall survival, p < 0.001; relapse-free survival, p = 0.003), PD1 (overall survival, p < 0.001; relapse-free survival, p = 0.003), and co-expression patterns of nuclear FAM83H and PD1 (overall survival, p < 0.001; relapse-free survival, p < 0.001) were the independent indicators of overall survival and relapse-free survival of breast carcinoma patients., Conclusions: This study suggests a close association between FAM83H expression and the infiltration of PD1-positive lymphoid cells in breast carcinomas and their expression as the prognostic indicators for breast carcinoma patients, and further studies are needed to clarify this relationship.

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs., Competing Interests: The authors declare no conflict of interest, financial or otherwise, in relation to this study or its publication.

Objective: Early chemoresistance and tumor mass progression are associated with poor prognosis in pancreatic ductal adenocarcinoma (PDAC). Circulating tumor cells (CTCs) have been studied as potential predictors of treatment response and prognosis in PDAC; however, this approach has yet to be applied in clinical practice. The aim of our study was to investigate the phenotypic characteristics of CTCs and determine their predictive value for PDAC progression., Methods: We prospectively enrolled 40 patients who were pathologically diagnosed with PDAC and collected blood samples at diagnosis, 2 months after diagnosis, and during disease progression or recurrence. We used a microfabricated filter-based enrichment system to retrieve and analyze CTCs, which were classified using immunofluorescence staining (CD45, EpCAM, and vimentin)., Results: Our study included 20 women and 20 men (median age, 66 years). Overall, 45% of the patients (18/40) had disseminated disease, and 77.5% (31/40) received chemotherapy. Multivariate analysis revealed that the total CTC count and carbohydrate antigen 19-9 level at 2 months after diagnosis were associated with disease progression ( P <0.05). Linear mixed model analysis revealed that the total CTC count and vimentin-positive CTCs were significantly correlated with treatment response during chemotherapy ( P =0.024 and 0.017, respectively). Kaplan-Meier analysis showed that total CTC positivity at 2 months was significantly associated with poor progression-free survival ( P =0.038)., Conclusion: Our study's findings suggest that CTCs can serve as predictive biomarkers of clinical outcomes in patients with PDAC receiving palliative chemotherapy. In particular, the total CTC count and vimentin-positive CTCs showed changes associated with the chemotherapy response., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lee, Jung, Shin, Park, Park, Jung, Leem, Kim, Jo, Chung, Park, Bang, Park and Song.)

Measuring, recording and analyzing spectral information of materials as its unique finger print using a ubiquitous smartphone has been desired by scientists and consumers. We demonstrated it as drug classification by chemical components with smartphone Raman spectrometer. The Raman spectrometer is based on the CMOS image sensor of the smartphone with a periodic array of band pass filters, capturing 2D Raman spectral intensity map, newly defined as spectral barcode in this work. Here we show 11 major components of drugs are classified with high accuracy, 99.0%, with the aid of convolutional neural network (CNN). The beneficial of spectral barcodes is that even brand name of drug is distinguishable and major component of unknown drugs can be identified. Combining spectral barcode with information obtained by red, green and blue (RGB) imaging system or applying image recognition techniques, this inherent property based labeling system will facilitate fundamental research and business opportunities., (© 2023. Springer Nature Limited.)

The primary function of animal nests is to protect developing offspring from hostile and fluctuating environments. Animal builders have been shown to adjust nest construction in response to changes in their environment. However, the extent of this plasticity, and its dependence on an evolutionary history of environmental variability, is not well understood. To test whether an evolutionary history with flowing water impacts male ability to adjust nests in response to flow regime, we collected three-spined sticklebacks ( Gasterosteus aculeatus ) from three lakes and three rivers, and brought them into reproductive condition in controlled laboratory aquaria. Males were then allowed to nest under both flowing and static conditions. Nest building behaviour, nest structure and nest composition were all recorded. In comparison to males building nests under static conditions, males building in flowing water took longer to construct their nests and invested more in nesting behaviour. Moreover, nests built in flowing water contained less material, were smaller, more compact, neater and more elongated than nests built under static conditions. Whether males came from rivers or lakes had little impact on nesting activities, or male capacity to adjust behaviours in response to flow treatment. Our findings suggest that aquatic animals which have experienced a stable environment over a long period of time retain plasticity in nest-building behaviours that allow them to adjust nests to ambient flow conditions. This ability may prove crucial in coping with the increasingly unpredictable flow regimes found in anthropogenically altered waterways and those resulting from global climate change. This article is part of the theme issue 'The evolutionary ecology of nests: a cross-taxon approach'.

Total polyphenol and total flavonoid assays were performed to characterize the relationships between the color of Peucedanum japonicum (PJ) seed coat and stem and the content of phytochemical compounds. The samples were divided into two groups based on their stem and seed coat color, with each group containing 23 samples. The stem color group was subdivided into green, light red, and red, whereas the seed coat color group was divided into light brown, brown, and dark brown. In the stem color group, the light red stems exhibited the highest content of phytochemical compounds, with levels over 10% higher than those of the stems of the other colors. Moreover, among the top ten samples with the highest total polyphenol content, eight samples were light red, and the light red group also exhibited the highest total flavonoid content among the examined color groups. In terms of the seed coat color, the plants grown from dark brown seeds exhibited the highest contents of both total polyphenols and total flavonoids. In conclusion, PJ plants with dark brown seeds and light red stems contained the highest levels of phytochemical compounds. Collectively, our findings provide a valuable basis for future seed selection of PJ for pharmaceutical purposes.

As the Korean society is aging rapidly, the issues on physical, social, economic, and mental disabilities of single-person households aged 65 years or older has also increased. This study aimed to investigate the nutrition-related dietary conditions of elderly people living alone and determine their dietary behavior by calculating the nutrition quotient for elderly (NQ-E). One hundred and three elderly people living alone who were basic living recipients were recruited from six senior welfare centers in Seoul, and the data were collected using a questionnaire from 19 July 2016 to 17 August 2016, with a 1:1 in-depth interview using the modified version of the NQ-E questionnaire. The data were analyzed using SPSS 27.0 for Mac (IBM Corp., Armonk, NY, USA); a p value of <0.05 was considered significant. The nutrition-related dietary conditions of the elderly living alone were limited, and many of them received support from the government, which helped improve their diet. The nutrition quotient score of the elderly living alone was 50.14, which was lower than the NQ-E mean score (57.6) of the Korean elderly and the NQ-E (62 points), which is the top 25% of the national survey subjects according to the criteria value presented by the Korean Nutrition Society. Elderly people living alone often have poor dietary habits and nutritional status. The NQ-E presented in this study can be used to evaluate the dietary conditions of the elderly and is expected to be used as an indicator for developing community programs for health promotion and evaluating their effectiveness.

Mori Folium ( Morus alba leaf, MF) and Mori Cortex Radicis ( Morus alba root cortex, MR) have been studied for their anti-obesity effects by enhancing the browning process and inhibiting adipogenesis. However, important aspects of their protective mechanisms have not been thoroughly investigated, which could aid in developing functional food. Thus, this study aims to determine the synergistic effects of MF and MR against obesity and its associated mechanisms. In an in vitro cell culture model of brown adipocytes, a 1:1 mixture of MF and MR showed a synergistic effect on the expression of brown adipocyte-specific genes, including Ucp-1 , Ppargc1a , Cbp/p300-interacting transactivator ( Cited ), Prdm16 , Tbx1 , and Fgf21 compared with either MF- or MR-treated conditions. Moreover, they demonstrated the involvement of cAMP and Ca2+ in induction of brown adipocyte-specific genes. In an in vivo model using HFD-fed mice, MF/MR significantly inhibited weight gain, plasma cholesterol, LDL, TG content, fat mass, and adipocyte size. Furthermore, MF/MR inhibited morphological alteration and the expressions of fatty acid synthesis genes such as Srebp1 and Fasn in the white adipose tissue. Thermogenesis genes were recovered in the brown adipose tissue with MF/MR supplementation, indicating that MF/MR regulated adipocytic dysmetabolism where AMPK signaling is involved. In conclusion, these results suggested that MF/MR regulates brown and beige adipocyte processes, providing one of the preventive functional food/herbal medicines against obesity and its associated metabolic diseases.

The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p = 0.036) in D1, 0.801 versus 0.753 (p < 0.001) in D2, and 0.774 versus 0.668 (p < 0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia., (© 2023. Springer Nature Limited.)

Pretreatment values of the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are well-established prognosticators in various cancers, including head and neck cancers. However, there are no studies on whether temporal changes in the NLR and PLR values after treatment are related to the development of recurrence. Therefore, in this study, we aimed to develop a deep neural network (DNN) model to discern cancer recurrence from temporal NLR and PLR values during follow-up after concurrent chemoradiotherapy (CCRT) and to evaluate the model's performance compared with conventional machine learning (ML) models. Along with conventional ML models such as logistic regression (LR), random forest (RF), and gradient boosting (GB), the DNN model to discern recurrences was trained using a dataset of 778 consecutive patients with primary head and neck cancers who received CCRT. There were 16 input features used, including 12 laboratory values related to the NLR and the PLR. Along with the original training dataset ( N = 778), data were augmented to split the training dataset ( N = 900). The model performance was measured using ROC-AUC and PR-AUC values. External validation was performed using a dataset of 173 patients from an unrelated external institution. The ROC-AUC and PR-AUC values of the DNN model were 0.828 ± 0.032 and 0.663 ± 0.069, respectively, in the original training dataset, which were higher than the ROC-AUC and PR-AUC values of the LR, RF, and GB models in the original training dataset. With the recursive feature elimination (RFE) algorithm, five input features were selected. The ROC-AUC and PR-AUC values of the DNN-RFE model were higher than those of the original DNN model (0.883 ± 0.027 and 0.778 ± 0.042, respectively). The ROC-AUC and PR-AUC values of the DNN-RFE model trained with a split dataset were 0.889 ± 0.032 and 0.771 ± 0.044, respectively. In the external validation, the ROC-AUC values of the DNN-RFE model trained with the original dataset and the same model trained with the split dataset were 0.710 and 0.784, respectively. The DNN model with feature selection using the RFE algorithm showed the best performance among the ML models to discern a recurrence after CCRT in patients with head and neck cancers. Data augmentation by splitting training data was helpful for model performance. The performance of the DNN-RFE model was also validated with an external dataset.

Stridor is a rare but important non-motor symptom that can support the diagnosis and prediction of worse prognosis in multiple system atrophy. Recording sounds generated during sleep by video-polysomnography is recommended for detecting stridor, but the analysis is labor intensive and time consuming. A method for automatic stridor detection should be developed using technologies such as artificial intelligence (AI) or machine learning. However, the rarity of stridor hinders the collection of sufficient data from diverse patients. Therefore, an AI method with high diagnostic performance should be devised to address this limitation. We propose an AI method for detecting patients with stridor by combining audio splitting and reintegration with few-shot learning for diagnosis. We used video-polysomnography data from patients with stridor (19 patients with multiple system atrophy) and without stridor (28 patients with parkinsonism and 18 patients with sleep disorders). To the best of our knowledge, this is the first study to propose a method for stridor detection and attempt the validation of few-shot learning to process medical audio signals. Even with a small training set, a substantial improvement was achieved for stridor detection, confirming the clinical utility of our method compared with similar developments. The proposed method achieved a detection accuracy above 96% using data from only eight patients with stridor for training. Performance improvements of 4%-13% were achieved compared with a state-of-the-art AI baseline. Moreover, our method determined whether a patient had stridor and performed real-time localization of the corresponding audio patches, thus providing physicians with support for interpreting and efficiently employing the results of this method., (© 2023. The Author(s).)

Background Differences in the clinical and radiological characteristics of SARS-CoV-2 Omicron subvariants have not been well studied. Purpose To compare clinical disease severity and radiologically severe pneumonia in patients with COVID-19 hospitalized during a period of either Omicron BA.1/BA.2 or Omicron BA.5 subvariant predominance. Materials and Methods This multicenter retrospective study, included patients registered in the Korean Imaging Cohort of COVID-19 database who were hospitalized for COVID-19 between January and December 2022. Publicly available relative variant genome frequency data were used to determine the dominant periods of Omicron BA.1/BA.2 subvariants (January 17 to June 20, 2022) and the Omicron BA.5 subvariant (July 4 to December 5, 2022). Clinical outcomes and imaging pneumonia outcomes based on chest radiography and CT were compared among predominant subvariants using multivariable analyses adjusted for covariates. Results Of 1916 confirmed patients with COVID-19 (mean age, 72 years ± 16 [SD]; 1019 males), 1269 were registered during the Omicron BA.1/BA.2 subvariant dominant period and 647 during the Omicron BA.5 subvariant dominant period. Patients in the BA.5 group showed lower odds of high-flow O 2 requirement (adjusted odds ratio [OR], 0.75 [95% CI: 0.57, 0.99]; P = .04), mechanical ventilation (adjusted OR, 0.49 [95% CI: 0.34, 0.72]; P < .001]), and death (adjusted OR, 0.47 [95% CI: 0.33, 0.68]; P <.001) than those in the BA.1/BA.2 group. Additionally, the BA.5 group had lower odds of severe pneumonia on chest radiographs (adjusted OR, 0.68 [95% CI: 0.53, 0.88]; P = .004) and higher odds of atypical pattern pneumonia on CT images (adjusted OR, 1.81 [95% CI: 1.26, 2.58]; P = .001) than the BA.1/BA.2 group. Conclusions Patients hospitalized during the period of Omicron BA.5 subvariant predominance had lower odds of clinical and pneumonia severity than those hospitalized during the period of Omicron BA.1/BA.2 predominance, even after adjusting for covariates. See also the editorial by Hammer in this issue.

The present study attempts to evaluate the acute and subacute toxicities of hypofractionated volumetric modulated arc therapy (HFX-VMAT) in patients with early breast cancer (EBC). It is a retrospective analysis of 23 patients treated with HFX-VMAT after breast-conserving surgery between September 2021 and February 2022. A total dose of 50.05 to 52.55 Gy was delivered, consisting of 40.05 Gy to the ipsilateral whole breast in 15 fractions of 2.67 Gy and a tumor bed boost dose of 10-12.5 Gy in 4-5 fractions. The primary endpoint was acute/subacute radiation pneumonitis (RP). The secondary endpoint was poor cosmesis, indicating acute/subacute radiation dermatitis. Chest computed tomography (CT) and the Common Terminology Criteria for Adverse Events v.5.0 were used to assess acute and subacute RP and dermatitis, respectively, during radiotherapy (RT) and at 3- and 6-months post-RT. The median follow-up duration was 3.8 months (range, 2.3-4.2). A total of seven patients developed RP. None of these patients presented RP-related symptoms; the diagnosis was based on radiologic findings observed on follow-up chest CT. Among the seven patients with RP, five had right-sided, and two had left-sided breast tumors (71.4 vs. 28.6%; P=0.026). Grade 1 erythema was observed in 19 patients (82.6%) and grade 2 erythema in four (17.4%). The mean target dose, D 105% (the dose received by 105% of the target volume), homogeneity index, mean lung dose, ipsilateral lung V 20 (the percentage volume receiving 20 Gy), and V 30 (the percentage volume receiving 30 Gy) for ipsilateral whole breast RT were significantly associated with RP (P=0.039, 0.047, 0.018, 0.015, 0.018 and 0.003, respectively.). HFX-VMAT showed tolerable acute/subacute toxicities. Therefore, HFX-VMAT is an effective and safe treatment option for EBC., Competing Interests: The authors declare that they have no competing interests., (Copyright: © Lee et al.)

Most recent survival prediction has been based on TNM staging, which does not provide individualized information. However, clinical factors including performance status, age, sex, and smoking might influence survival. Therefore, we used artificial intelligence (AI) to analyze various clinical factors to precisely predict the survival of patients with larynx squamous cell carcinoma (LSCC). We included patients with LSCC (N = 1026) who received definitive treatment from 2002 to 2020. Age, sex, smoking, alcohol consumption, Eastern Cooperative Oncology Group (ECOG) performance status, location of tumor, TNM stage, and treatment methods were analyzed using deep neural network (DNN) with multi-classification and regression, random survival forest (RSF), and Cox proportional hazards (COX-PH) model for prediction of overall survival. Each model was confirmed with five-fold cross validation, and performance was evaluated using linear slope, y-intercept, and C-index. The DNN with multi-classification model demonstrated the highest prediction power (1.000 ± 0.047, 0.126 ± 0.762, and 0.859 ± 0.018 for slope, y-intercept, and C-index, respectively), and the prediction survival curve showed the strongest agreement with the validation survival curve, followed by DNN with regression (0.731 ± 0.048, 9.659 ± 0.964, and 0.893 ± 0.017, respectively). The DNN model produced with only T/N staging showed the poorest survival prediction. When predicting the survival of LSCC patients, various clinical factors should be considered. In the present study, DNN with multi-class was shown to be an appropriate method for survival prediction. AI analysis may predict survival more accurately and improve oncologic outcomes., (© 2023. The Author(s).)

Background: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters., Methods: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs ( n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model., Results: Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm 3 (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm 3 m 3 in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e., , Rmse: 7975.6 mm 3 , the smallest among the other variable parameters)., Conclusions: The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs.

A novel probiotics-derived protein, P8, suppresses the growth of colorectal cancer (CRC). P8 can penetrate the cell membrane via endocytosis and cause cell cycle arrest in DLD-1 cells through down-regulation of CDK1/Cyclin B1. However, neither the protein involved in the endocytosis of P8 nor the cell cycle arrest targets of P8 are known. We identified two P8-interacting target proteins [importin subunit alpha-4 (KPNA3) and glycogen synthase kinase-3 beta (GSK3β)] using P8 as a bait in pull-down assays of DLD-1 cell lysates. Endocytosed P8 in the cytosol was found to bind specifically to GSK3β, preventing its inactivation by protein kinases AKT/CK1ε/PKA. The subsequent activation of GSK3β led to strong phosphorylation (S 33,37 /T 41 ) of β-catenin, resulting in its subsequent degradation. P8 in the cytosol was also found to be translocated into the nucleus by KPNA3 and importin. In the nucleus, after its release, P8 binds directly to the intron regions of the GSK3β gene, leading to dysregulation of GSK3β transcription. GSK3β is a key protein kinase in Wnt signaling, which controls cell proliferation during CRC development. P8 can result in a cell cycle arrest morphology in CRC cells, even when they are in the Wnt ON signaling state.

Context: Considering the absence of methods to find pancreatic cancer early, surveillance of high-risk groups is needed for early diagnosis., Objective: The study aimed to investigate the effect in the incidence of pancreatic cancer and the differences between new-onset diabetes mellitus (NODM) and long-standing DM (LSDM) since NODM group is a representative high-risk group., Methods: The Korean National Health Insurance Service-National Sample Cohort between 2002 and 2013 data were used. Regarding 88 396 people with DM (case group), we conducted a 1:1 propensity score matching to select a matched non-DM population (control group). To investigate the interaction between DM and the time variable distinguishing NODM and LSDM, we performed a multivariate time-dependent Cox regression analysis., Results: The incidence of pancreatic cancer was higher in the DM group compared to the non-DM group (0.52% vs 0.16%; P < .001). The DM group had shown different risk of pancreatic cancer development according to the duration since the DM diagnosis (NODM hazard ratio (HR): 3.81; 95% CI, 2.97-4.88; P < .001; LSDM HR: 1.53; 95% CI, 1.11-2.11; P < .001). When the NODM and the LSDM groups were compared, the risk of pancreatic cancer was higher in the NODM group than in the LSDM group (HR: 1.55; P = .020). In subgroup analysis, NODM group showed that men (HR = 4.42; 95% CI, 3.15-6.19; P < .001) and patients who were in their 50 seconds (HR = 7.54; 95% CI, 3.24-17.56; P < .001) were at a higher risk of developing pancreatic cancer than matched same sex or age control group (non-DM population), respectively., Conclusion: The risk of pancreatic cancer was greater in people with DM than in a non-DM population. Among people with DM, NODM showed a higher risk of pancreatic cancer than LSDM., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Gallbladder stones (GS) is associated with an increased risk of cardiovascular disease. However, the relationship between cholecystectomy for GS and acute coronary syndrome (ACS) is unknown. We investigated the ACS risk in patients with GS and its association with cholecystectomy. Data from the Korean National Health Insurance Service-National Sample Cohort from 2002 to 2013 was extracted. Overall, 64,370 individuals were selected through a 1:3 propensity score matching. Patients were stratified into two groups for comparison: the gallstone group, GS patients with or without cholecystectomy; and the control group, patients without GS or cholecystectomy. The gallstone group exhibited a higher risk of ACS than the control group (hazard ratio [HR], 1.30; 95% confidence interval [CI] 1.15-1.47; P < 0.0001). In the gallstone group, individuals without cholecystectomy had a higher risk of ACS development (HR: 1.35, 95% CI 1.17-1.55, P < 0.0001). Patients with GS with diabetes, hypertension, or dyslipidemia, had a higher risk of developing ACS than GS patients without the metabolic diseases (HR: 1.29, P < 0.001). The risk did not significantly differ after cholecystectomy compared to those without GS (HR: 1.15, P = 0.1924), but without cholecystectomy, the risk of ACS development was significantly higher than control group (1.30, 95% CI 1.13-1.50, P = 0.0004). Among patients without above metabolic disorders, cholecystectomy was still associated with increased ACS risk in the gallstone group (HR: 2.93, 95% CI 1.27-6.76, P = 0.0116). GS increased the risk of ACS. The effect of cholecystectomy on ACS risk differs according to the presence or absence of metabolic disorders. Thus, the decision to perform cholecystectomy for GS should consider both the ACS risk and the underlying disorders., (© 2023. The Author(s).)

This study investigated the rate at which radiologists miss or detect incidental breast cancers on chest CT and to compare the CT features between the two groups. This retrospective study evaluated chest CT examinations and medical records of patients who registered with the diagnosis code of "breast cancer" between January 2016 and December 2020, and who had undergone contrast enhanced chest CT 3-18 months before registration, during which they were unaware of any breast lesions. This study found that out of 84 patients, incidental breast cancer lesions were missed in 54 (64.3%) and detected in 30 (53.7%). The initial treatment was delayed in the missed breast lesions group ( p = 0.004). Breast lesions of smaller sizes (<9.0 mm, p = 0.01), or with lower enhancement ratios (<1.4, p = 0.009), were more likely to be missed. When three radiologists re-read the CTs with more attention to breast area, they detected breast cancers with higher accuracies (90.1%, 87.9%, and 81.3%). In summary, this study revealed that radiologists miss 64.3% of incidental breast cancers on chest CT, especially those of sub-centimeter sizes and weak enhancements.

Tumor-derived extracellular vesicles (tdEVs) are one of the most promising biomarkers for liquid biopsy-based cancer diagnostics, owing to the expression of specific membrane proteins of their cellular origin. The investigation of epithelial-to-mesenchymal transition (EMT) in cancer using tdEVs is an alternative way of evaluating the risk of malignancy transformation. An ultra-sensitive selection and detection methodology is an essential step in developing a tdEVs-based cancer diagnostic device. In this study, we developed an indium-tin-oxide (ITO) sensor integrated microfluidic device consisting of two main parts: 1) a multi-orifice flow-fractionation (MOFF) channel for extraction of pure EVs by removing blood cellular debris, and 2) an ITO sensor coupled with a geometrically activated surface interaction (GASI) channel for enrichment and quantification of tdEV. The microfluidic channel and the ITO sensors are assembled with a 3D printed magnetic housing to prevent sample leakage and to easily attach/detach the sensors to/from the microfluidic channel. The tdEVs were successfully captured on the specific antibody modified ITO surfaces in the integrated microfluidic channel. The integrated sensors showed an excellent linear response between 10 3 and 10 9 tdEVs/mL. Simultaneous evaluation of the epithelial and mesenchymal markers on the tdEV surfaces successfully revealed the EMT index of the corresponding pancreatic cancer cells. Our ITO sensor integrated microfluidic device showed excellent detection in the clinically relevant tdEVs-concentration range for patients with pancreatic cystic neoplasms. Hence, this system is expected to open a new avenue for liquid biopsy-based cancer prognostics and diagnostics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ([Formula: see text]) and patients with PD ([Formula: see text]), multiple systemic atrophy ([Formula: see text]), and progressive supranuclear palsy ([Formula: see text]) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research., (© 2023. The Author(s).)

Background Few reports have evaluated the effect of the SARS-CoV-2 variant and vaccination on the clinical and imaging features of COVID-19. Purpose To evaluate and compare the effect of vaccination and variant prevalence on the clinical and imaging features of infections by the SARS-CoV-2. Materials and Methods Consecutive adults hospitalized for confirmed COVID-19 at three centers (two academic medical centers and one community hospital) and registered in a nationwide open data repository for COVID-19 between August 2021 and March 2022 were retrospectively included. All patients had available chest radiographs or CT images. Patients were divided into two groups according to predominant variant type over the study period. Differences between clinical and imaging features were analyzed with use of the Pearson χ 2 test, Fisher exact test, or the independent t test. Multivariable logistic regression analyses were used to evaluate the effect of variant predominance and vaccination status on imaging features of pneumonia and clinical severity. Results Of the 2180 patients (mean age, 57 years ± 21; 1171 women), 1022 patients (47%) were treated during the Delta variant predominant period and 1158 (53%) during the Omicron period. The Omicron variant prevalence was associated with lower pneumonia severity based on CT scores (odds ratio [OR], 0.71 [95% CI: 0.51, 0.99; P = .04]) and lower clinical severity based on intensive care unit (ICU) admission or in-hospital death (OR, 0.43 [95% CI: 0.24, 0.77; P = .004]) than the Delta variant prevalence. Vaccination was associated with the lowest odds of severe pneumonia based on CT scores (OR, 0.05 [95% CI: 0.03, 0.13; P < .001]) and clinical severity based on ICU admission or in-hospital death (OR, 0.15 [95% CI: 0.07, 0.31; P < .001]) relative to no vaccination. Conclusion The SARS-CoV-2 Omicron variant prevalence and vaccination were associated with better clinical outcomes and lower severe pneumonia risk relative to Delta variant prevalence. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Little in this issue.

Cholangiocarcinoma (CCA) is a fatal disease often detected late in unresectable stages. Currently, there are no effective diagnostic methods or biomarkers to detect CCA early with high confidence. Analysis of tumor-derived extracellular vesicles (tEVs) harvested from liquid biopsies can provide a new opportunity to achieve this goal. Here, an advanced nanoplasmonic sensing technology is reported, termed FLEX (fluorescence-amplified extracellular vesicle sensing technology), for sensitive and robust single EV analysis. In the FLEX assay, EVs are captured on a plasmonic gold nanowell surface and immunolabeled for cancer-associated biomarkers to identify tEVs. The underlying plasmonic gold nanowell structures then amplify EVs' fluorescence signals, an effective amplification process at the single EV level. The FLEX EV analysis revealed a wide heterogeneity of tEVs and their marker levels. FLEX also detected small tEVs not detected by conventional EV fluorescence imaging due to weak signals. Tumor markers (MUC1, EGFR, and EPCAM) are identified in CCA, and this marker combination is applied to detect tEVs in clinical bile samples. The FLEX assay detected CCA with an area under the curve of 0.93, significantly better than current clinical markers. The sensitive and accurate nanoplasmonic EV sensing technology can aid in early CCA diagnosis., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Background: An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19., Objective: We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19., Methods: This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration., Results: The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859)., Conclusions: The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19., (©Hyun Woo Lee, Hyun Jun Yang, Hyungjin Kim, Ue-Hwan Kim, Dong Hyun Kim, Soon Ho Yoon, Soo-Youn Ham, Bo Da Nam, Kum Ju Chae, Dabee Lee, Jin Young Yoo, So Hyeon Bak, Jin Young Kim, Jin Hwan Kim, Ki Beom Kim, Jung Im Jung, Jae-Kwang Lim, Jong Eun Lee, Myung Jin Chung, Young Kyung Lee, Young Seon Kim, Sang Min Lee, Woocheol Kwon, Chang Min Park, Yun-Hyeon Kim, Yeon Joo Jeong, Kwang Nam Jin, Jin Mo Goo. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.02.2023.)

Background: Ligularia fischeri is a perennial herb isolated from plants of the Asteraceae family. Ligularia fischeri is distributed throughout Korea, Japan, eastern Siberia, and China., Aims: The aim of this study is to examine the intracellular inhibitory effect of Ligularia fischeri ethanol extract on melanin synthesis and expression of tyrosinase and tyrosinase-related protein 1 and 2. In addition, we analyzed the mitogen-activated protein kinase signaling pathway and microphthalmia-associated transcription factor in alpha-melanocyte-stimulating hormone-stimulated B16F10 melanoma cells., Methods: To assess the inhibition of melanogenesis in alpha-melanocyte-stimulating hormone-stimulated B16F10 melanoma cells, the expression of melanogenesis-related genes was investigated by quantitative real-time polymerase chain reaction, while western blotting was performed to determine protein expression levels., Results: We confirmed that the ethanol extract of Ligularia fischeri inhibited melanin synthesis in vitro by decreasing tyrosinase and tyrosinase-related protein 1 and 2 expression. Furthermore, we revealed that tyrosinase expression was regulated by the suppression of microphthalmia-associated transcription factor expression and activation of extracellular signal-regulated kinase phosphorylation. The ethanol extract of Ligularia fischeri inhibited melanogenesis by activating extracellular signal-regulated kinase phosphorylation and suppressing microphthalmia-associated transcription factor and tyrosinase expression., Conclusions: Ligularia fischeri ethanol extract may be used as an effective skin whitening agent in functional cosmetics., (© 2022 The Authors. Journal of Cosmetic Dermatology published by Wiley Periodicals LLC.)